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A temporal assessment of land-cover changes of the province Beykoz in Istanbul has been documented in this article. The study focuses on the acquisition and analysis of LANDSAT 5 TM images that reflect the drastic land-cover changes between the years 1984 and 2001 utilizing aerial photographs, orthophoto maps, standard topographic maps, and ground truth measurements. The status of the province, including its surrounding villages, were examined together with more specialized maps showing only the city center and part of the province that lies within the watershed of the Elmali Drinking Water Reservoir. The land distribution profiles were also calculated for each of the images to help authorities and decision-makers to better understand the main causes of such remarkable changes and to inform them of the changing quality conditions of the reservoir. Rapid, uncontrolled, and illegal urbanization accompanied by insufficient infrastructure has caused degradation of forests and barren lands in the province, especially within the past two decades. The proximity of the province to the reservoir's watershed, downtown Istanbul, and the transportation network has accelerated the land-cover changes whose adverse impacts on the reservoir water quality are sensed. It is intended that the data gathered and processed in this study will provide a basis for future sustainable urban planning and management activities.

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Soil salinization is one of the severe land-degradation problems due to its adverse effects on land productivity. Each year several hectares of lands are degraded due to primary or secondary soil salinization, and as a result, it is becoming a major economic and environmental concern in different countries. Spatio-temporal mapping of soil salinity is therefore important to support decisionmaking procedures for lessening adverse effects of land degradation due to the salinization. In that sense, satellite-based technologies provide cost effective, fast, qualitative and quantitative spatial information on saline soils.The main objective of this work is to highlight the recent remote sensing (RS) data and methods to assess soil salinity that is a worldwide problem. In addition, this study indicates potential linkages between salt-affected land and the prevailing climatic conditions of the case study areas being examined. Web of science engine is used for selecting relevant articles. "Soil salinity" is used as the main keyword for finding "articles" that are published from . Then, 3 keywords; "remote sensing", "satellite" and "aerial" were used to filter the articles. After that, 100 case studies from 27 different countries were selected. Remote sensing based researches were further overviewed regarding to their location, spatial extent, climate regime, remotely sensed data type, mapping methods, sensing approaches together with the reason of salinity for each case study. In addition, soil salinity mapping methods were examined to present the development of different RS based methods with time. Studies are shown on the Köppen-Geiger climate classification map. Analysis of the map illustrates that 63% of the selected case study areas belong to arid and semi-arid regions. This finding corresponds to soil characteristics of arid regions that are more susceptible to salinization due to extreme temperature, high evaporation rates and low precipitation.

Providing clean water at relevant quality and quantity is a challenge that regulatory authorities have to face in metropolitan cities that seem to develop at their limits of sustainability. Istanbul strives to face such a challenge for its population of over 10 million, through six surface water resources. Two approaches of classification for the reservoirs are presented, one based on current regulations and an alternative based on a more detailed classification. The results have shown that nutrient control is the primary issue, and one of the reservoirs has already exceeded the limits of being eutrophic, one is at mesotrophic conditions, and the remaining four are at the limit of being eutrophic, indicating the significance of making the correct decision and taking pertinent measures for management and control. It has been observed that the only mesotrophic resource, which also has the best general quality class, has no industry and a very low population density, whereas the one that is already eutrophic is also the one with the lowest quality class, has the highest population density, and has the greatest percentage of urban land use within its watershed.

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.