<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper aims to evaluate the presence and condition of vegetation by SAGA GIS. The study area covers northern coasts of Iceland including two fjords, the Eyjafjörður and the Skagafjörður, prosperous agricultural regions. The vegetation coverage in Iceland experience the impact of harsh climate, land use, livestock grazing, glacial ablation and volcanism. The data include the Landsat TM image. The methodology is based on computing raster bands for simulating Tassel Cap Transformation (wetness, greenness and brightness) and Enhanced Vegetation Index (EVI) sensitive to high biomass. The results include modelled three bands of brightness, greenness and wetness. Greenness variation shows the least values in ice-covered areas (-56.98 to -18.69). High values (-23.48 to 9.12) are in the valleys with dense vegetation, correlating with the geomorphology of the river network, the vegetation-free areas and ocean which corresponds to the peak of 30.87 to 41.19. The bell-shaped data distribution shows frequency 43.19–141.74 for vegetation indicating healthy state and canopy density. Maximal values are in ice-covered regions and glaciers (64°N-65°N). Very low values (0 to -20) show desertification and mountainous rocks. Moderate values (20-40) indicate healthy vegetation. The most frequent data: -28,17 to 11,8. The EVI shows data variations (-0.14 to 0.04). The study contributes both to the regional studies of Arctic Iceland and methodological approach of remote sensing data processing by SAGA GIS.</p>
</abstract>

The paper aims to evaluate the presence and condition of vegetation by SAGA GIS. The study area covers northern coasts of Iceland including two fjords, the Eyjafjörður and the Skagafjörður, prosperous agricultural regions. The vegetation coverage in Iceland experience the impact of harsh climate, land use, livestock grazing, glacial ablation and volcanism. The data include the Landsat TM image. The methodology is based on computing raster bands for simulating Tassel Cap Transformation (wetness, greenness and brightness) and Enhanced Vegetation Index (EVI) sensitive to high biomass. The results include modelled three bands of brightness, greenness and wetness. Greenness variation shows the least values in ice-covered areas (-56.98 to -18.69). High values (-23.48 to 9.12) are in the valleys with dense vegetation, correlating with the geomorphology of the river network, the vegetation-free areas and ocean which corresponds to the peak of 30.87 to 41.19. The bell-shaped data distribution shows frequency 43.19–141.74 for vegetation indicating healthy state and canopy density. Maximal values are in ice-covered regions and glaciers (64°N-65°N). Very low values (0 to -20) show desertification and mountainous rocks. Moderate values (20-40) indicate healthy vegetation. The most frequent data: -28,17 to 11,8. The EVI shows data variations (-0.14 to 0.04). The study contributes both to the regional studies of Arctic Iceland and methodological approach of remote sensing data processing by SAGA GIS.

SAGA GIS for Information Extraction on Presence and Conditions of Vegetation of Northern Coast of Iceland Based on the Landsat TM

Acta Biologica Marisiensis

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The paper aims to evaluate the presence and condition of vegetation by SAGA GIS. The study area covers northern coasts of Iceland including two fjords, the...

SAGA GIS for Information Extraction on Presence and Conditions of Vegetation of Northern Coast of Iceland Based on the Landsat TM

Publié en ligne: 05 janv. 2021

Pages: 10 - 21

Reçu: 17 sept. 2020

Accepté: 20 oct. 2020

DOI: https://doi.org/10.2478/abmj-2020-0007

Mots clés
Iceland, Landsat TM, SAGA GIS, cartography, vegetation index, machine learning, automatization, mapping

© 2020 Polina Lemenkova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

SAGA GIS for Information Extraction on Presence and Conditions of Vegetation of Northern Coast of Iceland Based on the Landsat TM

Publié en ligne: 05 janv. 2021

Pages: 10 - 21

Reçu: 17 sept. 2020

Accepté: 20 oct. 2020

DOI: https://doi.org/10.2478/abmj-2020-0007

Mots clésIceland, Landsat TM, SAGA GIS, cartography, vegetation index, machine learning, automatization, mapping

© 2020 Polina Lemenkova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Iceland, Landsat TM, SAGA GIS, cartography, vegetation index, machine learning, automatization, mapping