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

Transylvanian Review of Systematical and Ecological Research, 22(3), 17-34. DOI: 10.2478/trser-2020-0015

18 Pages Posted: 11 Feb 2021

See all articles by Polina Lemenkova

Polina Lemenkova

Schmidt Institute of Physics of the Earth, Russian Academy of Sciences

Date Written: December 4, 2020

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.

Keywords: Sentinel-2, SAGA GIS, Cameroon, Remote Sensing, Vegetation

JEL Classification: Y92, Q00, Q01, Q20, Q23, Q24, Q25, Q50, Q51, Q54, Q55, C00, C60, C63

Suggested Citation

Lemenkova, Polina, Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GIS (December 4, 2020). Transylvanian Review of Systematical and Ecological Research, 22(3), 17-34. DOI: 10.2478/trser-2020-0015, Available at SSRN: https://ssrn.com/abstract=3742870

Polina Lemenkova (Contact Author)

Schmidt Institute of Physics of the Earth, Russian Academy of Sciences ( email )

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HOME PAGE: http://https://www.researchgate.net/profile/Polina_Lemenkova

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