**Coastal Geomorphology**

[39] Sulsters WA. Mental mapping, viewing the urban landscapes of the mind. In: Proceedings of the International Conference "Doing, Thinking, Feeling Home: The Mental Geography of Residential Environment"; 2005 October 14-15; Delft, Netherlands. Netherlands: Delft University of Technology; 2005. Conference paper 1. [Internet] [Cited 2017 July 10]. Available from: https://repository.tudelft.nl/islandora/object/uuid%3

[40] IFRC. What is a Disaster? International Federation of Red Cross and Red Crescent Societies. [Internet] n.d. [Cited 2017 August 10]. Available from: http://www.ifrc.org/en/

Afc71de16-b485-4888-b6fe-a9d2771d9e4a?collection=research

158 Sea Level Rise and Coastal Infrastructure

what-we-do/disaster-management/about-disasters/what-is-a-disaster/

**Chapter 10**

Provisional chapter

**Coastal Geomorphology and Its Impacts**

Coastal depth and coastal geomorphology are the important research focuses of coastal waters and are important factors in coastal environment. Their measurements play an important role in safety of shipping, research of ocean science, simulation of coastal storm surge, construction of coastal facilities, management of coastal zone, detection of shoreline erosion, and so on. Remote sensing technology is an important method for water depth measurement because of its own advantages currently, and it has great value of practical application. East Gong Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6 and WorldView-2. The band ratio model with the highest accuracy is selected for depth inversion. The results show that the accuracy of the SPOT-6 image is the highest. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0–5 m. The inversion accuracy of 5–10 m is the highest, and the inversion error increases with the increase of depth. The factors affecting the accuracy of the water depth inversion are discussed. It is necessary to strengthen the research of remote sensor and inversion model in order to further improve

DOI: 10.5772/intechopen.73510

Keywords: coastal, geomorphology, depth, remote sensing, inversion, accuracy

The coastal waters are defined as waters within 20 nautical miles of the beach (Figure 1), even if the coast is uninhabited or inaccessible. Coastal waters are the interface between land and sea and have important ecological value because of their high productivity and system diversity (such as estuaries, coastal wetlands, coral reefs, mangroves, and upwelling areas). It is

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Coastal Geomorphology and Its Impacts

Tianqi Lu, Shengbo Chen and Yan Yu

Tianqi Lu, Shengbo Chen and Yan Yu

http://dx.doi.org/10.5772/intechopen.73510

the accuracy of inversion.

1. Introduction

Abstract

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Coastal Geomorphology and Its Impacts** Coastal Geomorphology and Its Impacts

DOI: 10.5772/intechopen.73510

Tianqi Lu, Shengbo Chen and Yan Yu Tianqi Lu, Shengbo Chen and Yan Yu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73510

#### Abstract

Coastal depth and coastal geomorphology are the important research focuses of coastal waters and are important factors in coastal environment. Their measurements play an important role in safety of shipping, research of ocean science, simulation of coastal storm surge, construction of coastal facilities, management of coastal zone, detection of shoreline erosion, and so on. Remote sensing technology is an important method for water depth measurement because of its own advantages currently, and it has great value of practical application. East Gong Island in South China Sea was taken as a typical study area. The band ratio model was established by using measured points and three multispectral images of Landsat-8, SPOT-6 and WorldView-2. The band ratio model with the highest accuracy is selected for depth inversion. The results show that the accuracy of the SPOT-6 image is the highest. Meanwhile, analyzing the error of inversion from different depth ranges, the accuracy of the inversion is lower in the range of 0–5 m. The inversion accuracy of 5–10 m is the highest, and the inversion error increases with the increase of depth. The factors affecting the accuracy of the water depth inversion are discussed. It is necessary to strengthen the research of remote sensor and inversion model in order to further improve the accuracy of inversion.

Keywords: coastal, geomorphology, depth, remote sensing, inversion, accuracy

#### 1. Introduction

The coastal waters are defined as waters within 20 nautical miles of the beach (Figure 1), even if the coast is uninhabited or inaccessible. Coastal waters are the interface between land and sea and have important ecological value because of their high productivity and system diversity (such as estuaries, coastal wetlands, coral reefs, mangroves, and upwelling areas). It is

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

mapping. Today, multibeam sounding equipment has developed over the past 40 years and develops toward the direction of wide coverage, narrow beam angle, multifunction integra-

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 163

The sonar sounding system based on the ship is still the main method of coastal bathymetry now. However, it has the shortcoming of time and labor cost and also cannot measure the area where the ship cannot sail. The method of coastal depth and coastal geomorphology measurement based on remote sensing has been developed to make up for the defect of site measurement. These methods include optical photogrammetry, multispectral remote sensing sounding, synthetic aperture radar (SAR) shallow sounding, satellite altimeter sounding, and airborne laser sounding [4, 5]. Optical photogrammetry extracts the underwater terrain by means of photogrammetry by using different angles to shoot underwater stereo images [6]. This method can only be carried out in the case of clear water, because it is influenced a lot by seawater turbidity, seawater suspension, solar illumination, seawave size, and other factors. Multispectral remote sensing sounding gets the information of depth by establishing a physical model or an empirical model based on the relationship between the radiation brightness of different optical bands and depth [7]. The underwater topography affects the scattering intensity of sea surface of SAR under the influence of tides and winds. The depth can be extracted from the scattering intensity information of SAR based on the physical process [8]. The measurement of satellite altimeter is based on sea level elevation obtained by altimetry satellite to restore the gravity field of ocean and then calculate the depth by gravity anomaly [9]. Airborne laser sounding is an active method of depth measurement; the depth can be measured by launching a laser of blue-green that can penetrate the water directly [10, 11]. This method can measure the coastal depth and the coastal geomorphology accurately and efficiently. Remote sensing technology can obtain depth information of a large area of ocean dynamically and rapidly. It has advantages of wide coverage, short period, and low cost. In particular, it can be used to obtain bathymetric data where ships are not accessible or find it difficult to enter. It is an important technical method of special regional water depth measurement and can make up for the deficiency of traditional

tion, and miniaturization gradually [3].

sounding method, so it has great value for practical applications.

The development of remote sensing technology of water depth can be traced back to the early last century. In the 1930s, the characteristics of water spectrum were researched. Clarke and James [12] first explored the relationship between absorption coefficient and wavelength of pure water in a wavelength ranging from 0.375 to 0.800 μm. Curcio and Petty [13] further investigated the relationship between absorption coefficient of 0.70–2.50 μm pure water and wavelength. It was found that pure water had the weakest absorption in the blue band near the wavelength of 0.475 μm, and the absorption coefficient tends to increase with the increase of the wavelength. Researchers at the American Environmental Research Institute of Michigan (ERIM) have been working on remote sensing sounding in the late 1960s. They used multispectral data such as MSS, TM, and aerial photo to study the bathymetric model. Lyzenga et al. [14, 15] proposed the quantitative analysis method of water depth measurement based on the bottom reflection model. Clark et al. [16] extracted water depth value from image data of Landsat TM1, TM2 band in the vicinity of Isla de Vieques through the linear multiband method. Mgengel and Spitzer [17] conducted the multidate mapping of the shallow seafloor nearby the Netherlands by using TM image. Bierwirth et al. [18] assumed that when water quality and sediments are homogeneous, it is possible to extract water depth and information

Figure 1. Schemata of marine waters.

necessary to investigate the coastal depth, coastal geomorphology, water quality, and water temperature, whether it is for reclamation, coastal tourism, shallow sea farming, or shallow sea energy exploration and other activities. Coastal waters have always been the focus of research at home and abroad because of it being most closely related to human activities.

Coastal depth and coastal geomorphology are the important research focuses of coastal waters and are important factors in coastal environment. Their measurements play an important role in safety of shipping, research of ocean science, simulation of coastal storm surge, construction of coastal facilities, monitoring of marine ecosystem, management of coastal zone, detection of shoreline erosion, and so on.

Bathymetry is mainly measured by a shipborne plumb line in earlier times. This kind of operation mode is inefficient, measurement points are sparse, and it is subject to ocean currents. The echo detector based on sonar technology was invented in the 1920s, which marked the entrance of a new era in ocean mapping. However, it has a number of weaknesses—the gap of bathymetric points is too wide, the topographic information is in the rough, the resolution and precision is low, and so on [1, 2]. For the need of oceanographic survey, a multibeam sounding system was developed based on echo sounder in the 1970s. The system forms hundreds of beam tracks by launching and receiving beams intersecting under the sea with the vertical bands of the direction of the ship. The depth of the seabed is calculated based on the time and geometry of the beam arrival, and we get the depth of the multipoint at a stripcovered area in the bottom of the ship. This system improves the efficiency greatly in ocean mapping. Today, multibeam sounding equipment has developed over the past 40 years and develops toward the direction of wide coverage, narrow beam angle, multifunction integration, and miniaturization gradually [3].

The sonar sounding system based on the ship is still the main method of coastal bathymetry now. However, it has the shortcoming of time and labor cost and also cannot measure the area where the ship cannot sail. The method of coastal depth and coastal geomorphology measurement based on remote sensing has been developed to make up for the defect of site measurement. These methods include optical photogrammetry, multispectral remote sensing sounding, synthetic aperture radar (SAR) shallow sounding, satellite altimeter sounding, and airborne laser sounding [4, 5]. Optical photogrammetry extracts the underwater terrain by means of photogrammetry by using different angles to shoot underwater stereo images [6]. This method can only be carried out in the case of clear water, because it is influenced a lot by seawater turbidity, seawater suspension, solar illumination, seawave size, and other factors. Multispectral remote sensing sounding gets the information of depth by establishing a physical model or an empirical model based on the relationship between the radiation brightness of different optical bands and depth [7]. The underwater topography affects the scattering intensity of sea surface of SAR under the influence of tides and winds. The depth can be extracted from the scattering intensity information of SAR based on the physical process [8]. The measurement of satellite altimeter is based on sea level elevation obtained by altimetry satellite to restore the gravity field of ocean and then calculate the depth by gravity anomaly [9]. Airborne laser sounding is an active method of depth measurement; the depth can be measured by launching a laser of blue-green that can penetrate the water directly [10, 11]. This method can measure the coastal depth and the coastal geomorphology accurately and efficiently. Remote sensing technology can obtain depth information of a large area of ocean dynamically and rapidly. It has advantages of wide coverage, short period, and low cost. In particular, it can be used to obtain bathymetric data where ships are not accessible or find it difficult to enter. It is an important technical method of special regional water depth measurement and can make up for the deficiency of traditional sounding method, so it has great value for practical applications.

necessary to investigate the coastal depth, coastal geomorphology, water quality, and water temperature, whether it is for reclamation, coastal tourism, shallow sea farming, or shallow sea energy exploration and other activities. Coastal waters have always been the focus of research

Coastal depth and coastal geomorphology are the important research focuses of coastal waters and are important factors in coastal environment. Their measurements play an important role in safety of shipping, research of ocean science, simulation of coastal storm surge, construction of coastal facilities, monitoring of marine ecosystem, management of coastal zone, detection of

Bathymetry is mainly measured by a shipborne plumb line in earlier times. This kind of operation mode is inefficient, measurement points are sparse, and it is subject to ocean currents. The echo detector based on sonar technology was invented in the 1920s, which marked the entrance of a new era in ocean mapping. However, it has a number of weaknesses—the gap of bathymetric points is too wide, the topographic information is in the rough, the resolution and precision is low, and so on [1, 2]. For the need of oceanographic survey, a multibeam sounding system was developed based on echo sounder in the 1970s. The system forms hundreds of beam tracks by launching and receiving beams intersecting under the sea with the vertical bands of the direction of the ship. The depth of the seabed is calculated based on the time and geometry of the beam arrival, and we get the depth of the multipoint at a stripcovered area in the bottom of the ship. This system improves the efficiency greatly in ocean

at home and abroad because of it being most closely related to human activities.

shoreline erosion, and so on.

Figure 1. Schemata of marine waters.

162 Sea Level Rise and Coastal Infrastructure

The development of remote sensing technology of water depth can be traced back to the early last century. In the 1930s, the characteristics of water spectrum were researched. Clarke and James [12] first explored the relationship between absorption coefficient and wavelength of pure water in a wavelength ranging from 0.375 to 0.800 μm. Curcio and Petty [13] further investigated the relationship between absorption coefficient of 0.70–2.50 μm pure water and wavelength. It was found that pure water had the weakest absorption in the blue band near the wavelength of 0.475 μm, and the absorption coefficient tends to increase with the increase of the wavelength. Researchers at the American Environmental Research Institute of Michigan (ERIM) have been working on remote sensing sounding in the late 1960s. They used multispectral data such as MSS, TM, and aerial photo to study the bathymetric model. Lyzenga et al. [14, 15] proposed the quantitative analysis method of water depth measurement based on the bottom reflection model. Clark et al. [16] extracted water depth value from image data of Landsat TM1, TM2 band in the vicinity of Isla de Vieques through the linear multiband method. Mgengel and Spitzer [17] conducted the multidate mapping of the shallow seafloor nearby the Netherlands by using TM image. Bierwirth et al. [18] assumed that when water quality and sediments are homogeneous, it is possible to extract water depth and information

of bottom reflectance using the visible spectrum of TM remote sensing to build the multiband model and applied the model to Shark bay. The result shows that there has a larger error when inversion of deeper water depth by using TM remote sensing image. Sandidge and Holyer [19] established the artificial neural network model using the correlation between bathymetric information and hyperspectral remote sensing images and then used the model to invert the depth information of the study area.

model with the highest accuracy was selected for the depth inversion. This study compares accuracy of inversion of remote sensing data that were acquired by three different sensors and analyzes the inversion accuracy of three remote sensing data in the range of water depths of

South China Sea is a marginal sea that is part of the Pacific Ocean. It is located south of mainland China. Toward the north of mainland China are Guangxi and Guangdong provinces; east is Philippines; southwest is Vietnam and peninsular Malaysia and the southernmost continent Zengmu reef close to Kalimantan Island. South China Sea is the largest and deepest sea in China. The waters within the nine-dotted line are territorial waters of China. The natural sea

Meanwhile, South China Sea is rich in marine oil and gas, mineral resources, coastal and island tourism resources, marine energy resources, port shipping resources, and tropical and subtropical biological resources. In addition, South China Sea is the most important distribution area of tropical ecosystems of the sea island, coral reef, mangrove forest, seagrass bed, and so on.

Seabed terrain of the South China Sea is complex and mainly comprises continental shelf, continental slope, and central deep-sea basin. The central deep-sea basin is located in the north from the center of South China Sea with slightly higher than the territory lying north-east, south-west gradually lower. The continental shelf is inclined to the sea basin with different slope tendencies along the continental margin and island arc and is the most extensive in the north and south. The steep continental slope lies between the central sea basin and continental shelf, and the continental shelf is divided into four continental slope areas of east, south, west, and north. The South China Sea islands are formed on the steps of the sea basin, Dongsha islands lie on the east sand steps of the northern continental slope area, Xisha islands and Zhongsha islands are located in the western slope area of Xisha steps and Zhongsha steps, respectively, and Nansha islands are formed on the Nansha steps in the southern continental slope area. In addition, there are many seamounts and seakolls in the South China Sea, such as the Jianfeng, Beipo, and Bijia seamounts in the northern continental margin, the Shuangfeng, Daimao, Xianbei, Shixing, Xiannan, Zhangzhong, Huangyan, and Zhongnan seamounts in the central deep-sea basin, and the Pearl seamount in the southern part of South China Sea [23]. East Gong Island waters, located in the north of South China Sea (Figure 2), have geographical

5.7800E and 109�6<sup>0</sup>

54.3000N. They belong to the Hainan Province, China, and experience tropical monsoon climate at low latitudes. The seawater in this area has strong penetrability and the maximum

, and the average depth is about 1200 m, more than 5000 m as the deepest point.

. The area of territorial waters of China is about

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 165

47.9400E and between 18�16<sup>0</sup>

9.1500N and

0–5, 5–10, 10–15, and 15–20 m.

2. Materials and methods

coordinates between 108�59<sup>0</sup>

depth is about 20 m.

18�22<sup>0</sup>

area of South China Sea is about 3.5 million km2

2.1. Study area

2.1 million km2

The research on water depth inversion by using multispectral remote sensing data has been rapidly developed. Three forms of models have been developed, theory interpretation model, semi-theoretical semi-empirical model, and statistical correlation model in the aspect of construction of remote sensing model for water sounding. Based on the radiation transfer equation in the water body, the theory interpretation model calculates water depth by measuring the optical parameter inside the water body. Currently, the common theory interpretation model is the Two-Stream Approximation Model [20]. Two-Stream refers that for any depth Z, water can be divided into two parts: above depth Z and below depth Z; thus, the light radiant flux of the water body can be decomposed into upward component and downward component. The radiation flux varies with the water depth can be estimated by studying the value or ratio of upward component and downward component. Due to the participation of water depth variable Z in the analytical process, it is possible to calculate the distribution of water depth using this model. Based on the radiation damping of light in the water, the semitheoretical semi-empirical model fulfills the remote sensing inversion of water by using the combination of the theoretical model with empirical model, and it can be classified into singleband model [21] and multiple model [22] on account of number of bands to be used. Compared to theoretical interpretation model, semi-theoretical semi-empirical model is simplified by using the combination of the theoretical model with the empirical value of research area. Taking advantage of the less required parameters during the calculative process and high accuracy of inversion, it has been widely used in currently remotely sensed bathymetric technology. As one of the widely used research technologies of remotely sensed bathymetric, the statistical correlation model derives water depth data through building the correlativity between radiance of remote sensing image and measured water depth [14, 15]. As compared to the theoretical interpretation model and semi-theoretical semi-empirical model, statistical correlation model does not require optical parameters on the inner water body, and the simple calculation is widely used. Nevertheless, due to the unique specific area of water in research, the correlation of measured water depth and radiance of remote sensing image cannot be guaranteed, thus leading to an undesirable result.

Not only is the implementation of the model a key factor to improve the accuracy of water depth inversion but also the quantity of remote sensing data can affect it. However, the previous research concentrated on comparing the accuracy of different inverse methods, paying less attention on analyzing error of different remote sensing data used for inverse water depth.

Take the sea area of East Gong Island as an example. The sea area of East Gong Island is located in the north of South China Sea. We established the band ratio models by using three multispectral images of Landsat-8, SPOT-6, WorldView-2 and measured points. The band ratio model with the highest accuracy was selected for the depth inversion. This study compares accuracy of inversion of remote sensing data that were acquired by three different sensors and analyzes the inversion accuracy of three remote sensing data in the range of water depths of 0–5, 5–10, 10–15, and 15–20 m.

### 2. Materials and methods

#### 2.1. Study area

of bottom reflectance using the visible spectrum of TM remote sensing to build the multiband model and applied the model to Shark bay. The result shows that there has a larger error when inversion of deeper water depth by using TM remote sensing image. Sandidge and Holyer [19] established the artificial neural network model using the correlation between bathymetric information and hyperspectral remote sensing images and then used the model to invert the

The research on water depth inversion by using multispectral remote sensing data has been rapidly developed. Three forms of models have been developed, theory interpretation model, semi-theoretical semi-empirical model, and statistical correlation model in the aspect of construction of remote sensing model for water sounding. Based on the radiation transfer equation in the water body, the theory interpretation model calculates water depth by measuring the optical parameter inside the water body. Currently, the common theory interpretation model is the Two-Stream Approximation Model [20]. Two-Stream refers that for any depth Z, water can be divided into two parts: above depth Z and below depth Z; thus, the light radiant flux of the water body can be decomposed into upward component and downward component. The radiation flux varies with the water depth can be estimated by studying the value or ratio of upward component and downward component. Due to the participation of water depth variable Z in the analytical process, it is possible to calculate the distribution of water depth using this model. Based on the radiation damping of light in the water, the semitheoretical semi-empirical model fulfills the remote sensing inversion of water by using the combination of the theoretical model with empirical model, and it can be classified into singleband model [21] and multiple model [22] on account of number of bands to be used. Compared to theoretical interpretation model, semi-theoretical semi-empirical model is simplified by using the combination of the theoretical model with the empirical value of research area. Taking advantage of the less required parameters during the calculative process and high accuracy of inversion, it has been widely used in currently remotely sensed bathymetric technology. As one of the widely used research technologies of remotely sensed bathymetric, the statistical correlation model derives water depth data through building the correlativity between radiance of remote sensing image and measured water depth [14, 15]. As compared to the theoretical interpretation model and semi-theoretical semi-empirical model, statistical correlation model does not require optical parameters on the inner water body, and the simple calculation is widely used. Nevertheless, due to the unique specific area of water in research, the correlation of measured water depth and radiance of remote sensing image cannot be

Not only is the implementation of the model a key factor to improve the accuracy of water depth inversion but also the quantity of remote sensing data can affect it. However, the previous research concentrated on comparing the accuracy of different inverse methods, paying less attention on analyzing error of different remote sensing data used for inverse water depth.

Take the sea area of East Gong Island as an example. The sea area of East Gong Island is located in the north of South China Sea. We established the band ratio models by using three multispectral images of Landsat-8, SPOT-6, WorldView-2 and measured points. The band ratio

depth information of the study area.

164 Sea Level Rise and Coastal Infrastructure

guaranteed, thus leading to an undesirable result.

South China Sea is a marginal sea that is part of the Pacific Ocean. It is located south of mainland China. Toward the north of mainland China are Guangxi and Guangdong provinces; east is Philippines; southwest is Vietnam and peninsular Malaysia and the southernmost continent Zengmu reef close to Kalimantan Island. South China Sea is the largest and deepest sea in China. The waters within the nine-dotted line are territorial waters of China. The natural sea area of South China Sea is about 3.5 million km2 . The area of territorial waters of China is about 2.1 million km2 , and the average depth is about 1200 m, more than 5000 m as the deepest point. Meanwhile, South China Sea is rich in marine oil and gas, mineral resources, coastal and island tourism resources, marine energy resources, port shipping resources, and tropical and subtropical biological resources. In addition, South China Sea is the most important distribution area of tropical ecosystems of the sea island, coral reef, mangrove forest, seagrass bed, and so on.

Seabed terrain of the South China Sea is complex and mainly comprises continental shelf, continental slope, and central deep-sea basin. The central deep-sea basin is located in the north from the center of South China Sea with slightly higher than the territory lying north-east, south-west gradually lower. The continental shelf is inclined to the sea basin with different slope tendencies along the continental margin and island arc and is the most extensive in the north and south. The steep continental slope lies between the central sea basin and continental shelf, and the continental shelf is divided into four continental slope areas of east, south, west, and north. The South China Sea islands are formed on the steps of the sea basin, Dongsha islands lie on the east sand steps of the northern continental slope area, Xisha islands and Zhongsha islands are located in the western slope area of Xisha steps and Zhongsha steps, respectively, and Nansha islands are formed on the Nansha steps in the southern continental slope area. In addition, there are many seamounts and seakolls in the South China Sea, such as the Jianfeng, Beipo, and Bijia seamounts in the northern continental margin, the Shuangfeng, Daimao, Xianbei, Shixing, Xiannan, Zhangzhong, Huangyan, and Zhongnan seamounts in the central deep-sea basin, and the Pearl seamount in the southern part of South China Sea [23].

East Gong Island waters, located in the north of South China Sea (Figure 2), have geographical coordinates between 108�59<sup>0</sup> 5.7800E and 109�6<sup>0</sup> 47.9400E and between 18�16<sup>0</sup> 9.1500N and 18�22<sup>0</sup> 54.3000N. They belong to the Hainan Province, China, and experience tropical monsoon climate at low latitudes. The seawater in this area has strong penetrability and the maximum depth is about 20 m.

Figure 2. Location of study area.

#### 2.2. Model principles

The spectral characteristics of the objects reflect their own attributes and status, so different objects have different spectral characteristics. The optical characteristics of water are determined by absorption and scattering properties of the optical active substance. The spectral proprieties of water derived by using remote sensing system to measure radiance of a range of wavelengths are the basics of inversion of water depth using remote sensing.

According to the Bouguer theorem [14], the changes of light radiation flux as water depth

In the formula, I<sup>0</sup> and I Zð Þ represent the light on the water surface and radiation flux of the water depth Z, respectively. K represents decay degree. Thus, we can obtain the simple model

In the formula, RE is the reflection received by the sensor; K is the decay coefficient of water; Rw is the reflectance of water; Rb is the reflectance of underwater; α is a comprehensive factor, which delivers the effects of solar radiance transmission in the water surface and atmosphere;

RE � Rw ¼ αRbe

<sup>¼</sup> Rb<sup>1</sup> Rb<sup>2</sup> e

RE<sup>1</sup> � Rw<sup>1</sup> RE<sup>2</sup> � Rw<sup>2</sup> �KZ (1)

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 167

�2KZ <sup>þ</sup> Rw (2)

�2KZ (3)

�2ð Þ <sup>K</sup>1�K<sup>2</sup> <sup>Z</sup> (4)

I Zð Þ¼ I0e

RE ¼ αRbe

fulfill exponential decay, namely:

Figure 3. Schemata of optical dissemination in water.

and sunlight reflects on the water surface.

Through ratio operation on bands 1 and 2, we can derive:

According to the equation above:

Further, it can be derived that:

[14, 16]:

The material compositions decide the spectral signature of water; it can also be affected by the statue of water. After a series of reflection and absorption of water, the radiance of sunlight reaching the sensor can be divided into three parts (Figure 3): (1) the solar radiance scatted by the atmosphere reaches the sensor; (2) the solar radiance reflected by water reaches the sensor; (3) the backward scattered light of water and reflected light of underwater return to the atmosphere and is intercepted by the sensor. This part is called water-leaving radiance which includes the information of water. The reflectance of a range of wavelengths usually has significant differences due to the water depth, which is the theoretical basis of quantitative inversion of water depth of remote sensing.

Band ratio model is developed on the basis of the single-band and dual-band models. It builds the linear or nonlinear statistical relation models between remote sensing data and synchronous measured depth based on the decay properties of light in water. This model combines the intrinsic optical quantum and preventative optical quantum of water according to the radiation transfer theory [24]. Using some assumption conditions to reduce the spatiotemporal differences of the unit intrinsic optical mass to invert the parameters of water, we simplify the model with approximate relationship, reducing the unknown value and interdependent relationship. Therefore, the band ratio model has certain physical significance and high inversion accuracy, thus can be used widely.

Figure 3. Schemata of optical dissemination in water.

2.2. Model principles

Figure 2. Location of study area.

166 Sea Level Rise and Coastal Infrastructure

inversion of water depth of remote sensing.

accuracy, thus can be used widely.

The spectral characteristics of the objects reflect their own attributes and status, so different objects have different spectral characteristics. The optical characteristics of water are determined by absorption and scattering properties of the optical active substance. The spectral proprieties of water derived by using remote sensing system to measure radiance of a range of

The material compositions decide the spectral signature of water; it can also be affected by the statue of water. After a series of reflection and absorption of water, the radiance of sunlight reaching the sensor can be divided into three parts (Figure 3): (1) the solar radiance scatted by the atmosphere reaches the sensor; (2) the solar radiance reflected by water reaches the sensor; (3) the backward scattered light of water and reflected light of underwater return to the atmosphere and is intercepted by the sensor. This part is called water-leaving radiance which includes the information of water. The reflectance of a range of wavelengths usually has significant differences due to the water depth, which is the theoretical basis of quantitative

Band ratio model is developed on the basis of the single-band and dual-band models. It builds the linear or nonlinear statistical relation models between remote sensing data and synchronous measured depth based on the decay properties of light in water. This model combines the intrinsic optical quantum and preventative optical quantum of water according to the radiation transfer theory [24]. Using some assumption conditions to reduce the spatiotemporal differences of the unit intrinsic optical mass to invert the parameters of water, we simplify the model with approximate relationship, reducing the unknown value and interdependent relationship. Therefore, the band ratio model has certain physical significance and high inversion

wavelengths are the basics of inversion of water depth using remote sensing.

According to the Bouguer theorem [14], the changes of light radiation flux as water depth fulfill exponential decay, namely:

$$I(Z) = I\_0 \mathfrak{e}^{-\mathbb{K}Z} \tag{1}$$

In the formula, I<sup>0</sup> and I Zð Þ represent the light on the water surface and radiation flux of the water depth Z, respectively. K represents decay degree. Thus, we can obtain the simple model [14, 16]:

$$R\_E = \alpha R\_b e^{-2KZ} + R\_w \tag{2}$$

In the formula, RE is the reflection received by the sensor; K is the decay coefficient of water; Rw is the reflectance of water; Rb is the reflectance of underwater; α is a comprehensive factor, which delivers the effects of solar radiance transmission in the water surface and atmosphere; and sunlight reflects on the water surface.

According to the equation above:

$$R\_E - R\_w = \alpha R\_b e^{-2kZ} \tag{3}$$

Through ratio operation on bands 1 and 2, we can derive:

$$\frac{R\_{E1} - R\_{w1}}{R\_{E2} - R\_{w2}} = \frac{R\_{b1}}{R\_{b2}} e^{-2(K\_1 - K\_2)Z} \tag{4}$$

Further, it can be derived that:

$$Z = -\frac{1}{2(K\_1 - K\_2)} \ln \frac{R\_{E1} - R\_{w1}}{R\_{E2} - R\_{w2}} + \frac{1}{2(K\_1 - K\_2)} \ln \frac{R\_{b1}}{R\_{b2}} \tag{5}$$

Assuming bands 1 and 2 keep constant reflectances on different substrates, Rb<sup>1</sup> Rb<sup>2</sup> is a constant. The difference in value of the decay coefficient of the two bands in the different types of water does not change.

