Post-Fire Hazard Detection (ALOS-2 & Landsat-8)


A study was carried out to investigate the use of Advanced Land Observing Satellite 2 (ALOS-2) equipped with an enhanced L-band SAR sensor imagery alongside Landsat-8 optical sensor in the detection and mapping of burnt and unburnt scars occurring after a bushfire(post-fire hazard detection) in Victoria, Australia.

The aim of the research was to analyze the use of backscatter intensity in the retrieval of burnt and unburnt areas(post-fire hazard detection). This was in relation to the geographical aspect of the study area. In this endeavour, we attempt to analyze the effect of bushfires on hilly-mountainous areas in Australia and compare the use of satellite SAR and optical imagery in the identification of burnt and unburnt patches within fire perimeter zones.


The analysis was explored using a contextual classifier Support Vector Machine (SVM), as SVM allows us to integrate spectral information and spatial context through the optimal smoothing parameter without degrading image quality. The training and test set datasets consisting of burnt and unburnt pixels were created from Landsat-8 scenes used as reference data. The backscatter intensity maps (acquired before and after the forest fires) from ALOS-2 data were compared and investigated, with a special concern on topographic influence removal.

The dual-polarization (HH and HV) have been used to improve the forest fire mapping capability. These change detection techniques were based on image feature differences, and index calculations such as normalized burn ratio. The burnt area and unburnt area were then classified via a threshold given by the pre-and post-disaster differences.


The classification result achieved an accuracy of 80% Landsat-8 and 89% ALOS-2. This result shows the limitations of burnt area mapping with ALOS-2 due to the effect local incidence angle and topography were of greater impact resulting in shadows. Nevertheless, the results in both areas verify the use of satellite SAR sensors and optical in forestry applications.

fire hazard detection
Figure 1: Bushfires and controlled burns between 2015 and 2020 (in yellow), from the Victoria bushfire database. In red: fires selected for this study in the period 2019/2020. Map source: National Geographic, Esri, Garmin, HERE, UNEP-WCMC, USGS, NASA, ESA, METI, NRCAN, GEBCO, NOAA, increment P Corp. The study area is indicated in blue
fire hazard detection - ALOS-2
Figure 2: ALOS-2 pre-fire (a) ([-0.47, 63.01] dB in HH)  and post-fire (b) ([-0.44, 47.13] dB in HH) intensity images covering the area of bushfires, respectively
fire hazard detection - LandSAT-8
Figure 3: Landsat-8 pre-fire (a) and post-fire (b) images covering the area of bushfires, respectively. The Band combination is R:G:B=7:5:2. Foreshowing changes before and after fire
fire hazard detection - SVM Classification map of SAR
Figure 4: Classified map of ALOS-2 imagery of study area.  The red colour represents burnt areas and the green represents unburnt areas.
fire hazard detection - SVM classification map of optical
Figure 5: Classified map of Landsat-8 optical imagery of study area. The red colour represents burnt areas and green represents unburnt areas

This research was conducted by Stella Chelangat Mutai (World Food Programme) and Dr Ling Chang (University of Twente, Netherlands)

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