A Seasonal-Window Ensemble-Based Thresholding Technique Used to Detect Active Fires in Geostationary Remotely Sensed Data

This article introduces a new algorithm to detect active fires in geostationary remotely sensed data. The algorithm calculated dynamic statistical multispectral thresholds based on, and sensitive to, biogeographical region, subseason, and time-of-day. The spectral characteristics of nonfire and noncloud mid-infrared values were found to vary with biogeographical region, subseason, and time-of-day. These differences were exploited to define a new seasonal Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) multivariate adaptive threshold geostationary satellite fire anomaly algorithm. The algorithm was demonstrated on 12 months of daytime data acquired from the geostationary satellite system, the Advanced Himawari Imager (on Himawari-8) over Australia (7.69 million kmnbsp;²nbsp;). The resulting hotspots were compared with those from the existing Moderate Resolution Imaging Spectrometer (MODIS) polar-orbiting fire-hotspot algorithm. The intercomparison showed that BRIGHT wildfire hotspots, detected using Himawari-8 and Interim Biogeographic Regionalisation of Australia (IBRA) data, were also detected by MODIS polar-orbiting fire hotspots 88% of the time. While MODIS hotspots were detected by BRIGHT hotspots only 39% of the time; the majority of the undetected MODIS hotspots had low radiative power. BRIGHT provides a new method for the remote sensing of active fires providing reliable observations at spatial and temporal scales useful for fire managers.