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

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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 hard-defined, heavily depending on user experience. We propose to use a user-friendly machine learning method, Random Forest™, 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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Document Type: Research Article

Publication date: 01 January 2017

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  • The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.

    Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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