Forest Fire Danger/Risk Forecasting: A Remote Sensing Approach
Subject2016 Horse River Fire
forest fire danger condition
human-caused ignition source
land surface temperature
moderate resolution imaging spectroradiometer (MODIS)
natural hazards and disasters
normalized difference vegetation index (NDVI)
normalized difference water index (NDWI)
very high spatial resolution
wildland-urban interface (WUI)
MetadataShow full item record
AbstractForest/wildland fires are natural disasters that create a significant threat to the communities living in the vicinity of the forested landscape. To minimize the risk concerning resiliency of those urban communities to forest fires, my overall objective was to develop primarily remote sensing (RS)-based models assessing potential risks at the wildland-urban interface (WUI) and making predictions of danger conditions in the environs forest/vegetation. I investigated the risks associated with WUI for the Fort McMurray community and danger conditions in the northern part of Alberta, Canada. For developing the risk modelling framework at WUI, I employed primarily a WorldView-2 satellite image acquired on June 06, 2016. I estimated structural damages due to the devastating 2016 Horse River wildland fire (HRF) that entered the community on May 03, 2016. Besides, I analyzed the presence of vegetation at the WUI to identify the associated risks according to the FireSmart Canada guidelines. My remote sensing-based estimates of the number of structural damages identified a strong linear relationship (i.e., r2 value of 0.97) with the ground-based estimates. Besides, all damaged structures were found associated with the existence of vegetation within the 30m buffer/priority zone of the WUI. It was revealed that approximately 30% of the areas of the WUI were vulnerable due to the presence of vegetation, in which approximately 7% were burned during the 2016 HRF event that led the structural damages. In addition, I developed a new medium-term (i.e., four days) model to forecast forest fire danger conditions using RS-derived biophysical variables of vegetation. I primarily employed Terra MODIS (moderate resolution imaging spectroradiometer)-derived four-day composites of daily surface temperature, normalized difference vegetation index and normalized difference water index. The model was able to detect about 75% of the fire events in the top two danger classes (i.e., very high and high) when evaluated with the historical ground-based forest fire occurrences during the fire seasons of 2015–2017. Besides, the model was able to predict the 2016 HRF event with about 67% agreement. Finally, I developed an operational near real-time (NRT) model to forecast forest fire danger conditions for a day to the next 8 days. Here, I employed Terra MODIS-acquired NRT data from NASA's LANCE (land, atmosphere near real-time capability for earth observing system), where data are made available to the public domain within 2.5 hours of satellite observation. The NRT model was successful in producing forecasted forest fire danger maps at any given time. These developed risk/forecast models would be very useful for the stakeholders in the forest fires management strategies of saving life, property, and community.
CitationAhmed, M. R. (2020). Forest Fire Danger/Risk Forecasting: A Remote Sensing Approach (Unpublished doctoral thesis). University of Calgary, Calgary, AB.
University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.