Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data
G. Caccamo A F , L. A. Chisholm A , R. A. Bradstock A B , M. L. Puotinen A and B. G. Pippen C D E
+ Author Affiliations
- Author Affiliations
A Institute for Conservation Biology and Environmental Management (ICBEM), School of Earth and Environmental Science, University of Wollongong, NSW 2522, Australia.
B Centre for Environmental Risk Management of Bushfires, University of Wollongong, 2522 NSW, Australia.
C Bushfire Cooperative Research Centre, Level 5, 340 Albert Street, East Melbourne, VIC 3002, Australia.
D The University of New South Wales at the Australian Defence Force Academy, Northcott Drive, Canberra, ACT 2600, Australia.
E Bushfire Dynamics and Applications Group, CSIRO Ecosystem Sciences, Bellenden Street, Crace, ACT 2911, Australia.
F Corresponding author. Email: gc996@uowmail.edu.au
International Journal of Wildland Fire 21(3) 257-269 https://doi.org/10.1071/WF11024
Submitted: 14 February 2011 Accepted: 12 June 2011 Published: 20 December 2011
Abstract
Live fuel moisture content is an important variable for assessing fire risk. Satellite observations provide the potential for monitoring fuel moisture across large areas. The objective of this study was to use data from the Moderate Resolution Imaging Spectroradiometer to monitor live fuel moisture content of three fire-prone vegetation types (shrubland, heathland and sclerophyll forest) in south-eastern Australia. The performances of four spectral indices (Normalised Difference Vegetation Index, Visible Atmospherically Resistant Index, Normalised Difference Infrared Index centred on 1650 nm and Normalised Difference Water Index) were compared. Models based on Visible Atmospherically Resistant Index and Normalised Difference Infrared Index centred on 1650 nm provided the best results (R2 values of 0.537 and 0.586). An empirical model based on these two indices was developed and its performance compared with a meteorological index traditionally used in this context, the Keetch–Byram Drought Index. The empirical model (R2 = 0.692) outperformed the meteorological index (R2 = 0.151), showing an enhanced capability to predict live fuel moisture content of the fire-prone vegetation types considered.
Additional keywords: fire risk, foliage water content, KBDI, spectral indices.
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