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Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GIS. / Леменкова, Полина Алексеевна.

в: Transylvanian Review of Systematical and Ecological Research, Том 22, № 3, 04.12.2020, стр. 17-34.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Леменкова, ПА 2020, 'Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GIS', Transylvanian Review of Systematical and Ecological Research, Том. 22, № 3, стр. 17-34. https://doi.org/10.2478/trser-2020-0015

APA

Леменкова, П. А. (2020). 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. https://doi.org/10.2478/trser-2020-0015

Vancouver

Леменкова ПА. Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GIS. Transylvanian Review of Systematical and Ecological Research. 2020 Дек. 4;22(3):17-34. https://doi.org/10.2478/trser-2020-0015

Author

Леменкова, Полина Алексеевна. / Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GIS. в: Transylvanian Review of Systematical and Ecological Research. 2020 ; Том 22, № 3. стр. 17-34.

BibTeX

@article{cd62215a39d64bf2b2a3a0129590d5bd,
title = "Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GIS",
abstract = "Vegetation of Cameroon includes a variety of landscape types with high biodiversity. Ecological monitoring of Yaound{\'e} 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",
author = "Леменкова, {Полина Алексеевна}",
year = "2020",
month = dec,
day = "4",
doi = "10.2478/trser-2020-0015",
language = "English",
volume = "22",
pages = "17--34",
journal = "Transylvanian Review of Systematical and Ecological Research",
issn = "2344-3219",
number = "3",

}

RIS

TY - JOUR

T1 - Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning by SAGA GIS

AU - Леменкова, Полина Алексеевна

PY - 2020/12/4

Y1 - 2020/12/4

N2 - 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.

AB - 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.

KW - Sentinel-2

KW - SAGA GIS

KW - Cameroon

KW - remote sensing

KW - vegetation

U2 - 10.2478/trser-2020-0015

DO - 10.2478/trser-2020-0015

M3 - Article

VL - 22

SP - 17

EP - 34

JO - Transylvanian Review of Systematical and Ecological Research

JF - Transylvanian Review of Systematical and Ecological Research

SN - 2344-3219

IS - 3

ER -

ID: 134181744