High Resolution Satellite Data Application

**69**

**Chapter 5**

**Abstract**

Detection

*and Jorge Montejano*

differences in the Mexican urban areas.

tion [5], size, scale, and form [6].

**1. Introduction**

advanced classification methods, GIS integration

High-Resolution Satellite Imagery

Mapping urban form at regional and local scales is a crucial task for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. Remotely sensed imagery is ideally used to monitor and detect urban areas that occur frequently as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into urban form map using the existing methods of manual interpretation and parametric image classification digitally. In this work, classification techniques of high-resolution satellite imagery were used to map 50 selected cities of study of the National Urban System in Mexico, during 2015–2016. In order to process the information, 140 RapidEye Ortho Tile multispectral satellite imageries with a pixel size of 5 m were downloaded, divided into 5 × 5 km tiles and then 639 tiles were generated. In each (imagery or tile), classification methods were tested, such as: artificial neural networks (RNA), support vector machines (MSV), decision trees (AD), and maximum likelihood (MV); after tests, urban and nonurban categories were obtained. The result is validated with an accuracy method that follows a stratified random sampling of 16 points for each tile. It is expected that these results can be used in the construction of spatial metrics that explain the

Classification for Urban Form

*Juan Manuel Núñez, Sandra Medina, Gerardo Ávila* 

**Keywords:** urban form, remote sensing, high-resolution satellite imagery,

Urbanization, as a process that manifests itself through the concentration of population in cities, is considered one of the most powerful and visible anthropogenic forces on the planet. Its influence is manifested on topics ranging from environmental changes on a global, regional, and local scale [1, 2], socioeconomic problems [3] to urban planning [4]. Thereby, several investigations use maps of urban areas to assess the influence of urbanization on natural and human environments and to estimate some important aspects of urbanization, such as its composi-

The urban form is the most visible result of the economic, social, cultural, and environmental driving forces of urban development [1]. Therefore, it is a spatial reflection of different processes across the evolution of a city and its characterization

## **Chapter 5**

## High-Resolution Satellite Imagery Classification for Urban Form Detection

*Juan Manuel Núñez, Sandra Medina, Gerardo Ávila and Jorge Montejano* 

## **Abstract**

 Mapping urban form at regional and local scales is a crucial task for discerning the influence of urban expansion upon the ecosystem and the surrounding environment. Remotely sensed imagery is ideally used to monitor and detect urban areas that occur frequently as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into urban form map using the existing methods of manual interpretation and parametric image classification digitally. In this work, classification techniques of high-resolution satellite imagery were used to map 50 selected cities of study of the National Urban System in Mexico, during 2015–2016. In order to process the information, 140 RapidEye Ortho Tile multispectral satellite imageries with a pixel size of 5 m were downloaded, divided into 5 × 5 km tiles and then 639 tiles were generated. In each (imagery or tile), classification methods were tested, such as: artificial neural networks (RNA), support vector machines (MSV), decision trees (AD), and maximum likelihood (MV); after tests, urban and nonurban categories were obtained. The result is validated with an accuracy method that follows a stratified random sampling of 16 points for each tile. It is expected that these results can be used in the construction of spatial metrics that explain the differences in the Mexican urban areas.

**Keywords:** urban form, remote sensing, high-resolution satellite imagery, advanced classification methods, GIS integration

### **1. Introduction**

Urbanization, as a process that manifests itself through the concentration of population in cities, is considered one of the most powerful and visible anthropogenic forces on the planet. Its influence is manifested on topics ranging from environmental changes on a global, regional, and local scale [1, 2], socioeconomic problems [3] to urban planning [4]. Thereby, several investigations use maps of urban areas to assess the influence of urbanization on natural and human environments and to estimate some important aspects of urbanization, such as its composition [5], size, scale, and form [6].

The urban form is the most visible result of the economic, social, cultural, and environmental driving forces of urban development [1]. Therefore, it is a spatial reflection of different processes across the evolution of a city and its characterization is a valuable source of information for urban planning. Ultimately, urban form is the result of the symbiotic interactions of infrastructures, people, and economic activities in a city that is constantly evolving in response to social, environmental, economic, and technological development [7].

In the cities, urban form is materialized by the heterogeneous physical alignment and characteristics of buildings, streets, and open spaces at different levels of spatial resolution. This high heterogeneity of materials and urban objects in terms of size, forms, and urban fabric morphology of the cities can be detected through the use of remote sensing imagery. This type of research provides very important information in relation to urban issues on planning, housing, health, transportation, and economic policies; especially for regions in developing countries that are less documented.

Most of the research efforts have been made for mapping urban landscapes at various scales and on the spatial resolution requirements of such mapping [8]. Different remote sensing techniques have already shown their value in mapping urban areas with different spatial, geometric, spectral, and temporal resolutions for different purposes. Therefore, the selection of an appropriate estimation method based on remotely sensed data characteristics is important.

Traditional remote sensing literature review suggests that major approaches include pixel-based image classification [9, 10], spectral index [11, 12], objectoriented algorithms [13, 14], and machine learning like artificial neural networks [15] and decision tree classification algorithm [16]. Techniques, such as data/image fusion, have also been explored [17]. Recent research has used high and very high spatial resolution remote sensing imagery to quantitatively describe the spatial structure of urban environments and characterize patterns of urban morphology [18].

Remote sensing approach compared with traditional methods for mapping the urban form provides certain advantages due to its convenience, efficiency, and coverage [19]. For this reason, the study of the detection of the urban form and its corresponding derived attributes through different types of satellite images is becoming of more interest [16, 20–23].

Regardless of the satellite imagery classification method employed for urban form detection, they can be divided into two categories: supervised and unsupervised methods. Those results obtained by the first ones usually produce a greater reliability, nevertheless they require more processing steps for the construction of training data.

For the supervised methods, the classifiers based on support vector machines (SVM) are very popular due to their good performance and robustness [24, 25]. Additionally, the methods based on the artificial neural networks (ANN) are also widely used for the classification of urban areas [26]. For example, Dridi et al. [27] combine multiple SVM for the mapping of urban extensions in the city of Algeria and compare them with ANN to support the experimental analysis to monitoring the spatiotemporal phenomenon of urban sprawl. Other supervised classification methods, such as decision tree (DT), regression model (RM), and maximum likelihood (ML), can also provide plausible results in the mapping of urban areas [28].

 In this work, we evaluated four supervised classification methods (SVM, ANN, DT, and ML) using satellite images of earth observation, to integrate with a GIS approach the mapping of the urban form in 50 Mexican cities. The rest of this document is organized as follows: in Section 2, the context of the cities selected for the test and the dataset used are briefly presented; in Section 3, it is described the methodology with the proposed classification strategy for urban mapping that includes the preprocessing of RapidEye images, the collection of training samples, the classification methods evaluating the validation strategy, and the postprocessing GIS approach. The experimental results obtained and their discussions are presented in Section 4. Finally, the conclusions of the work are expressed in Section 5.

## **2. Context**

## **2.1 Study area**

In Mexico, urbanization has been associated with increased prosperity and improvements in quality of life. Urban areas, lead in expanding coverage of basic and social services, also offer better access to other services and amenities, including health care and education. Moreover, Mexico's growing middle class and declining inequality in recent decades seem to be definitely urban phenomena [29].

There have been important changes on the spatial form of Mexican cities over the past 30 years: most notably urban growth is characterized as distant, dispersed, and disconnected. Between 1980 and 2010, the built-up area of Mexican cities expanded on average by a factor of seven and the urbanized area of the 11 biggest metropolitan areas with more than 1 million inhabitants in 2010 has even grown by a factor of nine (SEDESOL 2012). This rapid spatial transformation of most Mexican cities presents important challenges for their potential to promote green and inclusive growth. To solve these problems, different initiatives have made significant efforts to put in place measurement systems and to broaden information about urban dynamics.

An ambitious national initiative, the National Urban System (NUS) is a unified platform to support decision-making for urban and housing policies. The NUS, launched by Mexican federal agencies in 2012, exemplifies a significant effort to broaden information and understanding about urban dynamics and has been recognized as innovative among Latin American urban initiatives. This system is a reference to analyze spatial patterns of Mexican cities, their causes, and their impact and to provide an analytical basis to understand urban phenomenon.

The National Population Council (Consejo Nacional de Población, CONAPO) and the Secretariat of Social Development (Secretaria de Desarrollo Social, SEDESOL) put together the NUS on the basis of data from the Population and Housing Census (2010) with the objective of creating a system to support strategic planning and decision-making in urban areas and to provide all sectors (state governments, municipalities, academia, private sector, and general users) with integrated metropolitan and urban information on demographic and socioeconomic variables. The NUS comprises 384 cities with over 15,000 inhabitants each, out of which 59 are metropolitan areas, 78 conurbations (suburban centers), and 247 urban centers. About 81.2 million people or 72.3% of the country's population live in these 384 cities.

