Great Victoria Desert, Australia
Topic: Wildfire Behavior

Principle Investigators
Dr. Melba Crawford, Center for Space Research
Dr. Eric Pianka, Dept. of Zoology, University of Texas


In the Great Victoria Desert, which lies within the heart of Australia's outback, wildfires are a dominant factor in the desert ecosystem. The Great Victoria Desert is an arid environment in which there exists enough vegetation to support regular wildfires. Thes fires are usually started by lightning strikes from thunderstorms during the summer months and they can burn for several weeks. The fires spread unpredictably, missing occasional isolated patches of vegetation or even large tracts of vegetation (Channey, 1994). There are several factors which influence the frequency, extent, and geometry of the fires:
Understanding the fire behavior is the major focus of this research and it includes determining the status of the vegetation prior to burn, the amount of regrowth after a burn, and how long it takes the vegetation to recover until it is suitable for burn.

Data Analysis

For this research, Landsat Multi-Spectral Scanner (MSS) covering the period from 1979 to 1994 and two Thematic Mapper (TM) scenes were obtained from the Australia Centre for Remote Sensing (ACRES). In addition to the multispectral data, in 1993 CSR was also one of the first AIRSAR acquisitions using JPL's airborne polarametric radar.

To the right, subset of 1981 MSS scene over the testsite. The Yamarna basin is the leaf-shaped feature at the top of the image, and the firescars show up a bright white. Notice the unique shapes of the firescars.

The initial step in the study was to register all of the imagery to the Universal Tranverse Mercator (UTM) coordinate system. A second order transformation with an allowable RMS error of <0.5 was used with a nearest neighbor resampling.

Fire Extraction
After all of the images are registered, the fires from each year were extracted. Recently burned areas in the Great Victoria Desert are readily distinguishable in Landsat MSS imagery. Ash is highly adsorptive in the near-infrared bands whereas vegetation is highly reflective (Channey, 1994). Even years following a burn , the difference between the burned and non-burned vegetation differs enough to define sharp fire boundaries. The change detection was done by subtracting the previous year's brightness values from the current year's brightness values. From this resultant layer, a pyramid segmentation (Acton, 1994) method was used to accurately dilineate the fire boundaries. From this process, a map of each year's fires was created.

To see the fire statistics for each year, click here.

To see the fire map for all years, click here.

Radiometric Correction
Radiometric correction of satellite imagery is a difficult, yet necessary step in understanding temporal changes of vegetation. The brightness value which is recorded at the satellite sensor is often in need of correction due to sensor degradation and atmospheric attenuation. The MSS data in this study was atmospherically corrected based on a darkest pixel improvement method (Chavez, 1988) and then converted to surface reflectance. The computed reflectance values of known vegetation types were compared to those obtained from CSIRO using a laboratory spectrometer. The computed reflectance values compared reasonably close to the spectrometer results. These variations are expected since the ground response is not incuded in the CSIRO responses and the amount of pixel mixing in MSS data is fairly significant.

AIRSAR coverage
In October of 1993,JPL flew their Airborne SAR (AIRSAR) sensor over a target site known as Red Sands. They flew two passes over the testsite in two different modes, 20Mhz fully polarametric radar and 40Mhz Topsar Radar. The Southern strip, which was flown at 20Mhz, has a ground resolution of approximately 9 meters and has HH, VV, and HV polarizations of the C, L, and P bands. The northern strip, flown in Topsar Mode, has a ground resolution of approximately 4 meters and has HH, VV, and HV for the L and P band and the VV polarization for the C band.

The images look quite different in comparison to multispectral data since this is an active sensor rather than a passive sensor. It was hoped that the different wavelengths of the radar would be able to penetrate through the sand of Great Victoria Desert, but the flight was acquired during a heavy thunderstorm, so the amount of soil penetration is thought to be minimal. The AIRSAR is useful as a ground truth tool for gathering signiture files for MSS data, in that individual Marble Gum trees and other vegetation classes such as Mulga, thryptomene, and spinifex are easily seen in the AIRSAR data.

