<p><strong>Abstract.</strong> The purpose of the study was to compare performance of the classification methods, that are Rule Based (RB) classifier and Support Vector Machine (SVM), of Planetscope and Worldview-3 satellite images in order to produce land use / cover thematic maps. Six classes, which are deep water, shallow water, vegetation, agricultural area, soil and saline soil, were considered. After performing the classification process, accuracy assessment was employed based on the error matrices. The results showed that, both of the classification methods and satellite data were adequate to classify the area. Besides, classification accuracy was improved when Worldview-3 satellite and SVM method were used. The classification accuracies of RB classification of Planetscope and Worldview-3 were %87 and %94 respectively and the classification accuracies of SVM classification of Planetscope and Worldview-3 were %93 and %96 respectively.</p>

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Remote sensing data provides great opportunities in various steps of watershed management like characterization of watersheds that bear dynamic structure with large land, monitoring the physical variations within the basin, and conducting various scenario analyses to detect the response of the basin. The high resolution capacity of today's satellite images enables the production of land use/cover data of a basin in shorter period of time. In this study, it is aimed to demonstrate various aspects of remote sensing technology to be used in watershed management studies. For that purpose, MODIS, Landsat and Sentinel satellite data with different spatial resolutions were used to monitor the surface water bodies in Konya Closed Basin (KCB) of Turkey. In addition, high spatial Worldview-3 satellite data were used to extract detailed information about Akgol Wetland located in KCB. A methodology was developed on the utilization of remote sensing technology consisting of 3 main groups; field surveys, satellite images and ancillary data. In the study, 5 different spectral indices were applied to Sentinel 2 data to determine the areas of surface water bodies. Moreover, Support Vector Machine (SVM) method was applied to Worldview-3 satellite image to classify Akgol Wetland and its vicinity. The importance of establishing watershed information system together with a database reflecting the characteristics of watersheds was underlined. Various examples were given from KCB that is known as the largest closed basin of the country with a surface area of 5.426.480 ha. The basin owns 17 water bodies out of which 2 of them are RAMSAR sites. Within the scope of the study, information obtained from optical and synthetic aperture radar (SAR) satellite images in the basin were discussed. More accurate results were achieved by Sentinel 2 than MODIS and Landsat data. In addition, detailed information about the wetland were extracted by means of Worldview-3 data and water bodies were monitored in all weather conditions via Sentinel 1 SAR data.

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