**4.2 Land use change**

Satellite Landsat data was used to retrieve land use change around Sanya bay. Sanya River and the island between the two rivers was chose as land use change indexes (Yang 2008). From the satellite images, area of eastern and western Sanya Rivers reduced in 1999 compared with that in 1991. However the area of island between the two rivers enlarged more than 30%. The decreased river area, correspondently decreased the ecological wet land along the Sanya River, reduced the area for waste water cleaning. Seashore land use change was also retrieved with remote sensing data. From remote sensing data, the shape of costal line at the northeast of Lu Huitou peninsular changed greatly. In situ observation proved that buildings were constructed just along the coastal line. In situ observation also found that coast at the middle north of Lu Huitou peninsular was constructed as sea bath and sea diving area.

As water with high concentration of nutrients in Sanya River flow into Sanya Bay, it diffused by current and wave. The effluent plume streaming out of the Sanya river into the Sanya Bay flows straight for only a short distance before it is washed westward by the longshore current (Yang 2008). Sanya Bay is an open bay, the flows-in water only stay in a short time. Landsat TM data of 1991 was used to detect the diffuse pattern in Sanya Bay. From the satellite images, the water plume of Sanya River mainly distributed along the south of Sanya Bay. The reason is when tidal water rushed into Sanya Bay it splits and flows back from the south of Sanya Bay. In Sanya Bay, Seagrass mainly distributed along the north coast of Lu Huitou peninsular, the plume of Sanya River affected seagrass distribution in

**4. Impacts of environmental factors for seagrass distribution** 

Fig. 13. The diffuse pattern of flows-in water in Sanya Bay (Yang 2008).

Satellite Landsat data was used to retrieve land use change around Sanya bay. Sanya River and the island between the two rivers was chose as land use change indexes (Yang 2008). From the satellite images, area of eastern and western Sanya Rivers reduced in 1999 compared with that in 1991. However the area of island between the two rivers enlarged more than 30%. The decreased river area, correspondently decreased the ecological wet land along the Sanya River, reduced the area for waste water cleaning. Seashore land use change was also retrieved with remote sensing data. From remote sensing data, the shape of costal line at the northeast of Lu Huitou peninsular changed greatly. In situ observation proved that buildings were constructed just along the coastal line. In situ observation also found that coast at the middle north of Lu Huitou peninsular was constructed as sea bath and sea

**4.1 Effluent diffusion in Sanya Bay** 

great extent.

**4.2 Land use change** 

diving area.

Fig. 14. Landsat TM data of Land use change around Sanya Bay in 1999 compared with that of in 1991 (Yang 2008).

Seagrass Distribution in China with Satellite Remote Sensing 91

remote sensing to quantitative mapping of sparse seagrass species is still in its early

An optical model was proposed to simulate the radiation transfer in multi-layer, nonhomogenous, heterogeneous, natural media. This algorithm enables us to simulate radiation fields in a wide range of variations of optical characteristics of these layers and to analyze the mechanisms of the formation of the radiation characteristics inside and outside the layers, as well as to estimate any contribution of each region. Based on the algorithm, we appropriately removed the distorting influence of the water column on the remotely sensed signal to retrieve an estimate of the reflectance of seagrass. Implementation of the method was found to be effective for improving the accuracy of coastal habitat maps and essential for deriving empirical relationships between remotely sensed data and features of interest in the marine environment. Retrieved bottom reflectance was then used to study the optical characteristics of seagrass. Through spectrum analysis it was found that the wavelengths for the discrimination and mapping of seagrass meadows of Sanya Bay, South China Sea lay between 500-630 nm as well as 680-710 nm. An appropriate hyper spectral band set for the remote sensing of seagrass should include narrow bands (maximum 5-10 nm bandwidth) centered around 555, 650, 675 and 700 nm. If satellite images were used, the effect of atmosphere should be taken into account. Though the blue band is more easily affected by atmosphere, the accurate surface reflectance could be acquired with the development of the theory and models in atmosphere correction. The relationship between seagrass leaf area index (LAI) and hyperspectra is very important when satellite remote sensing data is