The study area corresponds to a 50 cities sample of the NUS that include three types of cities, classified on the basis of geographical delimitations defined by the NUS (**Figure 1**).

These 50 urban areas include:


Lázaro Cárdenas, (21) Uruapan, (22) Zitácuaro, (23) San Juan Bautista Tuxtepec, (24) Chetumal, (25) Ciudad Obregón, (26) Cárdenas, (27) Túxpam de Rodríguez Cano,and (28) Fresnillo.

 iii.*22 urban centers* that have more than 15,000 residents and that do not extend beyond the boundaries of their locality: (29) La Paz, (30) Ciudad del Carmen, (31) Ciudad Acuña, (32) Comitán de Domínguez, (33) San Cristóbal de las Casas, (34) Cuauhtémoc, (35) Delicias, (36) Hidalgo del Parral, (37) Victoria de Durango, (38) Salamanca, (39) Iguala de la Independencia, (40) Ciudad Guzmán, (41) Lagos de Moreno, (42) Apatzingán, (43) San Juan del Río, (44) Ciudad Valles, (45) Los Mochis, (46) Culiacán Rosales, (47) Mazatlán, (48) Navojoa, (49) Heroica Nogales, and (50) Ciudad Victoria.

## **2.2 Materials**

Urban areas were identified by looking at the layer of urban polygons of the geostatistical framework, version 5.0 of the National Institute of Statistics and Geography (Instituto Nacional de Estadística y Geografía, INEGI). Later, satellite images were obtained for the binary classification between urban and nonurban areas that covered the 50 study cities, for which 140 RapidEye images of the period 2015–2016 were acquired, through the Planet platform (www. planet.com).

The main characteristics of these images are: (a) spatial resolution of 5 m and covered area per image of 25 km2 ; (b) 5-band spectral resolution (blue 440–510 nm, green 520–590 nm, red 630–685 nm, red edge 690–730 nm, and near-infrared 760–850 nm); (c) 12-bit radiometric resolution, and (d) Universal Transverse Mercator (UTM) and WGS84 Horizontal Datum.

 Additionally, a digital elevation model (DEM) of the Mexican territory was downloaded to perform the radiometric and atmospheric corrections. Finally, for the collection of training samples, a Web Map Service (WMS) of a SPOT satellite

#### **Figure 1.**

*Selected cities of study, National Urban System and classification of city types. Source: Own elaboration based on data from the secretariat of social development (Secretaría de Desarrollo Social, SEDESOL).* 

images mosaic provided by the Mexico Reception Station (Estación de Recepción México, ERMEX) was used, at a resolution of 1.5 m in true color.

## **3. Methodology**

The methodology is split into five main steps as follows: strategy for satellite imagery download and preprocessing, training and validation sample selection, classification methods, GIS integration, and results evaluation.

## **3.1 Strategy for satellite imagery download and preprocessing**

In the first step, the entire Mexican territory was divided into nonoverlapping 5 × 5 km blocks, with the purpose of selecting blocks that cover the mosaics of the images related to the urban areas selected. A total of 639 blocks were selected to cover the 50 urban areas. Then, 140 RapidEye Ortho Tile multispectral scenes were downloaded through the Planet platform (www.planet.com) to cover all cities within the project. The satellite images were selected for the period 2015–2016, obtaining a homogeneous selection of acquisition dates and conditions of zero or little cloudiness.

Radiometric and atmospheric corrections were conducted to retrieve surface reflectance values by means of the atmospheric and topographic corrections software (ATCOR3) implemented in the ENVI virtual IDL machine [30]. Finally, mosaics by blocks were prepared for each of the 50 cities.

## **3.2 Training and validation sample selection**

To obtain training and validation samples, the generated blocks in the previous stage were used to cover the mosaics of the satellite imagery that corresponds to the selected. Training and validation data should be representative of the study area and of the classification scheme. Because urban is often a relatively rare class that covers only a small proportion of the landscape, spatial stratification with proportional class allocation (SpatialProp) was selected to be able to obtain high user's accuracy of urban class [31].

 In the SpatialProp strategy, the sample size is allocated to each class proportional to the areal coverage in the reference set, with the constraint that each spatial stratum receives an equal total sample size. For example, if the urban and nonurban classes comprised 25 and 75% of the area of the entire region, respectively, the sample allocation in each spatial stratum would be 25% urban and 75% nonurban. According to Jin et al. [31] in each 5 × 5 km block, 16 random samples are assigned to the urban and nonurban strata proportional allocation. For example, in our hypothetical situation, nonurban occupies 75% of the area and urban occupies 25%. Given the total sample size of 16, 12 nonurban pixels and 4 urban pixels will be selected following the designs of SpatialProp.

 For the 639 blocks employed for the 50 selected urban areas, 20,448 sampling and validation points were assigned. Later, each of the data points were verified with the related category based on the RapidEye mosaic and the Web Map Service (WMS) of a SPOT Image.

## **3.3 Classification methods**

 Machine-learning classification has become a major focus of the remote-sensing literature since it is generally able to model complex class signatures without

making assumptions about the data distribution, i.e., it is nonparametric [25]. A wide range of studies have generally found that these methods tend to produce higher accuracy compared to traditional parametric classifiers, especially for complex data with a high-dimensional feature space [32, 33].

 However, parametric maximum likelihood (ML) classifier method is the most commonly used remote-sensing classification method [34]. In this work, we evaluate the classification methods of artificial neural networks (ANN), support vector machines (SVM), decision tree (DT), and maximum likelihood (ML) for each city. For each of this classifier, we can measure the accuracy based on the use of an error matrix. Below, there is a brief description of each referred methods.

#### *3.3.1 Artificial neural networks (ANN)*

An artificial neural network is a massive parallel distributed processor made up of simple processing units, which has a natural propensity for storing experiential knowledge and can make it available for use [35]. The model is formed by artificial neurons that emulate biological neurons and the synaptic connections among them; it regulates them through the process of solving problem [36].

The network needs to be "trained" with a sufficiently large number of examples in order to be able to make the appropriate inferences. The procedure of training involves groups of input data together with the expected output data. Once the system of neurons has been trained, the network allows the processing of imprecise information, the generalization of known responses to new situations, and the prediction of outcomes. They are appropriate models for dealing with a large set of variables and their nonlinearity is convenient for the assessment of complex systems [37].

The links with the neurons located in the so-called hidden neuron layer take then different weights and are educated depending on the required output, thus they can model complex relationships among variables. The system requires feedforward and backpropagation processes to allow the network to get trained [38]. The visualization of this stage is accomplished through error analysis. If the error becomes smaller and asymptotic, the network will be ready to receive new input data and to predict an output [37].

The ANN models used in this study are of the multilayer perceptron ANN type, a model in which all neurons are fully connected to adjacent layers while layers are not connected to each other at all [39, 40]. There are three types of layers in a typical multilayer perceptron network: input layer, hidden layer, and output layer. This architecture is shown in **Figure 2**. In each case, the training of the proposed network was performed with a backpropagation algorithm which is a supervised learning procedure [41].

**Figure 2.**  *Artificial neural networks classifier. Source: adapted from [39].* 

The main tasks of remote sensing data analysis in which the application of ANN standard backpropagation for supervised learning is reported are classification, more commonly land cover classification [42, 43], unmixing [44, 45], and retrieval of biophysical parameters of cover [46]. Other applications of ANNs are also reported in change detection, data fusion, forecasting, preprocessing, georeferencing, and object recognition.

#### *3.3.2 Support vector machines (SVMs)*

Support vector machines are a supervised nonparametric statistical learning technique that has no assumption made on the underlying data distribution [47]. Initially, the method is presented with a set of labeled data instances and the SVM training algorithm aims to find a hyperplane that separates the dataset into a discrete predefined number of classes in a fashion consistent with the training examples [48]. Where, optimal separation hyperplane term is used to refer to the decision boundary that minimizes misclassifications, obtained in the training step and learning refers to the iterative process of finding a classifier with optimal decision boundary to separate the training patterns (in potentially high-dimensional space) and then to separate simulation data under the same configurations (dimensions) [49].

In its simplest form, SVM are linear binary classifiers that assign a given test sample a class from one of the two possible labels [47]. **Figure 3** illustrates a simple scenario of a two-class separable classification problem in a two-dimensional input space where the solution for a typical two-dimensional case where the subset of points that lies on the margin (called support vectors) is the only one that defines the hyperplane of maximum margin.

