Integration of Remote sensing and Google Earth Engine for land use/ land cover change analysis in small agricultural watershed (A case of Brante watershed, Ethiopia)

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Abstract The GEE is a cloud computing platform which provide the infrastructure to access and process large amounts of regularly updated earth observation data rapidly in a systematic using GEE. To classify and map LULC time series (2015-2021) of the Brante watershed supervised classification was performed using Random forest classifier and the image was classified into four classes which are agriculture, forest, grassland and wet land .In the 2015 grassland was the dominant type of Land use classified which covers about 62.5 % of the total study. But in 2021 agriculture was the dominant type of Land use classified which covers about 45.0% of the total study. The accuracy assessment indicate random forest classifier is good to prepare the land use/land cover map of the study area the Brante catchment. Furthermore, it is recommended by combining remote sensed techniques with in situ observations about changes in land use and ground truth observed data, the comprehensiveness of analyses of this study can further be enhanced.

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