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Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GISстатья
- Автор: Lemenkova Polina
- Журнал: Transylvanian Review of Systematical and Ecological Research
- Том: 22
- Номер: 3
- Год издания: 2020
- Издательство: Lucian Blaga University of Sibiu
- Местоположение издательства: Sibiu, Romania
- Первая страница: 17
- Последняя страница: 34
- DOI: 10.2478/trser-2020-0015
- Аннотация: 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.
- Добавил в систему: Леменкова Полина Алексеевна
Прикрепленные файлы
| № | Имя | Описание | Имя файла | Размер | Добавлен |
|---|---|---|---|---|---|
| 1. | Полный текст | Sentinel_2_for_High_Resolution_Mapping_o.pdf | 4,8 МБ | 22 апреля 2021 [polinalemenkova] |