Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data

Author： Chowdhury Ehsan H. Hassan Quazi K.

Publisher： MDPI

E-ISSN： 2072-4292|7|3|2431-2448

ISSN： 2072-4292

Source： Remote Sensing, Vol.7, Iss.3, 2015-03, pp. : 2431-2448

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Abstract

Forest 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 (T_S), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The T_S, 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.