An important generalization aspect of SVMs is that frequently not all the available training examples are used in the description and specification of the separating hyperplane. The subset of points that lie on the margin (called support vectors) is the only one that defines the hyperplane of maximum margin. If the two classes are not linearly separable, the SVM tries to find the hyperplane that maximizes the margin while, at the same time, minimizing a quantity proportional to the number of misclassification errors [50]. The tradeoff between margin and misclassification error is controlled by a user-defined constant [51]. SVM can also be extended to handle nonlinear decision surfaces. Boser et al. [52] propose a method of projecting

**Figure 3.**  *Linear support vector machine classifier. Source: adapted from [47].* 

#### *Satellite Information Classification and Interpretation*

the input data onto a high-dimensional feature space using kernel functions and formulating a linear classification problem in that feature space [53].

In case of nonlinear classification, SVM can perform the classification by using various types of kernels which turn nonlinear boundaries to linear ones in the high-dimensional space to define optimal hyperplane [54]. In this study, four types of kernels (linear, polynomial, radial basis function, and sigmoid) were used for the SVM classification.

#### *3.3.3 Decision tree (DT)*

 A decision tree is a flow chart like tree structure, defined as a classification procedure that recursively partitions a dataset into smaller subdivisions on the basis of a set of tests defined at each branch (or node) in the tree [55]. **Figure 4** illustrates a tree composed of a root node (formed from all of the data), a set of internal nodes (splits), and a set of terminal nodes (leaves). Each circle is a node at which tests (T) are applied recursively, in order to split the data into smaller groups. The labels (A, B, C) at each leaf node refer to the class label assigned to each observation.

In this framework, a DT classifier performs multistage classifications by using a series of binary decisions to place pixels into classes. Each decision divides the pixels in a set of images into two classes based on an expression. It is possible to divide each new class into two more classes based on another expression and defines as many decision nodes as needed. Decision trees have significant intuitive appeal because the classification structure is explicit and therefore easily interpretable since the results of the decisions are always classes. Furthermore, it is possible to use data from many different sources and files together to make a single DT classifier.

The construction of decision tree classifier does not require any domain knowledge of parameter setting, and therefore, is appropriate for satellite imagery classification [56]. The learning and classification steps of decision tree induction are simple and fast. In general, decision tree classifier has good accuracy. Decision tree induction algorithms have been used for classification in many applications areas, including remote sensing [57]. Decision trees have several advantages over traditional supervised classification procedures used in remote sensing such as l ISODATA clustering and maximum likelihood classifier algorithms [58]. In

**Figure 4.**  *Decision tree classifier. Source: adapted from [55].* 

particular, decision trees are strictly nonparametric and do not require assumptions regarding the distributions of the input data. In addition, they handle nonlinear relations between features and classes, they verify missing values and are capable of handling both numeric and categorical inputs in a natural manner [55].

## *3.3.4 Maximum likelihood (ML)*

Into the classic remote sensing image classification techniques, maximum likelihood (ML) classifier, widely implemented in commercial image-processing software packages, is the most frequently method used to pixel-wise classification [34]. ML classifier assumes that the statistics for each class in each band is normally distributed and calculates the probability that a given pixel belongs to a specific class. Unless the algorithm selects a probability threshold, all pixels are classified. Each pixel is assigned to the class that has the highest probability, that is, the maximum likelihood [41].

 Statistical techniques such as ML estimation usually assume that data distribution is known a priori [59]. The ML algorithm in remote sensing classification is parametric and depends on each class and is represented by a Gaussian probability density function, which is completely described by the mean vector and variance–covariance matrix using all available spectral bands, and if possible, ancillary information (**Figure 5**). The maximum likelihood classifier is based on an estimated probability density function for each of the reference classes under consideration, where the class statistics is obtained from the training data. Given these parameters, it is possible to compute the statistical likelihood of a pixel vector as a member of each spectral class [60].

The maximum likelihood classifier is simple and robust enough to accommodate modifications. With the advent of commercial high and very high spatial resolution sensor data, the ML classifier is appropriate for many urban applications [61]. In the context of the new generation of very high spatial resolution commercial satellite sensors, data from these sensors are high volume and they measure large spectral variations in urban land cover, so that in the absence of classifiers designed to deal with such data, simplicity in the maximum likelihood can accommodate large datasets, and the modifications outlined [62].

**Figure 5.**  *Maximum likelihood classifier. Source: adapted from [59].* 

#### **3.4 Validation strategy**

 In this step, the overall classification accuracies were determined from the error matrix by calculating the total percentage of pixels correctly classified for the classification methods of: (i) artificial neural networks (ANN); (ii) support vector machines (SVM) for linear (ML), polynomial (MP), radial basis function (MRBF), and sigmoid (MS) kernels; (iii) decision tree (DT); and (iv) maximum likelihood (ML). Since this assessment takes only the diagonal of the matrix into account, the Kappa coefficient, which is based on all the elements in the confusion matrix, was also calculated [63]. The overall accuracy and kappa values were determined using test datasets, obtained with the SpatialProp strategy for training and validation samples developed in Section 3.2.

 With the approach of more advanced digital satellite remote sensing techniques, the necessity of performing an accuracy assessment has received renewed interest [64]. Accurate assessment or validation is an important step in the processing of remote sensing data. At present, the geographic information systems and remote sensing communities are becoming more interested on accurate topics. Technological developments in the area of data processing offer more and more possibilities. In this work, the collection of training samples collected from a Web Map Service (WMS) of a SPOT satellite images mosaic at a resolution of 1.5 m in true color is used. The data collected by this method are comparable to the field data employed to assess the accuracy of these remote sensing products.

#### **3.5 GIS integration**

 The different nonparametric classifiers implemented in this work, such as an artificial neural network, decision tree, support vector machines, and the traditional maximum likelihood classifier, have their own strengths and limitations. For example, when sufficient training samples are available and the feature of land covers in a dataset is normally distributed, a maximum likelihood classifier may yield an accurate classification result. In contrast, when an image data are anomalously distributed, neural network and decision tree classifiers may demonstrate a better classification result [65, 66]. Some other times, machine-learning approaches provide a better classification result than ML, although some tradeoffs exist in classification accuracy, time consumption, and computing resources [67].

Previous research has indicated that the integration of two or more classifiers provides improved classification accuracy compared to the use of a single classifier [67–69]. A critical step is to develop suitable rules to combine the classification results from different classifiers. Some previous research has explored different techniques, such as a production rule, a sum rule, stacked regression methods, majority voting, and thresholds, to combine multiple classification results [69, 70].

In this step, we have employed a GIS approach to integrate the results of the ANN, SVM, DT, and ML classifiers to produce a better final map of urban form. Different urban mapping hybrid approaches have already been combined to achieve better results [71, 72]. In our approach, the matching results of two or more methods evaluated are combined by the superposition function with the results of the best evaluated method. Subsequently, through a selection of these attributes, the pixels of the urban and nonurban uses that were identified as the best results of the combination within a GIS environment are extracted. The resulting map was validated again, revealing that the most likely characteristics of urban and nonurban uses were present in the combined pixels. This integration GIS approach

*High-Resolution Satellite Imagery Classification for Urban Form Detection DOI: http://dx.doi.org/10.5772/intechopen.82729* 

has allowed the improvement of the results of the urban area classification for the selected cities of study. We suggested that this integration approach can be economically and immediately implemented in a standard GIS software package to produce urban form maps with higher accuracy from satellite images of high spatial resolution for the Mexican National Urban System.

### **4. Results and discussion**

 In this study, four different supervised classification methods were integrated to map urban forms of 50 selected cities of study in the National Urban System in Mexico. Maximum likelihood classifier which is a conventional classification method and the advanced classification methods: artificial neural networks, decision tree, and support vector machines for linear (ML), polynomial (MP), RBF (MR), sigmoid (MS) kernels. We found that the artificial neural network classifier (overall accuracy of 92.2%) turned out to be the better single classification method. Support vector machine (overall accuracy of 89.8%) and maximum likelihood (overall accuracy of 89.2%) had similar results. Decision tree classification method (overall accuracy of 87.8%) was the lower classification method. The results we obtained were evaluated by the overall accuracy which is computed by dividing the total number of correct pixels (i.e., the sum of the major diagonal) by the total number of pixels in the error matrix. Overall accuracy for ANN, DT, selected SVM models, and ML classifiers is summarized in **Figure 6**.

