Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data
AbstractForest fires are a critical natural disturbance in most of the forested ecosystems around the globe, including the Canadian boreal forest where fires are recurrent. Here, our goal was to develop a new daily-scale forest fire danger forecasting system (FFDFS) using remote sensing data and implement it over the northern part of Canadian province of Alberta during 2009–2011 fire seasons. The daily-scale FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived four-input variables, i.e., 8-day composite of surface temperature (TS), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The TS, NMDI, and NDVI variables were calculated during i period and PW during j day and then integrated to forecast fire danger conditions in five categories (i.e., extremely high, very high, high, moderate, and low) during j + 1 day. Our findings revealed that overall 95.51% of the fires fell under “extremely high” to “moderate” danger classes. Therefore, FFDFS has potential to supplement operational meteorological-based forecasting systems in between the observed meteorological stations and remote parts of the landscape. View Full-Text
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Chowdhury, E.H.; Hassan, Q.K. Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data. Remote Sens. 2015, 7, 2431-2448.
Chowdhury EH, Hassan QK. Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data. Remote Sensing. 2015; 7(3):2431-2448.Chicago/Turabian Style
Chowdhury, Ehsan H.; Hassan, Quazi K. 2015. "Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data." Remote Sens. 7, no. 3: 2431-2448.