Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland
International audience The vegetation indices (VIs) derived from the hyperspectral reflectance of vegetation are presented in this study for monitoring live green vegetation in the northern ecosystems of Iceland, along the fjords of Eyjafjörður and the Skagafjörður. The comparative analysis of the f...
Published in: | Advanced Research in Life Sciences |
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Online Access: | https://doi.org/10.2478/arls-2020-0021 https://hal.archives-ouvertes.fr/hal-03030414/file/25438050%20-%20Advanced%20Research%20in%20Life%20Sciences%20Hyperspectral%20Vegetation%20Indices.pdf https://hal.archives-ouvertes.fr/hal-03030414 |
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fttriple:oai:gotriple.eu:10670/1.6zplwl 2023-05-15T15:13:13+02:00 Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland Lemenkova, Polina Schmidt United Institute of Physics of the Earth Moscow (IPE) Russian Academy of Sciences Moscow (RAS) 2020-11-28 https://doi.org/10.2478/arls-2020-0021 https://hal.archives-ouvertes.fr/hal-03030414/file/25438050%20-%20Advanced%20Research%20in%20Life%20Sciences%20Hyperspectral%20Vegetation%20Indices.pdf https://hal.archives-ouvertes.fr/hal-03030414 en eng HAL CCSD Sciendo hal-03030414 doi:10.2478/arls-2020-0021 10670/1.6zplwl https://hal.archives-ouvertes.fr/hal-03030414/file/25438050%20-%20Advanced%20Research%20in%20Life%20Sciences%20Hyperspectral%20Vegetation%20Indices.pdf https://hal.archives-ouvertes.fr/hal-03030414 lic_creative-commons other Hyper Article en Ligne - Sciences de l'Homme et de la Société EISSN: 2543-8050 Advanced Research in Life Sciences Advanced Research in Life Sciences, Sciendo, 2020, 4 (1), pp.70-78. ⟨10.2478/arls-2020-0021⟩ Landsat TM QGIS NDVI Vegetation Index Cartography geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.2478/arls-2020-0021 2023-01-22T16:37:33Z International audience The vegetation indices (VIs) derived from the hyperspectral reflectance of vegetation are presented in this study for monitoring live green vegetation in the northern ecosystems of Iceland, along the fjords of Eyjafjörður and the Skagafjörður. The comparative analysis of the following VIs was performed: the NDVI, RVI, NRVI, TVI, CTVI, TTVI and SAVI. The methodology is based on the raster calculator band in a QGIS. The dataset includes a Landsat TM scene of 2013, UTM Zone 53, WGS84 captured from the GloVis. The computed bands include the NIR and R spectral bands and their combinations according to the algorithms of each of the seven VIs. The hyperspectral reflectance and crop canopy computations were applied to generate various scales of VIs and demonstrated following data range: NDVI: -0.91 to 0.65, RVI: 0.22 to 19.65, NRVI: 0.63 to 0.90, TVI: 0 to 1.12, CTVI: -0.64 to 1.07, TTVI: 0.70 to 1.18 and SAVI: -1.36 to 0.99 (roughly to 1.00). Of these, the RVI, NRVI, TVI and TTVI are adjusted to the positive values while the NDVI, CTVI and SAVI do include the negative diapason in the dataset due to the computing algorithm. The algorithms of the seven VIs are described and visualized in form of maps based on the multispectral remote sensing Landsat TM imagery identifying vegetated areas, their health condition and distribution of green areas against the bare soils, rocks, ocean water, lakes and ice-covered glaciers. The paper contributes both to the technical presentation of the QGIS functionality for the Landsat TM data processing by a raster calculator, and to the regional geographic studies of Iceland and Arctic ecosystems. Article in Journal/Newspaper Arctic Iceland Unknown Arctic Eyjafjörður ENVELOPE(-18.150,-18.150,65.500,65.500) Skagafjörður ENVELOPE(-19.561,-19.561,65.875,65.875) Advanced Research in Life Sciences 4 1 70 78 |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
Landsat TM QGIS NDVI Vegetation Index Cartography geo envir |
spellingShingle |
