Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier.
This study implemented and applied a binary ensemble classifier for identification of grazed vegetation communities on Macquarie Island from very high resolution Quickbird imagery. Rabbit grazing has severely affected Macquarie’s unique sub-Antarctic vegetation communities. The aim of this study was...
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ftunivtasmania:oai:eprints.utas.edu.au:6980 2023-05-15T13:36:46+02:00 Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier. Lucieer, A 2008-10 application/pdf https://eprints.utas.edu.au/6980/ https://eprints.utas.edu.au/6980/1/14arspc_lucieer_224.pdf http://www.14arspc.com/ en eng https://eprints.utas.edu.au/6980/1/14arspc_lucieer_224.pdf Lucieer, A 2008 , 'Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier.', paper presented at the 14th Australian Remote Sensing and Photogrammety Conference (ARSPC), 29 September-3 October 2008, Darwin. cc_utas 291003 Photogrammetry and Remote Sensing Conference or Workshop Item NonPeerReviewed 2008 ftunivtasmania 2020-05-30T07:20:49Z This study implemented and applied a binary ensemble classifier for identification of grazed vegetation communities on Macquarie Island from very high resolution Quickbird imagery. Rabbit grazing has severely affected Macquarie’s unique sub-Antarctic vegetation communities. The aim of this study was to identify the grazed areas from Quickbird imagery to map their spatial extent. Seven different soft classification algorithms were applied to classify the image into grazed vs. ‘other’ classes. The maximum likelihood classifier, supervised fuzzy c-means classifier (Euclidean distance, Mahalanobis distance, and k-nearest neighbour), and three support vector machine classifiers (SVM) were applied. An ensemble classifier based on the consensus rule was used to combine the seven classification results. A very high classification accuracy of 97% was achieved with the ensemble classifier, identifying grazed areas and providing an estimate of classification uncertainty. Conference Object Antarc* Antarctic Macquarie Island University of Tasmania: UTas ePrints Antarctic |
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Open Polar |
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University of Tasmania: UTas ePrints |
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ftunivtasmania |
language |
English |
topic |
291003 Photogrammetry and Remote Sensing |
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291003 Photogrammetry and Remote Sensing Lucieer, A Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier. |
topic_facet |
291003 Photogrammetry and Remote Sensing |
description |
This study implemented and applied a binary ensemble classifier for identification of grazed vegetation communities on Macquarie Island from very high resolution Quickbird imagery. Rabbit grazing has severely affected Macquarie’s unique sub-Antarctic vegetation communities. The aim of this study was to identify the grazed areas from Quickbird imagery to map their spatial extent. Seven different soft classification algorithms were applied to classify the image into grazed vs. ‘other’ classes. The maximum likelihood classifier, supervised fuzzy c-means classifier (Euclidean distance, Mahalanobis distance, and k-nearest neighbour), and three support vector machine classifiers (SVM) were applied. An ensemble classifier based on the consensus rule was used to combine the seven classification results. A very high classification accuracy of 97% was achieved with the ensemble classifier, identifying grazed areas and providing an estimate of classification uncertainty. |
format |
Conference Object |
author |
Lucieer, A |
author_facet |
Lucieer, A |
author_sort |
Lucieer, A |
title |
Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier. |
title_short |
Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier. |
title_full |
Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier. |
title_fullStr |
Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier. |
title_full_unstemmed |
Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier. |
title_sort |
mapping grazed vegetation communities on macquarie island using a binary ensemble classifier. |
publishDate |
2008 |
url |
https://eprints.utas.edu.au/6980/ https://eprints.utas.edu.au/6980/1/14arspc_lucieer_224.pdf http://www.14arspc.com/ |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic Macquarie Island |
genre_facet |
Antarc* Antarctic Macquarie Island |
op_relation |
https://eprints.utas.edu.au/6980/1/14arspc_lucieer_224.pdf Lucieer, A 2008 , 'Mapping grazed vegetation communities on Macquarie Island using a binary ensemble classifier.', paper presented at the 14th Australian Remote Sensing and Photogrammety Conference (ARSPC), 29 September-3 October 2008, Darwin. |
op_rights |
cc_utas |
_version_ |
1766083837315514368 |