In Eq. (5):

$$a = \frac{1}{2(K\_1 - K\_2)}, \; b = \frac{1}{2(K\_1 - K\_2)} \ln \frac{R\_{b1}}{R\_{b2}}, \; X\_i = R\_{Ei} - R\_{wi}$$

Then, Eq. (5) can be simplified as:

$$Z = a \ln\left(\frac{X\_1}{X\_2}\right) + b \tag{6}$$

inversion error. Adopted satellite remote sensing data are applied in the fourth quarter of 2013.

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 169

Generally, the remote sensing digital image shows the pixel DN value which is the digital expression without dimension. Using the DN value can only compare the same-scene image. Only by converting the image DN value into the radiation luminance value of the corresponding pixel can the remote sensing data obtained from different locations, at different times, and from different types of sensors be quantitatively compared and applied to meet the

The geometric distortion of the original image is very large because it is affected by the sensor platform latitude, height, and speed changes and by various factors such as panoramic distortion, Earth curvature, and the instantaneous field of view with a nonlinear characteristic of the sensor in the scanning, which has brought difficulties to the quantitative analysis. Therefore, the images must be corrected in order to use remote sensing images for analysis and research work. Remote sensing data need to do geometric accurate correction after obtained from the receiving department. In this study, the geometric correction of the remote sensing image is carried out by using the ground control point (GCP). Selecting the corresponding points as the control points to establish the correspondence between the distortion space and the correction space, all the pixels of the distortion space are transformed into the correction space, and the geometric correction of the remote sensing image is carried out by the correspondence between

The purpose of remote sensing is using sensors to efficiently collect electromagnetic radiation from the ground. However, the measured value of the remote sensing sensor is not the same as the actual spectral emissivity of the object due to the transmission of electromagnetic waves affected by the remote sensing sensor sensitivity analysis, the conditions of light conditions, and the role of human impact in the atmosphere and the sensor in the measurement process, so it is radiation distortion in the measured value. The main purpose of atmospheric correction is

Fast Line of Sight Atmospheric Analysis of Spectral Hypercube (FLAASH) is an atmospheric correction model of high spectral radiant energy image reflectance inversion, which can accurately compensate for atmospheric effects. The applicable wavelength range includes visibleto-near infrared and short-wave infrared. In this study, atmospheric correction module FLAASH in the Environment for Visualizing Images was used to realize the atmospheric

The distribution of measured points is shown in Figure 4. The reflectance values of the pixels

In this study, we build six band ratio models with blue, green, red, and near-infrared bands. The parameters of the band ratio model are regression analyzed by using the attribute

to eliminate the effects of atmospheric scattering on radiation distortion.

that corresponding to the measured points at each band were extracted.

It can be applied because the image time is close to the measured data collection time.

needs of the research. The process of conversion is called radiometric calibration.

the two sets of coordinates.

correction of the image.

3. Results

Eq. (6) is the band ratio model expression [16].

To some extent, the band ratio model eliminates the attenuation coefficient due to uneven water body and the effect of different reflectances in the bottom due to the differences of sediments. In addition, the band ratio model can also impair the sun elevation angle, surface wave, and satellite attitude; the scan angle changes such difference effects on the version of water depth.

#### 2.3. Data and preprocessing

The study used three types of data, including the US Landsat-8, the French SPOT-6 and the US WorldView-2 satellite data. The acquisition time, spectral values of the used band, and spatial resolution are listed in Table 1. Water depth-measured data used the single-point sonar data measured by Guangzhou Ocean Geological Survey in November 2014. There are multiple measured points in the same pixel due to the fact that the sounding points of the survey line are dense, which will cause the measured points at different depths of the pixel correspond to the digital number (DN) value of the same pixel and result in an increase in inversion error. Therefore, the number of sounding points becomes less in order to avoid increasing the


Table 1. Parameters of remote sensing satellite.

inversion error. Adopted satellite remote sensing data are applied in the fourth quarter of 2013. It can be applied because the image time is close to the measured data collection time.

Generally, the remote sensing digital image shows the pixel DN value which is the digital expression without dimension. Using the DN value can only compare the same-scene image. Only by converting the image DN value into the radiation luminance value of the corresponding pixel can the remote sensing data obtained from different locations, at different times, and from different types of sensors be quantitatively compared and applied to meet the needs of the research. The process of conversion is called radiometric calibration.

The geometric distortion of the original image is very large because it is affected by the sensor platform latitude, height, and speed changes and by various factors such as panoramic distortion, Earth curvature, and the instantaneous field of view with a nonlinear characteristic of the sensor in the scanning, which has brought difficulties to the quantitative analysis. Therefore, the images must be corrected in order to use remote sensing images for analysis and research work. Remote sensing data need to do geometric accurate correction after obtained from the receiving department. In this study, the geometric correction of the remote sensing image is carried out by using the ground control point (GCP). Selecting the corresponding points as the control points to establish the correspondence between the distortion space and the correction space, all the pixels of the distortion space are transformed into the correction space, and the geometric correction of the remote sensing image is carried out by the correspondence between the two sets of coordinates.

The purpose of remote sensing is using sensors to efficiently collect electromagnetic radiation from the ground. However, the measured value of the remote sensing sensor is not the same as the actual spectral emissivity of the object due to the transmission of electromagnetic waves affected by the remote sensing sensor sensitivity analysis, the conditions of light conditions, and the role of human impact in the atmosphere and the sensor in the measurement process, so it is radiation distortion in the measured value. The main purpose of atmospheric correction is to eliminate the effects of atmospheric scattering on radiation distortion.

Fast Line of Sight Atmospheric Analysis of Spectral Hypercube (FLAASH) is an atmospheric correction model of high spectral radiant energy image reflectance inversion, which can accurately compensate for atmospheric effects. The applicable wavelength range includes visibleto-near infrared and short-wave infrared. In this study, atmospheric correction module FLAASH in the Environment for Visualizing Images was used to realize the atmospheric correction of the image.

### 3. Results

<sup>Z</sup> ¼ � <sup>1</sup>

<sup>a</sup> <sup>¼</sup> <sup>1</sup>

Eq. (6) is the band ratio model expression [16].

Then, Eq. (5) can be simplified as:

2ð Þ K<sup>1</sup> � K<sup>2</sup>

does not change.

168 Sea Level Rise and Coastal Infrastructure

In Eq. (5):

water depth.

2.3. Data and preprocessing

Table 1. Parameters of remote sensing satellite.

2ð Þ K<sup>1</sup> � K<sup>2</sup>

Assuming bands 1 and 2 keep constant reflectances on different substrates, Rb<sup>1</sup>

, b <sup>¼</sup> <sup>1</sup>

ln RE<sup>1</sup> � Rw<sup>1</sup> RE<sup>2</sup> � Rw<sup>2</sup>

The difference in value of the decay coefficient of the two bands in the different types of water

2ð Þ K<sup>1</sup> � K<sup>2</sup>

X2 

To some extent, the band ratio model eliminates the attenuation coefficient due to uneven water body and the effect of different reflectances in the bottom due to the differences of sediments. In addition, the band ratio model can also impair the sun elevation angle, surface wave, and satellite attitude; the scan angle changes such difference effects on the version of

The study used three types of data, including the US Landsat-8, the French SPOT-6 and the US WorldView-2 satellite data. The acquisition time, spectral values of the used band, and spatial resolution are listed in Table 1. Water depth-measured data used the single-point sonar data measured by Guangzhou Ocean Geological Survey in November 2014. There are multiple measured points in the same pixel due to the fact that the sounding points of the survey line are dense, which will cause the measured points at different depths of the pixel correspond to the digital number (DN) value of the same pixel and result in an increase in inversion error. Therefore, the number of sounding points becomes less in order to avoid increasing the

Satellite products Landsat-8 SPOT-6 WorldView-2 Acquisition time 10/26/2013 12/7/2013 10/7/2013 Spectral value (μm) Blue: 0.483 Blue: 0.485 Blue: 0.480

Spatial resolution (m) 30 6 1.8

Green: 0.561 Green: 0.560 Green: 0.545 Red: 0.655 Red: 0.660 Red: 0.660 NIR: 0.865 NIR: 0.825 NIR: 0.833

<sup>Z</sup> <sup>¼</sup> aln <sup>X</sup><sup>1</sup>

þ

ln Rb<sup>1</sup> Rb<sup>2</sup>

1 2ð Þ K<sup>1</sup> � K<sup>2</sup> ln Rb<sup>1</sup> Rb<sup>2</sup>

, Xi ¼ REi � Rwi

þ b (6)

(5)

Rb<sup>2</sup> is a constant.

The distribution of measured points is shown in Figure 4. The reflectance values of the pixels that corresponding to the measured points at each band were extracted.

In this study, we build six band ratio models with blue, green, red, and near-infrared bands. The parameters of the band ratio model are regression analyzed by using the attribute

Figure 4. Measured points' distribution of the study area.

information of measured points of water depth which includes the reflectance value and water depth of each band of the image. Correlation coefficient is shown in Table 2. It can be seen from the table that the correlation coefficient of blue-near-infrared band ratio model of Landsat-8 image is the highest, and R<sup>2</sup> is equal to 0.5073; the correlation coefficient of greenred band ratio model of SPOT-6 is the highest, and R2 is equal to 0.7064; the correlation coefficient of blue-green band ratio model of WorldView-2 is the highest, and R<sup>2</sup> is equal to 0.6679 (Figure 5). Thus, we choose the above models as the final models to inverse the water depth.

trend of water depth is consistent along the direction of curve 'C'. It is verified that the

The average relative error was selected to measure the accuracy of the depth inversion. It is the

where <sup>E</sup> is average relative error, <sup>n</sup> is sample size, <sup>b</sup>xi is water depth of inversion, xi is measured

Calculating average relative error of water depth inversion from remote sensing image in a

Average relative errors of inversion water depth results are analyzed. The inversion errors of SPOT-6 image are the least in three ranges of water depths, 0–5, 5–10, and 10–15 m, respectively, 30.99, 13.62, and 21.68%. The inversion error in 0–5 m is bigger because of the location of this area; it lies in the zone of wave breaking, where waves can increase the sediment in the water. This affects the reflection of light on the water surface and the scattering in the water

<sup>b</sup>xi � xi xi

� � � �

� 100%

� � � �

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 171

(7)

ei j j <sup>¼</sup> <sup>1</sup> n Xn i

reliability of water depth inversion results from another aspect.

average of sum of absolute values of relative error of sample:

<sup>E</sup> <sup>¼</sup> <sup>1</sup> n Xn i

water depth, and ei is relative error.

Figure 5. Scatter plots of band ratio and depth.

different range, the results are shown in Table 3.

Putting the regression parameter derived from band ratio and values of corresponding water depth point to the corresponding band ratio model; the inversion result is shown in Figure 6.

It can be seen that the inversion results of three different sensors overall have good consistency. The water depths in the 'A' box in Figure 6 are bigger, and they are smaller in 'B' box. The


Table 2. Correlation coefficient of the band ratio models.

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 171

Figure 5. Scatter plots of band ratio and depth.

information of measured points of water depth which includes the reflectance value and water depth of each band of the image. Correlation coefficient is shown in Table 2. It can be seen from the table that the correlation coefficient of blue-near-infrared band ratio model of Landsat-8 image is the highest, and R<sup>2</sup> is equal to 0.5073; the correlation coefficient of greenred band ratio model of SPOT-6 is the highest, and R2 is equal to 0.7064; the correlation coefficient of blue-green band ratio model of WorldView-2 is the highest, and R<sup>2</sup> is equal to 0.6679 (Figure 5). Thus, we choose the above models as the final models to inverse the water

Putting the regression parameter derived from band ratio and values of corresponding water depth point to the corresponding band ratio model; the inversion result is shown in Figure 6. It can be seen that the inversion results of three different sensors overall have good consistency. The water depths in the 'A' box in Figure 6 are bigger, and they are smaller in 'B' box. The

Landsat-8 0.1698 0.0063 0.5073 0.0259 0.3163 0.2935 SPOT-6 0.2656 0.6528 0.3201 0.7064 0.2221 0.7008 WorldView-2 0.6679 0.3059 0.0110 0.0258 0.1261 0.2505

B/G B/R B/NIR G/R G/NIR R/NIR

depth.

Satellite Band ratio

Table 2. Correlation coefficient of the band ratio models.

Figure 4. Measured points' distribution of the study area.

170 Sea Level Rise and Coastal Infrastructure

trend of water depth is consistent along the direction of curve 'C'. It is verified that the reliability of water depth inversion results from another aspect.

The average relative error was selected to measure the accuracy of the depth inversion. It is the average of sum of absolute values of relative error of sample:

$$\overline{E} = \frac{1}{n} \sum\_{i}^{n} |e\_{i}| = \frac{1}{n} \sum\_{i}^{n} \left| \frac{\widehat{\mathbf{x}}\_{i} - \mathbf{x}\_{i}}{\mathbf{x}\_{i}} \times 100\% \right| \tag{7}$$

where <sup>E</sup> is average relative error, <sup>n</sup> is sample size, <sup>b</sup>xi is water depth of inversion, xi is measured water depth, and ei is relative error.

Calculating average relative error of water depth inversion from remote sensing image in a different range, the results are shown in Table 3.

Average relative errors of inversion water depth results are analyzed. The inversion errors of SPOT-6 image are the least in three ranges of water depths, 0–5, 5–10, and 10–15 m, respectively, 30.99, 13.62, and 21.68%. The inversion error in 0–5 m is bigger because of the location of this area; it lies in the zone of wave breaking, where waves can increase the sediment in the water. This affects the reflection of light on the water surface and the scattering in the water

and leads to the increase of the inversion error. The inversion error of the Landsat-8 image is

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 173

It can be seen from Figure 7 that the order of errors of the inversion water depth array from small to large are SPOT-6, WorldView-2, and Landsat-8 in the range of 0–5, 5–10, and water depth. However, the order changes in the range of 10–15 m. The inversion error of SPOT-6 image is still the least, but the inversion error of WorldView-2 is a little bigger than the inversion error of Landsat-8. When the water depth is greater than 15 m, the inversion relative error of Landsat-8 is the least, followed by WorldView-2, and the inversion relative error of

The remote sensing technology of water depth developed rapidly. The nature of remote sensor and the inversion model have an important influence on the inversion accuracy of water depth. In order to improve the accuracy of remote sensing inversion further, the research of remote

As the important collector of water depth information in remote sensing inversion of water depth, the nature of the remote sensor has an important influence on the accuracy of inversion. The data obtained by different sensors have their own characteristics in spectral resolution and spatial resolution. Therefore, the results of water depth information show some differences. The final effective information of the depth information received by the remote sensor is to highlight the depth information in water depth remote sensing. The chlorophyll, suspended sediment, chromophoric dissolved organic matter (CDOM), and other information of the water body as the noise in water depth remote sensing must be suppressed or removed, so requirements for the spectral resolution of the remote sensor are put forward. The technology of water depth remote sensing is an important auxiliary means for conventional bathymetry. The scope of engineering survey and the precision of the mapping are also certain requirements

least (32.5%) in the range of 15–20 m water depth.

Figure 7. Average relative error of depth inversion results in different depth ranges.

sensor and inversion model must be strengthened.

SPOT-6 is the most.

4. Discussions

4.1. Remote sensor

Figure 6. Depth inversion results.


Table 3. Average relative error of the depth inversion results.

Figure 7. Average relative error of depth inversion results in different depth ranges.

and leads to the increase of the inversion error. The inversion error of the Landsat-8 image is least (32.5%) in the range of 15–20 m water depth.

It can be seen from Figure 7 that the order of errors of the inversion water depth array from small to large are SPOT-6, WorldView-2, and Landsat-8 in the range of 0–5, 5–10, and water depth. However, the order changes in the range of 10–15 m. The inversion error of SPOT-6 image is still the least, but the inversion error of WorldView-2 is a little bigger than the inversion error of Landsat-8. When the water depth is greater than 15 m, the inversion relative error of Landsat-8 is the least, followed by WorldView-2, and the inversion relative error of SPOT-6 is the most.

### 4. Discussions

The remote sensing technology of water depth developed rapidly. The nature of remote sensor and the inversion model have an important influence on the inversion accuracy of water depth. In order to improve the accuracy of remote sensing inversion further, the research of remote sensor and inversion model must be strengthened.

#### 4.1. Remote sensor

Figure 6. Depth inversion results.

172 Sea Level Rise and Coastal Infrastructure

Table 3. Average relative error of the depth inversion results.

Depth (m) Landsat-8 (%) SPOT-6 (%) WorldView-2 (%)

–5 40.89 30.99 31.28 –10 22.72 13.62 22.31 –15 23.12 21.68 24.35 –20 32.50 33.65 33.22 Average 29.81 24.89 27.54 As the important collector of water depth information in remote sensing inversion of water depth, the nature of the remote sensor has an important influence on the accuracy of inversion. The data obtained by different sensors have their own characteristics in spectral resolution and spatial resolution. Therefore, the results of water depth information show some differences. The final effective information of the depth information received by the remote sensor is to highlight the depth information in water depth remote sensing. The chlorophyll, suspended sediment, chromophoric dissolved organic matter (CDOM), and other information of the water body as the noise in water depth remote sensing must be suppressed or removed, so requirements for the spectral resolution of the remote sensor are put forward. The technology of water depth remote sensing is an important auxiliary means for conventional bathymetry. The scope of engineering survey and the precision of the mapping are also certain requirements for the spatial resolution of remote sensor. In the future of the inversion of remote sensing, improving the system stability and signal-to-noise ratio of the remote sensor, using the hyperspectral remote sensing data to highlight the depth information, and the combination of remote sensing data with spectral resolution and spatial resolution, which will be of significance to improve the accuracy of remote sensing inversion, is necessary.

combine the measured water depth data and depth reflectance of satellite information on different scales so as to minimize the error caused by the asynchronization between the remote sensing image and the measured data is the key influencing factor of the parameter selection of current water depth model. In addition, the measured underwater terrain removes the influence of other factors (such as tidal range) at different times with geodetic datum as the standard. To some extent, the transit time of the satellite superposes the wave information because of transient imaging. Therefore, the correction of wave height is of great significance to improve the accuracy of inversion in the case of water depth remote sensing inversion in the

Coastal Geomorphology and Its Impacts http://dx.doi.org/10.5772/intechopen.73510 175

In terms of the geomorphological characteristics of coastal waters in tropical regions, this study selected three different kinds of sensors to find out the inverse water depth of remote sensing image, and the results showed that the highest accuracy of water depth inversion is SPOT-6 image. The least accuracy of water depth inversion is in the range of 0–5 m due to the effects of human activities offshore, which has a big decay coefficient; in addition, this area belongs to the ocean crushed zone, thus surface roughness of the ocean is big and measurable depth is small. On the one hand, the accuracy of water depth inversion is highest in the range of 5–10 m, the error of inversion turns to progressive tendency with the increase of depth, which means that there is a negative correlation between water depth and inverse accuracy. On the other hand, there is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of the sensor. In addition, many other factors such as suspended particle, yellow substance, and chlorophyll concentration also affect the accuracy of inversion. The remote sensing of water depth has the advantages of being macroscopic, dynamic, and objective. It has played a certain role in practical engineering and has a wide application prospect as one of the emerging technologies in the field of remote sensing and is an important complement to conventional water depth measurements. It is necessary to strengthen the research of remote sensor, the mechanism of water depth remote sensing, and the construction of the model in future research. The remote sensing spectral range and the band combination are scientifically selected to highlight the water depth information, and the influence of the water body and the material of the atmosphere on the water depth information are taken into account to further improve the accuracy and application range of the remote sensing inversion.

stormy sea with high-resolution remote sensing images.

5. Conclusion

Author details

Tianqi Lu, Shengbo Chen\* and Yan Yu

\*Address all correspondence to: chensb@jlu.edu.cn

College of Geo-exploration Science and Technology, Jilin University, Changchun, China

#### 4.2. Remote sensing model

The information received by remote sensor mainly includes water information and atmospheric information. Therefore, the improvement of remote sensing model must include two aspects: improvement of atmospheric model and water model.

Atmospheric information is the interference in remote sensing inversion of water depth, and it mainly includes the optical radiation of water depth information in the process of letting the water reach the remote sensor. On the one hand, it is weakened by the atmosphere. On the other hand, a lot of atmospheric scattering information is added to the depth information. Therefore, we must eliminate or suppress atmospheric interference before conducting depth inversion. The methods of traditional atmospheric correction are mainly realized by software. However, the atmosphere is a changing fluid constantly, and the differences in atmospheric properties at different times and locations can have different effects on the radiation transmission of light, which makes atmospheric radiation correction a very complicated process. It is of great significance to further improve the accuracy of water depth remote sensing inversion by strengthening the atmospheric radiation transmission theory and the atmospheric correction models, taking into account the influence of atmosphere with different heights on water depth information transmission for various regions and making interference removal and effective information outstanding.

The water with various properties affects the transmission process of the light wave in the water body directly. After the light wave enters the water body, on the one hand, the scattering and reflection of water material reduce the light radiation reached at the bottom, which, to some extent, lowers the intensity of water-leaving radiance. On the other hand, the transmission of light wave is affected by chlorophyll, suspended sediments, CDOM, and plankton so that water-leaving radiance reflects a combination of the suspended solids and dissolved substances in the water. The improvement of accuracy of water depth remote sensing must further strengthen the model construction of the water depth information in the process of light wave transmission in the water and determination of the optical parameters of the water body. Meanwhile, strengthen the study of the spectral reflection law of the suspended matter or dissolved material in the water body and eliminate or suppress the influence of the interference information to highlight the depth information.

#### 4.3. Others

At present, the water depth remote sensing inversion is mainly based on the relationship between the measured depth data and the corresponding water depth reflectance information, then establishing the model to obtain the depth information. It takes a long time to measure the underwater terrain, and remote sensing satellite transit is only a transient process. How to combine the measured water depth data and depth reflectance of satellite information on different scales so as to minimize the error caused by the asynchronization between the remote sensing image and the measured data is the key influencing factor of the parameter selection of current water depth model. In addition, the measured underwater terrain removes the influence of other factors (such as tidal range) at different times with geodetic datum as the standard. To some extent, the transit time of the satellite superposes the wave information because of transient imaging. Therefore, the correction of wave height is of great significance to improve the accuracy of inversion in the case of water depth remote sensing inversion in the stormy sea with high-resolution remote sensing images.

### 5. Conclusion

for the spatial resolution of remote sensor. In the future of the inversion of remote sensing, improving the system stability and signal-to-noise ratio of the remote sensor, using the hyperspectral remote sensing data to highlight the depth information, and the combination of remote sensing data with spectral resolution and spatial resolution, which will be of signifi-

The information received by remote sensor mainly includes water information and atmospheric information. Therefore, the improvement of remote sensing model must include two

Atmospheric information is the interference in remote sensing inversion of water depth, and it mainly includes the optical radiation of water depth information in the process of letting the water reach the remote sensor. On the one hand, it is weakened by the atmosphere. On the other hand, a lot of atmospheric scattering information is added to the depth information. Therefore, we must eliminate or suppress atmospheric interference before conducting depth inversion. The methods of traditional atmospheric correction are mainly realized by software. However, the atmosphere is a changing fluid constantly, and the differences in atmospheric properties at different times and locations can have different effects on the radiation transmission of light, which makes atmospheric radiation correction a very complicated process. It is of great significance to further improve the accuracy of water depth remote sensing inversion by strengthening the atmospheric radiation transmission theory and the atmospheric correction models, taking into account the influence of atmosphere with different heights on water depth information transmission for various regions and making interference removal and effective information outstanding. The water with various properties affects the transmission process of the light wave in the water body directly. After the light wave enters the water body, on the one hand, the scattering and reflection of water material reduce the light radiation reached at the bottom, which, to some extent, lowers the intensity of water-leaving radiance. On the other hand, the transmission of light wave is affected by chlorophyll, suspended sediments, CDOM, and plankton so that water-leaving radiance reflects a combination of the suspended solids and dissolved substances in the water. The improvement of accuracy of water depth remote sensing must further strengthen the model construction of the water depth information in the process of light wave transmission in the water and determination of the optical parameters of the water body. Meanwhile, strengthen the study of the spectral reflection law of the suspended matter or dissolved material in the water body and eliminate or suppress the influence of the interference

At present, the water depth remote sensing inversion is mainly based on the relationship between the measured depth data and the corresponding water depth reflectance information, then establishing the model to obtain the depth information. It takes a long time to measure the underwater terrain, and remote sensing satellite transit is only a transient process. How to

cance to improve the accuracy of remote sensing inversion, is necessary.

aspects: improvement of atmospheric model and water model.

information to highlight the depth information.

4.3. Others

4.2. Remote sensing model

174 Sea Level Rise and Coastal Infrastructure

In terms of the geomorphological characteristics of coastal waters in tropical regions, this study selected three different kinds of sensors to find out the inverse water depth of remote sensing image, and the results showed that the highest accuracy of water depth inversion is SPOT-6 image. The least accuracy of water depth inversion is in the range of 0–5 m due to the effects of human activities offshore, which has a big decay coefficient; in addition, this area belongs to the ocean crushed zone, thus surface roughness of the ocean is big and measurable depth is small. On the one hand, the accuracy of water depth inversion is highest in the range of 5–10 m, the error of inversion turns to progressive tendency with the increase of depth, which means that there is a negative correlation between water depth and inverse accuracy. On the other hand, there is no linear relationship between the accuracy of remote sensing water depth inversion and spatial resolution of remote sensing data, and it is affected by performance and parameters of the sensor. In addition, many other factors such as suspended particle, yellow substance, and chlorophyll concentration also affect the accuracy of inversion.

The remote sensing of water depth has the advantages of being macroscopic, dynamic, and objective. It has played a certain role in practical engineering and has a wide application prospect as one of the emerging technologies in the field of remote sensing and is an important complement to conventional water depth measurements. It is necessary to strengthen the research of remote sensor, the mechanism of water depth remote sensing, and the construction of the model in future research. The remote sensing spectral range and the band combination are scientifically selected to highlight the water depth information, and the influence of the water body and the material of the atmosphere on the water depth information are taken into account to further improve the accuracy and application range of the remote sensing inversion.

### Author details

Tianqi Lu, Shengbo Chen\* and Yan Yu

\*Address all correspondence to: chensb@jlu.edu.cn

College of Geo-exploration Science and Technology, Jilin University, Changchun, China

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[23] Yao BC. The geological structure and mineral resources of South China Sea. Chinese

[24] Li X, Chen SB, Wang XH. Study based on radioactive transfer model of the quantitative remote sensing of water bottom reflectance. Journal of Jilin University: Earth Science

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[15] Lyzenga DR. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International Journal of Remote Sensing. 1981;2(1):71-82. DOI: 10.1080/01431168108948342

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[6] Wang YN, Han L, Wang Y. Experimental research of underwater close-range photogram-

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10.1080/0143116031000066323


**Section 7**

**Coastal Water Quality**

**Coastal Water Quality**

**Chapter 11**

**Provisional chapter**

**Beach Users' Perceptions Toward Beach Quality and**

**Beach Users' Perceptions Toward Beach Quality and** 

DOI: 10.5772/intechopen.76614

**Crowding: A Case of Cenang Beach, Langkawi Island,**

**Crowding: A Case of Cenang Beach, Langkawi Island,** 

This chapter focuses on beach users' perceptions toward beach quality aspects and crowding as well as investigating beach users' main preferred activities and their motivations to choose Cenang beach in Langkawi Island as a major destination for holidaymakers in Malaysia. Questionnaire surveys on a total of 400 local and international beach users were carried out (January and February 2014). This study revealed that physical and morphological aspects of the beach have been recognized by beach users as the most important aspects of beach quality while environmental issues were ranked as the less important. Swimming and sunbathing were identified as the main preferred activities by users and landscape, water and sand cleanliness were identified as the most important reasons for choosing Cenang beach to visit. This study also found that the increased beach's sand availability does not necessarily reduce the degree of crowdedness felt by beach users.