After integrating the results obtained by city, using GIS approach, each evaluated method produces a result that has an impact on the spatial extent of the urban form, this is an important result. GIS approach showed an overall accuracy above the average of global reliabilities for each of the 50 selected cities of study; the average reliability for the methods evaluated in all the cities was 89.8%; when using GIS approach, this average reached 91.2%; this number is higher in 38 of the 50 cities evaluated. The approach used in this work has shown good results, although all the classifiers showed

**Figure 6.**  *Overall accuracy for ANN, DT, selected SVM models, and ML classifiers. Source: own elaboration.* 

*High-Resolution Satellite Imagery Classification for Urban Form Detection DOI: http://dx.doi.org/10.5772/intechopen.82729* 

#### **Figure 7.**

*(a) Metropolitan areas. Source: own elaboration. (b) Urban conurbations. Source: own elaboration. (c-1) Urban centers 29–38. Source: own elaboration. (c-2) Urban centers 40–50. Source: own elaboration.* 

very little differences in the spatial extent (within ±4%) of the urban class. The result for the 50 selected cities of study is shown as follows. **Figure 7a** shows the metropolitan areas, **Figure 7b** the urban conurbations, and **Figure 7c** the urban centers.

### **5. Conclusions**

Information about urban form maping is essential for proper planning and to examine how the recent urban growth has affected the economic performance and livability of cities. This methodological approach offers a spatially explicit inputs for adjusting urban policy frameworks and instruments in ways that support sustainable spatial development and make cities more productive and inclusive.

In this work, different advance classification methods have been tested for the high-resolution satellite imagery classification for urban form detection. SVM method proved to be better for classification problems of two classes. Its major advantage is the less parameters to make it operational and reach high accuracy rates. The employed methodology shows a great potential for the urban form mapping, which could help urban planners to understand and interpret complex urban characteristics with greater precision, where problems are often cited about satellite-based remotely sensed imagery [73].

 Furthermore, the proposed approach used to integrate results through GIS environment indicates a robust framework for addressing integrated classification problems in the field of remote sensing. This proposed approach allows to obtain better results when is used to integrate, on the basis that each of the integrated classification methods provides the best of its results to the benefit of a more accurate urban form classification.

Therefore, we believe this proposed approach has great practical value for several remote sensing problems and could be improved and applied to various urban applications in the near future. In this respect, this integration approach can be strengthened through the implementation of learning methods to manage the integration of the data and therefore obtain more and better reliable results. Finally, we are also interested in plainly analyzing the morphological characteristics of the urban form through the application of metrics that have, as primary input, the results obtained with this work.

### **Acknowledgements**

 The authors thank the anonymous reviewers for their comments and suggestions. We also thank the financial support granted by the Fondo Sectorial INEGI-CONACYT (278953-S0025-2016-1) project. Throughout the project we had the technical assistance of the Centro de Investigación en Ciencias de Información Geoespacial. For the technical support, we thank Sandra Medina and Gerardo Ávila, and specially we thank Gabriela Quiroz for the mapping making and visual design.

*High-Resolution Satellite Imagery Classification for Urban Form Detection DOI: http://dx.doi.org/10.5772/intechopen.82729* 

## **Author details**

Juan Manuel Núñez\*, Sandra Medina, Gerardo Ávila and Jorge Montejano Centro de Investigación en Ciencias de Información Geoespacial AC (CentroGeo), Mexico City, Mexico

\*Address all correspondence to: jnunez@centrogeo.edu.mx

© 2019 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.

## **References**

[1] Seto KC, Satterthwaite D. Interactions between urbanization and global environmental change. Current Opinion in Environment Sustainability. 2010;**2**(3):127-128

 [2] Núñez JM, Corona N, Ocampo P, Mohar A. Conectando el frente de agua marítimo de la zona costera norte de Yucatán con la zona metropolitana de Mérida. In: Iracheta A, Pedrotti C, Patricia R, editors. Suelo Urbano y Frentes de Agua: Debates y Propuestas en Iberoamérica. México: El Colegio Mexiquense, A.C.; 2017

[3] Caudillo C, Flores S. Tendencias espacio-temporales en la segregación. In: Tendencias territoriales determinantes del futuro de la Ciudad de México. México: Consejo Económico y Social de la Ciudad de México/Consejo Nacional de Ciencia y Tecnología/CentroGeo; 2016. pp. 153-175

[4] Mohar A. Tendencias territoriales determinantes del futuro de la Ciudad de México. Consejo Económico y Social de la Ciudad de México; 2016

 [5] Núñez JM. Mapeo de la composición urbana, contraste entre dispersión y formas compactas en el sur de la Ciudad de México. In: Rothe HQ , editor. Ciudad Compacta: Del concepto a la práctica. Universidad Nacional Autónoma de México, Ciudad de México; 2015

[6] Batty M. The size, scale, and shape of cities. Science. 2008;**319**(5864):769-771

[7] Besussi E, Chin N, Batty M, Longley P. The structure and form of urban settlements. In: Remote Sensing of Urban and Suburban Areas. Berlin, Heidelberg, New York: Springer-Verlag; 2010. pp. 13-31

[8] Weng QH. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sensing of Environment. 2012;**117**:34-49

[9] Guindon B, Zhang Y, Dillabaugh C. Landsat urban mapping based on a combined spectral-spatial methodology. Remote Sensing of Environment. 2004;**92**(2):218-232

[10] Schneider A, Friedl MA, Potere D. A new map of global urban extent from MODIS satellite data. Environmental Research Letters. 2009;**4**(4):11

[11] Ridd MK. Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities. International Journal of Remote Sensing. 1995;**16**(12):2165-2185

[12] Deng CB, Wu CS. BCI: A biophysical composition index for remote sensing of urban environments. Remote Sensing of Environment. 2012;**127**:247-259

[13] Bhaskaran S, Paramananda S, Ramnarayan M. Per-pixel and objectoriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography. 2010;**30**(4):650-665

[14] Zhou W, Troy A. An objectoriented approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing. 2008;**29**(11):3119-3135

[15] Zhang J, Foody GM. Fully-fuzzy supervised classification of sub-urban land cover from remotely sensed imagery: Statistical and artificial neural network approaches. International Journal of Remote Sensing. 2001;**22**(4):615-628

[16] Schneider A, Friedl MA, Potere D. Mapping global urban areas using

*High-Resolution Satellite Imagery Classification for Urban Form Detection DOI: http://dx.doi.org/10.5772/intechopen.82729* 

MODIS 500-m data: New methods and datasets based on 'urban ecoregions'. Remote Sensing of Environment. 2010;**114**(8):1733-1746

[17] Byun Y, Choi J, Han Y. An areabased image fusion scheme for the integration of SAR and optical satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2013;**6**(5):2212-2220

 [18] Puissant A, Zhang W, Skupinski G, editors. Urban morphology analysis by high and very high spatial resolution remote sensing. In: International Conference on Geographic Object-Based Image Analysis. 2012

[19] Duan YL, Shao XW, Shi Y, Miyazaki H, Iwao K, Shibasaki R. Unsupervised global urban area mapping via automatic labeling from ASTER and PALSAR satellite images. Remote Sensing. 2015;**7**(2):2171-2192

[20] Bartholome E, Belward AS. GLC2000: A new approach to global land cover mapping from earth observation data. International Journal of Remote Sensing. 2005;**26**(9):1959-1977

 [21] Gao F, De Colstoun EB, Ma RH, Weng QH, Masek JG, Chen J, et al. Mapping impervious surface expansion using medium-resolution satellite image time series: A case study in the Yangtze River Delta, China. International Journal of Remote Sensing. 2012;**33**(24):7609-7628

[22] Esch T, Marconcini M, Marmanis D, Zeidler J, Elsayed S, Metz A, et al. Dimensioning urbanization—An advanced procedure for characterizing human settlement properties and patterns using spatial network analysis. Applied Geography. 2014;**55**:212-228

[23] Sandoval H, Núñez JM. Cuantificación de la composición biofísica de los ambientes urbanos de

 la ciudad de Mérida, Yucatán basada en el análisis de imágenes Landsat TM/ ETM+/OLI (1986-2014). In: LCA C, LCB P, LCW Q, MET O, MIU C, MOG L, editors. Estudios Territoriales en México: Percepción Remota y Sistemas de Información Espacial. México: Universidad Autónoma de Ciudad Juárez; 2016

[24] Xian GZ. Remote Sensing Applications for the Urban Environment. Boca Raton, FL: CRC Press; 2015

 [25] Maxwell AE, Warner TA, Fang F. Implementation of machinelearning classification in remote sensing: An applied review. International Journal of Remote Sensing. 2018;**39**(9):2784-2817

[26] Mas JF, Flores JJ. The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing. 2008;**29**(3):617-663

[27] Dridi H, Bendib A, Kalla M. Analysis of urban sprawl phenomenon in Batna city (Algeria) by remote sensing technique. Analele Universităţii din Oradea, Seria Geografie. 2015;**2**:211-220

[28] Bhatta B. Analysis of Urban Growth and Sprawl from Remote Sensing Data. Berlin, Heidelberg, New York: Springer-Verlag; 2010. 170 p

 [29] Ferreira FH, Messina J, Rigolini J, López-Calva L-F, Lugo MA, Vakis R. Economic Mobility and the Rise of the Latin American Middle Class. Washington, DC: The World Bank; 2012

