Altitudinal forest-tundra ecotone categorization using texture-based classification
This study proposes a new technique involving texture-based image classification to categorize altitudinal FTEs by the degree of fragmentation of the interface. This allows a) universally adaptable altitudinal FTE categorization based on widely available satellite data products and b) assessment of...
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ftunivcam:oai:www.repository.cam.ac.uk:1810/294673 2024-02-04T10:01:53+01:00 Altitudinal forest-tundra ecotone categorization using texture-based classification Guo, W Rees, WG 2019 Undetermined application/vnd.openxmlformats-officedocument.wordprocessingml.document https://www.repository.cam.ac.uk/handle/1810/294673 https://doi.org/10.17863/CAM.41778 eng eng Elsevier BV http://dx.doi.org/10.1016/j.rse.2019.111312 Remote Sensing of Environment https://www.repository.cam.ac.uk/handle/1810/294673 doi:10.17863/CAM.41778 Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/ Altitudinal forest-tundra ecotone Sentinel-2 Fourier-based textural ordination (FOTO) Image classification Article 2019 ftunivcam https://doi.org/10.17863/CAM.41778 2024-01-11T23:32:59Z This study proposes a new technique involving texture-based image classification to categorize altitudinal FTEs by the degree of fragmentation of the interface. This allows a) universally adaptable altitudinal FTE categorization based on widely available satellite data products and b) assessment of sensitivity of altitudinal FTEs to shift with climate change at different locations based on the spatial distribution of the corresponding categories. The FTE categorization scheme used in this study corresponds partly to the globally occurring primary altitudinal FTE ‘forms.’ Specifically, ‘diffuse’ and ‘abrupt’ FTEs are recognized and separated. Normalized Difference Vegetation Index (NDVI) calculated from Sentinel-2 imagery is used for FTE delineation and categorization. A technique named FOurier-based Textural Ordination (FOTO) is implemented to extract textural information based on NDVI variations in image windows, and supervised classification is used to further separate these windows into FTE categories based on texture. The analysis is conducted on part of the Khibiny Mountains, Kola Peninsula, Russia, and further tested on six other study areas spread across the circumarctic region. The proposed method is able to adapt to different study areas with minimum changes in parameterization, and effectively extract altitudinal FTEs and categorize them into different FTE forms with satisfactory accuracies. Cambridge Trusts, Trinity College Cambridge, Fitzwilliam College Cambridge, China Scholarship Council Article in Journal/Newspaper kola peninsula Tundra Apollo - University of Cambridge Repository Kola Peninsula Khibiny ENVELOPE(33.210,33.210,67.679,67.679) |
institution |
Open Polar |
collection |
Apollo - University of Cambridge Repository |
op_collection_id |
ftunivcam |
language |
English |
topic |
Altitudinal forest-tundra ecotone Sentinel-2 Fourier-based textural ordination (FOTO) Image classification |
spellingShingle |
Altitudinal forest-tundra ecotone Sentinel-2 Fourier-based textural ordination (FOTO) Image classification Guo, W Rees, WG Altitudinal forest-tundra ecotone categorization using texture-based classification |
topic_facet |
Altitudinal forest-tundra ecotone Sentinel-2 Fourier-based textural ordination (FOTO) Image classification |
description |
This study proposes a new technique involving texture-based image classification to categorize altitudinal FTEs by the degree of fragmentation of the interface. This allows a) universally adaptable altitudinal FTE categorization based on widely available satellite data products and b) assessment of sensitivity of altitudinal FTEs to shift with climate change at different locations based on the spatial distribution of the corresponding categories. The FTE categorization scheme used in this study corresponds partly to the globally occurring primary altitudinal FTE ‘forms.’ Specifically, ‘diffuse’ and ‘abrupt’ FTEs are recognized and separated. Normalized Difference Vegetation Index (NDVI) calculated from Sentinel-2 imagery is used for FTE delineation and categorization. A technique named FOurier-based Textural Ordination (FOTO) is implemented to extract textural information based on NDVI variations in image windows, and supervised classification is used to further separate these windows into FTE categories based on texture. The analysis is conducted on part of the Khibiny Mountains, Kola Peninsula, Russia, and further tested on six other study areas spread across the circumarctic region. The proposed method is able to adapt to different study areas with minimum changes in parameterization, and effectively extract altitudinal FTEs and categorize them into different FTE forms with satisfactory accuracies. Cambridge Trusts, Trinity College Cambridge, Fitzwilliam College Cambridge, China Scholarship Council |
format |
Article in Journal/Newspaper |
author |
Guo, W Rees, WG |
author_facet |
Guo, W Rees, WG |
author_sort |
Guo, W |
title |
Altitudinal forest-tundra ecotone categorization using texture-based classification |
title_short |
Altitudinal forest-tundra ecotone categorization using texture-based classification |
title_full |
Altitudinal forest-tundra ecotone categorization using texture-based classification |
title_fullStr |
Altitudinal forest-tundra ecotone categorization using texture-based classification |
title_full_unstemmed |
Altitudinal forest-tundra ecotone categorization using texture-based classification |
title_sort |
altitudinal forest-tundra ecotone categorization using texture-based classification |
publisher |
Elsevier BV |
publishDate |
2019 |
url |
https://www.repository.cam.ac.uk/handle/1810/294673 https://doi.org/10.17863/CAM.41778 |
long_lat |
ENVELOPE(33.210,33.210,67.679,67.679) |
geographic |
Kola Peninsula Khibiny |
geographic_facet |
Kola Peninsula Khibiny |
genre |
kola peninsula Tundra |
genre_facet |
kola peninsula Tundra |
op_relation |
https://www.repository.cam.ac.uk/handle/1810/294673 doi:10.17863/CAM.41778 |
op_rights |
Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.17863/CAM.41778 |
_version_ |
1789968115815153664 |