Vegetation Classification

The images above depict the arid environment of the Great Victoria Desert. To the left, you can notice the red soil which is typical and large mulga trees line the horizon. The image on the right is an area of mature spinifex, which serves as an excellent fuel for wildfires.

Acton, S.T, Bovik, A.C., and Crawford, M.M., "Anisotropic Diffusion Pyramids for Image Segmentation," Proceedings of the 1st International Conference on Image Processing, Austin, TX, November 1994.

Acton, S.T. and Crawford, M.M., "Anisotropic Diffusion Pyramid Feature Extraction Algorithm: Application to Extraction of Fire Scar Features in Remotely Sensed Imagery," in progress, to be submitted to IEEE Transactions on Geoscience and Remote Sensing

Canney, L.M., "A Discrete Time Markov Random Field Model of Fire Behavior in Australia's Great Victoria Desert, M.S. Thesis, The University of Texas at Austin, May 1994

Crawford, M. and Pianka, E., "Disturbance, Spatial Heterogeneity, and Biotic Diverstiy in Arid Australia", Center for Space Research, University of Texas at Austin, 1991.

Crawford, M.M. and Usanmaz, G., "Hierarchical, Data Adaptive Supervised Nonparametric Image Classification," Proceedings of the 10th Australian Conference on Remote Sensing, Melbourne, Australia, March 1994.

Crawford, M.M., Acton, S., Usanmaz, G., Lee, S., and Jung, M., "Multiresolution Supervised Nonparametric Algorithm for Feature Extraction and Classification in Multispectral Imagery," Proceedings of the Conference on Image and Signal Processing for Remote Sensing, European Symposium on Satellite Remote Sensing, Rome, Italy, September 1994.

Crawford, M.M., Pianka, E., Canny, L., and Phillips, P, "Multisensor Modelling of the Impact of Fires on Vegetation in the Great Victoria Desert of Australia, Presented at the Conference on Multispectral Sensing of Forestry and Natural Resources, European Symposium on Satellite Remote Sensing, Rome, Italy, September 1994.

Crawford, M.M., Pianka, E.R., Neuwenschwander, A., Juang, W.J., Jung, M., and Chen, T.M., "Diversity in Arid Australia: Contributions of Airsar to Vegatation Mapping," Proceedings of the Australian Conference on 1993 AIRSAR Campaign, Sydney, Australia, November 1995.

Jung, M. and Crawford, M.M., "Contextual Simulation of Landscape Based on Remotely Sensed Data," Proceedings of the 1995 IEEE SW Symposium on Image Processing, San Antonio, TX, March 1996.

Jung, M. and Crawford, M.M., "Multiresolution Contextual Simulation of Landscape Based on Remotely Sensed Imagery from the Great Victoria Desert," Proceedings of the 1996 IGARSS Conference, Lincoln, NE, May 1996.

Curry, J., "A Time Series Model of the Spectral Response of Fires and Vegetation Regrowth in Landsat Imagery of Australia's Great Victoria Desert: An Initial Analysis," M.S. Thesis, The University of Texas at Austin, May 1996.

Curry, J., and Crawford M.M., "A Time Series Model of Regeneration of Vegetaton in the Great Victoria Desert of Australia from Landsat MSS Imagery", in progress, to be submitted to the International Journal on Remote Sensing.

Pianka, E. R., "Fire Ecology - Disturbance, Spatial Heterogeneity, and Biotic Diversity: Fire Succession in Arid Australia," National Geographic Research & Exploration, vol. 8, no. 3, pp. 352-371, 1994.

1. Chavez, P. "An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data", Remote Sensing of Environment, vol. 24, pp 459-479.


Last Modified: Fri Jul 19 1996 CSR/TSGC Team Web