AbstractFire in the tropical savannas of northern Australia is frequent and extensive, management is a continuous and iterative process. The remote sensed mapping of fire and its effects can be returned to managers quickly and cost effectively. The post-fire measure of the affect of fire on vegetation is defined here as fire severity.
This study developed a method of calibrating remotely sensed imagery for mapping fire severity. It coupled helicopter-based spectra and comprehensive ground measurements. Data were modelled against a fire severity index, the metrics of which were previously developed in collaboration with land managers.
The differenced normalised burn ratio (ΔNBR) was determined to be the best of a candidate set of models to separate severe from not-severe fire effects. Other models were assessed to discriminate low from moderate fire severity, the most parsimonious model used the shortwave infrared band at 1640 nm.
The reflectance models were applied to a time series of images from the MODIS sensor for the fire season of 2009 and assessed against validation data. Image differences were within 5 to 6 days, as a pre-determined window of algorithm applicability. Overall accuracy discriminating severe from not-severe fires was 94%. Discrimination within the not-severe category was only 60% due mostly to image availability in MODIS channel 6 and the coarseness of validation data.
As an outcome a fire severity mapping product, initially discriminating severe from not-severe fires, will be made available through the North Australian Fire Information mapping system to enhance its utility. Fire severity mapping will improve the ability of land managers to monitor the effects of fires and undertake strategic fire management planning. It will provide conservation land managers with greater parametric detail to more effectively assess fire effects on ecological communities, and it will improve the precision of greenhouse gas emissions calculations.
|Date of Award||Jan 2011|
|Supervisor||Lindsay B. Hutley (Supervisor) & Stefan Maier (Supervisor)|