Development of a Satellite-Based Forest Fire Danger Forecasting System and its Implementation Over the Forest Dominant Regions in Alberta, Canada
Creators: name: Chowdhury, Ehsan Hafiz
Publisher: name: Graduate Studies
Year: 2015
Subjects: subject: Remote Sensing
subject: Environmental Sciences
subject: Engineering--Environmental
subject: Surface temperature
subject: Normalized multiband drought index
subject: Normalized difference vegetation index
subject: Precipitable water
subject: Fire spot
subject: MODIS

description: Forest fire is a natural phenomenon in many ecosystems across the world. One of the most important components of forest fire management is forecasting of fire danger conditions. My aim was to develop a daily-scale forest fire danger forecasting system (FFDFS) using remote sensing inputs over the northern part of Canadian province of Alberta during 2009-2011 fire seasons. In this research, I critically analyzed the current operational fire danger forecasting systems and other remote sensing-based methods in order to determine the knowledge gaps. In general, the operational systems use point-based measurements of meteorological variables and generate danger maps upon employing interpolation techniques. It is possible to overcome the uncertainty associated with the interpolation techniques by using remote sensing data. It was observed that most of the fire danger monitoring systems focused on determining the danger during and/or after the period of image acquisition, thus unable to forecast the fire danger accurately. A limited number of studies were conducted to forecast fire danger conditions, which could be adaptable. In this thesis, I developed FFDFS’s useful for mid-term (i.e., 8-day) and daily-scale forecasting. The newly developed 8-day scale FFDFS uses Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 8-day composite of surface temperature (TS), normalized multiband drought index (NMDI), and normalized difference vegetation index (NDVI). In order to eliminate the data gaps in the input variables, I propose a gap-filling technique that considered both of the spatial and temporal dimensions. The input variables were calculated during the i period and then integrated to forecast the danger conditions into four categories during the i + 1 period. I observed that 90.94% of the fire fell under ‘very high’ to ‘moderate’ danger classes when compared with Alberta Environment and Sustainable Resource Development (ESRD) fire spots. As regards to operational perspective, I opted to develop daily-scale FFDFS comprised of MODIS-derived 8-day composite of TS, NDVI, and 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 into five categories during j+1 day. Results were significant with 95.51% of fires in the ‘extremely high’ to ‘moderate’ danger classes. Therefore, I infer that the refined FFDFS approach developed using remote sensing variables has operational value and can be routinely incorporated into meteorological based fire forecasting systems. Therefore, I apprehend that FFDFS could be used as an operational one; and has the potential to supplement information to the operational meteorological-based forecasting systems.