Development of an automated method for mapping fire history captured in Landsat TM and ETM plus time series across Queensland, Australia

Remote sensing can quantify past and present fire activity at spatial scales useful for a range of fire and vegetation management applications. In this study, we present a new automated approach to classifying burnt areas across the state of Queensland, Australia. The method is applied to complete time series of Landsat TM/ETM + imagery rather than single images and considers spectral (band 4, B4, and bands 4 + 5, B45), thermal, temporal and contextual information within a hierarchical framework. To maximise the available observations and the burnt area detected, we used imagery containing up to 60% cloud that was screened during pre-processing. Median filters were applied to smooth the time series and multi-date change detection used to locate negative outliers (large declines in reflectance relative to the median-smoothed time series). Watershed region growing was used to segment and map a larger spatial extent of the change while minimising commission errors. These segmented change objects were attributed as either burnt or unburnt using their thermal, reflective and contextual characteristics in a classification tree. Thermal information was found to be more important than reflective indices in the change attribution. Algorithm calibration used training data from ten Path/Rows located strategically across Queensland with four images sampled per path row (n = 40). Thresholds were optimised to maximise the burnt area detected while limiting under/over-growing of burnt area. Validation data covered a range of burnt areas from ten independent Path/Rows with ten images sampled across a range of burnt area fractions per Path/Row (n = 100). The results for burnt area mapping demonstrated an average producer's accuracy of 85% (range of 28 to 100% for individual images) and average user's accuracy of 71% (range of 4 to 99% for individual images). A morphological dilation of one pixel restricted to locations exhibiting a decline in B45 over time, increased the producer's accuracy by 4% but reduced the user's accuracy by 8%. The total accuracy for the burnt area classification was greater than 99%, however this is more a reflection of the small fraction of landscape represented by burnt area rather than the ability to detect burnt area. Areas frequently misdassified were related to areas of high spectral/land use change which included areas of cropping, frequently inundated land, and moisture/ground cover variations over dark soils. In this study, we applied a crop and water mask to minimise commission errors. Significantly, the results of this study demonstrate that an automated time series method for mapping burnt areas can be successfully applied across a diversity of land cover types. The method may be applied in similar savanna dominated environments but is likely to require modification to be applicable in other landscapes. (C) 2014 Elsevier Inc. All rights reserved.