Land use mapping from sentinel satellite imagery using remote sensing and GIS techniques

Abstract

Abstract The need to classify sentinel-2 satellite images to extract land use /land cover (LULC) are essential to analysis the processes of environment problems and to improve living conditions. Hence, this research aims to create LULC maps from sentinel-2 satellite images through Maximum likelihood (ML) algorithm using remote sensing and GIS. The selected study area for this research is Baghdad city because of it has a unique political stability and due to rapid urbanization, that lead to rise additional request for natural resources and affected on LULC in Baghdad city. After preprocessing and processing of satellite images, thematic maps were created and classified into five main classes based on visual interpretation and visit the field of the study area that containing: urban, vegetation, soil, asphalt roads, and water bodies. The results showed that classification accuracy assessment of ML algorithm is acceptable because of overall accuracy and Kappa index equal (86%, 0.82) respectively.