Land Cover Change Detection on Sentinel-2 Using GDAL and NDVI

Abstract

AbstractLand cover (LC) refers to the ground’s surface cover, which can be plant, urban development, or water, among other things. Land cover detection, delineation, and mapping using ongoing advances in sensor advances have seen an immense measure of exceptionally fine spatial goal (Sentinel-2) distantly detected symbolism being gathered for like clockwork (every five days). The Geospatial Data Abstraction Library (GDAL) is a geospatial programming library that is open source. Working in Python with spatial images enables us to channel the data, fetch the spatial data, catch, circle, and charge the raster or vector datasets with a successful use of the computational power giving a greater degree on information investigation. In the first place, we gather two date changes of Sentinel inferred information to research and guide locales that have gone through the land cover advances that you are keen on checking and planning. After collecting, we clip the Sentinel images to our study area using Rasterio and Fiona. By using Normalized Difference Vegetation Index (NDVI), we create maps of two collected dates of Sentinel-2. Finally, by differencing the resulted NDVI outputs, we create a final land cover change map. This study provided an important addition to the field, land cover change detection through GDAL; it has a lot of potential in a variety of geospatial applications.Keyword ListVegetationGDALSentinelRemote sensingLand cover