EVALUATION OF SENTINEL-2 MSI DATA FOR LAND USE / LAND COVER CLASSIFICATION USING DIFFERENT VEGETATION INDICES

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. In this study, Sentinel-2A level 1C data acquired in July 2018 were downloaded from Earth Explorer web page. A test site from 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 four bands with 10 m spatial resolution 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), were calculated for Sentinel-2 data. These calculated indices and red-edge band 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. Sentinel-2 data had a satisfactory performance in land cover classification; (the overall classification accuracy using the MLC classifier applied data set 2 was higher than the other three data set).