Classification Landsat-8Sentinel-2 Surface Water Cropland Urban and Bare land In recent years, Kabul city's rapid urbanization has adversely affected the urban land cover, such as surface water bodies and croplands. Surface water resources are threatened due to overpopulation in the city either qualitatively or quantitatively, also croplands are being lost with the development of urbanization activities through the city. To monitor and assess surface changes accurately, we classified the city area using satellite images of both Landsat-8 and Sentinel-2 and compared both of their findings. The Support Vector Machine classifier was applied to multi-senor data to classify four different land categories using the same training sites and samples with the same period. All the procedures were conducted in Google Earth Engine (GEE) cloud platform. The surface reflectance bands of both satellites were used for classification. Confusion matrixes were created using the same reference points for Sentinel-2 and Landsat-8 classification to compare the results and determine the best approach for classification of land cover. Results show that overall accuracy was 94.26% for Sentinel-2 while it was 85.04% for Landsat-8, similarly, the Kappa coefficient was calculated 91.7% and 78.3% for Sentinel-2 and Landsat-8, respectively.
The density of meteorological stations in most watersheds across the globe is far lower than recommended by the World Meteorological Organization (WMO). However, for some basins, including those used as pilot, an adequate quantity of weather stations is crucial for collecting high-accuracy data. This study aimed to 1) estimate the optimum number of meteorological stations and 2) demarcate the most appropriate sites for their installation considering physical and environmental factors directly and indirectly influencing both objectives, i.e. to develop a well-optimized weather station network. The Weighted Overlay method and six (6) environmental factors –- precipitation variance, slope, elevation, proximity of existing stations, land cover and land use, as well as distance from roads –- were applied to delineate the potential locations. All parameters were mapped out separately and then reclassified for scoring (0 to 100 scale) based on their significance. The Analytic Hierarchy Process (AHP) method was applied to determine the impact of each factor. Based on the analysis, the precipitation variance received 38% weight, while the distance from road was computed to reach only 3% weight. The Weighted Overlay map of the Karasu Watershed was delineated into corresponding highly suitable, moderately suitable, suitable, marginally suitable, and not suitable zones. Finally, the recommended station locations were validated using a hypsometric curve to ensure proper coverage of different elevations. The research will improve the climate change and water resource management applications by informing them with sufficient climatic data about the entire target area including all variations, as well as will help addressing the challenge of data shortage and thus increase the quality of future thematic research.
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