Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery
The transition zone between the boreal forest and Arctic tundra, the forest-tundra ecotone (FTE), is an area of high ecological and climatological significance. Despite its importance, a globally consistent high spatial resolution mapping is lacking. Accurate mapping of the FTE requires the use of s...
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ftninstnf:oai:brage.nina.no:11250/2659183 2023-05-15T14:54:47+02:00 Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery Guo, Wenkai Rees, Gareth Hofgaard, Annika Arctic 2020 application/pdf https://hdl.handle.net/11250/2659183 https://doi.org/10.1080/01431161.2020.1734254 eng eng Norges forskningsråd: 260400 Norges forskningsråd: 244557 Andre: Cambridge Trust Andre: University of Cambridge Andre: Fitzwilliam College Andre: Trinity College International Journal of Remote Sensing. 2020, 41 (16), 6384-6408. urn:issn:0143-1161 https://hdl.handle.net/11250/2659183 https://doi.org/10.1080/01431161.2020.1734254 cristin:1816258 6384-6408 41 International Journal of Remote Sensing 16 VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 Peer reviewed Journal article 2020 ftninstnf https://doi.org/10.1080/01431161.2020.1734254 2021-12-23T07:17:09Z The transition zone between the boreal forest and Arctic tundra, the forest-tundra ecotone (FTE), is an area of high ecological and climatological significance. Despite its importance, a globally consistent high spatial resolution mapping is lacking. Accurate mapping of the FTE requires the use of satellite remote sensing data. Here we use the Landsat Vegetation Continuous Fields (VCF) product and Reference point data to derive the location and characteristics of the FTE. An image texture-based supervised classification scheme is developed based on a study area in Central Eurasia to statistically exploit the spatial patterns of the transition zone. Texture statistics for the VCF image are derived from the grey-level co-occurrence matrix (GLCM) based on which the study area is classified into forest, tundra, and FTEs. Adaptive parameterization is implemented to achieve optimal classification performance in the study area. This method is further applied to six additional study areas around the circumarctic region to test its adaptability. In all study areas, this method achieves better FTE delineation results than previously reported methods, showing better classification accuracies (average of 0.826) and more realistic and complete representation of the FTE as shown by visual examination. This shows the universal applicability of the method and it is potential to be used to achieve more detailed and accurate circumarctic mapping of the FTE, which could serve as the basis of time series analysis of FTE positions, eventually contributing to a better understanding of the inter-relations between climate change and shifts in sub-arctic vegetation. acceptedVersion Article in Journal/Newspaper Arctic Climate change Tundra Norwegian Institute for Nature Research: Brage NINA Arctic International Journal of Remote Sensing 41 16 6384 6408 |
institution |
Open Polar |
collection |
Norwegian Institute for Nature Research: Brage NINA |
op_collection_id |
ftninstnf |
language |
English |
topic |
VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 |
spellingShingle |
VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 Guo, Wenkai Rees, Gareth Hofgaard, Annika Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery |
topic_facet |
VDP::Zoologiske og botaniske fag: 480 VDP::Zoology and botany: 480 |
description |
The transition zone between the boreal forest and Arctic tundra, the forest-tundra ecotone (FTE), is an area of high ecological and climatological significance. Despite its importance, a globally consistent high spatial resolution mapping is lacking. Accurate mapping of the FTE requires the use of satellite remote sensing data. Here we use the Landsat Vegetation Continuous Fields (VCF) product and Reference point data to derive the location and characteristics of the FTE. An image texture-based supervised classification scheme is developed based on a study area in Central Eurasia to statistically exploit the spatial patterns of the transition zone. Texture statistics for the VCF image are derived from the grey-level co-occurrence matrix (GLCM) based on which the study area is classified into forest, tundra, and FTEs. Adaptive parameterization is implemented to achieve optimal classification performance in the study area. This method is further applied to six additional study areas around the circumarctic region to test its adaptability. In all study areas, this method achieves better FTE delineation results than previously reported methods, showing better classification accuracies (average of 0.826) and more realistic and complete representation of the FTE as shown by visual examination. This shows the universal applicability of the method and it is potential to be used to achieve more detailed and accurate circumarctic mapping of the FTE, which could serve as the basis of time series analysis of FTE positions, eventually contributing to a better understanding of the inter-relations between climate change and shifts in sub-arctic vegetation. acceptedVersion |
format |
Article in Journal/Newspaper |
author |
Guo, Wenkai Rees, Gareth Hofgaard, Annika |
author_facet |
Guo, Wenkai Rees, Gareth Hofgaard, Annika |
author_sort |
Guo, Wenkai |
title |
Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery |
title_short |
Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery |
title_full |
Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery |
title_fullStr |
Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery |
title_full_unstemmed |
Delineation of the forest-tundra ecotone using texture-based classification of satellite imagery |
title_sort |
delineation of the forest-tundra ecotone using texture-based classification of satellite imagery |
publishDate |
2020 |
url |
https://hdl.handle.net/11250/2659183 https://doi.org/10.1080/01431161.2020.1734254 |
op_coverage |
Arctic |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Tundra |
genre_facet |
Arctic Climate change Tundra |
op_source |
6384-6408 41 International Journal of Remote Sensing 16 |
op_relation |
Norges forskningsråd: 260400 Norges forskningsråd: 244557 Andre: Cambridge Trust Andre: University of Cambridge Andre: Fitzwilliam College Andre: Trinity College International Journal of Remote Sensing. 2020, 41 (16), 6384-6408. urn:issn:0143-1161 https://hdl.handle.net/11250/2659183 https://doi.org/10.1080/01431161.2020.1734254 cristin:1816258 |
op_doi |
https://doi.org/10.1080/01431161.2020.1734254 |
container_title |
International Journal of Remote Sensing |
container_volume |
41 |
container_issue |
16 |
container_start_page |
6384 |
op_container_end_page |
6408 |
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1766326530892365824 |