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More than 72,000 hectares of western Tasmania were burnt in 2016 due to bushfires. Bushfires in Tasmania has high social, economical, and environmental impacts. The remote delineation of these bushfires has paramount importance for decision-making authorities to help people in emergencies and planning. Considering the fact that delineation uncertainty from Earth Observation [EO] data is inevitable, this study uses MODIS, Landsat and Sentinel-2 imageries covering the 2016 burnt areas from Tasmania. We test the hypothesis that the difference in Normalised Difference Vegetation Index (NDVI) before and after the fire event can detect the accurate delineation of burnt areas and hence the changes. MODIS, Landsat and Sentinel-2 products before and after fire are used independently in delineating and mapping bushfire boundaries. We map in three thematic classes burnt, damaged and both. Delineated boundaries are examined for uncertainty and error maps are produced. The uncertainty examination and validation are performed using ground truth data obtained from local fire authorities. Developed error metrics are used to obtain statistical measures like sensitivity, specificity, positive predictive value, negative predictive value, kappa coefficient and overall accuracy. Our results show that there is minimal difference in overall accuracy from both the sensors MODIS: [0.94 vs 0.92] and Sentinel [0.94 vs 0.93] for the classes burnt & damaged vs only burnt. Furthermore, we propose a conceptual framework for bushfire mapping uncertainty in a multiple-scale environment incorporating sensitive thematic parameters that could affect initiation of fire and blaze direction. The parameters considered in our framework are: vegetation type [landcover], vegetation density [vegetation indices], drought [soil moisture, air moisture, precipitation], temperature [air temperature, soil temperature], topography [elevation, slope, aspect, ruggedness, topography position index], and wind [speed, direction]).

Quantifying bushfire mapping uncertainty using single and multiscale approach: a case study from Tasmania, Australia