[30] Richter R, Schlapfer D. Geoatmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction. International Journal of Remote Sensing. 2002;**23**(13):2631-2649

[31] Jin HR, Stehman SV, Mountrakis G. Assessing the impact of training sample selection on accuracy of an urban classification: A case study in Denver, Colorado. International Journal of Remote Sensing. 2014;**35**(6):2067-2081

[32] Ghimire B, Rogan J, Galiano VR, Panday P, Neeti N. An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience & Remote Sensing. 2012;**49**(5):623-643

[33] Homer C, Huang CQ, Yang LM, Wylie B, Coan M. Development of a 2001 National Land-Cover Database for the United States. Photogrammetric Engineering and Remote Sensing. 2004;**70**(7):829-840

[34] Yu L, Liang L, Wang J, Zhao Y, Cheng Q, Hu L, et al. Meta-discoveries from a synthesis of satellite-based landcover mapping research. International Journal of Remote Sensing. 2014;**35**(13):4573-4588

 [35] Haykin S. Neural Networks: A Comprehensive Foundation. India: Prentice Hall PTR; 1994

[36] Atkinson PM, Tatnall A. Introduction neural networks in remote sensing. International Journal of Remote Sensing. 1997;**18**(4):699-709

[37] Canziani G, Ferrati R, Marinelli C, Dukatz F. Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes. Mathematical Biosciences and Engineering. 2008;**5**(4):691-711

[38] Basheer IA, Hajmeer M. Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods. 2000;**43**(1):3-31

[39] Minsky M, Papert S. Perceptron Expanded Edition. Cambridge, MA: MIT Press; 1969

[40] Rosenblatt F. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington, DC: Spartan; 1962

[41] Richards JA, Richards J. Remote Sensing Digital Image Analysis. Berlin, Heidelberg, New York: Springer-Verlag; 1999

[42] Fuller DO. Remote detection of invasive Melaleuca trees (Melaleuca quinquenervia) in South Florida with multispectral IKONOS imagery. International Journal of Remote Sensing. 2005;**26**(5):1057-1063

[43] Augusteijn MF, Folkert BA. Neural network classification and novelty detection. International Journal of Remote Sensing. 2002;**23**(14):2891-2902

[44] Liu WG, Wu EY. Comparison of non-linear mixture models: Sub-pixel classification. Remote Sensing of Environment. 2005;**94**(2):145-154

 [45] Mertens KC, Verbeke LPC, Westra T, De Wulf RR. Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients. Remote Sensing of Environment. 2004;**91**(2):225-236

[46] Lafont D, Guillemet B. Beam-filling effect correction with subpixel cloud fraction using a neural network. IEEE Transactions on Geoscience and Remote Sensing. 2005;**43**(5):1070-1077

[47] Mountrakis G, Im J, Ogole C. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 2011;**66**(3):247-259

[48] Vapnik V. Estimation of Dependences Based on Empirical Data. USA: Springer Science & Business Media; 2006

[49] Zhu GB, Blumberg DG. Classification using ASTER data and *High-Resolution Satellite Imagery Classification for Urban Form Detection DOI: http://dx.doi.org/10.5772/intechopen.82729* 

SVM algorithms; the case study of Beer Sheva, Israel. Remote Sensing of Environment. 2002;**80**(2):233-240

 [50] Pal M, Mather PM. Support vector machines for classification in remote sensing. International Journal of Remote Sensing. 2005;**26**(5):1007-1011

[51] Cortes C, Vapnik V. Supportvector networks. Machine Learning. 1995;**20**(3):273-297

 [52] Boser BE, Guyon IM, Vapnik VN, editors. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM; 1992

[53] Vapnik V. The Nature of Statistical Learning Theory. New York: Springer Science & Business Media; 2013

 [54] Ustuner M, Sanli FB, Dixon B. Application of support vector machines for land use classification using high-resolution RapidEye images: A sensitivity analysis. European Journal of Remote Sensing. 2015;**48**:403-422

[55] Friedl MA, Brodley CE. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment. 1997;**61**(3):399-409

[56] De Fries R, Hansen M, Townshend J, Sohlberg R. Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing. 1998;**19**(16):3141-3168

[57] Pal M. Random forest classifier for remote sensing classification. International Journal of Remote Sensing. 2005;**26**(1):217-222

[58] Sharma R, Ghosh A, Joshi PK. Decision tree approach for classification of remotely sensed satellite data using open source support. Journal of Earth System Science. 2013;**122**(5):1237-1247

[59] Burges CJC. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998;**2**(2):121-167

[60] Besag J. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society: Series B: Methodological. 1986;**48**(5-6):259-302

[61] Mesev V. Modified maximum likelihood classifications of urban land use: Spatial segmentation of prior probabilities. Geocarto International. 2001;**16**(4):41-48

[62] Majd MS, Simonetto E, Polidori L. Maximum likelihood classification of single high-resolution polarimetric SAR images in urban areas. Photogrammetrie, Fernerkundung, Geoinformation. 2012;(4):395-407

 [63] Lucas I, Janssen F, van der Wel FJ. Accuracy assessment of satellite derived land cover data: A review. Photogrammetric Engineering and Remote Sensing. 1994;**60**(4):479-426

 [64] Bharatkar PS, Patel R. Approach to accuracy assessment tor RS image classification techniques. International Journal of Scientific and Engineering Research. 2013;**4**(12):79-86

[65] Lu DS, Mausel P, Batistella M, Moran E. Comparison of land-cover classification methods in the Brazilian Amazon Basin. Photogrammetric Engineering and Remote Sensing. 2004;**70**(6):723-731

[66] Pal M, Mather PM. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment. 2003;**86**(4):554-565

[67] Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. 2007;**28**(5):823-870

[68] Huang Z, Lees BG. Combining non-parametric models for multisource predictive forest mapping. Photogrammetric Engineering and Remote Sensing. 2004;**70**(4):415-425

[69] Steele BM. Combining multiple classifiers: An application using spatial and remotely sensed information for land cover type mapping. Remote Sensing of Environment. 2000;**74**(3):545-556

[70] Liu W, Gopal S, Woodcock CE. Uncertainty and confidence in land cover classification using a hybrid classifier approach. Photogrammetric Engineering and Remote Sensing. 2004;**70**(8):963-971

[71] Lo CP, Choi J. A hybrid approach to urban land use/cover mapping using landsat 7 enhanced thematic mapper plus (ETM+) images. International Journal of Remote Sensing. 2004;**25**(14):2687-2700

 [72] Kuemmerle T, Radeloff VC, Perzanowski K, Hostert P. Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique. Remote Sensing of Environment. 2006;**103**(4):449-464

[73] Carlson T. Preface—Applications of remote sensing to urban problems. Remote Sensing of Environment. 2003;**86**(3):273-274

## **Chapter 6**

## Water Management in Irrigation Systems by Using Satellite Information

*Gema Marco Dos Santos, Ignacio Meléndez Pastor, Jose Navarro Pedreño and Ignacio Gómez Lucas* 

## **Abstract**

 Changes in agriculture are associated to the availability of resources and the economic and social demands. One of the more important transformations is to change rainfed into irrigated crops to increase the yield. In most of the cases, water resource and irrigation reservoirs are needed to maintain the yield. However, evaporation from ponds can be an important economic loss and an unsustainable strategy for water management, especially in arid and semiarid regions. Efficient methods for water storage should be established. In this study, a selected area located close to the city of Cartagena (Murcia) and the south of Alicante (Spain) has been studied, where there was an important transformation from rainfed to irrigated crops. Because of the high temperatures and insolation, the increment of the number of reservoirs detected by using remote sensing data and GIS tools may be inefficient for water management. The characterization of these reservoirs, to quantify the potential loss of water due to evaporation, has been done. The use of these tools for analysis could be interesting to find more efficient storage solutions (i.e., better spatial distribution of reservoirs, an increment of depth, and reduction of surface exposure) for improving the water storage and management.

**Keywords:** arid environments, evaporation, irrigated agriculture, spatial distribution, water storage

## **1. Introduction**

 Water management is one of the most important problems for future decades. Although there are areas of the planet where the water availability is naturally scarce due to the rain and temperature patterns, human pressure on this resource is accentuating the problem of scarcity. As reflected in the World Water Assessment Program published by UNESCO [1], there are three types of pressures or "drivers" on water systems: demographic, economic, and social. Population growth increases not only water consumption but also pollution, which is another way to decrease water availability. Furthermore, land occupation and urbanization affect the dynamics of the ecosystem due to soil sealing, and consequently, the hydrological cycle is altered (infiltration processes, aquifer recharge, etc.). Protecting ecosystems is highly important to maintain the goods and services they offer us, and it is so necessary for life. Economic growth has allowed the development of modern extraction

 and production techniques that aggravate water scarcity. Natural dynamics of water is affected; i.e., river flows are altered or the water table is reduced. The building of infrastructures that benefits the commerce of both products and services associated to water management has been increased. The change in the lifestyle of many countries is reflected in the amount of water consumed, principally in those in which access to drinking water is easy and immediate. In contrast, in developing countries where there is scarcity and water pollution, it is a great challenge. Therefore, there is a social inequality that must be resolved.

