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Heretofore, global burned area (BA) products are only available at coarsespatial resolution, since most of the current global BA products are producedwith the help of active fire detection or dense time-series change analysis,which requires very high temporal resolution. In this study, however, we focuson automated global burned area mapping approach based on Landsat images. Byutilizing the huge catalog of satellite imagery as well as the high-performancecomputing capacity of Google Earth Engine, we proposed an automated pipelinefor generating 30-meter resolution global-scale annual burned area map fromtime-series of Landsat images, and a novel 30-meter resolution global annualburned area map of 2015 (GABAM 2015) is released. GABAM 2015 consists ofspatial extent of fires that occurred during 2015 and not of fires thatoccurred in previous years. Cross-comparison with recent Fire_cci version 5.0BA product found a similar spatial distribution and a strong correlation($R^2=0.74$) between the burned areas from the two products, althoughdifferences were found in specific land cover categories (particularly inagriculture land). Preliminary global validation showed the commission andomission error of GABAM 2015 are 13.17% and 30.13%, respectively.

30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine