Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada
ABSTRACT Classification and regression tree (CART) analyses were undertaken to test the usefulness of including vegetation variables in mountain permafrost distribution models for five widely spaced study areas in the Yukon. Digital elevation model (DEM)‐derived variables, field‐derived vegetation v...
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crwiley:10.1002/ppp.719 2024-06-02T08:12:54+00:00 Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada Kremer, Marian Lewkowicz, Antoni G. Bonnaventure, Philip P. Sawada, Michael C. 2011 http://dx.doi.org/10.1002/ppp.719 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fppp.719 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.719 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Permafrost and Periglacial Processes volume 22, issue 2, page 163-178 ISSN 1045-6740 1099-1530 journal-article 2011 crwiley https://doi.org/10.1002/ppp.719 2024-05-03T10:51:17Z ABSTRACT Classification and regression tree (CART) analyses were undertaken to test the usefulness of including vegetation variables in mountain permafrost distribution models for five widely spaced study areas in the Yukon. Digital elevation model (DEM)‐derived variables, field‐derived vegetation variables and satellite imagery‐derived vegetation variables were employed individually to classify sites into permafrost probable, permafrost improbable and permafrost ‘uncertain’ categories. The vegetation variables were subsequently combined with the DEM‐derived set to see if they could improve the latter's accuracy. Overall training accuracies for the probable and improbable permafrost categories for 102 sites ranged from 81% to 92%. Remotely sensed imagery alone had the lowest overall training (81%) and testing (50%) accuracies. The CART that combined imagery and DEM‐based variables produced high overall accuracy for training (90%) and the highest for testing (77%), had few nodes classified as ‘uncertain’ and could be used to create permafrost probability maps of the study areas. CART analyses appear useful for predicting permafrost distribution because they can incorporate non‐linear relationships between independent variables and the presence of permafrost. Remotely sensed variables relating to vegetation, specifically a normalised difference vegetation index, improved the DEM‐based results, but required considerable additional effort for data collection and processing. Copyright © 2011 John Wiley & Sons, Ltd. Article in Journal/Newspaper permafrost Permafrost and Periglacial Processes Yukon Wiley Online Library Canada Yukon Permafrost and Periglacial Processes 22 2 163 178 |
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Open Polar |
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Wiley Online Library |
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crwiley |
language |
English |
description |
ABSTRACT Classification and regression tree (CART) analyses were undertaken to test the usefulness of including vegetation variables in mountain permafrost distribution models for five widely spaced study areas in the Yukon. Digital elevation model (DEM)‐derived variables, field‐derived vegetation variables and satellite imagery‐derived vegetation variables were employed individually to classify sites into permafrost probable, permafrost improbable and permafrost ‘uncertain’ categories. The vegetation variables were subsequently combined with the DEM‐derived set to see if they could improve the latter's accuracy. Overall training accuracies for the probable and improbable permafrost categories for 102 sites ranged from 81% to 92%. Remotely sensed imagery alone had the lowest overall training (81%) and testing (50%) accuracies. The CART that combined imagery and DEM‐based variables produced high overall accuracy for training (90%) and the highest for testing (77%), had few nodes classified as ‘uncertain’ and could be used to create permafrost probability maps of the study areas. CART analyses appear useful for predicting permafrost distribution because they can incorporate non‐linear relationships between independent variables and the presence of permafrost. Remotely sensed variables relating to vegetation, specifically a normalised difference vegetation index, improved the DEM‐based results, but required considerable additional effort for data collection and processing. Copyright © 2011 John Wiley & Sons, Ltd. |
format |
Article in Journal/Newspaper |
author |
Kremer, Marian Lewkowicz, Antoni G. Bonnaventure, Philip P. Sawada, Michael C. |
spellingShingle |
Kremer, Marian Lewkowicz, Antoni G. Bonnaventure, Philip P. Sawada, Michael C. Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada |
author_facet |
Kremer, Marian Lewkowicz, Antoni G. Bonnaventure, Philip P. Sawada, Michael C. |
author_sort |
Kremer, Marian |
title |
Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada |
title_short |
Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada |
title_full |
Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada |
title_fullStr |
Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada |
title_full_unstemmed |
Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada |
title_sort |
utility of classification and regression tree analyses and vegetation in mountain permafrost models, yukon, canada |
publisher |
Wiley |
publishDate |
2011 |
url |
http://dx.doi.org/10.1002/ppp.719 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fppp.719 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.719 |
geographic |
Canada Yukon |
geographic_facet |
Canada Yukon |
genre |
permafrost Permafrost and Periglacial Processes Yukon |
genre_facet |
permafrost Permafrost and Periglacial Processes Yukon |
op_source |
Permafrost and Periglacial Processes volume 22, issue 2, page 163-178 ISSN 1045-6740 1099-1530 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/ppp.719 |
container_title |
Permafrost and Periglacial Processes |
container_volume |
22 |
container_issue |
2 |
container_start_page |
163 |
op_container_end_page |
178 |
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1800759478775185408 |