Determination of Environmental Quality of a Drinking Water Reservoir by Remote Sensing, GIS and Regression Analysis

Coskun, H Gonca; Tanik, Aysegul; Alganci, Ugur; Cigizoglu, H Kerem

Determination of Environmental Quality of a Drinking Water Reservoir by Remote Sensing, GIS and Regression Analysis

2008

Coskun, H Gonca | Tanik, Aysegul | Alganci, Ugur | Cigizoglu, H Kerem

Istanbul, housing a population over ten million and with population increase rate of approximately twice that of Turkey, is one of the greatest metropolitan cities of the world. As a consequence of rapid population growth and industrial development, Omerli watershed is highly affected by wastewater discharges from the residential areas and industrial plants. The main objective of this study is to investigate the temporal assessment of the land-use/cover of the Omerli Watershed and the water quality changes in the Reservoir. The study is mainly focused on the acquisition and analysis of the Satellite Probatoire de l'Observation de la Terre (SPOT) (1993), Indian Remote Sensing satellite (IRS) (1996 and 2000) and Landsat Thematic Mapper (TM) (2004, 2005, and 2006) satellite images that reflect the drastic land-use/cover changes utilizing the ground truth measurements. The rapid, uncontrolled, and illegal urbanization coupled with insufficient infrastructure has caused the deterioration of the water quality within the past two decades in the Omerli watershed. The water quality analysis of the drinking water Reservoir within the watershed is investigated using 2006 dated Landsat TM satellite digital data. The results are compiled and compared with the water quality measurements of parameters like total nitrogen (TN), the total phosphorus (TP), chlorophyll a (CL) and total dissolved solids (TDS). The observed reflectance shows a strong relationship with the water quality parameters and thus, the satellite data proved to provide a useful index of TN, TP, CL and TDS. Moreover, the linkage between the water quality parameters and the individual band reflectance values are supported by multiple regression analysis.

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