Beyond being wise after the event: Combining spatial, temporal and spectral information for Himawari-8 early-stage wildfire detection

Abstract

Wildfires frequently occur around the world, which seriously threaten the ecology, environment, economic development, even human safety. In this work, we propose a novel framework for near-real-time and early-stage wildfire detection using Himawari-8 satellite 10-min data. Different from most of the existing methods, the proposed framework jointly combines spatial, temporal and spectral information for wildfire detection. The integrated time-series spatial variance, temporal difference and spectral difference can comprehensively judge the wildfire points and exclude disturbed points. Dispense with cloud detection and setting too many manual thresholds, a spatial–temporal–spectral recurrent neural network (STS-RNN) is developed to adaptively learn the time-series spatial–temporal–spectral curves. Compared with JAXA’s wildfire products, the proposed framework can more accurately detect the small, early-stage, day-time, night-time and forest wildfire points in three experimental scenarios. Especially for the early-stage wildfire detection, the proposed framework may provide the rapid alarm for the local fire department and emergency management agency. This greatly breaks through the limitations of existing wildfire detection methods.

Keywords

• Wildfire detection
• Himawari-8
• Spatial–temporal–spectral
• Near-real-time
• Early-stage