**Integrated Remote Sensing and GIS Applications for Sustainable Watershed Management: A Case Study from Cyprus**

Diofantos G. Hadjimitsis, Dimitrios D. Alexakis, Athos Agapiou, Kyriacos Themistocleous, Silas Michaelides and Adrianos Retalis

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/39307

**1. Introduction**

Due to the highly complex nature of both human and physical systems, the ability to com‐ prehend them and model future conditions using a watershed approach has taken a geo‐ graphic dimension. Satellite remote sensing and Geographic Information Systems (GIS) technology have played a critical role in all aspects of watershed management, from assess‐ ing watershed conditions through modeling impacts of human activities to visualizing im‐ pacts of alternative scenarios (Tim & Mallavaram, 2003).

The extreme weather phenomena and global warming noted in recent years has demonstrat‐ ed the necessity for effective flood risk management models. According to this paradigm, a considerable shift has been observed from structural defense against floods to a more com‐ prehensive approach, including appropriate land use, agricultural and forest practices (Alexakis et al., 2013a, 2013b; Barredo & Engelen, 2010; Lilesand & Kiefer, 2010; Michaelides et al., 2009). Land cover changes may be used to describe the dynamics of urban settlements and vegetation patterns as important indicators of urban ecological environments (Yinxin & Linlin, 2010). Satellite remote sensing provides an excellent source of data from which up‐ dated land use / land cover (LULC) changes can be extracted and analysed in an efficient way. In addition, effective monitoring and simulating of the urban sprawl phenomenon and its effects on land-use patterns and hydrological processes within the spatial limits of a wa‐ tershed are essential for effective land-use and water resource planning and management (Hongga et al., 2010; Hadjimitsis et al., 2004a, 2010a, 2010b). Several techniques have been

© 2013 Hadjimitsis et al.; licensee InTech. This is an open access article 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. © 2013 Hadjimitsis et al.; licensee InTech. This is a paper 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.

reported in order to improve classification results in terms of land use discrimination and accuracy of resulting classes in the processing of remotely sensed data (Agapiou et al., 2011). As a result of Very High Resolution (VHR) imagery, real world objects that were previously represented by very few pixels, are now represented by many pixels. Thus, techniques that take into account the spatial properties of an image region need to be developed and ap‐ plied. One such technique is texture analysis (Zhang & Zhu, 2011). Moreover, during the last years, spatial metrics have been largely used in landscape studies. According to Haralick et al. (1973), landscape metrics capture the inherent spatial structure of the environment and are used to enhance interpretation of spatial pattern of the landscape.

The main aim of this chapter is to integrate all the individual remote sensing methodologies related to watershed monitoring and management in a holistic approach. Specifically, differ‐ ent approaches such as development of erosion models, use of radar imagery for the detec‐ tion of areas prone to inundation phenomena, construction of Land Use /Land Cover (LULC) maps, optimization of classification methodologies and calculation of landscape metrics for the recording of urban sprawl will be presented thoroughly and will highlight the contribu‐

Integrated Remote Sensing and GIS Applications for Sustainable Watershed Management: A Case Study from Cyprus

tion of satellite remote sensing to the sustainable management of a catchment area.

Located in the central part of the island of Cyprus, the Yialias basin is about 110 km2

(Fig. 1). This study area is situated between longitudes 33°11´24.28´´ and 33°26´31.52´´ and latitudes 34°54´36.74´´ and 35°2´52.16´´. Cyprus is located in the Northeastern corner of the Mediterranean Sea and, therefore, has a typical eastern Mediterranean climate: the com‐ bined temperature–rainfall regime is characterized by cool-to-mild wet winters and warm-

in size

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http://dx.doi.org/10.5772/39307

**2. Study area**

**Figure 1.** The study area

to-hot dry summers (see Michaelides et al., 2009).

Several techniques have been reported to improve classification results in terms of land use discrimination and accuracy of resulting classes (Eiumnoh & Shrestha, 2000). However, the multispectral images acquired from different satellite sensors suffer from serious problems and errors, such as radiometric distortions, areas with low illumination, physical changes of the environment, etc. Recent studies have found that the accuracy of classification of remote sensing imagery does not increase by improving the applied algorithms, since classification mainly depends upon the physical and chemical parameters of the objects on the ground (Rongqun & Daolin, 2011).

Soil erosion is considered to be a major environmental problem, as it seriously threatens natu‐ ral resources, agriculture and the environment in a catchment area. Spatial and quantitative information of soil erosion contributes significantly to the soil conservation management, ero‐ sion control and general catchment area management (Prasannakumar et al., 2011). In recent years, there has been a growing awareness of the importance of problems directly related to erosion in the broader Mediterranean region. The widespread occurrence and importance of accelerated erosion in the Mediterranean region has driven to the development of models at scales ranging from individual farm fields to vast catchment areas and different types of ad‐ ministrative areas (Bou Kheir et al., 2008). In some parts of the Mediterranean region, erosion has reached a stage of irreversibility, while in some places there is no more soil left (Kouli et al., 2009). Although soil erosion is characterized as a natural phenomenon, human activities such as agriculture can accelerate it further (Karydas et al., 2009).

Recently, space-born microwave active remote sensing, especially Synthetic Aperture Radar (SAR) with its all-weather capability, can provide useful spatially distributed flood informa‐ tion that may be integrated with flood predictive models in the construction of an effective watershed management. Radar imagery is useful for the identification, mapping and meas‐ urement of streams, lakes and inundated areas. Most surface water features are detectable on radar imagery due to the contrast between the smooth water surface and the rough land surface (Lewis, 1998). The amount of moisture stored in the upper soil layer changes the die‐ lectric constant of the material and thus affects the SAR return. Because the dielectric con‐ stant of water is at least 10 times bigger than that of the dry soil, the presence of water in the top few centimeters of bare soil can easily be detected through the use of SAR imagery (Lil‐ lesand & Kiefer, 2000). In addition, the differences in the values between the dielectric con‐ stant of water and of dry soil at the microwave part of the spectrum plays a major role in the soil moisture estimation through the use of microwaves.

The main aim of this chapter is to integrate all the individual remote sensing methodologies related to watershed monitoring and management in a holistic approach. Specifically, differ‐ ent approaches such as development of erosion models, use of radar imagery for the detec‐ tion of areas prone to inundation phenomena, construction of Land Use /Land Cover (LULC) maps, optimization of classification methodologies and calculation of landscape metrics for the recording of urban sprawl will be presented thoroughly and will highlight the contribu‐ tion of satellite remote sensing to the sustainable management of a catchment area.
