Affordable Access

Access to the full text

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

Authors
  • Lemenkova, Polina1
  • 1 Laboratory of Regional Geophysics and Natural Disasters, Bolshaya Gruzinskaya Street, 303, 10, 1, RU-123995 , (Russia)
Type
Published Article
Journal
Transylvanian Review of Systematical and Ecological Research
Publisher
Sciendo
Publication Date
Dec 01, 2020
Volume
22
Issue
3
Pages
17–34
Identifiers
DOI: 10.2478/trser-2020-0015
Source
De Gruyter
Keywords
License
Green

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.

Report this publication

Statistics

Seen <100 times