Accurate determination of Land Use/Land Cover (LULC) categories has very important role for environmental monitoring and management applications. Classification of remotely sensed data is one of the popular method to determine LULC information in different scale. Many methods have been developed and applied to classify satellite images. Freely available Sentinel-2 MSI data is new generation remotely sensed data which can be used efficiently to determine the land use and land cover categories for environmental monitoring applications. Sentinel-2 MSI data contains blue, green, red, and near-infrared-1 bands at 10 m; red edge 1 to 3, near-infrared-2, and SWIR 1 and 2 at 20 m; and three atmospheric bands (band 1, band 9, and band 10) at 60 m. In this study, the three atmospheric bands were removed. Sentinel-2A level 1C data acquired in 2018 were downloaded from Earth Explorer web page. In this study, Çatalca District of İstanbul, Turkey was selected as the study area. Çatalca is very important district for İstanbul because of its valuable agricultural fields. Different land use/cover types have been defined in the selected study area such as; water surfaces, forest areas, agricultural fields (sunflowers), open mining area, settlements, and road. Sentinel-2 data gathered in 2018, was classified by maximum likelihood classification (MLC) method to investigate the potential of the data to determine the LULC types in selected region, as the first data set. Beside the original bands, different vegetation indices such as Normalized Difference Vegetation Index (NDVI), Green–red normalized difference vegetation index (GRNDVI), and Normalized difference red-edge index (NDRE) were calculated for Sentinel-2 data. These calculated indices were added to the original bands, and classified as the other data sets. The results of these 4 data sets of Sentinel-2 image were compared based on the field collected ground control data and error matrix.

Anahtar Kelimeler

Sentinel-2 MSI Land use land cover MLC NDVI GRNDVI NDRE

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