30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine
This provides a higher-resolution alternative to existing coarse global burned area products for researchers and policymakers in environmental monitoring, though it is incremental in improving spatial detail.
The study tackled the problem of generating high-resolution global burned area maps by developing an automated pipeline using Landsat images and Google Earth Engine, resulting in a 30-meter resolution global annual burned area map for 2015 with commission and omission errors of 13.17% and 30.13%, respectively.
Heretofore, global burned area (BA) products are only available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery as well as the high-performance computing capacity of Google Earth Engine, we proposed an automated pipeline for generating 30-meter resolution global-scale annual burned area map from time-series of Landsat images, and a novel 30-meter resolution global annual burned area map of 2015 (GABAM 2015) is released. GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with recent Fire_cci version 5.0 BA product found a similar spatial distribution and a strong correlation ($R^2=0.74$) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission error of GABAM 2015 are 13.17% and 30.13%, respectively.