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Land Cover Change Analysis with Python and Rasterio - Tutorial

Satellite imagery brought us the capacity to see the land surface on recent years but we haven’t been so successful to understand land cover dynamics and the interaction with economical, sociological and political factors. Some deficiencies for this analysis were found on the use of GIS commercial software, but there are other limitations in the way we apply logical and mathematical processes to a set of satellite imagery. Working with geospatial data on Python gives us the capability to filter, calculate, clip, loop, and export raster or vector datasets with an efficient use of the computational power providing a bigger scope on data analysis. This tutorial covers the complete procedure to create a land cover change raster from a comparison of generated vegetation index (NDVI) rasters by the use of Python and the Numpy and Rasterio libraries. Results of the NDVI for given years and NDVI change are plotted on Jupyter Lab as color grid and contour grid. Input data You can download the input data from this link: https://hatarilabs.com/ih-en/land-cov...