Sentinel-2 data classification for land use land cover mapping in northern Algeria

Abstract

The study of land use and land cover (LULC) changes is essential to understand the impact of human activities on the environment. The North of Algeria is a region that experiences high rates of change in LULC, making it a suitable study area. In this research, the potential of Sentinel-2 attributes for LULC classification in this region is evaluated using a deep learning-based approach. To improve the efficiency of the model, six reflectance-based indices are calculated to highlight the region of interest. The results are compared to the USGS land cover change data and show promising LULC change detection. In order to verify the presence of missed classes in our land use/land cover classification results, we employed a CNN-object detection method using high-resolution Planetscope images. This study demonstrates the potential of Sentinel-2 attributes for accurate LULC classification and change detection in the North of Algeria, which can be useful for monitoring land use patterns and planning sustainable land management practices.