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 webinar 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. An analysis of deforestation is performed based on the output data and online satellite imagery.
Intructor
Saul Montoya M.Sc
Hydrogeologist - Numerical Modeler
Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed an holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.
Event date
Friday, Abr 28 2022 6:00 p.m. Amsterdam Time
