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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Main Author: Lemenkova, Polina
Format: Article in Journal/Newspaper
Language:English
Published: Zenodo 2022
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.6347499
https://zenodo.org/record/6347499
id ftdatacite:10.5281/zenodo.6347499
record_format openpolar
spelling 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
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