Seagrass distribution in Xincun Bay spanning 15 years (1991-2006) was retrieved with satellite remote sensing. From the seagrass detection results, the resolution of satellite remote sensing image is very important for seagrass detection, so QuickBird data was more suitable for seagrass detection than Landsat TM and CBERS, especially when the seagrass distribution area was relatively small. Results in the paper proved that five classes can be classified clearly with QuickBird; however, only seagrass distribution contours can be

Though the accuracy of seagrass detection with satellite remote sensing can be affected by many factors, seagrass in Xincun Bay can be detected clearly for the sediment there was sand. Compared with satellite remote sensing data in 1991, the seagrass distribution area was reduced gradually and large areas of seagrass had disappeared by 2006. Human activities and extreme natural disasters were the main reasons for seagrass reduction, especially land use changes in recent years. The effect of land use change on seagrass distribution can be concluded as following: seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. Mainly because of land use change changed the water quality and

Except for hydrodynamic effect, distribution of seagrass was also affected by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water

development.

applied for detecting seagrass distribution.

detected with Landsat TM and CBERS data.

sediment type.

#### **4.3 Tendency of water chemical indexes by land use change in Sanya Bay**

Water quality of Sanya River and Sanya Bay, such as water transparency and water quality indexes, was provided by Sanya ecological field station and references. In situ water chemical data of inorganic nitrogen, phosphorus and chlorophyll a concentration was used for validation and correction of the results from satellite remote sensing data. Results showed that water quality of Sanya River degraded in 2002 compared with that in 1991(Fig.15); water quality in Sanya bay near the Sanya River mouth was also degraded. However, water quality in other part of Sanya bay changed little. Perhaps Sanya bay is an open bay, where water exchange rate is relatively high.

Fig. 15. Waste water discharge in Sanya Bay in 1991 and 2002 (Yang 2008).

#### **4.4 Relationship between land use change and seagrass distribution**

Seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. This mainly because distribution of seagrass was confined by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water mainly distributed in the southwest of Sanya Bay, which provided the suitable condition for large area continuous seagrass distributed in the area.

### **5. Discussions and conclusions**

As described in our investigation, much is known about the photophysiology of seagrass, while much is still required for us to effectively manage this important yet diminishing resource. New coastal ocean remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before, however, the application of ocean color

Water quality of Sanya River and Sanya Bay, such as water transparency and water quality indexes, was provided by Sanya ecological field station and references. In situ water chemical data of inorganic nitrogen, phosphorus and chlorophyll a concentration was used for validation and correction of the results from satellite remote sensing data. Results showed that water quality of Sanya River degraded in 2002 compared with that in 1991(Fig.15); water quality in Sanya bay near the Sanya River mouth was also degraded. However, water quality in other part of Sanya bay changed little. Perhaps Sanya bay is an

> 1991 2002 Year

Seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. This mainly because distribution of seagrass was confined by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water mainly distributed in the southwest of Sanya Bay, which provided the suitable condition for large area continuous seagrass

As described in our investigation, much is known about the photophysiology of seagrass, while much is still required for us to effectively manage this important yet diminishing resource. New coastal ocean remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before, however, the application of ocean color

**4.3 Tendency of water chemical indexes by land use change in Sanya Bay** 

open bay, where water exchange rate is relatively high.

3000

0

Fig. 15. Waste water discharge in Sanya Bay in 1991 and 2002 (Yang 2008).

**4.4 Relationship between land use change and seagrass distribution** 

500

1000

1500

Waste water discharge

distributed in the area.

**5. Discussions and conclusions** 

(ten thousands ton)

2000

2500

remote sensing to quantitative mapping of sparse seagrass species is still in its early development.