**Keywords:** beach user, perception, beach quality, crowding, Cenang beach

Tourism, in its all types and activities, is dependent upon the consumption of environmental resources and there is a mutual dependency between environment and tourism as the quality of environment is strongly in danger by tourism growth, and on the other hand, tourism development is vastly reliant on the quality of environment [1]. According to UNEP [1], coastal tourism is highly reliant on natural resources such as climate, landscape and ecosystem, and cultural resources like historic and cultural heritage, arts and crafts, tradition, and so on. Costal

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Malaysia**

**Abstract**

**1. Introduction**

**Malaysia**

Hamed Mehranian and Azizan Marzuki

Hamed Mehranian and Azizan Marzuki

http://dx.doi.org/10.5772/intechopen.76614

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Beach Users' Perceptions Toward Beach Quality and Crowding: A Case of Cenang Beach, Langkawi Island, Malaysia Beach Users' Perceptions Toward Beach Quality and Crowding: A Case of Cenang Beach, Langkawi Island, Malaysia**

DOI: 10.5772/intechopen.76614

Hamed Mehranian and Azizan Marzuki Hamed Mehranian and Azizan Marzuki

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.76614

#### **Abstract**

This chapter focuses on beach users' perceptions toward beach quality aspects and crowding as well as investigating beach users' main preferred activities and their motivations to choose Cenang beach in Langkawi Island as a major destination for holidaymakers in Malaysia. Questionnaire surveys on a total of 400 local and international beach users were carried out (January and February 2014). This study revealed that physical and morphological aspects of the beach have been recognized by beach users as the most important aspects of beach quality while environmental issues were ranked as the less important. Swimming and sunbathing were identified as the main preferred activities by users and landscape, water and sand cleanliness were identified as the most important reasons for choosing Cenang beach to visit. This study also found that the increased beach's sand availability does not necessarily reduce the degree of crowdedness felt by beach users.

**Keywords:** beach user, perception, beach quality, crowding, Cenang beach

#### **1. Introduction**

Tourism, in its all types and activities, is dependent upon the consumption of environmental resources and there is a mutual dependency between environment and tourism as the quality of environment is strongly in danger by tourism growth, and on the other hand, tourism development is vastly reliant on the quality of environment [1]. According to UNEP [1], coastal tourism is highly reliant on natural resources such as climate, landscape and ecosystem, and cultural resources like historic and cultural heritage, arts and crafts, tradition, and so on. Costal

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

tourism brings up the image of the resorts on the seaside, beaches, warm sands and plenty of sunshine [2] and according to Roca [3] sun, sea and sand tourism leads to an influx of people to the beaches However, tourists nowadays anticipate to have an experience on the beach that is more than sun, sand and sea. They seek a diverse range of recreational experiences like culinary, culture, natural beauty, sports, and so on. One of the main aims of this study is to understand the mutual interactions between beach user's and the physical environment.

different desires and requirements, and shifting priorities and opinions that are reported by policy-makers and beach users should be considered. Better understanding of how individuals perceive beach quality is very applicable for managers for planning strategies in integrated management [13]. This kind of beach users' analysis is an important part of sustainable beach development and affects managers' decision-making process. Numerous authors have also focused on the importance of users' perceptions of beach quality and discussed its dimensions [3, 18–20] and emphasized on the importance of assessment of people's opinions and requirements of recreational areas in order to guide beach management strategies [20–23]; however, it has been a big gap in South East of Asia's beach tourism and especially in Malaysia to do better planning and organize the visitors based on their perception and preferences. In other words, there is a lack of understanding of the beach goer's perceptions who are main custom-

Beach Users' Perceptions Toward Beach Quality and Crowding: A Case of Cenang Beach…

http://dx.doi.org/10.5772/intechopen.76614

183

As studies on users' perceptions of beach quality have been conducted over the years, but little work has concentrated on Malaysian beaches, this research explores beach users' perceptions toward beach quality to contribute to better understanding of beach goers' attitudes. This objective can be achieved by investigating the perception of different aspects of beach quality of beach users on Cenang beach in Langkawi Island to draw planning recommendation. By knowing the beach user's opinion about that region, beach managers will identify their weakness and strength to do better planning and implementing proper strategy to over-

The quality-based strategy's objective aims at obtaining sustainable development [12]. The importance of these strategies is based on their holistic, systemic and dynamic characteristics. These strategies are achievable by applying regional schemes. In these schemes, for all stakeholders (decision-makers, economic actors, NGOs and users), quality is a common aim and is a tool to obtain coastal zone management. In majority of quality-based strategies, public perception is a chief tool to improve quality. Beach quality is a key element of mass tourism [3]. In the perspective of tourism, the quality of a beach can be evaluated based on some elements like its setting, local access, security, the availability of services and infrastructure, and the quality of its sand and water [24–26]. In the perspective of tourism, the quality of a beach can be evaluated based on some elements like its setting, local access, security, the availability of services and infrastructure, and the quality of its sand and water [24–27]. According to Roca and Villares [28], in semi-natural beaches, users' satisfaction of beach quality is affected by natural beauty and conservation of the beach, and the beachgoer's profile. According to Espejel et al. [9], the ideal beach should be sandy where water is not deep with pleasant temperature, dangerous animals are absent, sand and water are clean, and no bad smells should be existent; it must be safe and basic infrastructure and services like access, lifeguards, bath-

rooms, lifeguards, shade, security, and minor shopping zones should be present.

According to Williams and Micallef [13], five elements, which are very significant in ascertaining a successful beach setting, are safety, water quality, facilities, scenery and litter. Williams

ers to use tourism products in coastal area.

come the shortage in that area.

**2. Literature review**

One of the possible outcomes of costal tourism would be a discrepancy between recreationists' demand and available sand [3] and perception of crowding happens when the quantity of encountered or observed people in a zone is too big [4] or when users' behavior interferes with an individual's own norms or objectives [5]. According to Manning [6], if visitors rate the environmental conditions worse than expected, this will have a fundamental influence on their perception of crowding. An increase in available sand on the beach elevates the satisfaction of visitors; however, not that much, since some other factors are also strongly important in beach users' perceptions toward the quality of the beach like its physical properties, facilities and landscape [7, 8]; therefore, an integrated management strategy that includes both social and natural resources conditions should be applied for managing perceived crowding [9].

The increasing attractiveness of an area which is conducted by local stakeholders and or investors results in concerns about overcrowding in that particular region [10]. This trend will emerge imbalance and disorder in the area which in long term will have numerous negative effects on natural and physical environment of the region. This issue has been in the center of attention of beach managers since mid-1970s; therefore, they should implement a multipurpose plan that is adaptable to changes in order to fulfill human and environmental requirements [3]. According to Van Maele et al. [11], in effective beach management, some elements should be considered; firstly natural environment and its dynamics life (the chemical, physical, and biological exchanges), secondly the necessities and opinions of the beach users and finally benefits for local residents and stakeholders. In general, managers should apply a quality-based strategy that public perception is a chief tool to improve the quality [12].

According to Williams and Micallef [13], better understanding of how individuals perceive the beach quality is very applicable for managers for planning strategies in integrated management. Pendleton et al. [14] also stated because of the complexity of the relationship between the natural environment and the human beings, trying to find how public, shape their attitudes and perceptions toward the quality of environment is essential; on one side, the quality of environment indirectly affects individual's recreational behavior through individual creations of perception of the environment. Alternatively, the human beings are able to directly influence the natural environmental quality via their behavior that is reliant on individual's perceptions of the environment. Once a resource like a beach undergoes degradation, the result would be a reduction in the quality of the experience of visitors [15].

As it was mentioned, one basic factor of natural resource management is implementation of users' perception [13, 16]. Beach management strategies are prepared on the basis of a broad understanding of coastal processes, provided by surveys and analysis. In other words, to improve the quality of beach and develop the facilities for beach users, there is a need to understand beach user's perceptions and priorities [17]. In this regard, the ability to meet different desires and requirements, and shifting priorities and opinions that are reported by policy-makers and beach users should be considered. Better understanding of how individuals perceive beach quality is very applicable for managers for planning strategies in integrated management [13]. This kind of beach users' analysis is an important part of sustainable beach development and affects managers' decision-making process. Numerous authors have also focused on the importance of users' perceptions of beach quality and discussed its dimensions [3, 18–20] and emphasized on the importance of assessment of people's opinions and requirements of recreational areas in order to guide beach management strategies [20–23]; however, it has been a big gap in South East of Asia's beach tourism and especially in Malaysia to do better planning and organize the visitors based on their perception and preferences. In other words, there is a lack of understanding of the beach goer's perceptions who are main customers to use tourism products in coastal area.

As studies on users' perceptions of beach quality have been conducted over the years, but little work has concentrated on Malaysian beaches, this research explores beach users' perceptions toward beach quality to contribute to better understanding of beach goers' attitudes. This objective can be achieved by investigating the perception of different aspects of beach quality of beach users on Cenang beach in Langkawi Island to draw planning recommendation. By knowing the beach user's opinion about that region, beach managers will identify their weakness and strength to do better planning and implementing proper strategy to overcome the shortage in that area.

### **2. Literature review**

tourism brings up the image of the resorts on the seaside, beaches, warm sands and plenty of sunshine [2] and according to Roca [3] sun, sea and sand tourism leads to an influx of people to the beaches However, tourists nowadays anticipate to have an experience on the beach that is more than sun, sand and sea. They seek a diverse range of recreational experiences like culinary, culture, natural beauty, sports, and so on. One of the main aims of this study is to understand the mutual interactions between beach user's and the physical environment.

182 Sea Level Rise and Coastal Infrastructure

One of the possible outcomes of costal tourism would be a discrepancy between recreationists' demand and available sand [3] and perception of crowding happens when the quantity of encountered or observed people in a zone is too big [4] or when users' behavior interferes with an individual's own norms or objectives [5]. According to Manning [6], if visitors rate the environmental conditions worse than expected, this will have a fundamental influence on their perception of crowding. An increase in available sand on the beach elevates the satisfaction of visitors; however, not that much, since some other factors are also strongly important in beach users' perceptions toward the quality of the beach like its physical properties, facilities and landscape [7, 8]; therefore, an integrated management strategy that includes both social and

natural resources conditions should be applied for managing perceived crowding [9].

The increasing attractiveness of an area which is conducted by local stakeholders and or investors results in concerns about overcrowding in that particular region [10]. This trend will emerge imbalance and disorder in the area which in long term will have numerous negative effects on natural and physical environment of the region. This issue has been in the center of attention of beach managers since mid-1970s; therefore, they should implement a multipurpose plan that is adaptable to changes in order to fulfill human and environmental requirements [3]. According to Van Maele et al. [11], in effective beach management, some elements should be considered; firstly natural environment and its dynamics life (the chemical, physical, and biological exchanges), secondly the necessities and opinions of the beach users and finally benefits for local residents and stakeholders. In general, managers should apply a quality-based strategy that public perception is a chief tool to improve the quality [12].

According to Williams and Micallef [13], better understanding of how individuals perceive the beach quality is very applicable for managers for planning strategies in integrated management. Pendleton et al. [14] also stated because of the complexity of the relationship between the natural environment and the human beings, trying to find how public, shape their attitudes and perceptions toward the quality of environment is essential; on one side, the quality of environment indirectly affects individual's recreational behavior through individual creations of perception of the environment. Alternatively, the human beings are able to directly influence the natural environmental quality via their behavior that is reliant on individual's perceptions of the environment. Once a resource like a beach undergoes degradation, the

As it was mentioned, one basic factor of natural resource management is implementation of users' perception [13, 16]. Beach management strategies are prepared on the basis of a broad understanding of coastal processes, provided by surveys and analysis. In other words, to improve the quality of beach and develop the facilities for beach users, there is a need to understand beach user's perceptions and priorities [17]. In this regard, the ability to meet

result would be a reduction in the quality of the experience of visitors [15].

The quality-based strategy's objective aims at obtaining sustainable development [12]. The importance of these strategies is based on their holistic, systemic and dynamic characteristics. These strategies are achievable by applying regional schemes. In these schemes, for all stakeholders (decision-makers, economic actors, NGOs and users), quality is a common aim and is a tool to obtain coastal zone management. In majority of quality-based strategies, public perception is a chief tool to improve quality. Beach quality is a key element of mass tourism [3]. In the perspective of tourism, the quality of a beach can be evaluated based on some elements like its setting, local access, security, the availability of services and infrastructure, and the quality of its sand and water [24–26]. In the perspective of tourism, the quality of a beach can be evaluated based on some elements like its setting, local access, security, the availability of services and infrastructure, and the quality of its sand and water [24–27]. According to Roca and Villares [28], in semi-natural beaches, users' satisfaction of beach quality is affected by natural beauty and conservation of the beach, and the beachgoer's profile. According to Espejel et al. [9], the ideal beach should be sandy where water is not deep with pleasant temperature, dangerous animals are absent, sand and water are clean, and no bad smells should be existent; it must be safe and basic infrastructure and services like access, lifeguards, bathrooms, lifeguards, shade, security, and minor shopping zones should be present.

According to Williams and Micallef [13], five elements, which are very significant in ascertaining a successful beach setting, are safety, water quality, facilities, scenery and litter. Williams and Micallef [13] also stated the importance of a variety of physical (local geology and geomorphology), biological (flora and fauna), socio-economic (recreational amenities, access, safety, landscape, archeology, and commercial interest) and environmental quality elements (hygiene, cleanliness and toilet facilities) which have been determined in the previous studies regarding the assessment of the beach quality.

that natural environment was the main motive for visiting Hallig Hooge for the majority of visitors. Most of the respondents did not feel disturbed by the quantity of other visitors they bumped into during their visit. Graefe and Moore [10] conducted a study in the Buck Island to measure certain indicators of quality in the visitor experience and probing the relationships between these indicators in which only few respondents reported that the encounters with others decreased their enjoyment. While 67% answered that other visitors had no influence on their experience and 33% noted that their enjoyment increased by encounters. It was revealed that perception of crowding is related to the level of experience, contact's location, expecta-

Beach Users' Perceptions Toward Beach Quality and Crowding: A Case of Cenang Beach…

http://dx.doi.org/10.5772/intechopen.76614

185

Needham et al. [29] conducted a survey in order to evaluate social and facility indicators at Kailua Beach Park on the east coast of Oahu, Hawaii. The findings revealed that swimmers/ waders and sunbathers were the major activity groups. Overall satisfaction of visitors was extremely high (90% were satisfied). Moreover, most of the respondents were satisfied with majority of features, particularly with not having to pay a fee to visit the park, the clean ocean water, and the absence of litter. On the other hand, they were most dissatisfied with the bathrooms. Totally, 38% of users reported that they felt crowded by the amount of people encountered. The number of sunbathers and swimmers encountered (32%) was the main reasons for visitors to feel crowded. In this study, majority of visitors encountered fewer people than the maximum number of people they would accept observing and felt that the number

In the study of Silva and Ferreira [30], in Portugal beaches, results showed that most of the people did not feel crowded in general, although the beach users stated that Tarquínio/Paraíso beach was slightly crowded by surfers in the water plane. The findings revealed that the presence of many people on the beach for majority of users increased their enjoyment, and it was not considered an important issue for them to escape the crowds. In order to identify negative and positive features of the beach quality regarding guide management and development of beaches, this study aims at analyzing beach users' perceptions of a variety of beach aspects based on the previous studies' findings. In future, this study investigates users' main pre-

ferred activities and their motivations to choose Cenang beach of Langkawi island.

South East Asia is currently one of the most important and fastest growing tourist destinations in the world with Singapore, Malaysia and Thailand in the top league possessing the basic resources for coastal tourism like sandy beaches, coral reefs, thousands of islands and a rich cultural heritage to complement coastal tourism development. The strong demand for coastal tourism in South East Asia comes not only from Europe, East Asia and Oceania but

Tropical islands with their insularity and unique combination of land forms and water and year round sunshine are particularly attractive for resort development [2]. The extraordinary beauty, cultural wealth, great variety of coastal areas, and duty-free zones have made Langkawi Island the preferred destination for many holiday makers in Asia and abroad, making coastal tourism an important tourism sector which employs a lot of people and generates

tions of visitors, and the number of other users contacted.

of encounters did not affect their enjoyment.

**3. The study area**

South East Asia itself [31].

Roca [3] assessed the beach occupancy and public perceptions of beach quality in six beaches in Spain. They divided quality parameters into four general groups as physical and morphological, environmental, facilities and services, and image and comfort. Characteristics of water, sand, beach dimensions and presence of waves, wind and rocks were studied in the physical and morphological group. The presence of items like waste and wastebaskets, toilet and shower, rain run-off, vegetation, fish and oil on water or sand, noise and animals were placed in environmental features group. Facilities and services group was divided into of stalls, deckchair, restaurants, life-saving equipment, walkway, beach and water game facilities, parking areas, and access subgroups. Finally, the quality of landscape, beach comfort, quality/price ratio and crowdedness were studied in image and comfort category. This study revealed that principal motive for visitors to choose a beach was landscape and the most favorable aspect for selecting a beach was cleanliness and sanitary conditions, followed by safety, attractiveness of landscape, tranquility, good accessibility and facilities. Beach goers were satisfied with morphological and physical aspects (water temperature and beach length are major parameters). Although noise that is considered as an indicator of crowding was perceived high in urban beaches, it still was above acceptable degree. Restaurants and bars were the highest and parking was the least scored parameters of facilities. Landscape was the most and number of users was the least favorable aspects related to image and comfort. It was concluded that evaluation of beach quality should be based on considering a variety of factors, not just density of beach users. Moreover, beach occupancy and users' perception are correlated but high degrees of occupancy do not essentially suggest low degrees of satisfaction. This implies that in beach users' perception, other factors like physical features, facilities, and landscape are vital in assessing beach quality and overall enjoyment of visitors' experience is not reduced by factors, which are associated with the quantity of users.

Duvat [12] conducted a study in France beaches in order to find users' perceptions of beach quality. In the study of Duvat [12], four major elements that affect the quality of the beach include coastal landscapes, the quality of the environment, quietness, and the natural properties of the beach. This study showed that users paid less attention to facilities in comparison with natural components. Majority of respondents expressed that physical characteristics of beaches play an important role in the quality of their visit including beach width and materials, sand quality and vegetation. Cervantes and Espejel [8] also conducted a study in four beaches in USA and Mexico. The results revealed that beaches with higher facilities and services were more appreciated by respondents. Marin et al. [18] also carried out a study in Italy to understand beach users' perceptions which beach and sea cleanliness were judged to be much more important than other issues. Majority of respondents reported beach quality and safety as good. About half of respondents judged that water quality is sufficient and the availability of recreational activities was stated as poor. In addition, crowding and its related noise were perceived high.

In a study to evaluate visitors' satisfaction and perception of crowding in a German national park on the island of Hallig Hooge conducted by Kalisch and Klaphake [5], it was found that natural environment was the main motive for visiting Hallig Hooge for the majority of visitors. Most of the respondents did not feel disturbed by the quantity of other visitors they bumped into during their visit. Graefe and Moore [10] conducted a study in the Buck Island to measure certain indicators of quality in the visitor experience and probing the relationships between these indicators in which only few respondents reported that the encounters with others decreased their enjoyment. While 67% answered that other visitors had no influence on their experience and 33% noted that their enjoyment increased by encounters. It was revealed that perception of crowding is related to the level of experience, contact's location, expectations of visitors, and the number of other users contacted.

Needham et al. [29] conducted a survey in order to evaluate social and facility indicators at Kailua Beach Park on the east coast of Oahu, Hawaii. The findings revealed that swimmers/ waders and sunbathers were the major activity groups. Overall satisfaction of visitors was extremely high (90% were satisfied). Moreover, most of the respondents were satisfied with majority of features, particularly with not having to pay a fee to visit the park, the clean ocean water, and the absence of litter. On the other hand, they were most dissatisfied with the bathrooms. Totally, 38% of users reported that they felt crowded by the amount of people encountered. The number of sunbathers and swimmers encountered (32%) was the main reasons for visitors to feel crowded. In this study, majority of visitors encountered fewer people than the maximum number of people they would accept observing and felt that the number of encounters did not affect their enjoyment.

In the study of Silva and Ferreira [30], in Portugal beaches, results showed that most of the people did not feel crowded in general, although the beach users stated that Tarquínio/Paraíso beach was slightly crowded by surfers in the water plane. The findings revealed that the presence of many people on the beach for majority of users increased their enjoyment, and it was not considered an important issue for them to escape the crowds. In order to identify negative and positive features of the beach quality regarding guide management and development of beaches, this study aims at analyzing beach users' perceptions of a variety of beach aspects based on the previous studies' findings. In future, this study investigates users' main preferred activities and their motivations to choose Cenang beach of Langkawi island.

### **3. The study area**

and Micallef [13] also stated the importance of a variety of physical (local geology and geomorphology), biological (flora and fauna), socio-economic (recreational amenities, access, safety, landscape, archeology, and commercial interest) and environmental quality elements (hygiene, cleanliness and toilet facilities) which have been determined in the previous studies

Roca [3] assessed the beach occupancy and public perceptions of beach quality in six beaches in Spain. They divided quality parameters into four general groups as physical and morphological, environmental, facilities and services, and image and comfort. Characteristics of water, sand, beach dimensions and presence of waves, wind and rocks were studied in the physical and morphological group. The presence of items like waste and wastebaskets, toilet and shower, rain run-off, vegetation, fish and oil on water or sand, noise and animals were placed in environmental features group. Facilities and services group was divided into of stalls, deckchair, restaurants, life-saving equipment, walkway, beach and water game facilities, parking areas, and access subgroups. Finally, the quality of landscape, beach comfort, quality/price ratio and crowdedness were studied in image and comfort category. This study revealed that principal motive for visitors to choose a beach was landscape and the most favorable aspect for selecting a beach was cleanliness and sanitary conditions, followed by safety, attractiveness of landscape, tranquility, good accessibility and facilities. Beach goers were satisfied with morphological and physical aspects (water temperature and beach length are major parameters). Although noise that is considered as an indicator of crowding was perceived high in urban beaches, it still was above acceptable degree. Restaurants and bars were the highest and parking was the least scored parameters of facilities. Landscape was the most and number of users was the least favorable aspects related to image and comfort. It was concluded that evaluation of beach quality should be based on considering a variety of factors, not just density of beach users. Moreover, beach occupancy and users' perception are correlated but high degrees of occupancy do not essentially suggest low degrees of satisfaction. This implies that in beach users' perception, other factors like physical features, facilities, and landscape are vital in assessing beach quality and overall enjoyment of visitors' experi-

ence is not reduced by factors, which are associated with the quantity of users.

was stated as poor. In addition, crowding and its related noise were perceived high.

In a study to evaluate visitors' satisfaction and perception of crowding in a German national park on the island of Hallig Hooge conducted by Kalisch and Klaphake [5], it was found

Duvat [12] conducted a study in France beaches in order to find users' perceptions of beach quality. In the study of Duvat [12], four major elements that affect the quality of the beach include coastal landscapes, the quality of the environment, quietness, and the natural properties of the beach. This study showed that users paid less attention to facilities in comparison with natural components. Majority of respondents expressed that physical characteristics of beaches play an important role in the quality of their visit including beach width and materials, sand quality and vegetation. Cervantes and Espejel [8] also conducted a study in four beaches in USA and Mexico. The results revealed that beaches with higher facilities and services were more appreciated by respondents. Marin et al. [18] also carried out a study in Italy to understand beach users' perceptions which beach and sea cleanliness were judged to be much more important than other issues. Majority of respondents reported beach quality and safety as good. About half of respondents judged that water quality is sufficient and the availability of recreational activities

regarding the assessment of the beach quality.

184 Sea Level Rise and Coastal Infrastructure

South East Asia is currently one of the most important and fastest growing tourist destinations in the world with Singapore, Malaysia and Thailand in the top league possessing the basic resources for coastal tourism like sandy beaches, coral reefs, thousands of islands and a rich cultural heritage to complement coastal tourism development. The strong demand for coastal tourism in South East Asia comes not only from Europe, East Asia and Oceania but South East Asia itself [31].

Tropical islands with their insularity and unique combination of land forms and water and year round sunshine are particularly attractive for resort development [2]. The extraordinary beauty, cultural wealth, great variety of coastal areas, and duty-free zones have made Langkawi Island the preferred destination for many holiday makers in Asia and abroad, making coastal tourism an important tourism sector which employs a lot of people and generates a noticeable share of gross value added. The main reason of choosing Langkawi Island as the research area is the growing number of tourists during past years. According to the official website of Municipal Council Langkawi Island, this Island is situated in North West of Peninsula Malaysia that is next to the Thai border and only 30 km away from mainland of state of Kedah. There are 104 beautiful islands that have made Langkawi Island as the jewel of Kedah. Since 2007, UNESCO has given the name of world Geo-park to this island. The most well-known beach of the island is Cenang beach, although there are some other famous beaches like Tengah beach and Datai beach, Cenang beach is the most popular and urbanized with suitable facilities and accommodations for beachgoers. This scenic beach is located in Western part of the Island and is covered by fine white sand and tall coconut trees and faces to the setting sun; therefore, the focus of tourists is in this particular beach.

**5. Findings and discussion**

*Number of hours spent at Cenang* 

**Table 1.** Beach users' profile.

*beach*

As it is shown in **Table 1**, the respondents were divided into two groups in terms of nationality; 44.2% were domestic tourists and 55.8% were international tourists which were from all five continents with Chinese and German visitors on top of the list. The statistics showed

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Origin Percentage Marital status Percentage Malaysian 44.2 Single 69.4 Others 55.8 Married 23.6 *Gender Percentage* Others 7

Female 52.8 *Educational background Percentage*

*Percentage* >5 times 16.5

Others 23.4

*Percentage*

*beach*

Male 47.2 No formal education 1 *Age Percentage* Primary 0.7 ≤ 24 49 Secondary 16.1 25–44 42.9 Tertiary 82.2 ≥ 45 8.1 *Monthly income Percentage Number of travel companion(s) Percentage* ≤USD1500 75.7 1–3 51.3 >USD1500 24.3 4–8 24.8 *Number of visit to Cenang beach Percentage* >8 16 1st time 45.4 Alone 8 2–5 times 38.1

<1 hour 9.5 *Amount of money spent at Cenang* 

1–3 hours 43.1 < RM50 53.9 >3 hours 47.4 RM50–100 29.6 *Main activity of beach users Percentage* >RM100 16.5 Swimming 32.3 *Reasons of choosing beach Percentage* Sunbathing 31 Nature and landscape 31.4 Variety of beach games 10.3 Water cleanliness 19.3 Exercising 3 Facility 8.5 Water-based activities 17.9 Reputation 13.3 F & B 5.5 Safety 4.1

### **4. Methodology**

In order to identify beach users' profile (socio-demographic characteristics, main activity and the reason of their choice) and perceptions toward beach quality and crowding, the questionnaire survey was carried out to collect the data from a total sample size of 400 respondents who were on sandy area of Cenang beach (January–February 2014). To obtain data from respondents, the questionnaire was prepared in four parts: the first part for identifying beach users' profile (17 items); the second part for evaluating beach users' perceptions toward beach quality regarding physical and morphological, environmental, facility and safety, landscape and design aspects (38 items) in a 5 Likert scale range from very unacceptable to very acceptable; the third part for evaluating beach users' perceptions toward beach crowding (8 items) in 4 Likert scale range from not at all crowded to extremely crowded and one multiple choice style; and the fourth part that aimed to identify beach users' future decision for visiting the beach based on their perception of the beach quality and crowding (5 items) in 5 Likert scale range from strongly disagree to strongly agree. This survey was carried out during peak hours in January 2014, which is according to Langkawi Development Authority (LADA) considered as one of the months that have the most visitors in Langkawi Island by 235,560 visitors in January 2013. Based on the researchers' observation, beach goers desired to come to the beach during 12–3 pm (for using more sunshine), and 5–7 pm (to watch the sunset).

In a further step, by applying Roca [3] methodology, in order to calculate the available sand per person, we measured Cenang beach's sandy area surface by using GPS and counted people on the beach during the peak hours when the weather was pleasant with plenty of sunshine, gentle blowing of wind and a good temperature. The sandy area of the beach was 81,000 m2 and the maximum number of beach users was 1273 persons. Individuals were selected through a systematic random sampling procedure. The starting point was chosen randomly, and a zigzag route was followed trying to cover the whole beach. The questionnaire was given to the person in every 15 steps on the way in order to ensure that there is a minimum separation of 5 me between respondents to minimize collective responses. The researchers tried to cover people with different nationalities, ages, sexes and different activities on the beach. Then, data were inserted in SPSS v.20 and the descriptive analysis was applied for all three parts of questionnaire.

### **5. Findings and discussion**

a noticeable share of gross value added. The main reason of choosing Langkawi Island as the research area is the growing number of tourists during past years. According to the official website of Municipal Council Langkawi Island, this Island is situated in North West of Peninsula Malaysia that is next to the Thai border and only 30 km away from mainland of state of Kedah. There are 104 beautiful islands that have made Langkawi Island as the jewel of Kedah. Since 2007, UNESCO has given the name of world Geo-park to this island. The most well-known beach of the island is Cenang beach, although there are some other famous beaches like Tengah beach and Datai beach, Cenang beach is the most popular and urbanized with suitable facilities and accommodations for beachgoers. This scenic beach is located in Western part of the Island and is covered by fine white sand and tall coconut trees and faces

In order to identify beach users' profile (socio-demographic characteristics, main activity and the reason of their choice) and perceptions toward beach quality and crowding, the questionnaire survey was carried out to collect the data from a total sample size of 400 respondents who were on sandy area of Cenang beach (January–February 2014). To obtain data from respondents, the questionnaire was prepared in four parts: the first part for identifying beach users' profile (17 items); the second part for evaluating beach users' perceptions toward beach quality regarding physical and morphological, environmental, facility and safety, landscape and design aspects (38 items) in a 5 Likert scale range from very unacceptable to very acceptable; the third part for evaluating beach users' perceptions toward beach crowding (8 items) in 4 Likert scale range from not at all crowded to extremely crowded and one multiple choice style; and the fourth part that aimed to identify beach users' future decision for visiting the beach based on their perception of the beach quality and crowding (5 items) in 5 Likert scale range from strongly disagree to strongly agree. This survey was carried out during peak hours in January 2014, which is according to Langkawi Development Authority (LADA) considered as one of the months that have the most visitors in Langkawi Island by 235,560 visitors in January 2013. Based on the researchers' observation, beach goers desired to come to the

beach during 12–3 pm (for using more sunshine), and 5–7 pm (to watch the sunset).