 For example, in the case of arid and semiarid areas [2], in which the amount of available water is limited due to the shortage and irregularity of rainfall, the development of irrigated agriculture has caused an increase in pressure on water resources. This affects highly negatively the agriculture, which is one of the biggest users of water with respect to the total demand of water (almost 80%) [3]. In these areas, where water is a limited resource, population growth exerts a great negative pressure on it. Agriculture must be able to supply the population even though the availability of water is the limiting factor for food production [4]. To guarantee the continuous supply of water for irrigation, small ponds are built to store the water and manage it according to their needs [5]. These ponds are usually shallow constructions located near the crops that will supply. However, it seems that the management of these small reservoirs is based on the experience of the farmer and not on contrasted technical criteria [6]. Water is a limited and essential resource for life that has to be managed efficiently, equitably, and allow future generations to have access to it. Therefore, the current management model should be changed to make sustainable use of available water resources and develop strategies that promote savings and minimize losses in irrigation [7].

 Evaporation is defined as a process by which liquid water turns to vapor state by heating it (energy breaks the bonds of the molecules) [8]. The main factors that influence evaporation are local climatic conditions such as air and water temperature, solar radiation, relative humidity, wind speed [9], and the geometry of the ponds, for example, evaporation is greater if the relationship between area and volume is large [10]. In areas with high insolation, the evaporation from the sheet of water represents a significant loss from the environmental and also economic point of view [11]. Different methods are being developed to avoid evaporation: there are chemical methods such as stearyl alcohol [12], floating modular systems that have different shapes and materials [13], floating photovoltaic panels [14], canvas, or suspended coverages [15]. Each method may be appropriate depending on the characteristics of the place where it will be installed (amount of water stored, area, costs, etc.) [16]. Therefore, it is necessary to study tools and develop management strategies that improve the efficiency of water consumption and obtain the potential evaporation from the ponds and reservoirs.

 The use of Geographic Information Systems (GIS) in the study of water resources allows us to know the dynamics of them, and therefore, models with different scenarios of water availability or demands can be developed [17]. With these models, different projections can be made in order to develop management scenarios more suited to the state of resources. This technology, GIS, is very suitable due to the amount of information that can be incorporated into the models, and the possibility of viewing the information in the form of maps [18]. In developing countries, this tool can help the management of its resources with a relative low cost and the large number of free images obtained over many years from remote sensing. Moreover, in those countries in which it is not possible to collect data in situ because of the cost, time, or access due to legal causes or because of war conflicts.

*Water Management in Irrigation Systems by Using Satellite Information DOI: http://dx.doi.org/10.5772/intechopen.82368* 

GIS combining remote sensing help to solve many problems related to resources management.

 Remote sensing is being a very useful method to delimit and study water bodies, especially due to the difficulty of obtaining continuous information. Due to the contrast between the reflectance of the sheet of water and that of the earth surface [10], it is possible, through satellite images, to study and monitor the water storage [19], to observe the changes in the surface of water bodies over time, study the evolution of the irrigation reservoirs of an area [20], estimate its evaporation (important in arid and semiarid zones) and volume. The water absorbs the energy in wavelengths of the near and medium infrared; therefore, the reflected energy of these is low and the water bodies appear in dark color in both the multispectral images and the grayscale images [21]. Moreover, satellite images facilitate the composition of RGB or false color images where water sheets can be detected and analyzed.

Facing of future scenarios of climate change [22], in which the availability and quality of water can be seriously affected [23], it is necessary to improve the use of water resources through the incorporation of new techniques and the modernization of infrastructures. This includes the application of regulations [24] that support integrated management techniques that guarantee a better resource quality and also promote citizen participation [25].

In this work, the combined use of remote sensing data and GIS tools, demonstrated with the example, the possibilities of managing and controlling water infrastructures and the evaporation of water in agriculture, is one of the major consumers of water.

### **2. A study case: Campo de Cartagena, southeast of the Iberian Peninsula**

#### **2.1 Study area**

 The study area is located beside the Mar Menor in Murcia and south of Alicante (**Figure 1**), Spain. This basin is a sedimentary plain formed by conglomerates, marls, sandstones, and clays [26] with approximately 152,000 ha. The Mar Menor is the biggest coastal lagoon of Spain that is included in the RAMSAR convention. It is in serious danger of pollution as a result of nitrogen and phosphate contributions from agriculture that cause the loss of its water's quality, the decrease of the diversity and elimination of autochthonous species, and induce the proliferation of algae blooms. The two factors that most affect this wetland are tourism (population growth) and agriculture; both generate polluting inputs that reach the Mar Menor through the different watercourses and infiltration processes. The climate is Bsh according to the Köppen classification, with low rainfalls (around 300 mm per year) of torrential type especially during the autumn. The average annual temperature is about 18°C, with hot summer (about 32–35°C in August) and mild winters (the temperature usually does not drop below 5°C) [27]. Precisely, the weather is one of the main reasons why so many tourists (both Spanish and foreign) come every year to visit the Region of Murcia (more than 1 million people in 2015–2016) [28], especially near the coast.

 Different improvements, mainly since the second half of the twentieth century in the region of Murcia and Alicante province, have favored the growth of population, principally located in coastal areas. This increase may be due to the improvement of communication channels (roads) and greater availability of water resources, which has allowed the development of agriculture. Agriculture is very important in the Region of Murcia, because of the good climate and a fertile soil

**Figure 1.** 

*Location of study area (Campo de Cartagena, Mar Menor watershed) in the region of Murcia and the south of the province of Alicante.* 

 in many river basins that allows suitable growth of crops, but the lack of water has limited the production. Therefore, the change of rainfed crops to irrigated crops was benefited by the capital investment (the Tajo-Segura water transfer in 1979, the exploitation of the aquifers and the obtaining of desalinated water), which increases the availability of water; the productivity of the crops has improved in spite of the severe shortage suffered by the area. La Pedrera reservoir (built in 1985), located in the province of Alicante, is responsible for regulating the water, which comes from the Tajo-Segura transfer canal (agricultural and urban supply). This reservoir maintains adequate water availability despite the severe scarcity suffered in the area [29]. In fact, Murcia exports between 20 and 30% of fruits and vegetables in Spain, especially to the European Union [30]. Even with the external contributions of water, it is not enough to supply the water needs of the area that often suffers serious droughts that cause cuts back not only for agriculture but also for urban supply. In addition, during the summer, the demand for water for agriculture is higher because of the large water deficit and high temperatures. This situation also coincides with the period of greatest urban demand in the area due to tourism [31], especially in some areas closer to the coast. For example, it is estimated that on the Costa Cálida, there were almost 4 million visitors in 2016 [28].

#### **2.2 Methodology**

The data were obtained from the National Geographic Institute (IGN). We used the geodesic reference system ETRS89 and UTM projection zone 30 [32].

The Mar Menor watershed was delimited with the Digital Terrain Model (MDT25 CC-BY 4.0 scne.es) and the GRASS software using the flow lines that run along the maximum slope. The basin covers 151,641 ha and is located mainly in the Region of Murcia and a part in the province of Alicante. All the reservoirs of the basin were digitized, one by one by, using high-quality orthophotos from the Plan

*Water Management in Irrigation Systems by Using Satellite Information DOI: http://dx.doi.org/10.5772/intechopen.82368* 

**Figure 2.**  *Old mills and the new irrigation systems in the Campo de Cartagena.* 

 Nacional de Ortofotografía Aérea (PNOA) (FotoPNOA 2004–2016 CC-BY 4.0 scne. es, pixel size of 25 cm). The same process was followed with old photographs taken from a photogrammetric flight along the period from 1973 to 1986 (Fotol 1973–1986 CC-BY 4.0 scne.es, scale 1: 18,000, pixel size between 27 and 45 cm). The digitization process was done with QGIS v.3.2. The ponds and reservoirs were marked with points to locate them, and then, they were digitalized to determine their surface taking into account the limits of the structure, when they were at maximum capacity. A field trip was also done to compare the results obtained from the images with the disposition in fact, checking close to a hundred elements (old mills, ponds, and small reservoirs) (**Figure 2**).

A heat map was developed from the density of points that identify the location of each irrigation ponds/reservoirs to better understand their distribution. Point interpolation aids to visualize in a map the concentration of these in a continuous surface. Three parameters are used to create a heat map: the cell size, the bandwidth, and the type of calculation used in the interpolation. The cell size will determine the degree of detail on the surface. The larger the cell size, the less continuous the color gradient that represents the concentration of points will be. The bandwidth (or search radius) is the area around each point that the GIS will take into account for density calculation. The type of calculation used in the most common interpolation is inverse distance weighting (IDW), which assigns more importance to the functions that are closer than to those that are furthest away [33]. In this case, we used a search radius of 5 km and 15 pixels of cell size.

 To estimate annual evaporation losses in the study area, we have used as reference the evaporation values published in the article"Regional assessment of evaporation from agricultural irrigation reservoirs in a semiarid climate" by Martínez Alvarez et al. [34]. They use measures done in the 2003 for the entire Segura River basin (located in the southeast of Spain, including the study area). In this article, authors estimate the evaporation losses using daily, monthly, and annual data on temperature, precipitation, relative humidity, wind speed, wind direction, and solar radiation of 74 agro-meteorological stations for the period 2000–2006. In addition, some of them have class-A pan evaporometer in which evaporation was calculated by a sensor that determinate the difference in water level. The class-A pan evaporometer standardized by the US National Weather Service is a 120.7-cm diameter and 15-cm-deep cylinder made of galvanized iron. It is elevated about 15 cm from the ground by a wooden platform. It must be located where the air circulates freely so that it does not affect the measurements [35]. They use 14,145 irrigation reservoirs for the entire Segura basin, which occupied 4901 ha. They obtain as a result the annual evaporation loss in the Segura basin taking into account the maximum surface area, which was 68.8 hm3 . Based upon this value and considering the surface, the evaporation value of water used as a reference is 0.014 hm3 year/ha. This helps us to estimate the evaporation loss estimation in our study area.

## **3. Results and discussion**

**Figure 3** presents the digitized points that indicate the location of the irrigation ponds for both periods. There is a clear increase in the number of points currently with respect to the previous period.

In the image a (**Figure 3**), the points do not appear distributed following any regular pattern; they are dispersed throughout the basin but especially near the coast and the urban cores, some of them forming small groups. In the top of the basin (NW), in the foothills of the Sierra de Carrascoy and El Valle, there are no irrigation ponds because at that time, mechanization and cultivation techniques did not allow working the land in areas with steep slopes. In the image b, there is a greater increase of irrigation ponds and small reservoirs. Grouping of points can be observed mainly in the center of the basin, which is quite flat, and in the top near La Pedrera reservoir. There is also a tendency for a high density of points near the coast as in the first image. In this case, due to the modern techniques, the irrigated crops occupy the foothills of the mountains.

 In order to understand and visualize better the irrigation ponds distribution patterns in the area and compare them between two periods, a heat map (**Figure 4**) was created from the density of points. These maps confirm in a very clear way the changes produced in the area.

#### **Figure 3.**

*Points marking the location of irrigation ponds in the Mar Menor basin in the 1973–1986 period (a) and nowadays (2016–2017) (b).* 

*Water Management in Irrigation Systems by Using Satellite Information DOI: http://dx.doi.org/10.5772/intechopen.82368* 

#### **Figure 4.**

*Heat maps from dot density of the identified ponds with old photographs (a) and those with current ones (b).* 

 In the first image (**Figure 4a**), high density of points (in green) is observed in the lower part of the basin and following the coastline. This location may be associated with the extraction of water from the subsurface aquifers, following the pattern of the traditional systems such as windmills. (A pond was situated near the mill so that the water could fall into it.) Moreover, extraction that is more efficient with pumps made possible to obtain water from the aquifers at a larger depth coming to cause an overexploitation of aquifers.

The arrival of water from the Tajo-Segura transfer in 1979 increased the availability of water and relieved the pressure on groundwater [36]. This situation benefited production and the expansion of intensive agriculture (with the corresponding construction of small reservoirs to store and supply water).

In the second image (**Figure 4b**), there is a generalized increment near the coast and a great increase in the upper part of the basin (NE). This difference could be explained by the construction in 1985 of the La Pedrera reservoir. It has 1272 ha and can store 246 hm3 . This reservoir receives water from the Tajo-Segura transfer and distributed to the Campo de Cartagena by a great canal and others supplied conductions.

 La Pedrera reservoir is also used for urban water supply through the Taibilla canal. Therefore, it is easier to supply the crop fields closest to the reservoir. Consequently, it has favored a greater development of greenhouses (**Figure 5**). They are grouped near the towns of San Pedro del Pinatar (Murcia) and Pilar de la Horadada in the south of Alicante.

#### **Figure 5.**

*Group of greenhouses near San Pedro del Pinatar in the study area (source: derived from FotoPNOA 2004–2016 CC-BY 4.0 scne.es).* 


#### **Table 1.**

*Values obtained from the irrigation pond digitalization and estimated values of evaporation in the study area for both periods.* 

After the analysis of the data and the digitalization of the irrigation ponds from the images in both periods, **Table 1** shows a summary of them. A total of 971 reservoirs were digitized from the data of the period 1973–1986. The sheet of water, according to the sum of the surface of all them, accounted to 88.63 ha. The average surface area of the reservoir/pond was 318 m2 .

In the second period (2016–2017), 3846 irrigation ponds were digitized from PNOA images. The total water surfaces were 1201.32 ha. The average surface area of the reservoirs increased to 1631 m2 .

These values indicate that the number of reservoirs in this area has almost incremented four times. For the average surface of ponds, the size at present is five times higher than before, however not necessarily deeper than the oldest. Therefore, the total area occupied by the reservoirs has increased fourteen times and the size of the surface of the reservoirs only five times for the last four decades.

To estimate the possible evaporation losses from the sheet of water of the small reservoirs/ponds, we took as reference the value given for the area of 0.014 hm3 year/ha [34]. Considering all the reservoirs to their maximum capacity, the values estimated for each period were as follows:


This means that there is a difference of approximately 15.62 hm3 /year, parallel to the increment of the surface exposure of reservoirs and ponds. This amount of water that can be lost is equal to that needed for the supply of a city of 300,000 inhabitants for a year considering the average water consumption in Spain for inhabitants [37].

Water scarcity in this area has always been a main concern for agricultural production. However, with the transfer from other river basins (i.e., Tajo river), water availability has been increased and along with the population growth and agricultural yield. This was reflected in the construction of reservoirs/ponds in the last years, which has been increased. With this increment, the potential evaporation of water from reservoirs and ponds has been dramatically increased by the way.

 According to a report managed by the Ministry of Agriculture and Water of the Region of Murcia with data from the Space Agency of Meteorology (AEMET), and with the collaboration of different universities and institutions, the evolution of rainfall does not follow a clear trend, which is a normal situation in that area with such irregularities. For temperatures, a slight tendency to increase is observed. In fact, according to this report from 1971 to 2009, the average annual temperature of the entire Region of Murcia increased from 15.5 to 17°C [38]. Therefore, the evaporation loss could be aggravated considering the scenarios based on the climate change and the increase in temperature. In this sense, evaporation can be over the values estimated in this work.

 In this line, it is important to study and develop measures to avoid water evaporation and improve the efficiency of the irrigation system. For this reason, it is convenient to study a better spatial distribution of reservoirs and reduce the number of them. Moreover, an increment of depth in their construction can facilitate to store the same amount of water with less surface exposure to evaporation. Finally, the use of some techniques to cover the ponds can reduce the water surface exposure.

### **4. Conclusions**

 Remote sensing data are very useful to study and analyze the amount of water stored and the management of irrigation systems. The use of these technologies, both GIS and remote sensing, can help in the management of decision-making about water resources.

The example given shows that the amount of water that could evaporate represents a significant loss. In this case, the amount of water that could evaporate is almost 14 times higher now. This matches with the increase in the total surface occupied by the irrigation ponds. With only a four-time increment in the number of reservoirs, the amount of water that could evaporate increases by 350%. Although it is an estimation, it is clear that water losses due to evaporation represent a high cost, especially in areas where this resource is scarce.

Despite the water limitations of the area, in the Mar Menor basin, there are many agricultural fields that generate tons of fruits and vegetables that provide a great social and economic benefit to region. Even with the different sources of water, there is still a water deficit that generates (especially during droughts periods) economic, social, and environmental instability.

 In addition, with the possible effects of climate change that indicates less precipitation and higher temperatures, it is expected that the amount of water resources available can be seriously affected especially in arid and semiarid areas such as Murcia and Alicante, which already suffer the effects of scarcity. Efforts

should be done applying techniques to reduce the evaporation. Therefore, saving the resource to avoid losses as much as possible and be able to supply a growing population is a priority.

## **Acknowledgements**

Thanks to the Instituto Nacional de Geografia (IGN) for the availability of social, economic, and environmental information in open source that facilitates the research in Spain.

