Abstract
Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over the user-selected extent and period, (ii) two tools implementing a BA stratified random sampling to select the scenes and dates for validation, and (iii) the BA Reference Perimeter tool to obtain highly accurate BA maps that focus on validating coarser BA products. Burned Area Mapping Tools (BAMTs) go beyond the previously implemented Burned Area Mapping Software (BAMS) because of GEE parallel processing capabilities and preloaded geospatial datasets. BAMT also allows temporal image composites to be exploited in order to obtain BA maps over a larger extent and longer temporal periods. The tools consist of four scripts executable from the GEE Code Editor. The tools’ performance was discussed in two case studies: in the 2019/2020 fire season in Southeast Australia, where the BA cartography detected more than 50,000 km2, using Landsat data with commission and omission errors below 12% when compared to Sentinel-2 imagery; and in the 2018 summer wildfires in Canada, where it was found that around 16,000 km2 had burned.
Highlights
Biomass burning is a significant disturbance that causes soil erosion and land-cover changes and releases greenhouse gas emissions into the atmosphere, affecting people’s lives and properties [1,2,3]
Fires are present in most types of vegetation in the world, especially grasslands, savannas, and forest, and they occur on all continents, with a significant incidence in Africa, which accounts for 70% of the global burned area [4,5,6]
The number of Landsat scenes processed in each period varied between 869 and 938, amounting to a total of 3653 scenes processed over the whole fire season
Summary
Biomass burning is a significant disturbance that causes soil erosion and land-cover changes and releases greenhouse gas emissions into the atmosphere, affecting people’s lives and properties [1,2,3]. Burned areas (BAs) must be detected accurately both spatially and temporally, for which satellite Earth observation has been much used over the last few decades, especially using coarse spatial resolution [5,6,7,8,9,10,11,12,13,14]. Global products at coarse spatial resolutions have significant omission errors [4,15,16,17,18], but creating products at medium resolution, more accurate, is quite challenging: It implies a heavy data-processing workload, and the temporal resolution is low (typically one image every 5 to 16 days). A few BA automatic algorithms have been developed, using time series data, especially using Landsat
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