Landsat TM QGIS NDVI Vegetation Index Cartography geo envir Lemenkova, Polina Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland |
topic_facet |
Landsat TM QGIS NDVI Vegetation Index Cartography geo envir |
description |
International audience The vegetation indices (VIs) derived from the hyperspectral reflectance of vegetation are presented in this study for monitoring live green vegetation in the northern ecosystems of Iceland, along the fjords of Eyjafjörður and the Skagafjörður. The comparative analysis of the following VIs was performed: the NDVI, RVI, NRVI, TVI, CTVI, TTVI and SAVI. The methodology is based on the raster calculator band in a QGIS. The dataset includes a Landsat TM scene of 2013, UTM Zone 53, WGS84 captured from the GloVis. The computed bands include the NIR and R spectral bands and their combinations according to the algorithms of each of the seven VIs. The hyperspectral reflectance and crop canopy computations were applied to generate various scales of VIs and demonstrated following data range: NDVI: -0.91 to 0.65, RVI: 0.22 to 19.65, NRVI: 0.63 to 0.90, TVI: 0 to 1.12, CTVI: -0.64 to 1.07, TTVI: 0.70 to 1.18 and SAVI: -1.36 to 0.99 (roughly to 1.00). Of these, the RVI, NRVI, TVI and TTVI are adjusted to the positive values while the NDVI, CTVI and SAVI do include the negative diapason in the dataset due to the computing algorithm. The algorithms of the seven VIs are described and visualized in form of maps based on the multispectral remote sensing Landsat TM imagery identifying vegetated areas, their health condition and distribution of green areas against the bare soils, rocks, ocean water, lakes and ice-covered glaciers. The paper contributes both to the technical presentation of the QGIS functionality for the Landsat TM data processing by a raster calculator, and to the regional geographic studies of Iceland and Arctic ecosystems. |
author2 |
Schmidt United Institute of Physics of the Earth Moscow (IPE) Russian Academy of Sciences Moscow (RAS) |
format |
Article in Journal/Newspaper |
author |
Lemenkova, Polina |
author_facet |
Lemenkova, Polina |
author_sort |
Lemenkova, Polina |
title |
Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland |
title_short |
Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland |
title_full |
Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland |
title_fullStr |
Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland |
title_full_unstemmed |
Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland |
title_sort |
hyperspectral vegetation indices calculated by qgis using landsat tm image: a case study of northern iceland |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://doi.org/10.2478/arls-2020-0021 https://hal.archives-ouvertes.fr/hal-03030414/file/25438050%20-%20Advanced%20Research%20in%20Life%20Sciences%20Hyperspectral%20Vegetation%20Indices.pdf https://hal.archives-ouvertes.fr/hal-03030414 |
long_lat |
ENVELOPE(-18.150,-18.150,65.500,65.500) ENVELOPE(-19.561,-19.561,65.875,65.875) |
geographic |
Arctic Eyjafjörður Skagafjörður |
geographic_facet |
Arctic Eyjafjörður Skagafjörður |
genre |
Arctic Iceland |
genre_facet |
Arctic Iceland |
op_source |
Hyper Article en Ligne - Sciences de l'Homme et de la Société EISSN: 2543-8050 Advanced Research in Life Sciences Advanced Research in Life Sciences, Sciendo, 2020, 4 (1), pp.70-78. ⟨10.2478/arls-2020-0021⟩ |
op_relation |
hal-03030414 doi:10.2478/arls-2020-0021 10670/1.6zplwl https://hal.archives-ouvertes.fr/hal-03030414/file/25438050%20-%20Advanced%20Research%20in%20Life%20Sciences%20Hyperspectral%20Vegetation%20Indices.pdf https://hal.archives-ouvertes.fr/hal-03030414 |
op_rights |
lic_creative-commons other |
op_doi |
https://doi.org/10.2478/arls-2020-0021 |
container_title |
Advanced Research in Life Sciences |
container_volume |
4 |
container_issue |
1 |
container_start_page |
70 |
op_container_end_page |
78 |
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1766343804085862400 |