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Early Stage Forest Fire Detection from Himawari-8 AHI Images Using a Modified MOD14 Algorithm Combined with Machine

Naoto Maeda1, Hideyuki Tonooka1

  • 1Graduate School of Science and Engineering, Ibaraki University, Hitachi 3168511, Japan.

Sensors (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

This study introduces a new method for early forest fire detection using satellite imagery. The approach achieves high precision and recall, significantly improving early fire identification capabilities.

Keywords:
AHIHimawari-8MOD14 algorithmcontextual classificationfire detectionforest firegeostationary satellitemachine learningrandom forestthermal anomaly

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Area of Science:

  • Earth Observation
  • Remote Sensing
  • Forestry Science

Background:

  • Early detection and rapid extinguishing of forest fires are crucial for mitigating their spread and impact.
  • Existing methods may miss early-stage fires, especially those with lower thermal anomalies.
  • Geostationary satellites offer high-temporal-resolution data valuable for monitoring dynamic events like fires.

Purpose of the Study:

  • To develop and evaluate an early-stage forest fire detection method using high-temporal-resolution satellite imagery.
  • To adapt the MODIS Thermal Anomaly (MOD14) algorithm for improved early fire detection.
  • To leverage the Advanced Himawari Imager (AHI) data for enhanced fire monitoring.

Main Methods:

  • Proposed an early stage fire detection method based on the MODIS Thermal Anomaly (MOD14) algorithm, adapted for AHI data.
  • Omitted potential fire pixel detection from MOD14 to avoid missing low-temperature early fire pixels.
  • Utilized a random forest classifier incorporating contextual parameters, AHI band values, solar zenith angle, and meteorological data.
  • Generated training data using a time-reversal approach with MOD14 products and time-series AHI images from Australia.

Main Results:

  • The proposed method achieved approximately 90% precision and recall in detecting fire pixels.
  • Contextual parameters were found to be particularly significant contributors to the random forest classifier's performance.
  • The method demonstrated effectiveness in identifying early-stage forest fires from satellite data.

Conclusions:

  • The developed method offers an effective approach for early forest fire detection using geostationary satellite data.
  • The inclusion of contextual parameters significantly enhances the accuracy of fire detection.
  • The method is adaptable to other satellite sensors, promising broader applications in forest fire management.