Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut
Thematic maps developed from remote sensing data are extremely useful for designing intensive field studies, particularly for large areas that are logistically challenging to access. The integrated watershed studies at the Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut, r...
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ftdoajarticles:oai:doaj.org/article:bbe7eed90f4a43c291673ab7955ee611 2023-05-15T14:23:43+02:00 Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut Jacqueline K.Y. Hung Paul Treitz 2020-12-01T00:00:00Z https://doi.org/10.1139/as-2019-0029 https://doaj.org/article/bbe7eed90f4a43c291673ab7955ee611 EN FR eng fre Canadian Science Publishing https://doi.org/10.1139/as-2019-0029 https://doaj.org/toc/2368-7460 doi:10.1139/as-2019-0029 2368-7460 https://doaj.org/article/bbe7eed90f4a43c291673ab7955ee611 Arctic Science, Vol 6, Iss 4, Pp 404-422 (2020) remote sensing environmental land-cover classification non-parametric support vector machine high arctic Environmental sciences GE1-350 Environmental engineering TA170-171 article 2020 ftdoajarticles https://doi.org/10.1139/as-2019-0029 2022-12-31T10:10:09Z Thematic maps developed from remote sensing data are extremely useful for designing intensive field studies, particularly for large areas that are logistically challenging to access. The integrated watershed studies at the Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut, rely heavily on land cover for establishing sampling locations regardless of the type of research being conducted (e.g., permafrost degradation, greenhouse gas exchange, surface water chemistry, etc.). Here, we present an environmental land-cover classification of the CBAWO that was developed through an iterative process employing parametric and non-parametric classification algorithms applied to WorldView-2 satellite data and topographic variables. The support vector machine classification of eight-band WorldView-2 spectral data and a topographic wetness index produced the highest classification accuracy for eight land-cover classes (overall classification accuracy: 90.7%; Kappa coefficient (κ): 0.89). This analysis also provided a more precise classification scheme, particularly in the context of the relationship between vegetation type and moisture regime. The environmental land-cover classification derived will better inform future integrated studies of the watershed and allow for upscaling of site-level characteristics to the watershed-scale using the updated vegetation classes. Article in Journal/Newspaper Arctic Arctic Nunavut permafrost Melville Island Directory of Open Access Journals: DOAJ Articles Arctic Cape Bounty ENVELOPE(-109.542,-109.542,74.863,74.863) Nunavut Arctic Science 6 4 404 422 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English French |
topic |
remote sensing environmental land-cover classification non-parametric support vector machine high arctic Environmental sciences GE1-350 Environmental engineering TA170-171 |
spellingShingle |
remote sensing environmental land-cover classification non-parametric support vector machine high arctic Environmental sciences GE1-350 Environmental engineering TA170-171 Jacqueline K.Y. Hung Paul Treitz Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut |
topic_facet |
remote sensing environmental land-cover classification non-parametric support vector machine high arctic Environmental sciences GE1-350 Environmental engineering TA170-171 |
description |
Thematic maps developed from remote sensing data are extremely useful for designing intensive field studies, particularly for large areas that are logistically challenging to access. The integrated watershed studies at the Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut, rely heavily on land cover for establishing sampling locations regardless of the type of research being conducted (e.g., permafrost degradation, greenhouse gas exchange, surface water chemistry, etc.). Here, we present an environmental land-cover classification of the CBAWO that was developed through an iterative process employing parametric and non-parametric classification algorithms applied to WorldView-2 satellite data and topographic variables. The support vector machine classification of eight-band WorldView-2 spectral data and a topographic wetness index produced the highest classification accuracy for eight land-cover classes (overall classification accuracy: 90.7%; Kappa coefficient (κ): 0.89). This analysis also provided a more precise classification scheme, particularly in the context of the relationship between vegetation type and moisture regime. The environmental land-cover classification derived will better inform future integrated studies of the watershed and allow for upscaling of site-level characteristics to the watershed-scale using the updated vegetation classes. |
format |
Article in Journal/Newspaper |
author |
Jacqueline K.Y. Hung Paul Treitz |
author_facet |
Jacqueline K.Y. Hung Paul Treitz |
author_sort |
Jacqueline K.Y. Hung |
title |
Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut |
title_short |
Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut |
title_full |
Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut |
title_fullStr |
Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut |
title_full_unstemmed |
Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut |
title_sort |
environmental land-cover classification for integrated watershed studies: cape bounty, melville island, nunavut |
publisher |
Canadian Science Publishing |
publishDate |
2020 |
url |
https://doi.org/10.1139/as-2019-0029 https://doaj.org/article/bbe7eed90f4a43c291673ab7955ee611 |
long_lat |
ENVELOPE(-109.542,-109.542,74.863,74.863) |
geographic |
Arctic Cape Bounty Nunavut |
geographic_facet |
Arctic Cape Bounty Nunavut |
genre |
Arctic Arctic Nunavut permafrost Melville Island |
genre_facet |
Arctic Arctic Nunavut permafrost Melville Island |
op_source |
Arctic Science, Vol 6, Iss 4, Pp 404-422 (2020) |
op_relation |
https://doi.org/10.1139/as-2019-0029 https://doaj.org/toc/2368-7460 doi:10.1139/as-2019-0029 2368-7460 https://doaj.org/article/bbe7eed90f4a43c291673ab7955ee611 |
op_doi |
https://doi.org/10.1139/as-2019-0029 |
container_title |
Arctic Science |
container_volume |
6 |
container_issue |
4 |
container_start_page |
404 |
op_container_end_page |
422 |
_version_ |
1766296194887188480 |