Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image
The paper presents the application of ArcGIS for environmental modelling of the landscapes in northern Iceland (17.00°W–23.00°W, 64.30°N–67.00°N). The aim was to explore the vegetation distribution by NDVI and ISOCLUST classification of the land cover types. Data include the Landsat TM image. Freely...
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ftdatacite:10.5281/zenodo.6347499 2023-05-15T15:12:17+02:00 Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image Lemenkova, Polina 2022 https://dx.doi.org/10.5281/zenodo.6347499 https://zenodo.org/record/6347499 en eng Zenodo https://dx.doi.org/10.15576/gll/2021.4.147 https://dx.doi.org/10.5281/zenodo.6347500 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 cartography Iceland remote sensing Arctic ArcGIS mapping image processing sustainable development environmental sustainability image analysis computer science article-journal ScholarlyArticle JournalArticle 2022 ftdatacite https://doi.org/10.5281/zenodo.6347499 https://doi.org/10.15576/gll/2021.4.147 https://doi.org/10.5281/zenodo.6347500 2022-04-01T13:31:07Z The paper presents the application of ArcGIS for environmental modelling of the landscapes in northern Iceland (17.00°W–23.00°W, 64.30°N–67.00°N). The aim was to explore the vegetation distribution by NDVI and ISOCLUST classification of the land cover types. Data include the Landsat TM image. Freely available satellite remote sensing data from the Landsat mission have been processed by GIS to deliver information on land cover types from image classification and NDVI vegetation index. Landsat products provide geospatial data on regional scale with moderate temporal (weekly) and spatial (30–10 m) resolution, making them useful for environmental monitoring and landscape studies. The tools include the ArcGIS software used for raster processing. Data processing was performed in the three steps: 1) comparative analysis of the visualized sixteen band combinations to assess the distinguishability of vegetation and other land cover types in colour composites; 2) computed NDVI standardized vegetation index; 3) unsupervised classification of the Landsat TM by the ISOCLUST algorithm. Large glaciers Hofsjökull and Langjökull were detected on various colour composites, and the visibility of the water/land borders is assessed (Blöndulón lake), agricultural areas near the Varmahlíð, vegetated areas around the Akrahreppur municipality. Computing the NDVI and using ISOCLUST by ArcGIS software enabled to distinguish various land cover types and map landscapes in the study area. The computed NDVI shown the presence and condition of vegetation, that is, a relative biomass in the area of northern Iceland. The NDVI was used based on the contrast of the two channels from a multispectral Landsat TM raster data. Article in Journal/Newspaper Arctic Blöndulón Hofsjökull Iceland Langjökull DataCite Metadata Store (German National Library of Science and Technology) Arctic Langjökull ENVELOPE(-20.145,-20.145,64.654,64.654) Blöndulón ENVELOPE(-19.649,-19.649,65.181,65.181) Akrahreppur ENVELOPE(-18.796,-18.796,65.355,65.355) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
cartography Iceland remote sensing Arctic ArcGIS mapping image processing sustainable development environmental sustainability image analysis computer science |
spellingShingle |
cartography Iceland remote sensing Arctic ArcGIS mapping image processing sustainable development environmental sustainability image analysis computer science Lemenkova, Polina Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image |
topic_facet |
cartography Iceland remote sensing Arctic ArcGIS mapping image processing sustainable development environmental sustainability image analysis computer science |
description |
The paper presents the application of ArcGIS for environmental modelling of the landscapes in northern Iceland (17.00°W–23.00°W, 64.30°N–67.00°N). The aim was to explore the vegetation distribution by NDVI and ISOCLUST classification of the land cover types. Data include the Landsat TM image. Freely available satellite remote sensing data from the Landsat mission have been processed by GIS to deliver information on land cover types from image classification and NDVI vegetation index. Landsat products provide geospatial data on regional scale with moderate temporal (weekly) and spatial (30–10 m) resolution, making them useful for environmental monitoring and landscape studies. The tools include the ArcGIS software used for raster processing. Data processing was performed in the three steps: 1) comparative analysis of the visualized sixteen band combinations to assess the distinguishability of vegetation and other land cover types in colour composites; 2) computed NDVI standardized vegetation index; 3) unsupervised classification of the Landsat TM by the ISOCLUST algorithm. Large glaciers Hofsjökull and Langjökull were detected on various colour composites, and the visibility of the water/land borders is assessed (Blöndulón lake), agricultural areas near the Varmahlíð, vegetated areas around the Akrahreppur municipality. Computing the NDVI and using ISOCLUST by ArcGIS software enabled to distinguish various land cover types and map landscapes in the study area. The computed NDVI shown the presence and condition of vegetation, that is, a relative biomass in the area of northern Iceland. The NDVI was used based on the contrast of the two channels from a multispectral Landsat TM raster data. |
format |
Article in Journal/Newspaper |
author |
Lemenkova, Polina |
author_facet |
Lemenkova, Polina |
author_sort |
Lemenkova, Polina |
title |
Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image |
title_short |
Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image |
title_full |
Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image |
title_fullStr |
Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image |
title_full_unstemmed |
Robust Vegetation Detection Using RGB Colour Composites and Isoclust Classification of the Landsat TM Image |
title_sort |
robust vegetation detection using rgb colour composites and isoclust classification of the landsat tm image |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://dx.doi.org/10.5281/zenodo.6347499 https://zenodo.org/record/6347499 |
long_lat |
ENVELOPE(-20.145,-20.145,64.654,64.654) ENVELOPE(-19.649,-19.649,65.181,65.181) ENVELOPE(-18.796,-18.796,65.355,65.355) |
geographic |
Arctic Langjökull Blöndulón Akrahreppur |
geographic_facet |
Arctic Langjökull Blöndulón Akrahreppur |
genre |
Arctic Blöndulón Hofsjökull Iceland Langjökull |
genre_facet |
Arctic Blöndulón Hofsjökull Iceland Langjökull |
op_relation |
https://dx.doi.org/10.15576/gll/2021.4.147 https://dx.doi.org/10.5281/zenodo.6347500 |
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.6347499 https://doi.org/10.15576/gll/2021.4.147 https://doi.org/10.5281/zenodo.6347500 |
_version_ |
1766342990797733888 |