In a further step, by applying Roca [3] methodology, in order to calculate the available sand per person, we measured Cenang beach's sandy area surface by using GPS and counted people on the beach during the peak hours when the weather was pleasant with plenty of sunshine, gentle blowing of wind and a good temperature. The sandy area of the beach was

selected through a systematic random sampling procedure. The starting point was chosen randomly, and a zigzag route was followed trying to cover the whole beach. The questionnaire was given to the person in every 15 steps on the way in order to ensure that there is a minimum separation of 5 me between respondents to minimize collective responses. The researchers tried to cover people with different nationalities, ages, sexes and different activities on the beach. Then, data were inserted in SPSS v.20 and the descriptive analysis was

and the maximum number of beach users was 1273 persons. Individuals were

to the setting sun; therefore, the focus of tourists is in this particular beach.

**4. Methodology**

186 Sea Level Rise and Coastal Infrastructure

81,000 m2

applied for all three parts of questionnaire.

As it is shown in **Table 1**, the respondents were divided into two groups in terms of nationality; 44.2% were domestic tourists and 55.8% were international tourists which were from all five continents with Chinese and German visitors on top of the list. The statistics showed


**Table 1.** Beach users' profile.

almost an equal representation of male and female respondents (47.2 vs. 52.8%). About 92% of respondents were below 45 years old. The majority of them were single (69.4%), and almost all of them attended at least secondary education (98.3%). Concerning their monthly income, 75.7% visitors claimed that they earn more than USD 1500 per month. More than half (51.3%) of the respondents were accompanied by 1–3 person(s) while about 41% had more than three companions and only 8% of them were alone on the beach. About 54.6% of recreationist visited Cenang beach more than 1 time and around 90% of them spent more than 1 h on the beach. The majority of beach users spent less than RM50 (USD16) while they were on the beach.

Three main activities of the beach users were in order: swimming (32.3%), sunbathing (31%) and water-based activities (17.9%). In terms of beach users' motivation for choosing Cenang beach to visit, three major reasons were nature and landscape (31.4%), water and sand cleanliness (19.3%) and its reputation (13.3%). It is notable that the reason of choosing the beach is different in every context. For example, Botero et al. [32] compared beach users' preferences in Caribbean and European settings. **Table 2** illustrates the differences of beach users' motivations in Malaysia with two previously mentioned regions. Regarding to their experience, a vast majority of respondents (85%) were satisfied with their visit in overall, and they evaluated overall beach quality as good by 94%. In specific, beach users identified that they were satisfied with landscape (16.6%), relaxed-friendly atmosphere (16.6%) and good weather (16.5%). On the other hand, they were dissatisfied with crowdedness (23.6%), litter on sand (18.1%) and noise (16.5%), which Marin et al. [18] stated that litter and dirty sea are the major elements of dislike in many beaches. Finally, in response to a question which asked them if they recommend this beach to their family and friends, almost all the respondents (96.2%) gave positive answer.

were scored highly, that indicates the presence of enough space for users to do different beach activities like exercising; moreover, vast beach dimensions (in this case approximately 2700 m length and 30 m width) increases the amount of available sand per person that may reduce the feeling of crowdedness among beach users. According to Roca [3], increase in sand availability makes beach users more satisfied. The sand of Cenang beach with its white color and fine texture positively affected the beach users. In addition, pleasant weather with average temperature of 28°C°C, the high number of sunny days with at least 8 h sunshine and the absence of intense waves and winds, which were highly appreciated by recreationists, made Langkawi Island and especially Cenang beach a desirable destination for holiday makers, in particular for those who are experiencing freezing winter days in their own countries. The respondents gave the lowest score for the color and temperature of water. This fact may imply that beach users expected more crystal clear and warmer water because of the popularity of the region for the mentioned characteristics. Yet, as coral reefs are absent and due to the high number of boats and jet skis that spill oil into the water in the vicinity of this beach, the color

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Beach users' rates for environmental aspect are shown in **Figure 2** that is the least evaluated among four aspects that might represent the high sensitivity of beach users to this aspect. The most accepted items in this group were the presence of vegetation which is one of the natural environmental quality features and shows the nature conservation in this beach in spite of its urbanization. Based on researchers' observation, there were some amount of fish (e.g., dead jellyfish) and algae on the beach, but users evaluated the absence of fish and algae as satisfactory items. Respondents scored sand cleanliness items in terms of the presence of abrasive and waste material on the sand below the level of acceptance. On the basis of researchers' observation, although mechanical cleaning of the beach was performed once a day every morning, this kind of machine could only remove gross objects and left back sharp and small items. Later in the day, plastic bags, cigarette butts, cans and pieces of broken glass could be found easily everywhere on the beach. This fact may imply the inadequate effort of local management to maintain the beach cleanliness and control the pollution due to the high use

of the water was not appreciated much from the beach users' point of view.

**Figure 1.** Physical and morphological aspect.

#### **5.1. Beach users' perceptions toward beach quality**

In this study, beach quality was analyzed in four different aspects: (1) physical and morphological, (2) environmental, (3) facility and safety and (4) landscape and design. In this study like studies of Roca [3] and Silva et al. [26], physical and morphological characteristics of the beach were highly appreciated by beach users while environmental aspects were the least scored. **Figure 1** shows the results for physical and morphological aspect of the beach that were the most highly scored by beach users, in comparison with other three aspects, for almost all the items. Items that are related to the beach dimensions (beach length and width)


**Table 2.** Beach users' preferences between Caribbean, Europe and Malaysia.

**Figure 1.** Physical and morphological aspect.

almost an equal representation of male and female respondents (47.2 vs. 52.8%). About 92% of respondents were below 45 years old. The majority of them were single (69.4%), and almost all of them attended at least secondary education (98.3%). Concerning their monthly income, 75.7% visitors claimed that they earn more than USD 1500 per month. More than half (51.3%) of the respondents were accompanied by 1–3 person(s) while about 41% had more than three companions and only 8% of them were alone on the beach. About 54.6% of recreationist visited Cenang beach more than 1 time and around 90% of them spent more than 1 h on the beach. The majority of beach users spent less than RM50 (USD16) while they were on the beach.

Three main activities of the beach users were in order: swimming (32.3%), sunbathing (31%) and water-based activities (17.9%). In terms of beach users' motivation for choosing Cenang beach to visit, three major reasons were nature and landscape (31.4%), water and sand cleanliness (19.3%) and its reputation (13.3%). It is notable that the reason of choosing the beach is different in every context. For example, Botero et al. [32] compared beach users' preferences in Caribbean and European settings. **Table 2** illustrates the differences of beach users' motivations in Malaysia with two previously mentioned regions. Regarding to their experience, a vast majority of respondents (85%) were satisfied with their visit in overall, and they evaluated overall beach quality as good by 94%. In specific, beach users identified that they were satisfied with landscape (16.6%), relaxed-friendly atmosphere (16.6%) and good weather (16.5%). On the other hand, they were dissatisfied with crowdedness (23.6%), litter on sand (18.1%) and noise (16.5%), which Marin et al. [18] stated that litter and dirty sea are the major elements of dislike in many beaches. Finally, in response to a question which asked them if they recommend this beach to their family and friends, almost all the respondents (96.2%) gave positive answer.

In this study, beach quality was analyzed in four different aspects: (1) physical and morphological, (2) environmental, (3) facility and safety and (4) landscape and design. In this study like studies of Roca [3] and Silva et al. [26], physical and morphological characteristics of the beach were highly appreciated by beach users while environmental aspects were the least scored. **Figure 1** shows the results for physical and morphological aspect of the beach that were the most highly scored by beach users, in comparison with other three aspects, for almost all the items. Items that are related to the beach dimensions (beach length and width)

**5.1. Beach users' perceptions toward beach quality**

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**Preference Caribbean Europe Malaysia**

3 Facilities Facility Reputation

5 Family/friendly atmosphere Litter Facility

**Table 2.** Beach users' preferences between Caribbean, Europe and Malaysia.

1 Water and sand quality Safety Nature and landscape 2 Relaxed/friendly atmosphere Water quality Water and sand cleanliness

4 Security and safety Scenery Variety of activities

were scored highly, that indicates the presence of enough space for users to do different beach activities like exercising; moreover, vast beach dimensions (in this case approximately 2700 m length and 30 m width) increases the amount of available sand per person that may reduce the feeling of crowdedness among beach users. According to Roca [3], increase in sand availability makes beach users more satisfied. The sand of Cenang beach with its white color and fine texture positively affected the beach users. In addition, pleasant weather with average temperature of 28°C°C, the high number of sunny days with at least 8 h sunshine and the absence of intense waves and winds, which were highly appreciated by recreationists, made Langkawi Island and especially Cenang beach a desirable destination for holiday makers, in particular for those who are experiencing freezing winter days in their own countries. The respondents gave the lowest score for the color and temperature of water. This fact may imply that beach users expected more crystal clear and warmer water because of the popularity of the region for the mentioned characteristics. Yet, as coral reefs are absent and due to the high number of boats and jet skis that spill oil into the water in the vicinity of this beach, the color of the water was not appreciated much from the beach users' point of view.

Beach users' rates for environmental aspect are shown in **Figure 2** that is the least evaluated among four aspects that might represent the high sensitivity of beach users to this aspect. The most accepted items in this group were the presence of vegetation which is one of the natural environmental quality features and shows the nature conservation in this beach in spite of its urbanization. Based on researchers' observation, there were some amount of fish (e.g., dead jellyfish) and algae on the beach, but users evaluated the absence of fish and algae as satisfactory items. Respondents scored sand cleanliness items in terms of the presence of abrasive and waste material on the sand below the level of acceptance. On the basis of researchers' observation, although mechanical cleaning of the beach was performed once a day every morning, this kind of machine could only remove gross objects and left back sharp and small items. Later in the day, plastic bags, cigarette butts, cans and pieces of broken glass could be found easily everywhere on the beach. This fact may imply the inadequate effort of local management to maintain the beach cleanliness and control the pollution due to the high use

**Figure 2.** Environmental aspect.

level and the high number of jet skis, boats and cars that are the possible sources of oil that contaminate the sand and water in Cenang beach.

**Figure 3** illustrates the mean scores of the items in facility and safety aspect. Results indicated that three most accepted items in this group were in order, access to the beach, restaurant/ bar and deckchair/umbrella, as they are anticipated to be adequately and in good condition in resort beaches, there are 20 restaurants/bars and around 802 deckchairs/umbrellas which seems good enough for Cenang beach. According to Roca [3], in tourist beaches, toilet and shower facilities should be in good conditions, but in our study, three items of public toilet, shower/foot-wash and trash-bins received the lowest rank in this group that may be because of low number or poor maintenance of these facilities.

in overall, it was the second most desirable aspect of the beach following physical and morphological aspect. Richness of landscape, comfort of beach, ease of access to the beach, and walkway on the sand were the four items that were valued highly and almost equal that show beach users desire to experience a comfortable recreational experience on the beach. Ease of access to the beach is due to the high number of access ways to the beach (five public pathways and numerous access through the private resorts and motels) and the high accessibility of Cenang beach which is close to the resorts and the most alive and active part of the island. Although Williams and Micallef [13] indicated that scenery is not a priority for users in urban beaches, the high score of the item of landscape richness indicates that users value the beautiful scenery in resort beaches like Cenang beach, as their first motive for choosing this beach to visit was also nature and landscape. Items of beach infrastructure (BBQ stuff, picnic table, sunshade and etc.) and signage were the least scored, and 16.5% of respondents stated that infrastructure was not present on the beach. This fact reflects the demand of beach users from local management

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authorities to provide them with these elements while designing an urban beach.

Findings of analysis of beach users' perceptions of beach crowdedness are summarized in this section. **Table 2** illustrates the degree of crowding that respondents felt by different groups of beach users. About 70% of respondents felt crowdedness by total number of beach users and water-based activity participants; moreover, almost 65 and 55% of beach users reported crowdedness was felt by sunbathers and swimmers. On the other hand, about 80 and 50% of beach users stated that they did not feel crowdedness by fishers and boaters respectively.

**5.2. Beach users' perceptions toward beach crowding**

**Figure 3.** Facility and safety aspects.

Researchers recorded only three public shower/foot-wash, two public toilets and seven trash-bins which seem to be few regards to beach dimensions. The probable reason for this deficiency can be due to the high number of beach accommodations and restaurants/bars, which mislead local authorities for providing more public services. Interestingly, 33.5% of respondents stated that security kiosk was absent; moreover, 30 and 26.8% reported that lifeguard tower and signposting of dangerous areas were not present on the beach. According to Williams and Micallef [13], the safety and security aspects are vital in urban beaches, but the absence of security kiosk, inappropriate location of lifeguard tower and few numbers of signposts of dangerous areas, based on researchers' records and beach users' perceptions may reveal that beach managers and authorities did not or paid less attention to the aspect of security and safety of the beach. In general, parking areas are controversial issues in overcrowded and urbanized beach [3]. Hence, in Cenang beach, as it is situated in an island, majority of beach users use the public transportation; as a result, the presence or absence of parking was not considered as a vital issue for them and it was evaluated as acceptable.

**Figure 4** shows the mean scores of users' perceptions of landscape and design aspect of the beach quality. Respondents had almost positive perceptions toward all items in this group, and

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**Figure 3.** Facility and safety aspects.

level and the high number of jet skis, boats and cars that are the possible sources of oil that

**Figure 3** illustrates the mean scores of the items in facility and safety aspect. Results indicated that three most accepted items in this group were in order, access to the beach, restaurant/ bar and deckchair/umbrella, as they are anticipated to be adequately and in good condition in resort beaches, there are 20 restaurants/bars and around 802 deckchairs/umbrellas which seems good enough for Cenang beach. According to Roca [3], in tourist beaches, toilet and shower facilities should be in good conditions, but in our study, three items of public toilet, shower/foot-wash and trash-bins received the lowest rank in this group that may be because

Researchers recorded only three public shower/foot-wash, two public toilets and seven trash-bins which seem to be few regards to beach dimensions. The probable reason for this deficiency can be due to the high number of beach accommodations and restaurants/bars, which mislead local authorities for providing more public services. Interestingly, 33.5% of respondents stated that security kiosk was absent; moreover, 30 and 26.8% reported that lifeguard tower and signposting of dangerous areas were not present on the beach. According to Williams and Micallef [13], the safety and security aspects are vital in urban beaches, but the absence of security kiosk, inappropriate location of lifeguard tower and few numbers of signposts of dangerous areas, based on researchers' records and beach users' perceptions may reveal that beach managers and authorities did not or paid less attention to the aspect of security and safety of the beach. In general, parking areas are controversial issues in overcrowded and urbanized beach [3]. Hence, in Cenang beach, as it is situated in an island, majority of beach users use the public transportation; as a result, the presence or absence of parking was

**Figure 4** shows the mean scores of users' perceptions of landscape and design aspect of the beach quality. Respondents had almost positive perceptions toward all items in this group, and

not considered as a vital issue for them and it was evaluated as acceptable.

contaminate the sand and water in Cenang beach.

**Figure 2.** Environmental aspect.

190 Sea Level Rise and Coastal Infrastructure

of low number or poor maintenance of these facilities.

in overall, it was the second most desirable aspect of the beach following physical and morphological aspect. Richness of landscape, comfort of beach, ease of access to the beach, and walkway on the sand were the four items that were valued highly and almost equal that show beach users desire to experience a comfortable recreational experience on the beach. Ease of access to the beach is due to the high number of access ways to the beach (five public pathways and numerous access through the private resorts and motels) and the high accessibility of Cenang beach which is close to the resorts and the most alive and active part of the island. Although Williams and Micallef [13] indicated that scenery is not a priority for users in urban beaches, the high score of the item of landscape richness indicates that users value the beautiful scenery in resort beaches like Cenang beach, as their first motive for choosing this beach to visit was also nature and landscape. Items of beach infrastructure (BBQ stuff, picnic table, sunshade and etc.) and signage were the least scored, and 16.5% of respondents stated that infrastructure was not present on the beach. This fact reflects the demand of beach users from local management authorities to provide them with these elements while designing an urban beach.

#### **5.2. Beach users' perceptions toward beach crowding**

Findings of analysis of beach users' perceptions of beach crowdedness are summarized in this section. **Table 2** illustrates the degree of crowding that respondents felt by different groups of beach users. About 70% of respondents felt crowdedness by total number of beach users and water-based activity participants; moreover, almost 65 and 55% of beach users reported crowdedness was felt by sunbathers and swimmers. On the other hand, about 80 and 50% of beach users stated that they did not feel crowdedness by fishers and boaters respectively.

half of the respondents (48.1%) stated that they will come back to Cenang beach, but not during peak season. About 42% of users indicated that they will come back to Cenang beach but earlier or later in day when less people are there. It means that many respondents are willing to be temporarily displaced as a result of the conditions they experienced that day. Moreover, 28.8% expressed that they will come back to Cenang beach, but they will change the way they think about this area. Majority of beach users are unlikely to try a product shift through shifting how they think about the beach and decide that it offers an altered kind of experience compared to what they first believed. Yet, 26% agreed that they would change their destination to other nearby beach and 37.8% agreed to go to other parts of Langkawi Island. In other

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words, most of the respondents are not willing to experience a spatial displacement.

to apply security kiosks and adjust lifeguard tower in a better area.

Although overall satisfaction of respondents in Cenang beach was very high, according to Needham et al. [29], global evaluations of satisfaction do not have much benefit for managers. Beach users were not satisfied with all aspects of beach quality. They were most satisfied with natural and physical characteristics of the beach and the most dissatisfied with environmental issues. These findings highlight the need of the attention and consideration of local managers and authorities. Nature conservation should be prioritized to other functions of the beach in order to assure satisfaction of users, as nature and landscape were the primary reasons for choosing this beach to visit. Moreover, based on beach users' perceptions, managers should pay more attention to beach sanitary and cleanliness issues and improve the facilities and services to the extent that there is no harm to the ecosystem. In specific, the result of this study reveals that beach users expressed the need for removing litter and abrasive material and install more public toilets, shower and foot-wash with better maintenance. In addition, more attention should be paid to beach's security and safety. For example, local management needs

Although available sand per person in Cenang beach was adequate, some respondents expressed that the beach was crowded. However, they reported that this feeling did not have much effect on their overall satisfaction; therefore, the perceptual carrying capacity of the area is not yet reached. This study confirms Roca [3] which stated that sand availability is not the only factor that increases users' satisfaction. Tourism is a family-based activity and especially people come in groups, so users of urban beaches expect the crowdedness and they can cope with it.

As tourism is a very fast growing sector in South East Asia, and Malaysia is one of the leaders in this field, tourism managers and local authorities should pay more attention to the tourists who are the end users of tourism product. Langkawi Island with its spectacular natural resources, especially its unique beaches has a great potential in tourism sector; therefore, any effort done for improving beach quality aspects along with consideration to the natural and heritage resources will increase the satisfaction of beach users. Consequently, this study attempted to fill the gap in knowledge about the importance of beach user's perceptions for beach managers in order to do better understanding of user's feeling about that particular region and help the managers to implement or improve their plans and strategies. The empirical findings of this research provide useful insight for the tourism marketers and local

**6. Conclusion**

**Figure 4.** Landscape and design aspects.

In this study, the calculated available sand surface per user was about 63 m2 . According to Sousa et al. [33], the results of studies from different parts of the world show that suitable sand availability is 5–25 m2 /user. The comparison between these numbers with the result of present study indicates that the sand availability in Cenang beach is much more than its global rate. Therefore, the beach users reported feeling of crowdedness is because of uneven distribution of facilities on the beach that cause beach users to concentrate in specific zones. It can be concluded that local management should implicate proper planning to de-concentrate facilities and services in order to avoid aggregation of beach users in particular spots in order to guarantee user's satisfaction during peak season. In this research, the result of analysis shows that the increased beach's sand availability does not necessarily reduce the degree of crowdedness felt by beach users.

Users were asked if the number of other people they have seen on the beach that day affected their enjoyment or not. The result shows that only 25.5% of respondents reported that their enjoyment was decreased by the number of other people, while the other 75% of them stated that their enjoyment was not affected or was increased due to the number of people they encountered on the beach. This finding is supported by the study of [10, 27, 29] that majority of respondents stated their enjoyment was not reduced by presence of other beach users. According to Needham et al. [29], although issues of crowdedness and high use levels are concerned in social aspect, crowding does not necessarily influence beach users' experience. It can be concluded that the perceptual carrying capacity of Cenang beach has not reached yet by this level of use.

#### **5.3. Beach users' decision for their future trip**

This section describes the future actions that respondents would take (based on their experience of beach quality and crowding) in case, if they could travel to Langkawi Island. Around half of the respondents (48.1%) stated that they will come back to Cenang beach, but not during peak season. About 42% of users indicated that they will come back to Cenang beach but earlier or later in day when less people are there. It means that many respondents are willing to be temporarily displaced as a result of the conditions they experienced that day. Moreover, 28.8% expressed that they will come back to Cenang beach, but they will change the way they think about this area. Majority of beach users are unlikely to try a product shift through shifting how they think about the beach and decide that it offers an altered kind of experience compared to what they first believed. Yet, 26% agreed that they would change their destination to other nearby beach and 37.8% agreed to go to other parts of Langkawi Island. In other words, most of the respondents are not willing to experience a spatial displacement.

### **6. Conclusion**

In this study, the calculated available sand surface per user was about 63 m2

sand availability is 5–25 m2

**Figure 4.** Landscape and design aspects.

192 Sea Level Rise and Coastal Infrastructure

crowdedness felt by beach users.

**5.3. Beach users' decision for their future trip**

Sousa et al. [33], the results of studies from different parts of the world show that suitable

of present study indicates that the sand availability in Cenang beach is much more than its global rate. Therefore, the beach users reported feeling of crowdedness is because of uneven distribution of facilities on the beach that cause beach users to concentrate in specific zones. It can be concluded that local management should implicate proper planning to de-concentrate facilities and services in order to avoid aggregation of beach users in particular spots in order to guarantee user's satisfaction during peak season. In this research, the result of analysis shows that the increased beach's sand availability does not necessarily reduce the degree of

Users were asked if the number of other people they have seen on the beach that day affected their enjoyment or not. The result shows that only 25.5% of respondents reported that their enjoyment was decreased by the number of other people, while the other 75% of them stated that their enjoyment was not affected or was increased due to the number of people they encountered on the beach. This finding is supported by the study of [10, 27, 29] that majority of respondents stated their enjoyment was not reduced by presence of other beach users. According to Needham et al. [29], although issues of crowdedness and high use levels are concerned in social aspect, crowding does not necessarily influence beach users' experience. It can be concluded that the perceptual carrying capacity of Cenang beach has not reached yet by this level of use.

This section describes the future actions that respondents would take (based on their experience of beach quality and crowding) in case, if they could travel to Langkawi Island. Around

/user. The comparison between these numbers with the result

. According to

Although overall satisfaction of respondents in Cenang beach was very high, according to Needham et al. [29], global evaluations of satisfaction do not have much benefit for managers. Beach users were not satisfied with all aspects of beach quality. They were most satisfied with natural and physical characteristics of the beach and the most dissatisfied with environmental issues. These findings highlight the need of the attention and consideration of local managers and authorities. Nature conservation should be prioritized to other functions of the beach in order to assure satisfaction of users, as nature and landscape were the primary reasons for choosing this beach to visit. Moreover, based on beach users' perceptions, managers should pay more attention to beach sanitary and cleanliness issues and improve the facilities and services to the extent that there is no harm to the ecosystem. In specific, the result of this study reveals that beach users expressed the need for removing litter and abrasive material and install more public toilets, shower and foot-wash with better maintenance. In addition, more attention should be paid to beach's security and safety. For example, local management needs to apply security kiosks and adjust lifeguard tower in a better area.

Although available sand per person in Cenang beach was adequate, some respondents expressed that the beach was crowded. However, they reported that this feeling did not have much effect on their overall satisfaction; therefore, the perceptual carrying capacity of the area is not yet reached. This study confirms Roca [3] which stated that sand availability is not the only factor that increases users' satisfaction. Tourism is a family-based activity and especially people come in groups, so users of urban beaches expect the crowdedness and they can cope with it.

As tourism is a very fast growing sector in South East Asia, and Malaysia is one of the leaders in this field, tourism managers and local authorities should pay more attention to the tourists who are the end users of tourism product. Langkawi Island with its spectacular natural resources, especially its unique beaches has a great potential in tourism sector; therefore, any effort done for improving beach quality aspects along with consideration to the natural and heritage resources will increase the satisfaction of beach users. Consequently, this study attempted to fill the gap in knowledge about the importance of beach user's perceptions for beach managers in order to do better understanding of user's feeling about that particular region and help the managers to implement or improve their plans and strategies. The empirical findings of this research provide useful insight for the tourism marketers and local managers to improve the current situation of beach tourism in Cenang beach and help them with the data to attract more beach tourists to that area while conserving the natural environment. Hence, by attracting more international and national tourist, the revenue will be increased and it benefits many other related industries.

[8] Cervantes O, Espejel I. Design of an integrated evaluation index for recreational beaches.

Beach Users' Perceptions Toward Beach Quality and Crowding: A Case of Cenang Beach…

http://dx.doi.org/10.5772/intechopen.76614

195

[9] Espejel I, Espinoza-Tenorio A, Cervantes-Rosas O, Popoca I, Mejia A, Delhumeau S. Proposal for an integrated risk index for the planning of recreational beaches: Use at seven Mexican arid sites. Journal of Coastal Research, SI 50 (Proceedings of the 9th

[10] Graefe AR, Moore RL. Monitoring the visitor experience at Buck Island Reef Monument. In: Vander S, Gail A, editors. Proceedings of the 1991 Northeastern Recreational Research

[11] Van Maele B, Pond K, Williams AT, Dubsky K. Public participation and consultation. In: Bartram J, Rees J, editors. Monitoring Bathing Waters: A Practical Guide to the Design and Implementation of Assessment and Monitoring Programmed. London and

[12] Duvat V. Public perception of beach quality: lessons learnt from a French case study. Paper presented at the Public perception of beach quality: lessons learnt from a French

[13] Williams AT, Micallef A. Beach Management: Principles and Practice. London, UK:

[14] Pendleton L, Martin N, Webster DG. Public perceptions of environmental quality: A survey study of beach use and perceptions in Los Angeles County. Marine Pollution

[15] Sowman MR. A procedure for assessing recreational carrying capacity of coastal resort

[16] Morgan R. Preferences and priorities of recreational beach users in Wales, UK. Journal of

[17] Choudri BS, Baawain M, Ahmed M. An overview of coastal and marine resources and their management in Sultanate of Oman. Journal of Environmental Management &

[18] Marin V, Palmisani F, Ivaldi R, Dursi R, Fabiano M. Users' perception analysis for sustainable beach management in Italy. Ocean & Coastal Management. 2009;**52**(5):268-277

[19] Morgan R, Jones TC, Williams AT. Opinions and perceptions of England and Wales heritage coast beach users: Some management implications from the Glamorgan Heritage

[20] Priskin J. Tourist perceptions of degradation caused by coastal nature-based recreation.

[21] Nordstrom KF, Mitteager WA. Perceptions of the value of natural and restored beach and dune characteristics by high school students in New Jersey, USA. Ocean & coastal

Ocean & Coastal Management. 2008;**51**(5):410-419

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Bulletin. 2001;**42**(11):1155-1160

case study. 2012

### **Acknowledgements**

The funding for this project is made possible through the research grant obtained from the Ministry of Higher Education, Malaysia under the Long-Term Research Grant Scheme 2011 [LRGS grant No.: JPT.S (BPKI)2000/09/01/015Jld.4(67)].

