Mapping sub-Antarctic cushion plants using random forests to combine very high resolution satellite imagery and terrain modelling

Bricher, Phillippa K., Lucieer, Arko, Shaw, Justine, Terauds, Aleks and Bergstrom, Dana M. (2013) Mapping sub-Antarctic cushion plants using random forests to combine very high resolution satellite imagery and terrain modelling. PLoS One, 8 8: e72093.1-e72093.15. doi:10.1371/journal.pone.0072093


Author Bricher, Phillippa K.
Lucieer, Arko
Shaw, Justine
Terauds, Aleks
Bergstrom, Dana M.
Title Mapping sub-Antarctic cushion plants using random forests to combine very high resolution satellite imagery and terrain modelling
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2013-08-01
Sub-type Article (original research)
DOI 10.1371/journal.pone.0072093
Open Access Status DOI
Volume 8
Issue 8
Start page e72093.1
End page e72093.15
Total pages 15
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Language eng
Formatted abstract
Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6-96.3%, κ = 0.849-0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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