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 (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.