### **Author details**

Hamed Mehranian1 and Azizan Marzuki1,2\*

\*Address all correspondence to: chik72@usm.my

1 School of Housing, Building and Planning, Universiti Sains Malaysia, Penang, Malaysia

2 Faculty of Education, Humanities, and Law, Flinders University, Adelaide, Australia

### **References**


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managers to improve the current situation of beach tourism in Cenang beach and help them with the data to attract more beach tourists to that area while conserving the natural environment. Hence, by attracting more international and national tourist, the revenue will be

The funding for this project is made possible through the research grant obtained from the Ministry of Higher Education, Malaysia under the Long-Term Research Grant Scheme 2011

1 School of Housing, Building and Planning, Universiti Sains Malaysia, Penang, Malaysia

[1] UNEP. Sustainable Coastal Tourism Planning. 2009. Available from: http://www.unep. fr/shared/publications/pdf/DTIx1091xPA-SustainableCoastalTourism-Planning.pdf [2] Wong PP. Coastal Tourism in Southeast Asia. Manila, Philippines: International Center

[3] Roca E. Bringing Public Perceptions in the Integrated Assessment of Coastal Systems

[4] Shelby B, Vaskey JJ, Heberlein TA. Comparative analysis of crowding in multiple location: Result from fifteen years of research. Leisure Sciences. 1989;**11**(4):269-291

[5] Kalisch D, Klaphake A. Visitors' satisfaction and perception of crowding in a German National Park: A case study on the island of Hallig Hooge. Forest Snow and Landscape

[6] Manning R. Crowding and carrying capacity in outdoor recreation: from normative standards to standards of quality. In: Leisure Studies: Prospects for the Twenty-First

[7] Budruk M, Manning RE, Valliere WA, Wans B. Perceived crowding at Boston Harbon Island National Park area. Paper presented at the Proceedings of the 2001 Northeastern Recreation Research Symposium. GTR-NE-289 Northeastern Research Station, NY. 2001

2 Faculty of Education, Humanities, and Law, Flinders University, Adelaide, Australia

for Living Aquatic Resources Management. 1991. 40 p. ISBN: 971-8709-07-X

[thesis doctoral] (Inédita), Universitat Autònoma de Barcelona. 2008

Century. State College, PA: Venture Publishing; 1999. pp. 323-334

increased and it benefits many other related industries.

[LRGS grant No.: JPT.S (BPKI)2000/09/01/015Jld.4(67)].

\*Address all correspondence to: chik72@usm.my

Research. 2007;**81**(1/2):109-122

and Azizan Marzuki1,2\*

**Acknowledgements**

194 Sea Level Rise and Coastal Infrastructure

**Author details**

Hamed Mehranian1

**References**


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**Chapter 12**

**Provisional chapter**

**Remote Sensing Retrieval Study of the Surface Kinetic**

Wind and current are significant parameters in the hydrodynamic processes, making a significant effect on the expansion of the Yangtze (Changjiang River) Diluted Water, sediment transport, resuspension and shelf circulation in the Yangtze Estuary. They are indispensable as input parameters in the numerical simulation of these phenomena. Synthetic aperture radar (SAR) can acquire data with different resolutions (down to 1 m) and coverage (up to 400 km) over a site during day or night time under all weather conditions, being capable of providing ocean surface kinetic parameters with high resolution. SAR images were collected to verify and improve the validity of wind direction retrieval by 2D fast Fourier transformation (FFT) method, wind speed by CMOD4 model and current by Doppler frequency method. These SAR-retrieved wind and current results were analyzed and assessed against in situ data and corresponding numerically simulated surface wind and current fields. Comparisons to the in situ and simulations show that 1) SAR can measure sea surface wind fields with a high resolution at sub-km scales and provide a powerful complement to conventional wind measurement techniques. 2) The Doppler shift anomaly measurements from SAR images are able to capture quantitative surface currents, thus are helpful to reveal the multi-scale upper layer dynamics around the East China Sea. **Keywords:** multi-source remote sensing images, sea surface wind, sea surface current,

**Remote Sensing Retrieval Study of the Surface Kinetic** 

DOI: 10.5772/intechopen.72461

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

Sea surface wind and current, directly related to almost all ocean water movement, are one of the most basic and crucial parameters in studies of hydrodynamic, ecological processes

**Parameters in the Yangtze Estuary and Its Adjacent**

**Parameters in the Yangtze Estuary and Its Adjacent** 

**Waters**

**Waters**

Shengbo Chen and Lihua Wang

Shengbo Chen and Lihua Wang

http://dx.doi.org/10.5772/intechopen.72461

**Abstract**

**1. Introduction**

Additional information is available at the end of the chapter

fast Fourier transformation, Doppler frequency

Additional information is available at the end of the chapter


**Provisional chapter**

### **Remote Sensing Retrieval Study of the Surface Kinetic Parameters in the Yangtze Estuary and Its Adjacent Waters Parameters in the Yangtze Estuary and Its Adjacent Waters**

**Remote Sensing Retrieval Study of the Surface Kinetic** 

DOI: 10.5772/intechopen.72461

Shengbo Chen and Lihua Wang Shengbo Chen and Lihua Wang Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72461

#### **Abstract**

[22] Tudor DT, Williams AT. Public perception and opinion of visible beach aesthetic pollution: The utilisation of photography. Journal of Coastal Research. 2003;**19**(4):1104-1115

[23] Villares M, Roca E, Serra J, Montori C. Social perception as a tool for beach planning: A case study on the Catalan coast. Journal of Coastal Research. Special Issue 48. Coastal Geomorphology in Spain: Proceedings of the III Spanish Conference on Coastal

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[25] Pereira LCC, Jiménez JA, Medeiros C, da Costa Rauquı́rio M. The influence of the environmental status of Casa Caiada and Rio Doce beaches (NE-Brazil) on beaches users.

[26] Silva JS, Leal MMV, Araújo MCB, Barbosa SCT, Costa MF. Spatial and temporal patterns of use of Boa Viagem Beach, Northeast Brazil. Journal of Coastal Research. 2008;

[27] Silva IR, Pereira LCC, Sousa RC, Oliveira SMO, Guimarães D de O, Costa RM da. Amazon Beaches (São Luís, Brazil): Recreational Use, environmental indicators, and perception of beachgoers. Journal of Coastal Research, SI 64 (Proceedings of the 11th

[28] Roca E, Villares M. Public perceptions for evaluating beach quality in urban and semi-

[29] Needham MD, Tynon JF, Ceurvorst RL, Collins RL, Connor WM, Culnane MJW. Recreation carrying capacity and management at Kailua Beach Park on Oahu, Hawaii. Final project report for Hawaii Coral Reef Initiative – Research Program. Corvallis: Oregon State University, Department of Forest Ecosystems and Society; 2008. 74 p [30] Silva SF, Ferreira JC. Beach Carrying Capacity: The physical and social analysis at Costa de Caparica, Portugal. Journal of Coastal Research: Special Issue 65 - International

[31] Wong PP. Coastal tourism development in Southeast Asia: Relevance and lessons for coastal zone management. Ocean & Coastal Management. 1998;**38**(2):89-109

[32] Botero C, Anfuso A, Williams AT, Zielinski S, Silva CP, Cervantes O, Silva L, Cabrera JA. Reasons for beach choice: European and Caribbean perspectives. In Conley, DC, Masselink G, Russell PE, O'Hare TJ, editors. Proceedings 12th International Coastal Symposium(Plymouth, England). Journal of Coastal Research, Special Issue No. 65.

[33] de Sousa RC, Pereira LCC, Silva NIS, Olivera SM, Pinto KST, da Costa RM. Recreational carrying capacity of three Amazon macrotidal beaches during the peak vacation season.

natural environments. Ocean & Coastal Management. 2008;**51**(4):314-329

Geomorphology. 2006. pp. 118-123

**24**(sp1):79-86

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2013. pp. 880-885

Ocean & Coastal Management. 2003;**46**(11):1011-1030

International Coastal Symposium). 2011. pp. 1287-1291

Coastal Symposium Vol. 1. 2013. pp. 1039-1044

Journal of Coastal Research SI. 2011;**64**:1292-1296

Wind and current are significant parameters in the hydrodynamic processes, making a significant effect on the expansion of the Yangtze (Changjiang River) Diluted Water, sediment transport, resuspension and shelf circulation in the Yangtze Estuary. They are indispensable as input parameters in the numerical simulation of these phenomena. Synthetic aperture radar (SAR) can acquire data with different resolutions (down to 1 m) and coverage (up to 400 km) over a site during day or night time under all weather conditions, being capable of providing ocean surface kinetic parameters with high resolution. SAR images were collected to verify and improve the validity of wind direction retrieval by 2D fast Fourier transformation (FFT) method, wind speed by CMOD4 model and current by Doppler frequency method. These SAR-retrieved wind and current results were analyzed and assessed against in situ data and corresponding numerically simulated surface wind and current fields. Comparisons to the in situ and simulations show that 1) SAR can measure sea surface wind fields with a high resolution at sub-km scales and provide a powerful complement to conventional wind measurement techniques. 2) The Doppler shift anomaly measurements from SAR images are able to capture quantitative surface currents, thus are helpful to reveal the multi-scale upper layer dynamics around the East China Sea.

**Keywords:** multi-source remote sensing images, sea surface wind, sea surface current, fast Fourier transformation, Doppler frequency

### **1. Introduction**

Sea surface wind and current, directly related to almost all ocean water movement, are one of the most basic and crucial parameters in studies of hydrodynamic, ecological processes

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

and global climatic change [1, 2], including the expansion of Yangtze (Changjiang River) Diluted Water (CDW) and shelf circulation in the Yangtze Estuary. In the summer, CDW extends to the northeast as a plume. While during winter it clings to the Chinese coast to the southwest in a narrow band. In the estuary, the prevailing monsoon climate results in stronger northerly winds during winter and weaker southerly winds during summer. Winddriven Ekman transport cause the CDW distribution presenting significant seasonal variation [3, 4]. Therefore, it is indispensable to take the high-resolution ocean surface wind into consideration in the accurate numerical simulation of these phenomena. At present, there is lack of high-resolution in situ wind data in the East China Sea (ECS). Prevailing wind vector products are based on meteorological models and satellite-borne scatterometer (SCAT) measurements with only a resolution of around 25 km [5]. This resolution is insufficient to meet the calculation accuracy of the numerical model. SAR can acquire data with different resolutions (down to 1 m) and coverage (up to 400 km) over a site during day or night time under all weather conditions, being capable of retrieving ocean surface wind vectors with high resolution. The ERS-1 was launched in 1991 by European Space Agency, since then, SAR images have been continuously measuring the various global features and observing the ocean surface, such as ocean surface winds, waves, and currents [6–8]. Despite variations of wind field including direction and speed, SAR images have the capability to reveal high-resolution patterns, which can render possible the exciting prospect of measuring ocean surface wind from space, especially in the coastal regions.

currents. The corresponding SAR images based on the Doppler frequency anomaly methods have been successfully applied to observe the Agulhas Current [12, 14], the Gulf Stream [11], the Norwegian Atlantic Current [18] and coastal current in the Yangtze Estuary. Therefore, SAR will play an increasingly critical role in the quantitative studies of ocean surface flow characteristics. In addition, the development of SAR Doppler technology will provide new opportunities for routinely observing and simulating mesoscale ocean processes and coastal

Remote Sensing Retrieval Study of the Surface Kinetic Parameters in the Yangtze Estuary…

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199

For sea surface wind direction extraction, 2D fast Fourier transformation (FFT) in the spectral domain was employed. The processing steps were as follows in order to obtain high-resolution wind direction information. Firstly, all pixels in SAR images not affected by the local ocean surface wind, such as land, surface slicks, ships and artifacts, were masked. Secondly, SAR images were divided into sub-images, which was set to 6.4 × 6.4 km to quantitatively express the resolution of wind direction. The reason for setting this scale is that the winddriven streak characterizes typically present km-scale spacing. Next, we used the 2D FFT in SAR scenes to obtain the Fourier spectra and filters to eliminate high-frequency speckle in Fourier spectra. Fourthly, we constructed the regression according to the least-squares estimation, and set the energy densities for wavelengths between 500 and 2000 m. At last, the orientation of the wind streaks with an 180° directional ambiguity was extracted, which is perpendicular to the regression line. The directional ambiguity was subsequently removed

For sea surface wind speed extraction, the GMF CMOD4 was employed here. And it was originally designed to derive wind speed from SCAT, the SCAT instruments operate at C-band with VV polarization. SAR also operate at the same wavelength, therefore, the GMF CMOD4 is suitable for SAR images with VV polarization. The relationship between wind speed, wind

is the NRCS in linear units; *φ* is the wind direction versus antenna look direction; u10

is wind speed; *θ* is nadir incident angle; and *A*, *b*, *c*, and *B* are model parameters depending

The GMF CMOD4 employed here was developed and validated using a large amount of measured data. For the C-band SAR images with HH polarization, a hybrid model including CMOD4 and the polarization ratio [20, 21] were employed. A flowchart of sea surface wind

*B*

(1)

direction, and NRCS (the GMF) is generally expressed by the form (1) [19]

*σ*<sup>0</sup> = *A*(*u*10, *θ*) [1 + *b*(*u*10, *θ*)cos*φ* + *c*( *u*10, *θ*)cos*ϕ*]

current phenomena.

**2.1. Sea surface wind**

according to the QuikSCAT or ECMWF wind products.

on radar frequency, polarization, u10, and *θ*.

retrieval scheme is shown in **Figure 1**.

**2. Methods**

where σ0

For high resolution (~1 km) surface current measurements, it is highly necessary to have a good regular observing system. From the perspectives of economic, ecological and hydrodynamic, these data are of great importance for assimilation in ocean and shelf circulation models, which is capable of providing sufficient predictions of the continuous changes in the estuaries. At present, there are several techniques/equipment currently employed to observe sea flows, including current meter moorings, acoustic Doppler current profilers, drifters as well as remote sensing methods from satellites and ground based High Frequency Radar systems. Pandian et al. [9] discussed their inherent advantages and disadvantages of these instruments and techniques. Geostrophic currents derived from satellite altimetry [10], are now being used regularly in global and regional circulation models. However, it only has a spatial resolution of 25 km, which is too coarse to apply in the coastal regions. High-resolution imaging SARs have been demonstrated to have the promising capabilities for retrieving surface current estimates with resolution of 2–10 km [11–14]. Two techniques have emerged notably the along-track interferometry SAR (AT-INSAR) requiring a split antenna [13, 15, 16] and the single-antenna SAR based on Doppler method [11]. Chapron et al. [17] pioneered to derive and discuss the slant range radar-detected velocity of the ocean surface roughness from Advanced SAR (ASAR) based on Doppler measurements of moving ocean surfaces, probably caused by the small-scale disturbances such as capillary waves. Moreover, their studies presented that the Doppler centroid anomalies observed by ASAR are of a geophysical properties. The Doppler anomalies are generated by the relative motion between ocean surface and radar platform, which are solely connected to the movement of the sea surface roughness elements. These anomalies can reflect the combined action of wind, waves and currents. The corresponding SAR images based on the Doppler frequency anomaly methods have been successfully applied to observe the Agulhas Current [12, 14], the Gulf Stream [11], the Norwegian Atlantic Current [18] and coastal current in the Yangtze Estuary. Therefore, SAR will play an increasingly critical role in the quantitative studies of ocean surface flow characteristics. In addition, the development of SAR Doppler technology will provide new opportunities for routinely observing and simulating mesoscale ocean processes and coastal current phenomena.

### **2. Methods**

and global climatic change [1, 2], including the expansion of Yangtze (Changjiang River) Diluted Water (CDW) and shelf circulation in the Yangtze Estuary. In the summer, CDW extends to the northeast as a plume. While during winter it clings to the Chinese coast to the southwest in a narrow band. In the estuary, the prevailing monsoon climate results in stronger northerly winds during winter and weaker southerly winds during summer. Winddriven Ekman transport cause the CDW distribution presenting significant seasonal variation [3, 4]. Therefore, it is indispensable to take the high-resolution ocean surface wind into consideration in the accurate numerical simulation of these phenomena. At present, there is lack of high-resolution in situ wind data in the East China Sea (ECS). Prevailing wind vector products are based on meteorological models and satellite-borne scatterometer (SCAT) measurements with only a resolution of around 25 km [5]. This resolution is insufficient to meet the calculation accuracy of the numerical model. SAR can acquire data with different resolutions (down to 1 m) and coverage (up to 400 km) over a site during day or night time under all weather conditions, being capable of retrieving ocean surface wind vectors with high resolution. The ERS-1 was launched in 1991 by European Space Agency, since then, SAR images have been continuously measuring the various global features and observing the ocean surface, such as ocean surface winds, waves, and currents [6–8]. Despite variations of wind field including direction and speed, SAR images have the capability to reveal high-resolution patterns, which can render possible the exciting prospect of measuring ocean

For high resolution (~1 km) surface current measurements, it is highly necessary to have a good regular observing system. From the perspectives of economic, ecological and hydrodynamic, these data are of great importance for assimilation in ocean and shelf circulation models, which is capable of providing sufficient predictions of the continuous changes in the estuaries. At present, there are several techniques/equipment currently employed to observe sea flows, including current meter moorings, acoustic Doppler current profilers, drifters as well as remote sensing methods from satellites and ground based High Frequency Radar systems. Pandian et al. [9] discussed their inherent advantages and disadvantages of these instruments and techniques. Geostrophic currents derived from satellite altimetry [10], are now being used regularly in global and regional circulation models. However, it only has a spatial resolution of 25 km, which is too coarse to apply in the coastal regions. High-resolution imaging SARs have been demonstrated to have the promising capabilities for retrieving surface current estimates with resolution of 2–10 km [11–14]. Two techniques have emerged notably the along-track interferometry SAR (AT-INSAR) requiring a split antenna [13, 15, 16] and the single-antenna SAR based on Doppler method [11]. Chapron et al. [17] pioneered to derive and discuss the slant range radar-detected velocity of the ocean surface roughness from Advanced SAR (ASAR) based on Doppler measurements of moving ocean surfaces, probably caused by the small-scale disturbances such as capillary waves. Moreover, their studies presented that the Doppler centroid anomalies observed by ASAR are of a geophysical properties. The Doppler anomalies are generated by the relative motion between ocean surface and radar platform, which are solely connected to the movement of the sea surface roughness elements. These anomalies can reflect the combined action of wind, waves and

surface wind from space, especially in the coastal regions.

198 Sea Level Rise and Coastal Infrastructure

#### **2.1. Sea surface wind**

For sea surface wind direction extraction, 2D fast Fourier transformation (FFT) in the spectral domain was employed. The processing steps were as follows in order to obtain high-resolution wind direction information. Firstly, all pixels in SAR images not affected by the local ocean surface wind, such as land, surface slicks, ships and artifacts, were masked. Secondly, SAR images were divided into sub-images, which was set to 6.4 × 6.4 km to quantitatively express the resolution of wind direction. The reason for setting this scale is that the winddriven streak characterizes typically present km-scale spacing. Next, we used the 2D FFT in SAR scenes to obtain the Fourier spectra and filters to eliminate high-frequency speckle in Fourier spectra. Fourthly, we constructed the regression according to the least-squares estimation, and set the energy densities for wavelengths between 500 and 2000 m. At last, the orientation of the wind streaks with an 180° directional ambiguity was extracted, which is perpendicular to the regression line. The directional ambiguity was subsequently removed according to the QuikSCAT or ECMWF wind products.

For sea surface wind speed extraction, the GMF CMOD4 was employed here. And it was originally designed to derive wind speed from SCAT, the SCAT instruments operate at C-band with VV polarization. SAR also operate at the same wavelength, therefore, the GMF CMOD4 is suitable for SAR images with VV polarization. The relationship between wind speed, wind direction, and NRCS (the GMF) is generally expressed by the form (1) [19]

$$
\sigma^0 = A(\mu\_{1\nu'}\theta) \left[ 1 + b(\mu\_{1\nu'}\theta) \text{cos}\phi + c \left( \mu\_{1\nu'}\theta \right) \text{cos}\rho \right]^\beta \tag{1}
$$

where σ0 is the NRCS in linear units; *φ* is the wind direction versus antenna look direction; u10 is wind speed; *θ* is nadir incident angle; and *A*, *b*, *c*, and *B* are model parameters depending on radar frequency, polarization, u10, and *θ*.

The GMF CMOD4 employed here was developed and validated using a large amount of measured data. For the C-band SAR images with HH polarization, a hybrid model including CMOD4 and the polarization ratio [20, 21] were employed. A flowchart of sea surface wind retrieval scheme is shown in **Figure 1**.

**Figure 1.** Flowchart of wind retrieval from SAR image.

#### **2.2. Sea surface current**

In the processing of SAR images, the Doppler centroid frequency of the SAR signal *f DC* is an important input parameter to obtain high resolution SAR images. For ASAR WSM scene in the range direction, a systematic grid of Doppler centroid frequencies has 100 pixels, while in the azimuth direction it contains a given number, which is dependent on the scene coverage. The cross-track pixel spacing is about 3.5 km in the far range direction and 9 km in the near range direction, while in the azimuth direction it is about 8 km (1) [22–25].

*f*

denotes radar incidence angle.

**3. Results and discussion**

**3.1. SAR-retrieved wind fields**

where *ke*

ASAR image.

*<sup>g</sup>* = *f DC* − *f DP* − *f err* − *f*

*V* = −*fg*/*ke*sin (3)

**Figure 2.** The NRCS of ASAR WSM scene over Yangtze estuary on 31 January 2005 (left) and 5 February 2005 (right). Superimposed points on the right plot are the Doppler centroid grids. Arrows denote azimuth and range directions of

Remote Sensing Retrieval Study of the Surface Kinetic Parameters in the Yangtze Estuary…

**Figure 3a** and **b** showed the estimated wind directions by 2D FFT in the spectral domain at the Dajishan and Tanhu meteorological stations. Since the wind shadowing are visible in the SAR image, we can directly remove the 180° directional ambiguities. The sea surface wind directions retrieved from SAR scene are approximate to the observed measurements, which were presented in **Table 1**. Results from SAR scene and by WRF model are generally in good agreement with the observed values. Particularly, the difference between the SAR-retrieved wind direction and the observed measurement is less than 5°. When comparing wind speed retrieved from SAR scene and the WRF model with observed data, the results showed both retrieved wind speed are a little lower than the observed data. Generally, wind direction and wind speed derived from the SAR data are slightly better than the WRF model outputs.

Two ERS-2 SAR images obtained over the Yangtze coastal area on 4 May 2006 were mosaicked and presented in **Figure 3c**. The upper SAR data were captured at 02:27 UTC and lower at

is 112 m−1 for the radar wavelength of 5.6 cm of the Envisat ASAR instrument and θ

*<sup>w</sup>* (2)

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The velocity of satellite along track relative to the rotating Earth results in a frequency motion *f DP*. Using CFI software complied in C language, the parameters of *f DP* and footprint geolocation can be precisely computed at any look angle and any orbit time [23, 25].

There are several influence factors contaminating the geophysical Doppler frequency shift information, including radial discontinuities, antenna mis-pointing, strong discrete targets, low signal-to-noise ratio areas and Doppler estimator bias. Therefore, the estimation errors *f err* must be removed first [11, 12, 22–24]. The scenes with enough land pixels for each range line number from the adjacent orbits/acquisition time were applied as reference image to eliminate the Doppler anomaly biases relying on elevation angle (**Figure 2**). Hansen et al. [23] introduced the details of Doppler centroid anomaly.

Wind-induced streaks are presented in the ASAR images. So, 2D FFT can be employed to extract the wind direction and CMOD4 to calculate the wind speed. Based on the CDOP model [24], we applied the ASAR derived-wind vectors to yield an estimation of the wind contributions to the Doppler frequency. In turn, these Doppler contributions from wind induced *f <sup>w</sup>* were then removed.

The geophysical Doppler anomaly *f g* can be calculated using the following Eq. (2) and can be converted with Eq. (3) to the surface current fields [23, 25] (2)

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**Figure 2.** The NRCS of ASAR WSM scene over Yangtze estuary on 31 January 2005 (left) and 5 February 2005 (right). Superimposed points on the right plot are the Doppler centroid grids. Arrows denote azimuth and range directions of ASAR image.

$$f\_{\mathcal{g}} = f\_{\text{DC}} - f\_{\text{DP}} - f\_{\text{ev}} - f\_w \tag{2}$$

$$V = -\pi f \mathbf{g} / k \text{esin} \theta \tag{3}$$

where *ke* is 112 m−1 for the radar wavelength of 5.6 cm of the Envisat ASAR instrument and θ denotes radar incidence angle.

#### **3. Results and discussion**

**2.2. Sea surface current**

200 Sea Level Rise and Coastal Infrastructure

**Figure 1.** Flowchart of wind retrieval from SAR image.

*f*

*f*

induced *f*

In the processing of SAR images, the Doppler centroid frequency of the SAR signal *f*

near range direction, while in the azimuth direction it is about 8 km (1) [22–25].

tion can be precisely computed at any look angle and any orbit time [23, 25].

*g*

converted with Eq. (3) to the surface current fields [23, 25] (2)

*DP*. Using CFI software complied in C language, the parameters of *f*

introduced the details of Doppler centroid anomaly.

*<sup>w</sup>* were then removed.

The geophysical Doppler anomaly *f*

important input parameter to obtain high resolution SAR images. For ASAR WSM scene in the range direction, a systematic grid of Doppler centroid frequencies has 100 pixels, while in the azimuth direction it contains a given number, which is dependent on the scene coverage. The cross-track pixel spacing is about 3.5 km in the far range direction and 9 km in the

The velocity of satellite along track relative to the rotating Earth results in a frequency motion

There are several influence factors contaminating the geophysical Doppler frequency shift information, including radial discontinuities, antenna mis-pointing, strong discrete targets, low signal-to-noise ratio areas and Doppler estimator bias. Therefore, the estimation errors

*err* must be removed first [11, 12, 22–24]. The scenes with enough land pixels for each range line number from the adjacent orbits/acquisition time were applied as reference image to eliminate the Doppler anomaly biases relying on elevation angle (**Figure 2**). Hansen et al. [23]

Wind-induced streaks are presented in the ASAR images. So, 2D FFT can be employed to extract the wind direction and CMOD4 to calculate the wind speed. Based on the CDOP model [24], we applied the ASAR derived-wind vectors to yield an estimation of the wind contributions to the Doppler frequency. In turn, these Doppler contributions from wind

can be calculated using the following Eq. (2) and can be

*DC* is an

*DP* and footprint geoloca-

#### **3.1. SAR-retrieved wind fields**

**Figure 3a** and **b** showed the estimated wind directions by 2D FFT in the spectral domain at the Dajishan and Tanhu meteorological stations. Since the wind shadowing are visible in the SAR image, we can directly remove the 180° directional ambiguities. The sea surface wind directions retrieved from SAR scene are approximate to the observed measurements, which were presented in **Table 1**. Results from SAR scene and by WRF model are generally in good agreement with the observed values. Particularly, the difference between the SAR-retrieved wind direction and the observed measurement is less than 5°. When comparing wind speed retrieved from SAR scene and the WRF model with observed data, the results showed both retrieved wind speed are a little lower than the observed data. Generally, wind direction and wind speed derived from the SAR data are slightly better than the WRF model outputs.

Two ERS-2 SAR images obtained over the Yangtze coastal area on 4 May 2006 were mosaicked and presented in **Figure 3c**. The upper SAR data were captured at 02:27 UTC and lower at

An ERS-2 SAR imaged on 4 May 2006, at 02:27 UTC, covering the Yangtze and adjacent area was shown in **Figure 3d**. Wind vectors with a resolution of 5 km were superimposed on the SAR image. According to the wind shadowing visible in SAR image, the 180 degree wind direction ambiguities were removed. The wind direction in **Figure 3d** was closely analogous to that on the coarse grid of the WRF model (the upper part of **Figure 3c**). While close to the coastal area of Yangtze Estuary, the wind direction was slightly changed westward, however, it was not effectively simulated by the WRF model. In the upper part of **Figure 3c**, wind speed

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**Figure 4a** and **b** showed the scatterplots of QuikSCAT products vs. SAR results. From these robust results, we could find that both QuikSCAT and SAR images are valid data sources to obtain sea surface wind fields. QuikSCAT is still best suitable for open ocean measurements due to its larger coverage, although it can only yield wind fields with resolution up to 12.5 km. However, wind fields can remarkably change over a few km, even over a smaller scale in the coastal areas. SAR images with high resolution (up to 1 m) are capable of producing sub-km resolution wind vectors. Therefore, an improved global wind product can be obtained by combining QuikSCAT wind products in open ocean areas with SAR-derived

The wind scatterplots of WRF vs. SAR were plotted in **Figure 4c** and **d**. The results suggested that wind fields computed by WRF and extracted by SAR do not correspond as closely as those between SAR and QuikSCAT. Furthermore, when CMOD4 adopted to extract wind speeds from SAR images, it would lead to underestimation in high wind speeds larger than

From **Table 1** and **Figure 3c**, we could find that wind directions retrieved from SAR images are in good agreement with the observed data at the Tanhu and Dajishan. Specifically, the discrepancies are less than 5°. These promising results may be explained as follows. The algorithm for wind direction extraction based on 2D FFT was improved by finding average position of the first three maximum spectral value instead of applying the position of single spectrum peak. This process was helpful to enhance the stability of the wind direction information extraction. In addition, wind direction from SAR image based on 2D FFT method is dependent on the orientation of typical km-scale surface features. When atmospheric conditions are relatively steady or sea surface wind speed is very small, precise wind directions would become difficult to derive. The wind speed of the example SAR image is large, about 8–10 m/s. Therefore, it can be deduced that wind direction can be precisely extracted by 2D FFT method from SAR images when wind speed is larger than about 7–8 m/s. On the other hand, large wind direction discrepancies between SAR-retrieved and in situ observations are probably due to non-wind-driven features imaged in SAR scene at the same scale as winddriven. These features are not related with the ocean surface wind direction, e.g., ocean waves. Along the coast of Yangtze Estuary, the SAR-retrieved wind vector results presented larger variability in direction and much better detail information in structure than the WRF outputs (**Figure 3d**). This is dependent on the high spatial resolution of SAR snapshot imaging a highly turbulent wind field, while relatively low resolution of the WRF numerical model cannot capture such small-scale signals. Moreover, there are several factors may result in the

derived from SAR image was very close to the outputs from the WRF model.