An optical model was proposed to simulate the radiation transfer in multi-layer, nonhomogenous, heterogeneous, natural media. This algorithm enables us to simulate radiation fields in a wide range of variations of optical characteristics of these layers and to analyze the mechanisms of the formation of the radiation characteristics inside and outside the layers, as well as to estimate any contribution of each region. Based on the algorithm, we appropriately removed the distorting influence of the water column on the remotely sensed signal to retrieve an estimate of the reflectance of seagrass. Implementation of the method was found to be effective for improving the accuracy of coastal habitat maps and essential for deriving empirical relationships between remotely sensed data and features of interest in the marine environment. Retrieved bottom reflectance was then used to study the optical characteristics of seagrass. Through spectrum analysis it was found that the wavelengths for the discrimination and mapping of seagrass meadows of Sanya Bay, South China Sea lay between 500-630 nm as well as 680-710 nm. An appropriate hyper spectral band set for the remote sensing of seagrass should include narrow bands (maximum 5-10 nm bandwidth) centered around 555, 650, 675 and 700 nm. If satellite images were used, the effect of atmosphere should be taken into account. Though the blue band is more easily affected by atmosphere, the accurate surface reflectance could be acquired with the development of the theory and models in atmosphere correction. The relationship between seagrass leaf area index (LAI) and hyperspectra is very important when satellite remote sensing data is applied for detecting seagrass distribution.

Seagrass distribution in Xincun Bay spanning 15 years (1991-2006) was retrieved with satellite remote sensing. From the seagrass detection results, the resolution of satellite remote sensing image is very important for seagrass detection, so QuickBird data was more suitable for seagrass detection than Landsat TM and CBERS, especially when the seagrass distribution area was relatively small. Results in the paper proved that five classes can be classified clearly with QuickBird; however, only seagrass distribution contours can be detected with Landsat TM and CBERS data.

Though the accuracy of seagrass detection with satellite remote sensing can be affected by many factors, seagrass in Xincun Bay can be detected clearly for the sediment there was sand. Compared with satellite remote sensing data in 1991, the seagrass distribution area was reduced gradually and large areas of seagrass had disappeared by 2006. Human activities and extreme natural disasters were the main reasons for seagrass reduction, especially land use changes in recent years. The effect of land use change on seagrass distribution can be concluded as following: seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. Mainly because of land use change changed the water quality and sediment type.

Except for hydrodynamic effect, distribution of seagrass was also affected by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water

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#### **6. Acknowledgment**

The National Basic Research Program of China (973 Program) under grant No.2010CB951203; the National Natural Sciences Foundation of China under grant No. 41176161 and No. 40876092; the National Natural Sciences Foundation of Guangdong Province under grant No.8351030101000002.

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**5** 

*2Geologist, Perugia* 

*Italy* 

**The Use of Remote Sensed Data and GIS** 

Thematic maps in the Earth Sciences are an essential tool for the representation, analysis and visualization of geological processes. Among the large variety of thematic maps, geomorphological maps are particularly useful in understanding natural phenomena

Geomorphological maps report the erosion and depositional relief landforms, including submarine ones, highlighting the morphographic and morphometric characters and interpreting the endogenous and exogenous morphological processes, both past or present, that produce and shape the topographic relief. In this kind of maps, the chronological sequence is also reported, distinguishing between active and inactive landforms. The geomorphological mapping, in addition to its scientific value, is the necessary starting point of different studies such applied geology and environmental protection investigations for

A major problem with geomorphological information is that it is extremely complex to be

associated with human activities (Dramis & Bisci, 1998 and references within).

In particular, the reproduced information can be summarized as follows:




**1. Introduction** 

socio-economic improvement.

represented due to the huge amount of data.







**to Produce a Digital Geomorphological** 

**Map of a Test Area in Central Italy** 

Laura Melelli1, Lucilia Gregori1 and Luisa Mancinelli2 *1Department of Earth Sciences, University of Perugia, Perugia* 
