4.3 Article

Fire Risk Prediction Using Remote Sensed Products: A Case of Cambodia

Journal

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
Volume 83, Issue 1, Pages 19-25

Publisher

AMER SOC PHOTOGRAMMETRY
DOI: 10.14358/PERS.83.1.19

Keywords

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Funding

  1. Hundred Talents Program of Chinese Academy of Sciences [Y34004101A]
  2. Comparative Study on Global Environmental Change Using Remote Sensing Technology [41120114001]
  3. National Natural Science Foundation of Major International (regional) Collaborative Research Project and High Resolution Earth Observation Systems [14CNIC-032079-32]

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Forest fire is threatening human life in monsoon countries, such as Cambodia, which suffers from forest fire frequently. Developing an efficient method to predict fire risk for large areas is becoming significantly important. However, the methods used in fire risk prediction are mostly based on field-based meteorological data, and the coefficients are harddefined, heavily depending on user experience. We propose to use a user-friendly machine learning method, Random Forest(TM), to train a regression model by synthesizing publicly available remote sensed products to predict fire risk ratings at pixel-level in eight-day advance. The structure of our model synthesizes features in three-time intervals T1, T2, and T3 to predict fire occurrence probability in T4. The experiment demonstrates the efficiency of such model in predicting fire occurrence with a correlation coefficient of 0.987 and mean square error being 0.00285. It results in a practical way to predict fire risk and prevent fires.

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