20 m/s and overestimation in small speeds lower than 3 m/s (**Figure 4d**).

wind fields in coastal areas.

**Figure 3.** Low-wavenumber of sea surface wind direction at (a) Dajishan and (b) Tanhu; wind vectors (c) from two ERS-2 SAR scenes on 4 May 2006, and (d) 5 km resolution wind field from SAR image.


**Table 1.** Extracted wind field results and observed wind vectors.

02:28 UTC, respectively. The wind vector results were superimposed on the SAR images. The black arrows denoted the wind retrieved from SAR images and the white arrows presented those calculated by WRF model. The observed data at the Dajishan and Tanhu meteorological stations were also superimposed. Since the wind from WRF model are available on a 1-h basis, we interpolated the wind vectors into the precise SAR acquisition time using the natural neighbor method. According to **Figure 3c**, roughly speaking, both in wind direction and magnitude, the vectors derived from SAR are in good agreement with the outputs by the WRF model.

An ERS-2 SAR imaged on 4 May 2006, at 02:27 UTC, covering the Yangtze and adjacent area was shown in **Figure 3d**. Wind vectors with a resolution of 5 km were superimposed on the SAR image. According to the wind shadowing visible in SAR image, the 180 degree wind direction ambiguities were removed. The wind direction in **Figure 3d** was closely analogous to that on the coarse grid of the WRF model (the upper part of **Figure 3c**). While close to the coastal area of Yangtze Estuary, the wind direction was slightly changed westward, however, it was not effectively simulated by the WRF model. In the upper part of **Figure 3c**, wind speed derived from SAR image was very close to the outputs from the WRF model.

**Figure 4a** and **b** showed the scatterplots of QuikSCAT products vs. SAR results. From these robust results, we could find that both QuikSCAT and SAR images are valid data sources to obtain sea surface wind fields. QuikSCAT is still best suitable for open ocean measurements due to its larger coverage, although it can only yield wind fields with resolution up to 12.5 km. However, wind fields can remarkably change over a few km, even over a smaller scale in the coastal areas. SAR images with high resolution (up to 1 m) are capable of producing sub-km resolution wind vectors. Therefore, an improved global wind product can be obtained by combining QuikSCAT wind products in open ocean areas with SAR-derived wind fields in coastal areas.

The wind scatterplots of WRF vs. SAR were plotted in **Figure 4c** and **d**. The results suggested that wind fields computed by WRF and extracted by SAR do not correspond as closely as those between SAR and QuikSCAT. Furthermore, when CMOD4 adopted to extract wind speeds from SAR images, it would lead to underestimation in high wind speeds larger than 20 m/s and overestimation in small speeds lower than 3 m/s (**Figure 4d**).

From **Table 1** and **Figure 3c**, we could find that wind directions retrieved from SAR images are in good agreement with the observed data at the Tanhu and Dajishan. Specifically, the discrepancies are less than 5°. These promising results may be explained as follows. The algorithm for wind direction extraction based on 2D FFT was improved by finding average position of the first three maximum spectral value instead of applying the position of single spectrum peak. This process was helpful to enhance the stability of the wind direction information extraction. In addition, wind direction from SAR image based on 2D FFT method is dependent on the orientation of typical km-scale surface features. When atmospheric conditions are relatively steady or sea surface wind speed is very small, precise wind directions would become difficult to derive. The wind speed of the example SAR image is large, about 8–10 m/s. Therefore, it can be deduced that wind direction can be precisely extracted by 2D FFT method from SAR images when wind speed is larger than about 7–8 m/s. On the other hand, large wind direction discrepancies between SAR-retrieved and in situ observations are probably due to non-wind-driven features imaged in SAR scene at the same scale as winddriven. These features are not related with the ocean surface wind direction, e.g., ocean waves.

Along the coast of Yangtze Estuary, the SAR-retrieved wind vector results presented larger variability in direction and much better detail information in structure than the WRF outputs (**Figure 3d**). This is dependent on the high spatial resolution of SAR snapshot imaging a highly turbulent wind field, while relatively low resolution of the WRF numerical model cannot capture such small-scale signals. Moreover, there are several factors may result in the

02:28 UTC, respectively. The wind vector results were superimposed on the SAR images. The black arrows denoted the wind retrieved from SAR images and the white arrows presented those calculated by WRF model. The observed data at the Dajishan and Tanhu meteorological stations were also superimposed. Since the wind from WRF model are available on a 1-h basis, we interpolated the wind vectors into the precise SAR acquisition time using the natural neighbor method. According to **Figure 3c**, roughly speaking, both in wind direction and magnitude, the vectors derived from SAR are in good agreement with the outputs by

**Figure 3.** Low-wavenumber of sea surface wind direction at (a) Dajishan and (b) Tanhu; wind vectors (c) from two ERS-2

**Observed data SAR-retrieved WRF model Observed data SAR-retrieved WRF model**

SAR scenes on 4 May 2006, and (d) 5 km resolution wind field from SAR image.

**Table 1.** Extracted wind field results and observed wind vectors.

**Test site Wind direction (°) Wind speed (m/s)**

Dajishan 340 343.0 335.0 9.8 8.6 8.3 Tanhu 345 340.0 329.6 10.3 9.9 9.5

the WRF model.

202 Sea Level Rise and Coastal Infrastructure

coefficient R2

and WRF results are 0.95 and 0.82, respectively. The high R2

of SAR wind speeds with QuikSCAT products showed high R2

to the R2 = 0.93 manifested by Monaldo et al. [26].

*DC*, *f*

surface kinetic parameters and features.

**3.2. SAR-retrieved current fields**

*g*

**Figures 5** and **6** showed *f*

quency anomaly *f*

from the platform.

in the case-by-case comparisons of SAR-retrieved wind direction with QuikSCAT

Remote Sensing Retrieval Study of the Surface Kinetic Parameters in the Yangtze Estuary…

*DP*, the raw Doppler anomaly and the geophysical Doppler fre-

from ASAR WSM scene on 31 January 2005 and 5 February 2005, respec-

FFT method used to deduce wind direction is reliable. The wind speed linear regression analysis between SAR images and QuikSCAT has a bias of 0.16 m/s and root mean square error of 1.04 m/s. These results are slightly better than those for the linear analysis of wind speeds from SAR and WRF model, whose values are 0.27 m/s and 1.60 m/s, respectively. The comparisons

Therefore, for C-band SAR images with VV polarization, the algorithm based on 2D FFT extracting sea surface wind direction and the CMOD4 model computing wind speed are prominent and promising to obtain sea surface wind field. Especially in the coastal areas, the large spatial coverage and multi-resolution (especially high resolution) of SAR scene, with its all day, all weather capability, makes it indispensable in the observation of detailed sea

tively. There were large variability of the raw Doppler centroid anomaly over interaction zone between land and sea, even over land areas (**Figure 5c** and **6c**). However, the Doppler frequency anomaly should be zero over land since it is immobile relative to the Earth. Along the azimuth direction, the strong backscatter signal gradient is one of the main sources of Doppler frequency bias, which is particularly exhibited over the coastline areas. In addition, the erroneous Doppler frequencies in the range direction are obvious as vertical stripes of increased or decreased Doppler anomalies, and present at the transition area between different sub-swaths. The biases are also from artifacts. The error correction were therefore proceeded both in the azimuth and range directions. The root mean square offset over land was reduced by 13.7 and 12.1 Hz that was from 24.5, 21.4 Hz of the raw Doppler anomaly to 10.8, 9.3 Hz (**Table 2**). And it was further reduced to 6.2, 6.1 Hz, respectively (**Table 2**) after removing the outlying values. The geophysical Doppler anomaly after removal of the wind-induced Doppler frequency were shown in **Figures 5d** and **6d**. Surface current fields according to the Eq. (3) were calculated and presented in **Figure 7a** and **b**, respectively. The SAR Doppler method produced Doppler velocity with a resolution about 8 km in azimuth direction and 4 km in range direction. The negative range Doppler velocity values correspond to the sea surface velocities towards the sensor platform, whereas positive values suggest a current away

In **Figure 7a** of surface Doppler velocities from ASAR WSM scene on 31 January 2005, there is a distinct directional change located at about 31.5°N. In the Hangzhou Bay area, a southeasterly current is encountered. At the time of image acquisition, the wind streak was clearly visible on the SAR scene and exhibited a qualitative correlation with the SAR backscatter signal. 2D FFT and CMOD4 model were adopted to extract the wind direction and wind speed information, respectively. Results showed that the northwesterly wind increased from

values indicated the improved 2D

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value of 0.92, which approaches

**Figure 4.** Scatterplots of wind results from QuikSCAT vs. SAR and WRF vs. SAR.

spatial inhomogeneity in the wind field. In the Yangtze Estuary, the variable topography is one of the factor. The improved SAR wind retrieval method in this study indeed resolve spatial inhomogeneity of the variable sea surface wind vector.

A linear regression of sea surface wind direction retrieved from SAR images and QuikSCAT wind direction presented a bias of −2.18° and root mean square error of 19.3°. These results are better than those between SAR measurements and WRF outputs, whose values are 2.73° and 34.20°, respectively. The ECS is located in a subtropical monsoon climate area. SAR images can effectively capture the homogeneous distribution of wind. Therefore, the 2D FFT method in the spectral domain is well suitable for extracting wind direction in the ECS. The correlation coefficient R2 in the case-by-case comparisons of SAR-retrieved wind direction with QuikSCAT and WRF results are 0.95 and 0.82, respectively. The high R2 values indicated the improved 2D FFT method used to deduce wind direction is reliable. The wind speed linear regression analysis between SAR images and QuikSCAT has a bias of 0.16 m/s and root mean square error of 1.04 m/s. These results are slightly better than those for the linear analysis of wind speeds from SAR and WRF model, whose values are 0.27 m/s and 1.60 m/s, respectively. The comparisons of SAR wind speeds with QuikSCAT products showed high R2 value of 0.92, which approaches to the R2 = 0.93 manifested by Monaldo et al. [26].

Therefore, for C-band SAR images with VV polarization, the algorithm based on 2D FFT extracting sea surface wind direction and the CMOD4 model computing wind speed are prominent and promising to obtain sea surface wind field. Especially in the coastal areas, the large spatial coverage and multi-resolution (especially high resolution) of SAR scene, with its all day, all weather capability, makes it indispensable in the observation of detailed sea surface kinetic parameters and features.

#### **3.2. SAR-retrieved current fields**

spatial inhomogeneity in the wind field. In the Yangtze Estuary, the variable topography is one of the factor. The improved SAR wind retrieval method in this study indeed resolve spa-

A linear regression of sea surface wind direction retrieved from SAR images and QuikSCAT wind direction presented a bias of −2.18° and root mean square error of 19.3°. These results are better than those between SAR measurements and WRF outputs, whose values are 2.73° and 34.20°, respectively. The ECS is located in a subtropical monsoon climate area. SAR images can effectively capture the homogeneous distribution of wind. Therefore, the 2D FFT method in the spectral domain is well suitable for extracting wind direction in the ECS. The correlation

tial inhomogeneity of the variable sea surface wind vector.

**Figure 4.** Scatterplots of wind results from QuikSCAT vs. SAR and WRF vs. SAR.

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**Figures 5** and **6** showed *f DC*, *f DP*, the raw Doppler anomaly and the geophysical Doppler frequency anomaly *f g* from ASAR WSM scene on 31 January 2005 and 5 February 2005, respectively. There were large variability of the raw Doppler centroid anomaly over interaction zone between land and sea, even over land areas (**Figure 5c** and **6c**). However, the Doppler frequency anomaly should be zero over land since it is immobile relative to the Earth. Along the azimuth direction, the strong backscatter signal gradient is one of the main sources of Doppler frequency bias, which is particularly exhibited over the coastline areas. In addition, the erroneous Doppler frequencies in the range direction are obvious as vertical stripes of increased or decreased Doppler anomalies, and present at the transition area between different sub-swaths. The biases are also from artifacts. The error correction were therefore proceeded both in the azimuth and range directions. The root mean square offset over land was reduced by 13.7 and 12.1 Hz that was from 24.5, 21.4 Hz of the raw Doppler anomaly to 10.8, 9.3 Hz (**Table 2**). And it was further reduced to 6.2, 6.1 Hz, respectively (**Table 2**) after removing the outlying values. The geophysical Doppler anomaly after removal of the wind-induced Doppler frequency were shown in **Figures 5d** and **6d**. Surface current fields according to the Eq. (3) were calculated and presented in **Figure 7a** and **b**, respectively. The SAR Doppler method produced Doppler velocity with a resolution about 8 km in azimuth direction and 4 km in range direction. The negative range Doppler velocity values correspond to the sea surface velocities towards the sensor platform, whereas positive values suggest a current away from the platform.

In **Figure 7a** of surface Doppler velocities from ASAR WSM scene on 31 January 2005, there is a distinct directional change located at about 31.5°N. In the Hangzhou Bay area, a southeasterly current is encountered. At the time of image acquisition, the wind streak was clearly visible on the SAR scene and exhibited a qualitative correlation with the SAR backscatter signal. 2D FFT and CMOD4 model were adopted to extract the wind direction and wind speed information, respectively. Results showed that the northwesterly wind increased from

**Figure 5.** The Doppler centroid grid of ASAR WSM scene on 31 January 2005. (a) *f DC*. (b) *f DP*. (c) the raw Doppler centroid anomaly and (d) *f g* .

8 to 11 m/s with the distance from the shoreline. At the Tanhushan meteorological station, it is particularly the case at low tide, so the tidal current should be relatively low. However, the range Doppler velocity here is relatively large in **Figure 7a**. This was probably related to the underwater topography and the combination action of the ocean wind, wave and current.

In **Figure 7b** of the range Doppler velocity on 5 February 2005, the Doppler currents located from 122.5°E to west range from −1.2 to −0.2 m/s. This corresponds with a westerly/southwesterly sea surface current. In the area located from 122.5°E to east, the range Doppler currents are mostly positive, corresponding an easterly/northeasterly surface current. Negative strong Doppler velocity occur in the Hangzhou Bay areas. At the scene acquisition time, wind directions retrieved by 2D FFT method are from the northeast, i.e. towards the radar sensor. Wind speeds calculated by CMOD4 model are between 9 and 11 m/s. At the Tanhushan station, Doppler velocity is −0.25 m/s at 40 minutes after high tide. At the remaining four tidal stations, the Doppler velocities were very variable even if they all located at about 2–3 h after high tide. Therefore, we could deduce that any Doppler velocity map such as **Figure 6a** and **b** represent the wind, wave and current patterns in a rather complicated way. Local variables in the wind, wave field and underwater topography would exacerbate the interpretation of geophysical Doppler velocity.

The estimated Doppler velocity fields in the above two images showed that the strongest Doppler velocities appeared in the Hangzhou Bay area, where the velocities are up to 0.8– 1.0 m/s. These high current values are mainly influenced by the interaction of wind, wave and tide. As the two cases at spring tide, the retrieved Doppler velocities represent the relatively intense currents. The ability of SAR image to extract strong surface currents based on the Doppler frequency method was also shown in the Agulhas Return Current area [14]. However, large Doppler velocities are usually related to strong NRCS gradients in the SAR signal. Accordingly, the strong NRCS would lead to the overestimation of Doppler velocity. Therefore, the error correction of Doppler shift in azimuth direction must be sufficient, if not,

**raw After azimuthal correction After bias correction After outliers removal**

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*DC*. (b) *f*

*DP*. (c) the raw Doppler centroid

**Figure 6.** The Doppler centroid grid of ASAR WSM scene on 5 February 2005. (a) *f*

**RMS of Doppler anomaly/Hz**

**Table 2.** Doppler centroid anomaly bias over land of the scenes.

31 January 2005 24.5 19.0 10.8 6.2 05 February 2005 21.4 16.1 9.3 6.1

anomaly and (d) *f*

**Acquisition time of ASAR scene**

*g* .

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**Figure 6.** The Doppler centroid grid of ASAR WSM scene on 5 February 2005. (a) *f DC*. (b) *f DP*. (c) the raw Doppler centroid anomaly and (d) *f g* .


**Table 2.** Doppler centroid anomaly bias over land of the scenes.

8 to 11 m/s with the distance from the shoreline. At the Tanhushan meteorological station, it is particularly the case at low tide, so the tidal current should be relatively low. However, the range Doppler velocity here is relatively large in **Figure 7a**. This was probably related to the underwater topography and the combination action of the ocean wind, wave and current.

*DC*. (b) *f*

*DP*. (c) the raw Doppler centroid

**Figure 5.** The Doppler centroid grid of ASAR WSM scene on 31 January 2005. (a) *f*

In **Figure 7b** of the range Doppler velocity on 5 February 2005, the Doppler currents located from 122.5°E to west range from −1.2 to −0.2 m/s. This corresponds with a westerly/southwesterly sea surface current. In the area located from 122.5°E to east, the range Doppler currents are mostly positive, corresponding an easterly/northeasterly surface current. Negative strong Doppler velocity occur in the Hangzhou Bay areas. At the scene acquisition time, wind directions retrieved by 2D FFT method are from the northeast, i.e. towards the radar sensor. Wind speeds calculated by CMOD4 model are between 9 and 11 m/s. At the Tanhushan station, Doppler velocity is −0.25 m/s at 40 minutes after high tide. At the remaining four tidal stations, the Doppler velocities were very variable even if they all located at about 2–3 h after high tide. Therefore, we could deduce that any Doppler velocity map such as **Figure 6a** and **b** represent the wind, wave and current patterns in a rather complicated way. Local variables in the wind, wave field and underwater topography would exacerbate the interpretation of

geophysical Doppler velocity.

anomaly and (d) *f*

*g* .

206 Sea Level Rise and Coastal Infrastructure

The estimated Doppler velocity fields in the above two images showed that the strongest Doppler velocities appeared in the Hangzhou Bay area, where the velocities are up to 0.8– 1.0 m/s. These high current values are mainly influenced by the interaction of wind, wave and tide. As the two cases at spring tide, the retrieved Doppler velocities represent the relatively intense currents. The ability of SAR image to extract strong surface currents based on the Doppler frequency method was also shown in the Agulhas Return Current area [14]. However, large Doppler velocities are usually related to strong NRCS gradients in the SAR signal. Accordingly, the strong NRCS would lead to the overestimation of Doppler velocity. Therefore, the error correction of Doppler shift in azimuth direction must be sufficient, if not,

**Figure 7.** Surface range Doppler velocities from WSM images (a) on 31 January 2005 and (b) on 5 February 2005. The color scale is given in unit of m/s.

it will make a negatively effect on Doppler velocity estimation. In addition, more attention and analysis should be taken in the region near the land-sea boundaries.

The SAR imaging geometry with regard to the sea surface current field derived from Doppler method is highly significant to the Doppler velocity quality. In the Yangtze Estuary, the flow is along a southeast/northwest axis. Since the ENVISAT ASAR is right-looking imaging radar, the descending track configuration is well suitable for capturing spatial variations of current field. Moreover, the descending track SAR image the Yangtze Estuary mouth at the high radar incidence angle (**Figure 7a**), which is helpful to reduce the retrieval error from the effect of incidence angle.

For the Doppler centroid anomaly method, in order to examine and assess its capability for retrieving surface current from SAR images, we compared the Doppler current, both from SAR ascending and descending pass, with FVCOM outputs. In general, both in magnitude and directions, they exhibited the similar surface current field features (**Figure 8a** and **b**). For quantitative comparison, we extracted two transects from ASAR results and FVCOM surface flow maps (**Figure 8a** and **b**), one at about 30.5°N latitude on ASAR descending pass, the other at about 30.7°N latitude on ASAR ascending image.

ignored if taking the retrieval error into the consideration. Whereas, the difference increased to 0.24 m/s on 31 January 2005 and 0.18 m/s on 5 February 2005 below 30° radar incidence angle. These results were well matched with the previous studies [11, 12, 27] and further corroborated and revealed a considerable increase in the ASAR Doppler velocity error below 30°

**Figure 8.** ASAR Doppler velocity (a) on 31 January 2005 and (b) on 5 February 2005. Superimposed were the FVCOM surface currents as arrows. The color scale is given in unit of m/s. Transects of ASAR Doppler and FVCOM velocities (c)

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The dominant current direction of FVCOM result on 31 January 2005 is southerly-southwesterly (**Figure 8a**) from 122.2°E to east. The ASAR-retrieved current only capture the surface current velocity in the range direction, i.e. westerly-northwesterly or easterly-southeasterly. Therefore, the Doppler range Doppler velocities are rather weak, below +/− 0.15 m/s in the area from 31.0°N to north. On the contrary, the current direction simulated by FVCOM on 5 February 2005 (**Figure 8b**), principally presented motions easterly and northeasterly from 122.5°E to east. This direction corresponds well with the slant range direction of ASAR, i.e. the line of sight direction, at least at south of about 31.5°N. Therefore, the retrieval of surface range Doppler current on 5 February 2005 is more accurate and yields a better measurement

radar incidence angle.

of the real local sea surface current.

on 31 January 2005 and (d) on 5 February 2005.

In both transects, surface current directions derived from ASAR images and simulated by FVCOM are in good agreement (**Figure 8c** and **d**). As a whole, the comparison of velocity is also robust provided the different speed is within +/−0.2 m/s. For the surface current velocities retrieved from the ASAR, the maximum values in both cases are up to1.0 m/s, whereas the maximal velocity simulated by FVCOM is only 0.6 m/s on 5 February 2005 and 0.8 m/s on 31 January 2005. The maximum discrepancy is 0.35 m/s located at about 121.2° E on 5 February 2005, and 0.42 m/s at 122.7°E on 31 January 2005. The corresponding incidence angle is 24.0° and 27.5°, respectively, which are both below 30°. We further computed the average velocity difference below and above 30° radar incidence angle, between Doppler velocities and FVCOM outputs. The results showed that above 30° radar incidence angle, the difference was only 0.09 m/s on 31 January 2005 and 0.10 m/s on 5 February 2005, i.e., the difference could be Remote Sensing Retrieval Study of the Surface Kinetic Parameters in the Yangtze Estuary… http://dx.doi.org/10.5772/intechopen.72461 209

it will make a negatively effect on Doppler velocity estimation. In addition, more attention

**Figure 7.** Surface range Doppler velocities from WSM images (a) on 31 January 2005 and (b) on 5 February 2005. The

The SAR imaging geometry with regard to the sea surface current field derived from Doppler method is highly significant to the Doppler velocity quality. In the Yangtze Estuary, the flow is along a southeast/northwest axis. Since the ENVISAT ASAR is right-looking imaging radar, the descending track configuration is well suitable for capturing spatial variations of current field. Moreover, the descending track SAR image the Yangtze Estuary mouth at the high radar incidence angle (**Figure 7a**), which is helpful to reduce the retrieval error from the effect of

For the Doppler centroid anomaly method, in order to examine and assess its capability for retrieving surface current from SAR images, we compared the Doppler current, both from SAR ascending and descending pass, with FVCOM outputs. In general, both in magnitude and directions, they exhibited the similar surface current field features (**Figure 8a** and **b**). For quantitative comparison, we extracted two transects from ASAR results and FVCOM surface flow maps (**Figure 8a** and **b**), one at about 30.5°N latitude on ASAR descending pass, the

In both transects, surface current directions derived from ASAR images and simulated by FVCOM are in good agreement (**Figure 8c** and **d**). As a whole, the comparison of velocity is also robust provided the different speed is within +/−0.2 m/s. For the surface current velocities retrieved from the ASAR, the maximum values in both cases are up to1.0 m/s, whereas the maximal velocity simulated by FVCOM is only 0.6 m/s on 5 February 2005 and 0.8 m/s on 31 January 2005. The maximum discrepancy is 0.35 m/s located at about 121.2° E on 5 February 2005, and 0.42 m/s at 122.7°E on 31 January 2005. The corresponding incidence angle is 24.0° and 27.5°, respectively, which are both below 30°. We further computed the average velocity difference below and above 30° radar incidence angle, between Doppler velocities and FVCOM outputs. The results showed that above 30° radar incidence angle, the difference was only 0.09 m/s on 31 January 2005 and 0.10 m/s on 5 February 2005, i.e., the difference could be

and analysis should be taken in the region near the land-sea boundaries.

other at about 30.7°N latitude on ASAR ascending image.

incidence angle.

color scale is given in unit of m/s.

208 Sea Level Rise and Coastal Infrastructure

**Figure 8.** ASAR Doppler velocity (a) on 31 January 2005 and (b) on 5 February 2005. Superimposed were the FVCOM surface currents as arrows. The color scale is given in unit of m/s. Transects of ASAR Doppler and FVCOM velocities (c) on 31 January 2005 and (d) on 5 February 2005.

ignored if taking the retrieval error into the consideration. Whereas, the difference increased to 0.24 m/s on 31 January 2005 and 0.18 m/s on 5 February 2005 below 30° radar incidence angle. These results were well matched with the previous studies [11, 12, 27] and further corroborated and revealed a considerable increase in the ASAR Doppler velocity error below 30° radar incidence angle.

The dominant current direction of FVCOM result on 31 January 2005 is southerly-southwesterly (**Figure 8a**) from 122.2°E to east. The ASAR-retrieved current only capture the surface current velocity in the range direction, i.e. westerly-northwesterly or easterly-southeasterly. Therefore, the Doppler range Doppler velocities are rather weak, below +/− 0.15 m/s in the area from 31.0°N to north. On the contrary, the current direction simulated by FVCOM on 5 February 2005 (**Figure 8b**), principally presented motions easterly and northeasterly from 122.5°E to east. This direction corresponds well with the slant range direction of ASAR, i.e. the line of sight direction, at least at south of about 31.5°N. Therefore, the retrieval of surface range Doppler current on 5 February 2005 is more accurate and yields a better measurement of the real local sea surface current.

Although the range Doppler velocity results involve spatial change, an obvious correlation exhibits between FVCOM outputs and ASAR Doppler velocities in Yangtze Estuary. The correlation coefficient is 0.56 for the 31 January 2005 case and 0.59 for the 5 February case. In consistence with the previous studies [12, 14, 18], the accuracy of range Doppler velocity fields are affected by the radar parameters, including radar wavelength, polarization, incidence angle and antenna information. Nevertheless, the surface current retrieval based on the Doppler frequency anomaly method is undoubtedly helpful to obtain mesoscale ocean dynamics and definitely reveal sea surface features combined with local environmental changes.

[2] Genovese SJ, Witman JD. Wind-mediated diel variation in flow speed in a Jamaican back reef environment: Effects on ecological processes. Bulletin of Marine Science. 2004;

Remote Sensing Retrieval Study of the Surface Kinetic Parameters in the Yangtze Estuary…

http://dx.doi.org/10.5772/intechopen.72461

211

[3] Zhu JR, Li YP, Shen HT. Numerical simulation of the wind field's impact on the expansion of the Changjiang River diluted water in summer. Oceanologia et Limnologia Sinica.

[4] Chang PH, Isobe A. A numerical study on the Changjiang diluted water in the yellow and East China seas. Journal of Geophysical Research. 2003;**108**(C9):1-17. DOI:

[5] Horstmann J, Koch W, Lehner S, Tonboe R. Wind retrieval over the ocean using synthetic aperture radar with C-band HH polarization. IEEE Transactions on Geoscience

[6] Johannessen JA. Coastal observing systems: The role of synthetic aperture radar. Johns

[7] Fichaux N, Ranchin T. Combined extraction of high spatial resolution wind speed and wind direction from SAR images: A new approach using wavelet transform. Can.

[8] Christiansen MB, Koch W, Horstmann J, Hasager CB, Nielsen M. Wind resource assess-

[9] Pandian PK, Emmanuel O, Ruscoe JP, et al. An overview of recent technologies on wave and current measurement in coastal and marine applications. Journal of Oceanography

[10] Kerbaol V, Collard F. SAR-derived coastal and marine applications: From research to operational products. IEEE Journal of Oceanic Engineering. 2005;**30**(3):472-486

[11] Chapron B, Collard F, Ardhuin F. Direct measurements of ocean surface velocity from space: Interpretation and validation. Journal of Geophysical Research. 2005;**110**:C07008

[12] Johannessen JA, Chapron B, Collard F, et al. Direct ocean surface velocity measurements from space: Improved quantitative interpretation of Envisat ASAR observations.

[13] Romeiser R, Thompson DR. Numerical study on the along-track interferometric radar imaging mechanism of oceanic surface currents. IEEE Transactions on Geoscience and

[14] Rouault MJ, Mouche A, Collard F, et al. Mapping the Agulhas current from space: An assessment of ASAR surface current velocities. Journal of Geophysical Research.

