This coffeesode discusses a spatial feature-based (STF) method for detecting forest fires using Himawari-8/9 satellite imagery and SRTM DEM data in the context of recent forest fires in L.A. The discussion focuses on a research based on STF method which reconstructs surface temperature using a Random Forest model, which outperforms other machine learning models tested. Fire detection leverages the difference between reconstructed and observed surface temperatures, combined with a spatial contextual algorithm. The research discussed has a resulting algorithm exhibits high accuracy, achieving a zero omission error rate and a comprehensive evaluation index exceeding 0.74 when compared to existing fire detection datasets.

Reference- ao, H.; Yang, Z.; Zhang, G.; Liu, F. Forest Fire Detection Based on Spatial Characteristics of Surface Temperature. Remote Sens. 2024, 16, 2945.
https://doi.org/10.3390/rs16162945

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