Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada
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...
Published in: | Permafrost and Periglacial Processes |
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Online Access: | https://doi.org/10.1002/ppp.719 |
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ftrepec:oai:RePEc:wly:perpro:v:22:y:2011:i:2:p:163-178 2023-05-15T17:55:23+02:00 Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada Marian Kremer Antoni G. Lewkowicz Philip P. Bonnaventure Michael C. Sawada https://doi.org/10.1002/ppp.719 unknown https://doi.org/10.1002/ppp.719 article ftrepec https://doi.org/10.1002/ppp.719 2020-12-04T13:31:03Z 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 Yukon RePEc (Research Papers in Economics) Yukon Canada Permafrost and Periglacial Processes 22 2 163 178 |
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Open Polar |
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RePEc (Research Papers in Economics) |
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ftrepec |
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unknown |
description |
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 |
Marian Kremer Antoni G. Lewkowicz Philip P. Bonnaventure Michael C. Sawada |
spellingShingle |
Marian Kremer Antoni G. Lewkowicz Philip P. Bonnaventure Michael C. Sawada Utility of Classification and Regression Tree Analyses and Vegetation in Mountain Permafrost Models, Yukon, Canada |
author_facet |
Marian Kremer Antoni G. Lewkowicz Philip P. Bonnaventure Michael C. Sawada |
author_sort |
Marian Kremer |
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 |
url |
https://doi.org/10.1002/ppp.719 |
geographic |
Yukon Canada |
geographic_facet |
Yukon Canada |
genre |
permafrost Yukon |
genre_facet |
permafrost Yukon |
op_relation |
https://doi.org/10.1002/ppp.719 |
op_doi |
https://doi.org/10.1002/ppp.719 |
container_title |
Permafrost and Periglacial Processes |
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22 |
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2 |
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163 |
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178 |
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1766163311502557184 |