[15] Romeiser R, Suchand S, Runge H, et al. First analysis of TerraSAR-X along-track InSAR-derived current fields. IEEE Transactions on Geoscience and Remote Sensing.

ment from C-band SAR. Remote Sensing of Environment. 2006;**105**(1):68-81

1997;**28**(1):72-79 (in Chinese with English abstract)

and Remote Sensing. 2000;**38**(5):2122-2131

Hopkins APL Technical Digest. 2000;**21**:7-14

J. Remote Sensing. 2002;**28**(3):510-516

and Marine Science. 2010;**1**(1):1-10

Geophysical Research Letter. 2008;**35**:L22608

Remote Sensing. 2000;**38**(1):446-458

2010;**115**:C10026

2010;**48**(2):820-829

**75**(2):281-293

10.1029/2002JC001749

The geophysical Doppler anomaly can be obtained from the ASAR WSM scenes using Doppler centroid grid, due to the precise attitude of the ASAR platform [11]. Yet, biases negatively affect the Doppler centroid frequency, subsequently affect the retrieval accuracy of the range Doppler velocity. Therefore, for the extraction of accurate range Doppler surface velocity estimation, in turn, the more real surface current, error corrections and bias removal are extremely required.

The comparison and validation of ASAR-retrieved Doppler current against the flow simulated by FVCOM showed promising results in both direction and magnitude. Therefore, the Doppler frequency method is capable of extracting innovative measurement of surface current at Yangtze Estuary. These range Doppler velocities from ASAR scenes based on Doppler frequency method are valuable because they can capture and render the multi-scale ocean dynamics around the East China Sea. In addition, the SAR Doppler velocities possess the capability to yield sufficiently and precisely spatial information for validation of high resolution ocean and coastal simulation models in the near future. Further processing and analyzing SAR scenes, together with in situ measurement at the Yangtze Estuary, will undoubtedly promote and implement routine observation of multi-scale sea surface dynamic.

### **Author details**

Shengbo Chen1 and Lihua Wang2 \*

\*Address all correspondence to: wanglh@cuit.edu.cn

1 College of Geo-exploration Science and Technology, Jilin University, Changchun, China

2 College of Resources and Environment, Chengdu University of Information Technology, Chengdu, China

### **References**

[1] Blanton J, Wenner E, Werner F, Knott D. Effects of wind-generated coastal currents on the transport of blue crab megalopae on a shallow continental shelf. Bulletin of Marine Science. 1995;**57**(3):739-752

[2] Genovese SJ, Witman JD. Wind-mediated diel variation in flow speed in a Jamaican back reef environment: Effects on ecological processes. Bulletin of Marine Science. 2004; **75**(2):281-293

Although the range Doppler velocity results involve spatial change, an obvious correlation exhibits between FVCOM outputs and ASAR Doppler velocities in Yangtze Estuary. The correlation coefficient is 0.56 for the 31 January 2005 case and 0.59 for the 5 February case. In consistence with the previous studies [12, 14, 18], the accuracy of range Doppler velocity fields are affected by the radar parameters, including radar wavelength, polarization, incidence angle and antenna information. Nevertheless, the surface current retrieval based on the Doppler frequency anomaly method is undoubtedly helpful to obtain mesoscale ocean dynamics and

The geophysical Doppler anomaly can be obtained from the ASAR WSM scenes using Doppler centroid grid, due to the precise attitude of the ASAR platform [11]. Yet, biases negatively affect the Doppler centroid frequency, subsequently affect the retrieval accuracy of the range Doppler velocity. Therefore, for the extraction of accurate range Doppler surface velocity estimation, in turn, the more real surface current, error corrections and bias removal are

The comparison and validation of ASAR-retrieved Doppler current against the flow simulated by FVCOM showed promising results in both direction and magnitude. Therefore, the Doppler frequency method is capable of extracting innovative measurement of surface current at Yangtze Estuary. These range Doppler velocities from ASAR scenes based on Doppler frequency method are valuable because they can capture and render the multi-scale ocean dynamics around the East China Sea. In addition, the SAR Doppler velocities possess the capability to yield sufficiently and precisely spatial information for validation of high resolution ocean and coastal simulation models in the near future. Further processing and analyzing SAR scenes, together with in situ measurement at the Yangtze Estuary, will undoubtedly

definitely reveal sea surface features combined with local environmental changes.

promote and implement routine observation of multi-scale sea surface dynamic.

1 College of Geo-exploration Science and Technology, Jilin University, Changchun, China

2 College of Resources and Environment, Chengdu University of Information Technology,

[1] Blanton J, Wenner E, Werner F, Knott D. Effects of wind-generated coastal currents on the transport of blue crab megalopae on a shallow continental shelf. Bulletin of Marine

extremely required.

210 Sea Level Rise and Coastal Infrastructure

**Author details**

Shengbo Chen1

Chengdu, China

**References**

and Lihua Wang2

Science. 1995;**57**(3):739-752

\*Address all correspondence to: wanglh@cuit.edu.cn

\*


[16] Thompson DR, Jensen JR. Synthetic-aperture radar interferometry applied to shipgenerated internal waves in the 1989 loch Linnhe experiment. Journal of Geophysical Research Oceans. 1993;**98**(C6):10259-10269

**Chapter 13**

**Provisional chapter**

**Spatio-Temporal Analysis of Sea Surface Temperature**

Sea surface temperature (SST) is an important parameter in determining the atmospheric and oceanic circulations, and satellite thermal infrared remote sensing can obtain the SST with very high spatio-temporal resolutions. The study first validated the accuracy of TERRA MODIS SST daytime and nighttime products with the timing SST measurements from the ships in the East China Sea (ECS) in February, May, August and November, 2001, and then the daily variation of daytime and nighttime SST difference was analyzed. Using 16-year MODIS SST monthly products data from February 2000 to January 2016, when all SST monthly products in February, May, August and November were averaged respectively, the seasonal spatial distribution pattern of SST in the ECS was discovered. After monthly sea surface temperature anomaly was finally processed by the empirical orthogonal function (EOF), the interannual variability of SST in the ECS was discussed. The results show that the MODIS SST daily products have a good accuracy with a mean absolute percentage error (MAPE) below 5%. The SST difference between day and night is the largest in winter, followed by spring, then for autumn and the smallest in summer, while the diurnal SST difference is very low for the same season in the different seas. The SST in the ECS displays the obvious seasonal spatial distribution pattern, in which the SST of winter is gradually increasing from north to south, while local temperature difference is the largest for 26.5°C in a year. In comparison, the SST in summer tends uniform and the difference is not more than 5°C in the whole sea. From the EOF analysis of SST anomaly, the interannual variability of SST in the ECS is affected by the East Asian monsoon, the latitudinal difference of solar radiation, the offshore circulation and the

**Spatio-Temporal Analysis of Sea Surface Temperature** 

DOI: 10.5772/intechopen.73217

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

and reproduction in any medium, provided the original work is properly cited.

**Keywords:** spatio-temporal analysis, sea surface temperature, East China Sea, MODIS

**in the East China Sea Using TERRA/MODIS Products**

**in the East China Sea Using TERRA/MODIS Products** 

**Data**

**Data**

Shaoqi Gong and Kapo Wong

Shaoqi Gong and Kapo Wong

http://dx.doi.org/10.5772/intechopen.73217

**Abstract**

submarine terrain.

SST product, empirical orthogonal function

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter


**Provisional chapter**

### **Spatio-Temporal Analysis of Sea Surface Temperature in the East China Sea Using TERRA/MODIS Products Data in the East China Sea Using TERRA/MODIS Products Data**

**Spatio-Temporal Analysis of Sea Surface Temperature** 

DOI: 10.5772/intechopen.73217

Shaoqi Gong and Kapo Wong Additional information is available at the end of the chapter

Shaoqi Gong and Kapo Wong

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.73217

#### **Abstract**

[16] Thompson DR, Jensen JR. Synthetic-aperture radar interferometry applied to shipgenerated internal waves in the 1989 loch Linnhe experiment. Journal of Geophysical

[17] Chapron B, Collard F, Kerbaol V. Satellite synthetic aperture radar sea surface Doppler measurements. Proceedings of the 2nd Workshop Coastal and Marine Applications

[18] Hansen MW, Johannessen JA, Dagestad KF, et al. Monitoring the surface inflow of Atlantic water to the Norwegian Sea using Envisat ASAR. Journal of Geophysical

[19] Stoffelen A, Anderson D. Scatterometer data interpretation: Estimation and validation of the transfer function CMOD4[J]. Journal of Geophysical Research: Oceans. 1997;

[20] Elfouhaily T, Thompson DR, Vandemark D, Chapron BA. New bistatic model for electromagnetic scattering from perfectly conducting random surfaces. Waves in Random

[21] Thompson DR, Elfouhaily TM, Chapron B. Polarization ratio for microwave backscattering from the ocean surface at low to moderate incidence angles. Geoscience and Remote

Sensing (IGARSS), IEEE International Symposium Proceedings. 1999;**3**:1671-1673 [22] Dagestad KF, Hansen MW, Johannessen JA, et al. Inverting consistent surface current

[23] Hansen MW, Collard F, Dagestad KF, et al. Retrieval of sea surface range velocities from Envisat ASAR Doppler centroid measurements. IEEE Transactions on Geoscience and

[24] Mouche A, Dagestad KF, Collard F, et al. On the use of Doppler shift for sea surface wind retrieval from SAR. IEEE Transactions on Geoscience and Remote Sensing. 2012;

[25] Wang L, Zhou Y, Ge J, et al. Mapping sea surface velocities in the Changjiang coastal zone with advanced synthetic aperture radar[J]. Acta Oceanologica Sinica. 2014;**33**(11):141-149

[26] Monaldo FM, Thompson DR, Pichel WG, Clemente-Colon PA. Systematic comparison of QuikSCAT and SAR ocean surface wind speeds. IEEE Transactions on Geoscience and

[27] Collard F, Mouche A, Chapron B, Danilo C, et al. Routine high resolution observation of selected major surface currents from space. Proceedings of the Conference of

fields from SAR. Frascati, Roma: European Space Research Institute; 2010

Synthetic Aperture Radar (SAR). Svalbard, Norway: ESA; 2004

Research Oceans. 1993;**98**(C6):10259-10269

Research. 2011;**116**:C12008

Media. 1999;**9**(3):281-294

Remote Sensing. 2011;**49**(10):3582-3592

Remote Sensing. 2004;**42**(2):283-291

SEASAR. Frascati, Italy: ESA; 2008

**102**(C3):5767-5780

212 Sea Level Rise and Coastal Infrastructure

**50**(7):2901-2909

Sea surface temperature (SST) is an important parameter in determining the atmospheric and oceanic circulations, and satellite thermal infrared remote sensing can obtain the SST with very high spatio-temporal resolutions. The study first validated the accuracy of TERRA MODIS SST daytime and nighttime products with the timing SST measurements from the ships in the East China Sea (ECS) in February, May, August and November, 2001, and then the daily variation of daytime and nighttime SST difference was analyzed. Using 16-year MODIS SST monthly products data from February 2000 to January 2016, when all SST monthly products in February, May, August and November were averaged respectively, the seasonal spatial distribution pattern of SST in the ECS was discovered. After monthly sea surface temperature anomaly was finally processed by the empirical orthogonal function (EOF), the interannual variability of SST in the ECS was discussed. The results show that the MODIS SST daily products have a good accuracy with a mean absolute percentage error (MAPE) below 5%. The SST difference between day and night is the largest in winter, followed by spring, then for autumn and the smallest in summer, while the diurnal SST difference is very low for the same season in the different seas. The SST in the ECS displays the obvious seasonal spatial distribution pattern, in which the SST of winter is gradually increasing from north to south, while local temperature difference is the largest for 26.5°C in a year. In comparison, the SST in summer tends uniform and the difference is not more than 5°C in the whole sea. From the EOF analysis of SST anomaly, the interannual variability of SST in the ECS is affected by the East Asian monsoon, the latitudinal difference of solar radiation, the offshore circulation and the submarine terrain.

**Keywords:** spatio-temporal analysis, sea surface temperature, East China Sea, MODIS SST product, empirical orthogonal function

Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

### **1. Introduction**

Sea surface temperature (SST) can display the comprehensive results of solar radiation, ocean-atmosphere interactions and oceanic inner dynamic and thermal processes. It is not only an important physical parameter for studying the exchange of water vapor and heat between sea surface and atmosphere but also provides an useful index for oceanographic studies such as ocean circulation, water mass, ocean front, upwelling current, seawater mixing [1] and ocean ecological environment [2]. Since SST anomalies of 0.5–2.0 K in the Pacific Ocean during El Nino or La Nina is sufficient to cause abnormality in oceanic and atmospheric circulations and global weather patterns, the global ocean surface temperature should be observed continuously [3]. With the development of satellite thermal infrared remote sensing for recent more than 30 years, the SST retrieved from thermal infrared images with the very high spatio-temporal resolutions, large-scale and periodic characteristics will be useful data sources for oceanography. At present, the NOAA/AVHRR Pathfinder SST product was applied widely in some researches about SST in the local sea [4–7]. Since the low spatial resolution with 0.25° × 0.25°, AVHRR Pathfinder SST product is inconvenient to the research for local sea. The sensor MODIS onboard TERRA and AQUA satellite in the Earth Observation System can revisit four times per day, which provides the SST with the high spatio-temporal resolutions for the oceanographic research [8, 9]. In view of the advantage and less reports on the MODIS SST products, this chapter will use the MODIS SST products data to analyze the spatio-temporal variation of SST in the East China Sea (ECS). It is helpful to discover the SST change mechanisms and its influence factors in the ECS, discuss the relationship between China offshore and ENSO and understand the effect of China offshore on continental climate.

Current, while the coastal currents are produced by the runoff with low salinity from rivers and the wind currents affected by the East Asian monsoon. However, these ocean circulations will result in the spatial and temporal variations of sea surface temperature and salinity in the ECS.

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Since satellite TERRA respectively passes once by the ECS in the day and night, NASA/GSFC develops the MODIS SST daytime and nighttime daily products, and monthly products are

**2.2. Sea surface temperature**

**Figure 1.** Distribution of ship locations and East China Sea.

### **2. Data and methods**

#### **2.1. Research area**

The East China Sea (ECS) is located in the east of China's continent and is a broad continental shelf bounding the North Pacific Ocean in the west, which covers an area of about 1.22 × 10<sup>6</sup> km2 and includes Bohai Sea, Huanghai Sea and Donghai Sea within the Korean Peninsula, Kyushu, Ryukyu Islands and Taiwan island. The submarine terrain is very complex in the ECS, the depth contour is paralleled with the coastline and the topography is leaned from northwest toward southeast and is the steepest in the southeastern continental margin. Bohai Sea approximates an inland sea with a mean depth of 18 m and the largest depth of 85 m, while Huanghai Sea is a semi-enclosed sea with a mean depth of 40 m, which has a largest depth of 140 m in the north of Jeju Island. Donghai Sea is a marginal sea where the mean depth of continental shelf is 72 m, of which whole sea is 349 m and the largest depth is 2700 m in the west of Okinawa Island. The ECS undergoes temperate and subtropical climate, where the northerly wind prevails in winter and the southerly wind does in summer. Influenced by the solar radiation, sea surface temperature is gradually increasing from north to south. The ocean circulation in the ECS is determined jointly by the offshore circulation and coastal currents. The offshore circulation includes Kuroshio Current, Taiwan Warm Current, Tsushima Current and Huanghai Warm Current, while the coastal currents are produced by the runoff with low salinity from rivers and the wind currents affected by the East Asian monsoon. However, these ocean circulations will result in the spatial and temporal variations of sea surface temperature and salinity in the ECS.

#### **2.2. Sea surface temperature**

**1. Introduction**

214 Sea Level Rise and Coastal Infrastructure

**2. Data and methods**

**2.1. Research area**

Sea surface temperature (SST) can display the comprehensive results of solar radiation, ocean-atmosphere interactions and oceanic inner dynamic and thermal processes. It is not only an important physical parameter for studying the exchange of water vapor and heat between sea surface and atmosphere but also provides an useful index for oceanographic studies such as ocean circulation, water mass, ocean front, upwelling current, seawater mixing [1] and ocean ecological environment [2]. Since SST anomalies of 0.5–2.0 K in the Pacific Ocean during El Nino or La Nina is sufficient to cause abnormality in oceanic and atmospheric circulations and global weather patterns, the global ocean surface temperature should be observed continuously [3]. With the development of satellite thermal infrared remote sensing for recent more than 30 years, the SST retrieved from thermal infrared images with the very high spatio-temporal resolutions, large-scale and periodic characteristics will be useful data sources for oceanography. At present, the NOAA/AVHRR Pathfinder SST product was applied widely in some researches about SST in the local sea [4–7]. Since the low spatial resolution with 0.25° × 0.25°, AVHRR Pathfinder SST product is inconvenient to the research for local sea. The sensor MODIS onboard TERRA and AQUA satellite in the Earth Observation System can revisit four times per day, which provides the SST with the high spatio-temporal resolutions for the oceanographic research [8, 9]. In view of the advantage and less reports on the MODIS SST products, this chapter will use the MODIS SST products data to analyze the spatio-temporal variation of SST in the East China Sea (ECS). It is helpful to discover the SST change mechanisms and its influence factors in the ECS, discuss the relationship between China offshore and ENSO and understand the effect of China offshore on continental climate.

The East China Sea (ECS) is located in the east of China's continent and is a broad continental shelf bounding the North Pacific Ocean in the west, which covers an area of about 1.22 × 10<sup>6</sup> km2 and includes Bohai Sea, Huanghai Sea and Donghai Sea within the Korean Peninsula, Kyushu, Ryukyu Islands and Taiwan island. The submarine terrain is very complex in the ECS, the depth contour is paralleled with the coastline and the topography is leaned from northwest toward southeast and is the steepest in the southeastern continental margin. Bohai Sea approximates an inland sea with a mean depth of 18 m and the largest depth of 85 m, while Huanghai Sea is a semi-enclosed sea with a mean depth of 40 m, which has a largest depth of 140 m in the north of Jeju Island. Donghai Sea is a marginal sea where the mean depth of continental shelf is 72 m, of which whole sea is 349 m and the largest depth is 2700 m in the west of Okinawa Island. The ECS undergoes temperate and subtropical climate, where the northerly wind prevails in winter and the southerly wind does in summer. Influenced by the solar radiation, sea surface temperature is gradually increasing from north to south. The ocean circulation in the ECS is determined jointly by the offshore circulation and coastal currents. The offshore circulation includes Kuroshio Current, Taiwan Warm Current, Tsushima Current and Huanghai Warm Since satellite TERRA respectively passes once by the ECS in the day and night, NASA/GSFC develops the MODIS SST daytime and nighttime daily products, and monthly products are

**Figure 1.** Distribution of ship locations and East China Sea.

**Figure 2.** SST scatter plots between MODIS daily products and ship measurements.

aggregated by the mean of daily products in 1 month. In order to use MODIS SST monthly products in the study, MODIS SST daytime and nighttime products are validated with the timing SST measurements from the ships in the ECS in February, May, August and November of 2001, which represent for the winter, spring, summer and autumn. Due to 1-hour shipmeasuring interval of SST, MODIS SST daily products can match well with those of ship measurements. And the distribution of ship locations can be seen from **Figure 1**. The ship SST measurements are available from the China Meteorological Data Network (http://data.cma. cn/). The 16-year MODIS SST monthly products from February 2000 to January 2016 are used to analyze the temporal variation of SST in the ECS. All the MODIS SST products data with the spatial resolution of 4 km were downloaded from the global ocean color network (https:// oceancolor.gsfc.nasa.gov/).

**Figure 3.** SST variation charts for every day and night in the East China Sea ((a–c) stands for Bohai Sea, Huanghai Sea

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**Bohai Sea Huanghai Sea Donghai Sea**

Winter 3.569 2.887 Spring 2.248 2.262 2.526 Summer 0.696 0.598 0.518 Autumn 1.043 1.332 1.001

**Table 1.** Average diurnal temperature difference for the different seasons in three seas.

and Donghai Sea).

#### **2.3. Data processing method**

In order to validate the accuracy of MODIS SST daily products, ship SST measurements were screened by comparing with the passing time of satellite TERRA, and time difference of both data is limited within 1 hour. The measuring locations of ships according to their geographical

Spatio-Temporal Analysis of Sea Surface Temperature in the East China Sea Using TERRA/MODIS… http://dx.doi.org/10.5772/intechopen.73217 217

**Figure 3.** SST variation charts for every day and night in the East China Sea ((a–c) stands for Bohai Sea, Huanghai Sea and Donghai Sea).


**Table 1.** Average diurnal temperature difference for the different seasons in three seas.

aggregated by the mean of daily products in 1 month. In order to use MODIS SST monthly products in the study, MODIS SST daytime and nighttime products are validated with the timing SST measurements from the ships in the ECS in February, May, August and November of 2001, which represent for the winter, spring, summer and autumn. Due to 1-hour shipmeasuring interval of SST, MODIS SST daily products can match well with those of ship measurements. And the distribution of ship locations can be seen from **Figure 1**. The ship SST measurements are available from the China Meteorological Data Network (http://data.cma. cn/). The 16-year MODIS SST monthly products from February 2000 to January 2016 are used to analyze the temporal variation of SST in the ECS. All the MODIS SST products data with the spatial resolution of 4 km were downloaded from the global ocean color network (https://

**Figure 2.** SST scatter plots between MODIS daily products and ship measurements.

In order to validate the accuracy of MODIS SST daily products, ship SST measurements were screened by comparing with the passing time of satellite TERRA, and time difference of both data is limited within 1 hour. The measuring locations of ships according to their geographical

oceancolor.gsfc.nasa.gov/).

216 Sea Level Rise and Coastal Infrastructure

**2.3. Data processing method**

**Figure 4.** SST seasonal distribution maps in the East China Sea: (a) winter; (b) spring; (c) summer and (d) autumn.


coordinates are corresponding to the images of MODIS SST daily products, and the median of the 3 × 3 neighborhood pixels around the central location is calculated in the SST images. The SST scatter plots between MODIS and ships are drawn for the different seasons, day and night, respectively (**Figure 2**), and the relative errors are calculated for MODIS SST daily

**Figure 5.** Spatial distribution maps of first four SST eigenvectors in the ECS: a-d for the SST eigenvector from first to

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products.

fourth, respectively.

**Table 2.** Contributions of the first six eigenvectors to variance.

Spatio-Temporal Analysis of Sea Surface Temperature in the East China Sea Using TERRA/MODIS… http://dx.doi.org/10.5772/intechopen.73217 219

**Figure 5.** Spatial distribution maps of first four SST eigenvectors in the ECS: a-d for the SST eigenvector from first to fourth, respectively.

**Figure 4.** SST seasonal distribution maps in the East China Sea: (a) winter; (b) spring; (c) summer and (d) autumn.

**Eigenvector 1 2 3 4 5 6** Percent 21.22 7.24 4.57 3.07 2.80 1.99 Cumulative percent 21.22 28.46 33.03 36.10 38.90 40.89

**Table 2.** Contributions of the first six eigenvectors to variance.

218 Sea Level Rise and Coastal Infrastructure

coordinates are corresponding to the images of MODIS SST daily products, and the median of the 3 × 3 neighborhood pixels around the central location is calculated in the SST images. The SST scatter plots between MODIS and ships are drawn for the different seasons, day and night, respectively (**Figure 2**), and the relative errors are calculated for MODIS SST daily products.

To analyze the SST difference between day and night in the ECS, the vector boundary maps of Bohai Sea, Huanghai Sea and Donghai Sea were overlapped on the images of MODIS SST daytime and nighttime products, and the mean of all the pixels within the zone of each sea is calculated after the outliers were removed. The calculated SST values of three seas in the daytime and nighttime in 2001 are used to draw the daily SST variation chart (**Figure 3**). The average diurnal SST difference is calculated for the three seas in the different seasons (**Table 1**).

images were carried out the empirical orthogonal function analysis, and the eigenvalues and eigenvectors of SST were calculated in the ECS [12–14]. **Table 2** shows the contributions of the first six eigenvectors to SST variance, and then the first four SST eigenvectors map in the ECS and their corresponding time coefficient charts are shown in **Figures 5** and **6**, respectively.

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Since the MODIS SST global daytime and nighttime products were generated by MODIS raw images which were obtained by simultaneous satellite TERRA passes through the earth and undergo the preprocess, retrieval and mosaicking. Then, the monthly products were aggregated by the mean of daily products in a month. Utilizing MODIS SST monthly products to analyze the SST spatio-temporal variation in the ECS, the accuracy of MODIS SST daily products should be evaluated first. MODIS SST daytime and nighttime products in February, May, August and November, 2001, which represent for the winter, spring, summer and autumn, selected to compare with the ship SST measurements, and the time difference of both SST is not more than 1 hour. **Figure 2** shows the SST scatter plots between MODIS and ship for the different seasons, day and night, respectively. It can be seen from **Figure 2** that all the matched SST points are located near the scatter plot 1:1 line. The slopes of majority scatter plots are above 0.902 except for summer of 0.466 and autumn of 0.825, and the correlation coefficients of majority scatter plots are more than 0.936 besides summer of 0.642, which indicate that MODIS SST daily products are in a very good agreement with the ship SST measurements. The biases in all the scatter plots are below 0, indicating that MODIS SST daily products are little lower than that of ships. Gentemann [15] compared the AUQA/MODIS sea surface temperature with in situ SST measurements made by drifting and moored buoys, and the bias of MODIS SST was found to be −0.13°C. This result is similar to the bias of daytime, winter and autumn in the study. Qiu et al. [16] validated AVHRR SST with drifting buoy SST in the northern South China Sea and showed the biases of AVHRR SST are −0.43 and −0.33°C for daytime and nighttime, respectively. These biases are also closed to that of MODIS SST in this study, which are −0.107 and −0.487°C for daytime and nighttime, respectively. The mean absolute percentage errors (MAPE) in all the scatter plots are below 5%, indicating MODIS SST daily products have a low uncertainty, high precision and good quality. The number of MODIS matched SST points in the daytime is 43, and the bias, root mean square error (RMSE) and MAPE are −0.107, 0.913°C and 2.61%, respectively (**Figure 2(a)**), which are lower than those in the nighttime that are −0.487, 1.325°C and 3.98%, respectively (**Figure 2(b)**). This figure shows that the accuracy of MODSI SST daytime product is superior to the nighttime one. Comparing three error indices of MODIS SST daily products in the different seasons (**Figure 2(c–f)**), the errors in autumn are the smallest, the second for winter except for MAPE of 4.40%, then for summer and the errors in spring are the biggest. Hence, the accuracy of MODIS SST daily products indicates seasonal variation in descending order from autumn to winter, to summer and then to spring. On the whole, MODIS SST daily products have a good

**3. Results and discussion**

accuracy with the MAPE below 5%.

**3.1. Accuracy evaluation of MODIS SST daily products**

The mean of MODIS SST daytime monthly products for February, May, August and November from February 2000 to January 2016 was calculated after the outliers were excluded in the SST images. The average SST images of the four months stand for winter, spring, summer and autumn were used to analyze the seasonal variations of SST in the ECS. When the average SST images of the four months are clipped by the vector map of ECS, the SST seasonal distribution maps in the ECS are shown in **Figure 4**.

In order to analyze the interannual variability of SST in the ECS, all the MODIS SST daytime monthly products were firstly carried out the climatological mean, that is, all the SST monthly products from January to December were averaged after the outliers were removed in the SST images. Secondly, the each monthly product was subtracted to the corresponding monthly mean SST so that the seasonal and inner-annual cyclicity of SST is eliminated, then the monthly sea surface temperature anomaly was obtained [10, 11]. Finally, 192 SST anomaly

**Figure 6.** Time coefficient charts of first four SST eigenvectors in the ECS. (a-d) stands for the time coefficient of SST eigenvector from first to fourth, respectively.

images were carried out the empirical orthogonal function analysis, and the eigenvalues and eigenvectors of SST were calculated in the ECS [12–14]. **Table 2** shows the contributions of the first six eigenvectors to SST variance, and then the first four SST eigenvectors map in the ECS and their corresponding time coefficient charts are shown in **Figures 5** and **6**, respectively.

### **3. Results and discussion**

To analyze the SST difference between day and night in the ECS, the vector boundary maps of Bohai Sea, Huanghai Sea and Donghai Sea were overlapped on the images of MODIS SST daytime and nighttime products, and the mean of all the pixels within the zone of each sea is calculated after the outliers were removed. The calculated SST values of three seas in the daytime and nighttime in 2001 are used to draw the daily SST variation chart (**Figure 3**). The average diurnal SST difference is calculated for the three seas in the different seasons (**Table 1**).

The mean of MODIS SST daytime monthly products for February, May, August and November from February 2000 to January 2016 was calculated after the outliers were excluded in the SST images. The average SST images of the four months stand for winter, spring, summer and autumn were used to analyze the seasonal variations of SST in the ECS. When the average SST images of the four months are clipped by the vector map of ECS, the SST seasonal distribution

In order to analyze the interannual variability of SST in the ECS, all the MODIS SST daytime monthly products were firstly carried out the climatological mean, that is, all the SST monthly products from January to December were averaged after the outliers were removed in the SST images. Secondly, the each monthly product was subtracted to the corresponding monthly mean SST so that the seasonal and inner-annual cyclicity of SST is eliminated, then the monthly sea surface temperature anomaly was obtained [10, 11]. Finally, 192 SST anomaly

**Figure 6.** Time coefficient charts of first four SST eigenvectors in the ECS. (a-d) stands for the time coefficient of SST

maps in the ECS are shown in **Figure 4**.