## **Conflict of interest**

Authors expressed that there is no conflict of interest.

## **Author details**

Gema Marco Dos Santos, Ignacio Meléndez Pastor, Jose Navarro Pedreño\* and Ignacio Gómez Lucas Department of Agrochemistry and Environment, University Miguel Hernández of Elche, Alicante, Spain

\*Address all correspondence to: jonavar@umh.es

© 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.

*Water Management in Irrigation Systems by Using Satellite Information DOI: http://dx.doi.org/10.5772/intechopen.82368* 

## **References**

[1] Nations U. Water in a Changing World [Internet]. Vol. 11, World Water. 2009. 349 p. Available from: http://www.esajournals. org/doi/abs/10.1890/1051- 0761(2001)011[1027:WIACW]2.0 .CO;2

[2] Martínez Fernández J, Selma MAE. The dynamics of water scarcity on irrigated landscapes: Mazarrón and Aguilas in South-Eastern Spain. System Dynamics Review. 2004;**20**(2):117-137

[3] European Environment Agency. El agua en la agricultura. 2012. Available from: https://www.eea.europa.eu/es/ articles/el-agua-en-la-agricultura

[4] El-Beltagy AMM. Impact of climate change on arid lands agriculture. Agriculture & Food Security. 2012;**1**:3

[5] Juan M, Casas J, Bonachela S, Fuentes-Rodríguez F, Gallego I, Elorrieta MA. Construction characteristics and management practices of in-farm irrigation ponds in intensive agricultural systems— Agronomic and environmental implications. Irrigation and Drainage. 2012;**61**(5):657-665

[6] Cazorla MJ. Gestión ecosistémica de las balsas de riego del litoral mediterráneo andaluz [thesis]. Almería University; 2012

[7] Hamdy A, Abu-Zeid M, Lacirignola C. Water crisis in the mediterranean: Agricultural water demand management. Water International. 1995;**20**(4):176-187

[8] USGS. The Water Cycle: Evaporation [Internet]. 2016. Available from: https://water.usgs.gov/edu/ watercycleevaporation.html

[9] Benzaghta MA, Mohamad TA. Evaporation from reservoir and

reduction methods: An overview and assessment study. Domascus, Syria Medinah, Kingdom Saudi Arab: Int Eng Conv; 2009. 9 p

[10] Ibarra D, Salvador M, Conesa C. Estimación de evaporación en balsas de riego mediante el empleo de técnicas de teledetección. Estudio aplicado a la vertiente litoral sur de la Región de Murcia. Vol. 82014

[11] Lopez Moreno JI. Estimación de pérdidas de agua por evaporación en embalses del Pirineo. Cuadernos de Investigación Geográfica. 2008;**34**:61-81

[12] Gugliotti M, Baptista MS, Politi MJ. Reduction of evaporation of natural water samples by monomolecular films. Journal of the Brazilian Chemical Society. 2005;**16**(6 A):1186-1190

[13] Segal L, Burstein L. Retardation of water evaporation by a protective float. Water Resources Management. 2010;**24**(1):129-137

[14] KYOCERA TCL Solar Begins Operation of Japan's Largest 13.7 MW Floating Solar Power Plant [Internet]. 2018. Available from: http://www. kyocerasolar.eu/index/news/news\_ details.L3NvbGFyX2VsZWN0cml jX3N5c3RlbXMvbmV3cy8yMDE4 L0tZT0NFUkFfVENMX1Nvb GFyX2JlZ2luc19vcGVyYXRpb25fb2Z fSmFwYW5fc19sYXJnZXN0Xz EzXzdNV19GbG9hdGluZ19Tb2xhc l9Qb3dlcl9QbGFudA~~.html

 [15] Yao X, Zhang H, Lemckert C, Brook A. Evaporation Reduction by Suspended and Floating Covers: Overview, Modelling and Efficiency. Urban Water Security Research Alliance Technical Report No. 28; 2010

[16] Craig I, Green A, Scobie M, Schmidt E. Controlling Evaporation Loss from

Water Storages. Natl Cent Eng Agric. 1000580/1; 2005. p. 207

[17] Kawsar R. Water Resource Managment and Remote Sensing, A Prospective Issue that Requires Considerable Attention [Internet]. 2015. Available from: http://geoawesomeness. com/water-resource-managment-andremote-sensing-a-prospective-issuethat-requires-considerable-attention/

[18] Tsihrintzis VA, Hamid R, Fuentes HR. Use of geographic information systems (GIS) in water resources: A review. Water Resources Management. 1996;**10**(4):251-277

 [19] Pipitone C, Maltese A, Dardanelli G, Lo Brutto M, La Loggia G. Monitoring water surface and level of a reservoir using different remote sensing approaches and comparison with dam displacements evaluated via GNSS. Remote Sensing. 2018;**10**(1):1-24

[20] Lin YCW, Hsiao L, Cheng K. A Multi-Decadal Change Analysis for Irrigation Ponds in Taoyuan, Taiwan Using Multi-source Data2018. pp. 1-16

[21] Kite G, Pietroniro A. Remote sensing of surface water. In: Schultz GA, Engman ET, editors. Remote Sensing in Hydrology and Water Management. Berlin, Heidelberg: Springer; 2000. pp. 217-238

[22] Porter JR, Xie L, Challinor AJ, Cochrane K, Howden SM, Iqbal MM, et al. Food security and food production systems. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL, editors. Climate Change 2014: Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate

Change. Cambridge, United Kingdom and New York: Cambridge University Press; 2014. pp. 485-533

[23] Hudson NW. Water Conservation. In: FAO Land and Water Development Division, editor. Soil and Water Conservation in Semi-Arid Areas. Bedford, United Kingdom; 1988. p. 185

[24] Vargas-Amelin EPP. The challenge of climate change in Spain: Water resources, agriculture and land. Journal of Hydrology. 2014;**518**:243-249

 [25] FAO & WWC. Towards a Water Critical Perspectives for Policy-makers. FAO. 2015. p. 62

[26] Pérez Ruzafa A, Marcos C, Pérez Ruzafa IM. 30 años de estudios en la laguna costera del Mar Menor: de la descripción del ecosistema a la comprensión de los procesos y la solución de los problemas ambientales. In: Instituto Euromediterráneo del agua, editor. El Mar Menor: Estado del conocimiento actual. 1st ed. 2009. pp. 18-40

[27] Blázquez MP, Pelegrín GB, Díaz MF. Ficha informativa de los humedales Ramsar. Mar Menor; 2006

[28] Mompeán PA, Vegas Juez AM. Turismo en la región de murcia 2016. Instituto de Turismo de la Región de Murcia: ITREM; 2016

[29] Confederación Hidrográfica del Segura. Embalse de La Pedrera [Internet]. 2018. Available from: https://www.chsegura.es/chs/ cuenca/infraestructuras/embalses/ embalsedelaPedrera/index.html

 [30] FEDEX. Exportación/importación españolas de frutas y hortalizas [Internet]. 2018. Available from: http://www.fepex.es/datos-del-sector/ produccion-frutas-hortalizas

 [31] Candela L, Domingo F, Berbel JAJJ. An overview of the main water *Water Management in Irrigation Systems by Using Satellite Information DOI: http://dx.doi.org/10.5772/intechopen.82368* 

conflicts in Spain: Proposals for problem-solving. In: El Moujabber M, Ouessar M, Laureano P, Rodríguez R, editors. Water Culture and Water Conflict in the Mediterranean Area. Options Méditerranéennes: Série A. Séminaires Méditerranéens; no. 83. 2008. pp. 197-203

[32] Real Decreto 1071/2007, de 27 de julio, por el que se regula el sistema geodésico de referencia oficial en España

[33] Dempsey C. Heat Maps in GIS [Internet]. GIS Lounge. 2012. Available from: https://www.gislounge.com/ heat-maps-in-gis/

[34] Martínez Alvarez V, González-Real MM, Baille A, Maestre Valero JF, Gallego Elvira B. Regional assessment of evaporation from agricultural irrigation reservoirs in a semiarid climate. Agricultural Water Management. 2008;**95**(9):1056-1066

 [35] Toribio MIS. In: Sociedad Española de Geomorfología, editor. Métodos para el estudio de la evaporación y evapotranspiración. 1st ed. Logroño: Geomorga Ediciones; 1992

[36] Gil Meseguer E. Los paisajes agrarios de la región de Murcia. Papeles de Geografía. 2006;**43**:19-30

[37] Instituto Nacional de Estadística. España en cifras 2017. Madrid; 2018

 [38] Victoria Jumilla F. Cambio Climático en la Región de Murcia. Murcia: Consejería de Agricultura y Agua; 2010

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Section 5

Spatial Data for Natural

Features Monitoring

## Section 5
