Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index

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BushfireTopic: 
Ecology and Biodiversity
Fire Management
ResearchAdoption: 
Prescribed burning
Project Reference: 
Multi-scale patterns in Ecological Processes and Fire Regime Impacts
TitleMapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index
Publication TypeJournal Article
Year of Publication2008
AuthorsBoer, MM, Macfarlane, C, Norris, J, Sadler, RJ, Wallace, J, Grierson, P
JournalRemote Sensing of Environment
Volume112
Issue12
Pagination4358 - 4369
Date Published12/2008
AbstractRemote sensing is the most practical method available to managers of fire-prone forests for quantifying and mapping fire impacts. Differenced Normalised Burn Ratio (ΔNBR) is among the most widely used spectral indices for the mapping of burn severity but is difficult to interpret in terms of fire-related changes in key biophysical attributes and processes. We propose to quantify burn severity as a change in the leaf area index (ΔLAI) of a stand. LAI is a key biophysical attribute of forests, and is central to understanding their water and carbon cycles. Previous studies have suggested that changes in canopy LAI may be a major contributor to ΔNBR and to the composite burn index (CBI) that is frequently used in combination with the NBR to assess burn severity on the ground. We applied remotely-sensed ΔLAI to map burn severity in jarrah (Eucalyptus marginata) forest in south-western Australia burnt during the January 2005 Perth Hills wildfires. Ground-based digital photography was used to measure LAI in typical stands representing the full range of canopy densities present in the study area as well as variation in the time since the last fire. Regression models for the prediction of LAI were developed using NBR, the Normalised Difference Vegetation Index (NDVI) or the Simple Ratio (SR) as the independent variable. All three LAI models had equally high coefficients of determination (R2: 0.87) and small root mean squared errors (RMSE: 0.27–0.28). ΔLAI was calculated as the difference between pre- and post-fire LAI, predicted using imagery from January 2004 and February 2005, respectively. The area affected by the January 2005 fire and the burn severity patterns within that area were mapped using ΔLAI and ΔNBR. Landscape patterns of burn severity obtained from differencing pre- and post-fire LAI were similar to those mapped by ΔNBR. We conclude that fire-affected areas and burn severity patterns in the northern jarrah forest can be objectively mapped using remotely-sensed changes in LAI, while offering the important advantage over NBR of being readily interpretable in the wider context of ecological forest management.
DOI10.1016/j.rse.2008.08.005
Short TitleRemote Sensing of Environment
Refereed DesignationRefereed