**1. Introduction**

Monitoring, protecting, and improving the quality of water in lakes and reservoirs is critical for targeting conservation efforts and improving the quality of the environment (Ritchie et al., 1994; Nellis et al., 1998). The standard traditional mapping and monitoring techniques of lakes have already become too expensive compared with the information achieved for environ‐ mental use (Östlund et al., 2001).

Sustainable management of freshwater resources has gained importance at regional (e.g., European Union, 2000) and global scales (United Nations, 2002, 2006; World Water Council, 2006), and 'Integrated Water Resources Management' has become the corresponding scientific paradigm (IPCC, 2007). Water resources, both in terms of quantity and quality, are critically influenced by human activity, including agriculture and land-use change, construction and management of reservoirs, pollutant emissions, and water and wastewa‐ ter treatment (IPCC, 2008).

Traditional water quality monitoring typically involves costly and time consuming insitu boat surveys in which in situ measurements or water samples are collected and returned to laboratory for testing of water quality indicators e.g. chlorophyll-a (indicator of algae) and suspended solids. This method allows accurate measurements within a water body but only at discrete points, they can't give the real-time spatial overview that is necessary for the global assessment and monitoring of water quality (Curran et al., 1987; Wang et al., 2004; Brivio et al., 2001).

The challenge of water-quality management associated with the principle of sustainable development has been of concern to many researchers and managers in the last decade. A variety of models have been developed for supporting missions of water-quality management.

© 2013 Papoutsa and Hadjimitsis; 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 Papoutsa and Hadjimitsis; 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.

Technologies are becoming more and more important for water-quality management, due to the rapid development of computational problem-solving tools and the enhancement of scientific approaches for information support (Huang & Xia, 2001).

average by 35-40%. Cyprus as a semi-arid country with a highly variable climate, it is predicted that there will be increasing water shortages with the growing water demand in the years to come (Iacovides, 2007; Tsiourtis, 1999). It is important to mention that Cyprus lies heavily on water storage in dams to satisfy its water needs. Today in Cyprus there are 108 dams varying from small ponds to major dams. The fact that the storage capacity of surface reservoirs has reached 304,7 million cubic meters (MCM) of water from a mere 6 MCM in 1960, is a truly impressive achievement when compared to other countries of the same size and level of

Remote Sensing for Water Quality Surveillance in Inland Waters: The Case Study of Asprokremmos Dam in Cyprus

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One of the most important challenges for the Cyprus Water Development Department is the implementation of the European Water Framework Directive (WFD; 2000/60/EC) for inland surface waters including the 108 existing reservoirs. WFD aims at achieving "good water status" and establishes a framework for the protection of all waters including inland surface waters, transitional (estuarine) waters, coastal waters and groundwater by 2015 (Mostert, 2003; Borja et al., 2004). Moreover, WFD sets new objectives for the condition of Europe's water and introduces new means and processes for achieving these objectives. Specific details are also given of the monitoring requirements for different types of water, as well as the assessment and monitoring performance quality standards that should be achieved. For these reasons, monitoring is critical to surface water status within the WFD, as it will determine its classifi‐ cation and the necessity for additional measures in order to achieve the objectives in the

Remote sensing technology can become a valuable tool for obtaining information on the processes taking place in the surface of inland water bodies. Satellite remote sensing techniques show more important advantages than traditional sampling with emphasis on the synoptic coverage; it is remarkable that only a single Landsat TM image covers almost all the 108 reservoirs existing in Cyprus; and the high frequency of image captures (Hadjimitsis, 1999; Hadjimitsis et al., 2004a; Hadjimitsis et al., 2010a). Previous studies of using satellite remote sensing in the Cyprus region emphasized the importance of using such techniques for systematic monitoring of water quality either for coastal or inland water bodies due to the good weather conditions in the island (Hadjimitsis et al., 2000; Hadjimitsis et al., 2010a). Moreover remote sensing allows the spatial and temporal assessment of various physical, biological and ecological parameters of water bodies giving the opportunity to examine a large area by applying the suitable algorithm (Hadjimitsis et al., 2006; Hadjimitsis & Clayton, 2009; Papoutsa et al., 2010). Remotely sensed images can give an indication of the physical properties in surface water bodies and can be used to design or improve in-situ sampling monitoring programs by locating appropriate the sampling stations (Dekker et al., 1995). The role of remote sensing technology is there‐ fore under scrutiny, given its potential capacity for systematic observations at scales ranging from local to global and for the provision of data archives extending back over several decades (Rosenqvist et al., 2003). These issues also highlight a need for the exchange

of information between remote sensing scientists and various organizations.

The storage of surface waters in large dams in Cyprus is of vital importance in supplying the local areas for irrigation and potable water supply purposes (Hadjimitsis et al., 2007). At the

development as Cyprus.

Directive (Chen et al., 2004).

The principal benefit of satellite remote sensing for inland water quality monitoring is the production of synoptic views without the need of costly in-situ sampling. Synoptic, multisensor satellite data products and imagery have become increasingly valuable tools for the assessment of water quality in inland and nears-shore coastal waters. Remote sensing of lakes using satellite images has the potential to produce a truly synoptic tool for the monitoring of water quality variables such as chlorophyll a (chl-a), total suspended sediment (TSS), sus‐ pended minerals (SM), turbidity, Secchi Disk Depth (SDD), particulate organic carbon and coloured dissolved organic matter (CDOM) (Allan et al., 2011; Hadjimitsis, 1999; Mayo et al., 1995; Zhang et al., 2002).

Many researchers have attempted to develop algorithms or models for monitoring water quality in different types of inland water bodies from several satellite sensors such as *Landsat MSS, TM or ETM+ data* (Baban, 1993; Mayo et al., 1995; Östlund et al., 2001; Olmanson, Bauer, & Brezonik, 2008; Lillesand et al., 1983; Wang et al., 2004), *SPOT HVR data* (Lathrop & Lillesand 1989; Chacon-Torres et al., 1992; Jensen et al., 1993; Bhatti, Suttinon, & Nasu, 2011), *MODIS data* (Chen, Hu, & Muller-Karger, 2006; Doxaran et al., 2009; Dall'Olmo et al., 2005), *NOAA AVHRR data* (Prangsma & Roozekrans, 1989; Stumpf & Pennock, 1989; Carrick et al., 1994; Woodruff et al., 1999; Ruhl et al., 2001; Chen et al., 2004), *MERIS data* (Ruiz-Verdú et al., 2008; Guanter et al., 2010; Bresciani et al., 2012), *ASTER data* (Kishino et al., 2005; Nas et al., 2009), *IRS-1C data* (Xu et al., 2010; Sheela et al., 2011), *Hyperion data* (Kutser, 2004; Wang et al., 2005; Giardino et al., 2007), *IKONOS and QuickBird data* (Sawaya et al., 2003; Ekercin, 2007; Oyama et al., 2009). Statistical techniques have been used to investigate the correlation between spectral wavebands or waveband combinations and the desired water quality parameters. Predictive equations for water quality parameters have been developed after these correlations have been determined.

This Chapter describes how remote sensing has been used to monitor water quality in large dams in Cyprus using spectroradiometric measurements and satellite imagery.
