Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning By SAGA GIS

Lemenkova, Polina

doi:10.2478/trser-2020-0015

Abstract

Vegetation of Cameroon includes a variety of landscape types with high biodiversity. Ecological monitoring of Yaoundé requires visualization of vegetation types in context of climate change. Vegetation Indices (VIs) derived from Sentinel-2 multispectral satellite image were analyzed in SAGA GIS to separate wetland biomes, as well as savannah and tropical rainforests. The methodology includes computing 6 VIs: NDVI, DVI, SAVI, RVI, TTVI, CTVI. The VIs shown correlation of data with vegetation distribution rising from wetlands, grassland, savanna, and shrub land towards tropical rainforests, increasing values along with canopy greenness, while also being inversely proportional to soils, urban spaces and Sanaga River. The study contributed to the environmental studies of Cameroon and demonstration of the satellite image processing.

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Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning By SAGA GIS

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