220 Sea Level Rise and Coastal Infrastructure

eigenvector from first to fourth, respectively.

### **3.1. Accuracy evaluation of MODIS SST daily products**

Since the MODIS SST global daytime and nighttime products were generated by MODIS raw images which were obtained by simultaneous satellite TERRA passes through the earth and undergo the preprocess, retrieval and mosaicking. Then, the monthly products were aggregated by the mean of daily products in a month. Utilizing MODIS SST monthly products to analyze the SST spatio-temporal variation in the ECS, the accuracy of MODIS SST daily products should be evaluated first. MODIS SST daytime and nighttime products in February, May, August and November, 2001, which represent for the winter, spring, summer and autumn, selected to compare with the ship SST measurements, and the time difference of both SST is not more than 1 hour. **Figure 2** shows the SST scatter plots between MODIS and ship for the different seasons, day and night, respectively. It can be seen from **Figure 2** that all the matched SST points are located near the scatter plot 1:1 line. The slopes of majority scatter plots are above 0.902 except for summer of 0.466 and autumn of 0.825, and the correlation coefficients of majority scatter plots are more than 0.936 besides summer of 0.642, which indicate that MODIS SST daily products are in a very good agreement with the ship SST measurements. The biases in all the scatter plots are below 0, indicating that MODIS SST daily products are little lower than that of ships. Gentemann [15] compared the AUQA/MODIS sea surface temperature with in situ SST measurements made by drifting and moored buoys, and the bias of MODIS SST was found to be −0.13°C. This result is similar to the bias of daytime, winter and autumn in the study. Qiu et al. [16] validated AVHRR SST with drifting buoy SST in the northern South China Sea and showed the biases of AVHRR SST are −0.43 and −0.33°C for daytime and nighttime, respectively. These biases are also closed to that of MODIS SST in this study, which are −0.107 and −0.487°C for daytime and nighttime, respectively. The mean absolute percentage errors (MAPE) in all the scatter plots are below 5%, indicating MODIS SST daily products have a low uncertainty, high precision and good quality. The number of MODIS matched SST points in the daytime is 43, and the bias, root mean square error (RMSE) and MAPE are −0.107, 0.913°C and 2.61%, respectively (**Figure 2(a)**), which are lower than those in the nighttime that are −0.487, 1.325°C and 3.98%, respectively (**Figure 2(b)**). This figure shows that the accuracy of MODSI SST daytime product is superior to the nighttime one. Comparing three error indices of MODIS SST daily products in the different seasons (**Figure 2(c–f)**), the errors in autumn are the smallest, the second for winter except for MAPE of 4.40%, then for summer and the errors in spring are the biggest. Hence, the accuracy of MODIS SST daily products indicates seasonal variation in descending order from autumn to winter, to summer and then to spring. On the whole, MODIS SST daily products have a good accuracy with the MAPE below 5%.

### **3.2. Variation of the diurnal SST difference in the ECS**

Utilizing MODIS SST daytime and nighttime products in February, May, August and November 2001, the average SST within Bohai Sea, Huanghai Sea and Donghai Sea was calculated every day after the outliers were excluded in the SST product images. **Figure 3** shows the SST variation charts for daytime, nighttime and diurnal difference in the three seas, and x-axis stands for the day of year, 32–59 for February, 121–151 for May, 213–243 for August and 306–335 for November. From **Figure 3**, it is clear that the daytime SST is higher than nighttime one and the diurnal SST difference is positive. Except that the matched number of daytime and nighttime SST is a few in Bohai Sea in winter, the diurnal SST difference in the other two seas is the largest in winter, and that in spring is the second for three seas, while the difference in autumn is very small, and the smallest for summer. **Table 1** shows the average diurnal SST differences for the three seas in the different seasons. The average diurnal difference is 3.569 and 2.887°C for Huanghai Sea and Donghai Sea, respectively, in winter, and that is 2.248, 2.262 and 2.526°C in the Bohai Sea, Huanghai Sea and Donghai Sea, respectively, in spring, then 1.043, 1.332 and 1.001°C for the three seas in autumn, while in summer for the three seas is 0.696, 0.598 and 0.518°C, respectively. Thus, the diurnal SST difference is very distinguishing, while that becomes little in the different seas for the same season. The variation of diurnal SST difference could have a good relationship with the length of daytime and nighttime in the different seasons. The daytime is short and nighttime is long in the north hemisphere in winter, and the solar radiation is absorbed less by the sea surface in the daytime due to short sunshine duration, while the more energy is emitted from the sea surface in the nighttime, which results in the large diurnal SST difference in winter, and the vice versa for summer. Since spring and autumn are the transitional season between winter and summer, the variation of diurnal SST difference in spring and autumn is moderate.

and middle of the Sea. The minimum temperature is about 1.5°C in the West Korea Bay, then for the coasts of Korean Peninsula and Shandong Peninsula about 3.0°C. The possible factors for the low SST in these areas are the influence of the continental air temperature and the coastal currents [1, 17]. The maximum temperature can reach at 15.8°C in the Korea Strait, then in the middle of Huanghai Sea about 14.0°C, which is affected by the Huanghai Warm Current and results from the extend of currents with high temperature and salinity from the east of Jeju Island to west by north. The average SST is 17.6°C in the Donghai Sea, and the low temperature zone is below 11°C and situated in the northwest of the Sea and the western coastal waters included the narrow area near the coast from the mouth of the Yangtze River to Taiwan strait, which is mainly affected by the coastal currents in the Donghai Sea shelf. However, the temperature in the open waters is very high between 16.0 and 25.5°C which is

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Since spring is the transitional season from winter to summer, the SST spatial distribution pattern in spring is very different from that of winter. As seen from **Figure 4(b)**, the SST in the Bohai Sea is contrary to the winter, which the temperature is high in the coastal shallows and low in the profundal zone. The average SST is 14.2°C in the Bohai Sea in spring, and the maximum temperature is close to 20.0°C in Liaodong Bay, Bohai Bay and Laizhou Bay while the minimum one is about 10.0°C in the north of Bohai strait. The SST in the Bohai Sea is first controlled by the terrain and solar radiation and second by the circulation [17]. The average SST is 14.9°C in the Huanghai Sea, and the temperature is much lower in the northeast of the Sea, especially in the West Korea Bay, then for the southern coastal waters of Korean Peninsula where the minimum temperature is about 9.0°C. The temperature is very high in the southwestern coastal waters and the Korea Strait where it is close to 19.0°C. The average SST is 22.4°C in the Donghai Sea, and the SST in the middle-north of the Sea is relatively low for 17°C or so, then the medium is in the eastern and western coastal waters for 20.8°C, while the temperature is very high in the southeastern open waters and it is above 25°C, especially

Due to the strong solar radiation in summer, the SST in the ECS trends uniform and the temperature difference in the northern-southern sea area is not more than 5°C (**Figure 4(c)**). The average SST in the Bohai Sea is 24.7°C, the maximum temperature reaches to 27°C in the Laizhou Bay, while the minimum one is about 23.5°C in the west of Bohai Strait and the middle of Bohai Sea. The cold waters are caused by the upwelling current from the deep seawater. In the Huanghai Sea, the SST is relatively lower between 23 and 24.5°C in the eastern coastal waters that are located in the West Korea Bay and west of Korean Peninsula. Then, the SST is also low about 24°C in the western coastal waters of Shandong Peninsula and the north of Jiangsu. The low SST zone is formed by the gradient submarine terrain and the strong tides together [10, 18]. The SST in middle of the Huanghai Sea is very uniform and high for 25°C or so, hence the summer average temperature is 25.2°C in the Huanghai Sea. The SST difference is very little in the Donghai Sea with the mean for 28°C. The low temperature waters are still situated in the western coasts, which are the coasts of Zhejiang and Fujian provinces with the minimum temperature at 25.5°C. The low temperature zone is mainly formed by the coastal upwelling current. However, the overall SST is very high in the open waters of Donghai Sea

influenced jointly by the Kuroshio current and Taiwan warn current [6, 18].

in the waters around Taiwan reaching to 28°C.

with the peak at 29°C.

#### **3.3. Seasonal spatial pattern of SST in the ECS**

**Figure 4** shows the SST seasonal spatial distribution maps drawn by the average MODIS SST monthly products for February, May, August and November from February 2000 to January 2016, and **Figure 4a–d** stands for winter, spring, autumn and summer. Since the East China Sea is located in the East Asian monsoon zone, the SST displays the typical seasonal variability.

**Figure 4(a)** shows the SST spatial distribution map in winter, in which the SST is gradually increasing from north to south in winter and local temperature difference is the largest for 26.5°C in a year. This is due to the much change of solar radiation with the latitude in winter. In the Bohai Sea, the variation of SST is generally not large, and the average temperature is 1.6°C in the whole sea. The minimum temperature is about −1.22°C in the top of Liaodong Bay, while the maximum one is 3.9°C in the Bohai strait. The obvious variation of SST is seen the north-south and east-west direction in the Bohai Sea, which the SST is increasing to 3.0°C from Liaodong Bay to Laizhou Bay for the north-south direction and is decreasing to −0.8°C from Bohai strait to Bohai Bay for the east-west direction. This SST distribution pattern displays the characteristic of low temperature in the shallows and high one in the profundal zone. The average SST is 6.9°C in the Huanghai Sea, and the low temperature zone is located in the eastern and western coastal waters, while the high temperature area is in the southeast and middle of the Sea. The minimum temperature is about 1.5°C in the West Korea Bay, then for the coasts of Korean Peninsula and Shandong Peninsula about 3.0°C. The possible factors for the low SST in these areas are the influence of the continental air temperature and the coastal currents [1, 17]. The maximum temperature can reach at 15.8°C in the Korea Strait, then in the middle of Huanghai Sea about 14.0°C, which is affected by the Huanghai Warm Current and results from the extend of currents with high temperature and salinity from the east of Jeju Island to west by north. The average SST is 17.6°C in the Donghai Sea, and the low temperature zone is below 11°C and situated in the northwest of the Sea and the western coastal waters included the narrow area near the coast from the mouth of the Yangtze River to Taiwan strait, which is mainly affected by the coastal currents in the Donghai Sea shelf. However, the temperature in the open waters is very high between 16.0 and 25.5°C which is influenced jointly by the Kuroshio current and Taiwan warn current [6, 18].

**3.2. Variation of the diurnal SST difference in the ECS**

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tion of diurnal SST difference in spring and autumn is moderate.

**3.3. Seasonal spatial pattern of SST in the ECS**

Utilizing MODIS SST daytime and nighttime products in February, May, August and November 2001, the average SST within Bohai Sea, Huanghai Sea and Donghai Sea was calculated every day after the outliers were excluded in the SST product images. **Figure 3** shows the SST variation charts for daytime, nighttime and diurnal difference in the three seas, and x-axis stands for the day of year, 32–59 for February, 121–151 for May, 213–243 for August and 306–335 for November. From **Figure 3**, it is clear that the daytime SST is higher than nighttime one and the diurnal SST difference is positive. Except that the matched number of daytime and nighttime SST is a few in Bohai Sea in winter, the diurnal SST difference in the other two seas is the largest in winter, and that in spring is the second for three seas, while the difference in autumn is very small, and the smallest for summer. **Table 1** shows the average diurnal SST differences for the three seas in the different seasons. The average diurnal difference is 3.569 and 2.887°C for Huanghai Sea and Donghai Sea, respectively, in winter, and that is 2.248, 2.262 and 2.526°C in the Bohai Sea, Huanghai Sea and Donghai Sea, respectively, in spring, then 1.043, 1.332 and 1.001°C for the three seas in autumn, while in summer for the three seas is 0.696, 0.598 and 0.518°C, respectively. Thus, the diurnal SST difference is very distinguishing, while that becomes little in the different seas for the same season. The variation of diurnal SST difference could have a good relationship with the length of daytime and nighttime in the different seasons. The daytime is short and nighttime is long in the north hemisphere in winter, and the solar radiation is absorbed less by the sea surface in the daytime due to short sunshine duration, while the more energy is emitted from the sea surface in the nighttime, which results in the large diurnal SST difference in winter, and the vice versa for summer. Since spring and autumn are the transitional season between winter and summer, the varia-

**Figure 4** shows the SST seasonal spatial distribution maps drawn by the average MODIS SST monthly products for February, May, August and November from February 2000 to January 2016, and **Figure 4a–d** stands for winter, spring, autumn and summer. Since the East China Sea is located in the East Asian monsoon zone, the SST displays the typical seasonal variability. **Figure 4(a)** shows the SST spatial distribution map in winter, in which the SST is gradually increasing from north to south in winter and local temperature difference is the largest for 26.5°C in a year. This is due to the much change of solar radiation with the latitude in winter. In the Bohai Sea, the variation of SST is generally not large, and the average temperature is 1.6°C in the whole sea. The minimum temperature is about −1.22°C in the top of Liaodong Bay, while the maximum one is 3.9°C in the Bohai strait. The obvious variation of SST is seen the north-south and east-west direction in the Bohai Sea, which the SST is increasing to 3.0°C from Liaodong Bay to Laizhou Bay for the north-south direction and is decreasing to −0.8°C from Bohai strait to Bohai Bay for the east-west direction. This SST distribution pattern displays the characteristic of low temperature in the shallows and high one in the profundal zone. The average SST is 6.9°C in the Huanghai Sea, and the low temperature zone is located in the eastern and western coastal waters, while the high temperature area is in the southeast Since spring is the transitional season from winter to summer, the SST spatial distribution pattern in spring is very different from that of winter. As seen from **Figure 4(b)**, the SST in the Bohai Sea is contrary to the winter, which the temperature is high in the coastal shallows and low in the profundal zone. The average SST is 14.2°C in the Bohai Sea in spring, and the maximum temperature is close to 20.0°C in Liaodong Bay, Bohai Bay and Laizhou Bay while the minimum one is about 10.0°C in the north of Bohai strait. The SST in the Bohai Sea is first controlled by the terrain and solar radiation and second by the circulation [17]. The average SST is 14.9°C in the Huanghai Sea, and the temperature is much lower in the northeast of the Sea, especially in the West Korea Bay, then for the southern coastal waters of Korean Peninsula where the minimum temperature is about 9.0°C. The temperature is very high in the southwestern coastal waters and the Korea Strait where it is close to 19.0°C. The average SST is 22.4°C in the Donghai Sea, and the SST in the middle-north of the Sea is relatively low for 17°C or so, then the medium is in the eastern and western coastal waters for 20.8°C, while the temperature is very high in the southeastern open waters and it is above 25°C, especially in the waters around Taiwan reaching to 28°C.

Due to the strong solar radiation in summer, the SST in the ECS trends uniform and the temperature difference in the northern-southern sea area is not more than 5°C (**Figure 4(c)**). The average SST in the Bohai Sea is 24.7°C, the maximum temperature reaches to 27°C in the Laizhou Bay, while the minimum one is about 23.5°C in the west of Bohai Strait and the middle of Bohai Sea. The cold waters are caused by the upwelling current from the deep seawater. In the Huanghai Sea, the SST is relatively lower between 23 and 24.5°C in the eastern coastal waters that are located in the West Korea Bay and west of Korean Peninsula. Then, the SST is also low about 24°C in the western coastal waters of Shandong Peninsula and the north of Jiangsu. The low SST zone is formed by the gradient submarine terrain and the strong tides together [10, 18]. The SST in middle of the Huanghai Sea is very uniform and high for 25°C or so, hence the summer average temperature is 25.2°C in the Huanghai Sea. The SST difference is very little in the Donghai Sea with the mean for 28°C. The low temperature waters are still situated in the western coasts, which are the coasts of Zhejiang and Fujian provinces with the minimum temperature at 25.5°C. The low temperature zone is mainly formed by the coastal upwelling current. However, the overall SST is very high in the open waters of Donghai Sea with the peak at 29°C.

Autumn is also the transitional season from summer to winter, and the SST in this season drops most quickly and becomes gradually lower from south to north in the ECS (**Figure 4(d)**). In the Bohai Sea, the SST in the shallows near the coast turns low about 10°C, such as Liaodong Bay, Bohai Bay and Laizhou Bay, while that in the middle of the sea is relatively high about 13.5°C, so the average SST within the whole Bohai Sea is 13.0°C in autumn. In the Huanghai Sea, the SST is relatively low in the eastern and western coastal waters, especially in the West Korea Bay with the temperature between 12 and 13.5°C. There is a warm water zone from south to west, even reaching to the Qingdao coast where the SST is within 16.5–20.0°C. This is probably caused by the Huanghai coastal current, which is evolved from the convergence of southward Huanghai Warm Current and inland freshwater from the Shandong Peninsula [18]. And the average SST of whole Huanghai is 16.2°C in autumn. In the Donghai Sea, the SST gets gradually low from south to north and the largest temperature difference approaches to 7°C, then the average SST is 23.3°C within the whole sea. The western coastal waters are still the low temperature zone where the SST is within 15.0–20.5°C, and the SST in the west of Kyushu Island is also very low about 22°C. There is a low temperature water area in the north of Taiwan Island at 23°C, which has something with the anticyclonic mesoscale eddy in autumn. However, the relatively high SST is in the east of Taiwan Island with the maximum one of 27°C.

shows that the SST of the majority of sea areas rises except that around the Jeju Island falls. The SST variability of fourth eigenvector indicates the effect of submarine terrain on the SST interannual variability of ECS (**Figure 5(d)**). The central area of positive variability is situated in the Bohai Sea, the western coast of Huanghai Sea and the northwest of Donghai Sea where the variability is above 0.004°C, while that of negative variability is below −0.008°C in the west of Korean Peninsula. These areas are near the coasts with lower water depth and are the SST influenced easily by the coastal currents and the continental atmospheric temperature. Hence, the SST variability is very small in the middle of Huanghai Sea and the south of Donghai Sea due to the greater water depth. **Figure 6(d)** shows more positive time coefficients and less negative ones in the chart of fourth SST eigenvector, which means that the SST of the wide range sea area is increasing and that of only small-scale area is decreasing. Furthermore, the maximum positive time coefficient and minimum negative one are smaller than other eigenvectors, indicating that the SST variability becomes more insignificant for the fourth eigenvector.

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Based on the validation of MODIS SST daytime and nighttime products using the ship SST measurements in the East China Sea (ECS), this chapter discusses the variation of diurnal SST difference for the three seas in the different seasons and analyzes the SST seasonal and inter-

**1.** Comparison with the ship SST measurements, the MODIS SST daily products have a good accuracy with a mean absolute percentage error below 5%. The accuracy of MODSI SST daytime products is superior to the nighttime ones. The accuracy of MODIS SST daily products indicates the seasonal variation in descending order from autumn to winter, to

**2.** Analyzing the SST of daytime and nighttime for the three seas in the ECS, the diurnal SST difference is the largest in winter, followed by spring, then for autumn, and the smallest in summer, while the difference is very little for the same season in the different seas. The variation of diurnal SST difference could have a good relationship with the length of day-

**3.** The SST in the ECS displays the obvious seasonal spatial distribution pattern, in which the SST in winter is gradually increasing from north to south and local temperature difference is the largest for 26.5°C in a year, while the SST in summer tends uniform due to the strong solar radiation and the difference is not more than 5°C in the whole sea. Since spring and autumn are the transitional seasons between winter and summer, the SST changes quickly in spring and autumn. The SST seasonal spatial variability in the ECS is mainly attributed to the solar radiation, continental atmospheric temperature, coastal currents and offshore circulation, such as Huanghai Warm Current, Tsushima Warm Current, Kuroshio Current and Taiwan Warm Current. **4.** From the EOF analysis of SST anomaly, the interannual variability of SST in the ECS is affected by the East Asian monsoon, the latitudinal difference of solar radiation, the offshore

annual variability in the ECS. Conclusions can be summarized as:

**4. Conclusions**

summer and then to spring.

time and nighttime in the different seasons.

circulation and the submarine terrain.

### **3.4. The EOF analysis of the SST interannual variability in the ECS**

**Figure 5** shows the spatial distribution maps of first four SST eigenvectors in the ECS, and their contributions to the SST variance are 21.22%, 7.24%, 4.57% and 3.07% (**Table 2**). The SST variability of first eigenvector is completely positive in the ECS, and this shows that the SST variation tendency appears a good consistence in the whole Sea and indicates further the influence of East Asian monsoon climate on the SST interannual variability of ECS [19]. The SST variability is relatively high above 0.006°C in the Huanghai Sea, then for the north of Donghai Sea, which is caused by the Huanghai Warm Current in winter and the cold water mass in summer [17]. **Figure 6(a)** shows the time coefficient chart of first SST eigenvector; when the time coefficient is positive, the SST within the whole Sea is increasing. And the SST falls for the negative time coefficient. The SST variability of second eigenvector displays the opposite distribution pattern in the north and south, and it is positive in the Bohai Sea and Huanghai Sea and is negative in the Donghai Sea (**Figure 5(b)**), which indicates the influence of latitude and solar radiation on the SST interannual variability [20, 21]. The latitude is relatively high in the Bohai Sea and Huanghai Sea, where the seasonal difference of solar radiation is very large, so the SST interannual variability is a little higher there, but the contrary for the Donghai Sea. In the time coefficient chart of second SST eigenvector (**Figure 6(b)**), when the time coefficient is positive, the SST in the Bohai Sea and Huanghai Sea will rise and that in the Donghai Sea will drop. While the time coefficient is negative, the SST in the northern and southern Sea will occur the opposite variation. The SST variability of third eigenvector reflects the influence of ocean current (**Figure 5(c)**) [18, 21], the variability is mainly negative and the negative central area is located in the middle of Bohai Sea, the north of Huanghai Sea and the west of Taiwan Island where are affected by the Huanghai Warm Current and coastal current. The sea area with negative variability is very small, which mainly distributes in the around Jeju Island included the southeast of Huanghai Sea and the east of Donghai Sea, and they are often controlled by the Tsushima Current. As seen from **Figure 6(c)**, most of the time coefficients are negative. This shows that the SST of the majority of sea areas rises except that around the Jeju Island falls. The SST variability of fourth eigenvector indicates the effect of submarine terrain on the SST interannual variability of ECS (**Figure 5(d)**). The central area of positive variability is situated in the Bohai Sea, the western coast of Huanghai Sea and the northwest of Donghai Sea where the variability is above 0.004°C, while that of negative variability is below −0.008°C in the west of Korean Peninsula. These areas are near the coasts with lower water depth and are the SST influenced easily by the coastal currents and the continental atmospheric temperature. Hence, the SST variability is very small in the middle of Huanghai Sea and the south of Donghai Sea due to the greater water depth. **Figure 6(d)** shows more positive time coefficients and less negative ones in the chart of fourth SST eigenvector, which means that the SST of the wide range sea area is increasing and that of only small-scale area is decreasing. Furthermore, the maximum positive time coefficient and minimum negative one are smaller than other eigenvectors, indicating that the SST variability becomes more insignificant for the fourth eigenvector.

### **4. Conclusions**

Autumn is also the transitional season from summer to winter, and the SST in this season drops most quickly and becomes gradually lower from south to north in the ECS (**Figure 4(d)**). In the Bohai Sea, the SST in the shallows near the coast turns low about 10°C, such as Liaodong Bay, Bohai Bay and Laizhou Bay, while that in the middle of the sea is relatively high about 13.5°C, so the average SST within the whole Bohai Sea is 13.0°C in autumn. In the Huanghai Sea, the SST is relatively low in the eastern and western coastal waters, especially in the West Korea Bay with the temperature between 12 and 13.5°C. There is a warm water zone from south to west, even reaching to the Qingdao coast where the SST is within 16.5–20.0°C. This is probably caused by the Huanghai coastal current, which is evolved from the convergence of southward Huanghai Warm Current and inland freshwater from the Shandong Peninsula [18]. And the average SST of whole Huanghai is 16.2°C in autumn. In the Donghai Sea, the SST gets gradually low from south to north and the largest temperature difference approaches to 7°C, then the average SST is 23.3°C within the whole sea. The western coastal waters are still the low temperature zone where the SST is within 15.0–20.5°C, and the SST in the west of Kyushu Island is also very low about 22°C. There is a low temperature water area in the north of Taiwan Island at 23°C, which has something with the anticyclonic mesoscale eddy in autumn. However, the

relatively high SST is in the east of Taiwan Island with the maximum one of 27°C.

**Figure 5** shows the spatial distribution maps of first four SST eigenvectors in the ECS, and their contributions to the SST variance are 21.22%, 7.24%, 4.57% and 3.07% (**Table 2**). The SST variability of first eigenvector is completely positive in the ECS, and this shows that the SST variation tendency appears a good consistence in the whole Sea and indicates further the influence of East Asian monsoon climate on the SST interannual variability of ECS [19]. The SST variability is relatively high above 0.006°C in the Huanghai Sea, then for the north of Donghai Sea, which is caused by the Huanghai Warm Current in winter and the cold water mass in summer [17]. **Figure 6(a)** shows the time coefficient chart of first SST eigenvector; when the time coefficient is positive, the SST within the whole Sea is increasing. And the SST falls for the negative time coefficient. The SST variability of second eigenvector displays the opposite distribution pattern in the north and south, and it is positive in the Bohai Sea and Huanghai Sea and is negative in the Donghai Sea (**Figure 5(b)**), which indicates the influence of latitude and solar radiation on the SST interannual variability [20, 21]. The latitude is relatively high in the Bohai Sea and Huanghai Sea, where the seasonal difference of solar radiation is very large, so the SST interannual variability is a little higher there, but the contrary for the Donghai Sea. In the time coefficient chart of second SST eigenvector (**Figure 6(b)**), when the time coefficient is positive, the SST in the Bohai Sea and Huanghai Sea will rise and that in the Donghai Sea will drop. While the time coefficient is negative, the SST in the northern and southern Sea will occur the opposite variation. The SST variability of third eigenvector reflects the influence of ocean current (**Figure 5(c)**) [18, 21], the variability is mainly negative and the negative central area is located in the middle of Bohai Sea, the north of Huanghai Sea and the west of Taiwan Island where are affected by the Huanghai Warm Current and coastal current. The sea area with negative variability is very small, which mainly distributes in the around Jeju Island included the southeast of Huanghai Sea and the east of Donghai Sea, and they are often controlled by the Tsushima Current. As seen from **Figure 6(c)**, most of the time coefficients are negative. This

**3.4. The EOF analysis of the SST interannual variability in the ECS**

224 Sea Level Rise and Coastal Infrastructure

Based on the validation of MODIS SST daytime and nighttime products using the ship SST measurements in the East China Sea (ECS), this chapter discusses the variation of diurnal SST difference for the three seas in the different seasons and analyzes the SST seasonal and interannual variability in the ECS. Conclusions can be summarized as:


### **Acknowledgements**

This work was funded by the National Research and Development Program of China (NO. 2016YFC1402003). The authors thank the China Meteorological Data Network (http://data. cma.cn/) and the global ocean color network (https://oceancolor.Gsfc.nasa.gov/) for providing the ship SST measurements and TERRA/MODIS SST products, respectively. The authors also appreciate the help from Dr. Xue Li in Xiamen University, China for the SST EOF analysis.

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Spatio-Temporal Analysis of Sea Surface Temperature in the East China Sea Using TERRA/MODIS…

http://dx.doi.org/10.5772/intechopen.73217

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### **Author details**

Shaoqi Gong1 \* and Kapo Wong2

\*Address all correspondence to: shaoqigong@163.com

1 School of Geography and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing, China

2 Chinese University of Hong Kong, Center for Housing Innovations, Shatin, Hong Kong

### **References**


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**Acknowledgements**

226 Sea Level Rise and Coastal Infrastructure

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Technology, Nanjing, China

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\* and Kapo Wong2

\*Address all correspondence to: shaoqigong@163.com

Shaoqi Gong1

**References**

This work was funded by the National Research and Development Program of China (NO. 2016YFC1402003). The authors thank the China Meteorological Data Network (http://data. cma.cn/) and the global ocean color network (https://oceancolor.Gsfc.nasa.gov/) for providing the ship SST measurements and TERRA/MODIS SST products, respectively. The authors also appreciate the help from Dr. Xue Li in Xiamen University, China for the SST EOF analysis.

1 School of Geography and Remote Sensing, Nanjing University of Information Science and

2 Chinese University of Hong Kong, Center for Housing Innovations, Shatin, Hong Kong

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## *Edited by Yuanzhi Zhang, Yijun Hou and Xiaomei Yang*

Sea level rise and coastal erosion had drawn an increasing awareness recently as the repercussion of increase of sea level and coastal erosion would reshape the earth's system and induce a tremendous loss in ecological or economics cost. Governments are dedicated to meliorate the occurrence of these phenomena, or else all creations on the earth will suffer from the catastrophe. Global warming is one of the crucial factors resulting in the increase of sea level and coastal erosion. Remote sensing and geographic information systems (GIS) technologies are thoroughly adopted and applied to monitor the dynamic change of the nature system, such as coastal land use and land cover, sea level rise, and coastal infrastructure.

Published in London, UK © 2018 IntechOpen © Keith Camilleri / unsplash

Sea Level Rise and Coastal Infrastructure

Sea Level Rise

and Coastal Infrastructure

*Edited by Yuanzhi Zhang, Yijun Hou* 

*and Xiaomei Yang*