SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness
The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural si...
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ftdatacite:10.5281/zenodo.4835821 2023-05-15T15:17:14+02:00 SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness Lemenkova, Polina 2021 https://dx.doi.org/10.5281/zenodo.4835821 https://zenodo.org/record/4835821 en eng Zenodo https://dx.doi.org/10.2478/contagri-2021-0011 https://dx.doi.org/10.5281/zenodo.4835822 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY machine learning data analysis remote sensing GIS geoinformatics big data Iceland vegetation indices NDVI SAGA GIS cartography mapping environment sustainable development Text Journal article article-journal ScholarlyArticle 2021 ftdatacite https://doi.org/10.5281/zenodo.4835821 https://doi.org/10.2478/contagri-2021-0011 https://doi.org/10.5281/zenodo.4835822 2021-11-05T12:55:41Z The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural significance. Vegetation in Iceland is fragile due to the harsh climate, climate change, overgrazing and volcanic activity, which increase soil erosion. The study was conducted on a Landsat TM image using SAGA GIS as a technical tool for raster bands calculations. The NDVI dataset shows a range from -0.56 to 0.24, with 0 indicating ‘no vegetation’, and negative values – ‘other surfaces’ (e.g. rocks, open terrain). The DVI, compared to the NDVI, shows statistically non-normalized values ranging from - 112 to 0, with extreme negative values while the coastal vegetation areas are badly distinguished from the water areas. The NRVI shows an extent from -0.24 to 0.48 with higher values for vegetation. The NRVI reduces topographic, solar and atmospheric effects and creates a normal data distribution. RVI shows a range in a dataset from 0.2 to 3.2 with vegetation in the river valleys clearly visible and depicted, while the water areas have values 0.8 to 1.0. The CTVI shows corrected TVI, in a data range -0.10 to 1.10, as the dataset of NDVI were negative. The TVI dataset ranges from 0.44 to 0.80 with the ice-covered areas and glaciers distinguishable and water values within a range from 0.60 to 0.64 and the vegetation from 0.60 to 0.44. The TTVI dataset ranges from 0.40 to 0.80 performing similarly to the TVI, but more refined with vegetation values 0.64 to 0.68. SAVI dataset ranges from -0.80 to 0.30 with minimized effects of soil on the vegetation through a constant soil adjustment factor added into the NDVI formula. The paper presents a comparison of eight VIs for Arctic vegetation monitoring. The overall behavior of SAGA GIS in calculation and mapping of the VIs is effective in terms of their use for vegetation mapping of the region. Text Arctic Climate change Iceland ice covered areas DataCite Metadata Store (German National Library of Science and Technology) Arctic Skagafjörður ENVELOPE(-19.561,-19.561,65.875,65.875) Eyjafjörður ENVELOPE(-18.150,-18.150,65.500,65.500) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
machine learning data analysis remote sensing GIS geoinformatics big data Iceland vegetation indices NDVI SAGA GIS cartography mapping environment sustainable development |
spellingShingle |
machine learning data analysis remote sensing GIS geoinformatics big data Iceland vegetation indices NDVI SAGA GIS cartography mapping environment sustainable development Lemenkova, Polina SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness |
topic_facet |
machine learning data analysis remote sensing GIS geoinformatics big data Iceland vegetation indices NDVI SAGA GIS cartography mapping environment sustainable development |
description |
The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural significance. Vegetation in Iceland is fragile due to the harsh climate, climate change, overgrazing and volcanic activity, which increase soil erosion. The study was conducted on a Landsat TM image using SAGA GIS as a technical tool for raster bands calculations. The NDVI dataset shows a range from -0.56 to 0.24, with 0 indicating ‘no vegetation’, and negative values – ‘other surfaces’ (e.g. rocks, open terrain). The DVI, compared to the NDVI, shows statistically non-normalized values ranging from - 112 to 0, with extreme negative values while the coastal vegetation areas are badly distinguished from the water areas. The NRVI shows an extent from -0.24 to 0.48 with higher values for vegetation. The NRVI reduces topographic, solar and atmospheric effects and creates a normal data distribution. RVI shows a range in a dataset from 0.2 to 3.2 with vegetation in the river valleys clearly visible and depicted, while the water areas have values 0.8 to 1.0. The CTVI shows corrected TVI, in a data range -0.10 to 1.10, as the dataset of NDVI were negative. The TVI dataset ranges from 0.44 to 0.80 with the ice-covered areas and glaciers distinguishable and water values within a range from 0.60 to 0.64 and the vegetation from 0.60 to 0.44. The TTVI dataset ranges from 0.40 to 0.80 performing similarly to the TVI, but more refined with vegetation values 0.64 to 0.68. SAVI dataset ranges from -0.80 to 0.30 with minimized effects of soil on the vegetation through a constant soil adjustment factor added into the NDVI formula. The paper presents a comparison of eight VIs for Arctic vegetation monitoring. The overall behavior of SAGA GIS in calculation and mapping of the VIs is effective in terms of their use for vegetation mapping of the region. |
format |
Text |
author |
Lemenkova, Polina |
author_facet |
Lemenkova, Polina |
author_sort |
Lemenkova, Polina |
title |
SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness |
title_short |
SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness |
title_full |
SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness |
title_fullStr |
SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness |
title_full_unstemmed |
SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness |
title_sort |
saga gis for computing multispectral vegetation indices by landsat tm for mapping vegetation greenness |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://dx.doi.org/10.5281/zenodo.4835821 https://zenodo.org/record/4835821 |
long_lat |
ENVELOPE(-19.561,-19.561,65.875,65.875) ENVELOPE(-18.150,-18.150,65.500,65.500) |
geographic |
Arctic Skagafjörður Eyjafjörður |
geographic_facet |
Arctic Skagafjörður Eyjafjörður |
genre |
Arctic Climate change Iceland ice covered areas |
genre_facet |
Arctic Climate change Iceland ice covered areas |
op_relation |
https://dx.doi.org/10.2478/contagri-2021-0011 https://dx.doi.org/10.5281/zenodo.4835822 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.4835821 https://doi.org/10.2478/contagri-2021-0011 https://doi.org/10.5281/zenodo.4835822 |
_version_ |
1766347489673216000 |