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Use of remote sensing-derived variables in developing a forest fire danger forecasting system

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  • Ehsan Chowdhury
  • Quazi Hassan

Abstract

Our aim was to develop a remote sensing-based forest fire danger forecasting system (FFDFS) and its implementation in forecasting 2011 fire season in the Canadian province of Alberta. The FFDFS used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 8-day composites of surface temperature, normalized multiband drought index, and normalized difference vegetation index as input variables. In order to eliminate the data gaps in the input variables, we propose a gap-filling technique by considering both of the spatial and temporal dimensions. These input variables were calculated during the i period and then integrated to forecast the fire danger conditions into four categories (i.e., very high, high, moderate, and low) during the i + 1 period. It was observed that 98.19 % of the fire fell under “very high” to “moderate” danger classes. The performance of this system was also demonstrated its ability to forecast the worst fires occurred in Slave Lake and Fort McMurray region during mid-May 2011. For example, 100 and 94.0 % of the fire spots fell under “very high” to “high” danger categories for Slave Lake and Fort McMurray regions, respectively. Copyright The Author(s) 2013

Suggested Citation

  • Ehsan Chowdhury & Quazi Hassan, 2013. "Use of remote sensing-derived variables in developing a forest fire danger forecasting system," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 321-334, June.
    • Handle: RePEc:spr:nathaz:v:67:y:2013:i:2:p:321-334
    DOI: 10.1007/s11069-013-0564-7
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    References listed on IDEAS

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    1. Litao Wang & Yi Zhou & Weiqi Zhou & Shixing Wang, 2013. "Fire danger assessment with remote sensing: a case study in Northern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 819-834, January.
    2. Govind, Ajit & Chen, Jing Ming & Bernier, Pierre & Margolis, Hank & Guindon, Luc & Beaudoin, Andre, 2011. "Spatially distributed modeling of the long-term carbon balance of a boreal landscape," Ecological Modelling, Elsevier, vol. 222(15), pages 2780-2795.
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    Cited by:

    1. Abdullah Al Saim & Mohamed H. Aly, 2022. "Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA," Geographies, MDPI, vol. 2(1), pages 1-17, January.
    2. Faisal, Abdullah Al & Kafy, Abdulla - Al & Afroz, Farzana & Rahaman, Zullyadini A., 2023. "Exploring and forecasting spatial and temporal patterns of fire hazard risk in Nepal's tiger conservation zones," Ecological Modelling, Elsevier, vol. 476(C).

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