Spatio-temporal assessment of land use and cover classification with Google Earth Engine

Published: 22 February 2024| Version 1 | DOI: 10.17632/9hcwfgf6rs.1


Assessment of land use land cover (LULC) classification in a tropical region (Pra River Basin, Ghana) in West Africa experiencing exponential Anthropocene. Five classes were mapped out. Script used was based on shapefile and Landsat images which were imported into GEE after pre processing in QGIS. Indicators such as Bare Soil Index (BSI), Normalized Difference vegetation index (NDVI), Modified Normalized Difference Water Index (MNDWI) were applied to improve on the accuracy of the classes. Adopting new approaches to LULC does not only improve the understanding of GIS and Remote sensing in environmental management, it also less time consuming relative to the older approaches.



Kwame Nkrumah University of Science and Technology, Universite d'Abomey-Calavi, West African Science Service Centre on Climate Change and Adapted Land Use


Remote Sensing, Mapping Land Use, Land Use Change, Landsat-8


Bundesministerium für Bildung und Forschung