An enhanced NHI algorithm configuration for fire detection and mapping

The devastating fire events occurring during the intense fire season of 2023 have shown the importance of developing efficient fire detection methods capable of supporting the fire management activities. An enhanced configuration of the Normalized Hotspot Indices (NHI) algorithm has been developed in this direction to improve the fire mapping by satellite through near infrared (NIR) and short-wave infrared (SWIR) data (up to 20 m spatial resolution) from the Operational Land Imager (OLI/OLI2) and the Multispectral Instrument (MSI) aboard Landsat-8/9 (L8/9) and Sentinel-2 (S2) satellites, respectively. In this work, we show the results achieved by investigating the fire events occurring in California, Hawaii islands (USA), Yellowknife (Canada), Tenerife islands (Spain), Greece and Australia also through comparison with information from operational Landsat Fire and Thermal Anomaly (LFTA) product. Results of an extended validation analysis performed using information from well-established databases show that the enhanced NHI algorithm configuration enabled an accurate mapping of fire fronts with a very number of omission and commission errors. Moreover, the algorithm flagged up to 99% of fire pixels from the LFTA product over California and detected up to 70% of additional fire pixels, in night-time conditions, which better detailed the fire fronts and provided unique information about small-fire outbreaks. The effective integration of S2 (daytime) and L8/9 (daytime/night-time) observations, demonstrates that the enhanced NHI algorithm configuration may be used with success to analyse the dynamic evolution of flaming fronts by assessing/complementing information from satellite products at high-temporal/low-spatial resolution. The next implementation of the algorithm on from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellite and the Flexible Combined Imager (FCI) of the Meteosat Third Generation (MTG) opens some interesting perspectives also regarding its usage for the near-real time monitoring of wildfires