An evaluation of the utility of two classifiers for mapping woody vegetation using remote sensing

Acevedo Cattaneo, S and Jones, S 2012, 'An evaluation of the utility of two classifiers for mapping woody vegetation using remote sensing', in Colin Arrowsmith, Chris Bellman, William Cartwright, Karin Reinke, Mark Shortis, Mariela Soto-Berelov, Lola Suarez Barranco (ed.) Proceedings of the 2012 Geospatial Science Research 2 Symposium (GSR_2), Melbourne, Australia, 10-12 December 2012, pp. 1-15.


Document type: Conference Paper
Collection: Conference Papers

Title An evaluation of the utility of two classifiers for mapping woody vegetation using remote sensing
Author(s) Acevedo Cattaneo, S
Jones, S
Year 2012
Conference name GSR_2
Conference location Melbourne, Australia
Conference dates 10-12 December 2012
Proceedings title Proceedings of the 2012 Geospatial Science Research 2 Symposium (GSR_2)
Editor(s) Colin Arrowsmith, Chris Bellman, William Cartwright, Karin Reinke, Mark Shortis, Mariela Soto-Berelov, Lola Suarez Barranco
Publisher RMIT University
Place of publication Melbourne, Australia
Start page 1
End page 15
Total pages 15
Abstract Native vegetation is vulnerable to fragmentation and loss of condition due to various environmental and anthropogenic drivers. Regrowth can vary depending upon species composition within that vegetation community. As well as maintaining biodiversity native vegetation provides a range of ecosystem services. Up-to date and reliable information on the distribution of native vegetation is essential to support decisions that minimise loss of biodiversity and maximise functionality of ecosystems. Remotely sensed data is an ideal tool for this purpose. As such, this study aims to assess the accuracy of Maximum Likelihood Classification (MLC) and Spectral Angle Mapper (SAM) algorithms to distinguished woody vegetation from non-woody vegetation in Creswick, Victoria, Australia. The use of RapidEye imagery, image fusion (spectral information combined with textual information (ALOS-PALSAR)), and the use of a filter (majority filter) was also evaluated. The classification accuracy of MLC and SAM was used as the determining factor for identifying a suitable mapping method to distinguish woody native vegetation from non-native woody vegetation (particularly, Pinus spp., Pine and Eucalyptus spp., Blue Gum) in the forest. The results demonstrated that the use of MLC on RapidEye imagery enabled the Native, Pine and Blue Gum to be accurately mapped with an overall accuracy of 88%. Kappa statistics show that there was a significant difference between MLC and SAM algorithms independent of the image type input. However, when the same algorithm was applied on each image type, no significant difference was found. The use of fused images as well as a filter, did not improve the accuracy of the classification. Considering cost and time in registering and processing the images as well as the computational time of images filtering, the use of these methods does not provide benefit in this case study.
Subjects Cartography
Geospatial Information Systems
Photogrammetry and Remote Sensing
Keyword(s) woody/non-woody vegetation
classification
data fusion
majority filter
remote sensing
Copyright notice Copyright © 2012 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
ISBN 9780978252715
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