POST-FIRE HAZARD DETECTION USING ALOS-2 RADAR AND LANDSAT-8 OPTICAL IMAGERY
- 1International Fund for Agricultural Development, Kenya
- 2Dept. of Earth Observation and Science, ITC, University of Twente, The Netherlands
Keywords: Post-fire, SAR, SVM, Bushfire
Abstract. This study investigates the use of Advanced Land Observing Satellite 2 (ALOS-2) equipped with an enhanced L-band SAR sensor imagery alongside with Landsat-8 optical sensor in detection and mapping of burnt and unburnt scars occurring after a bushfire in Victoria, Australia. The bushfires had recently occurred in the period of 2018–2019. 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 polarizations (HH and HV) have been used to improve the forest fire mapping capability. These change detection techniques were based on image feature differences, index calculation 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 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 application.