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Open AccessArticle

Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

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Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
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Environmental Monitoring and Science Division, Alberta Environment and Parks, 3535 Research Road NW, University Research Park, Calgary, AB T2L 2K8, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2101; https://doi.org/10.3390/rs11182101
Received: 20 August 2019 / Revised: 5 September 2019 / Accepted: 6 September 2019 / Published: 9 September 2019
(This article belongs to the Special Issue Environmental Modelling and Remote Sensing)
Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events. View Full-Text
Keywords: 2016 Horse River wildfire; forest fire danger condition; human-caused ignition source; land surface temperature; normalized difference vegetation index; normalized difference water index 2016 Horse River wildfire; forest fire danger condition; human-caused ignition source; land surface temperature; normalized difference vegetation index; normalized difference water index
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Ahmed, M.R.; Hassan, Q.K.; Abdollahi, M.; Gupta, A. Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale. Remote Sens. 2019, 11, 2101.

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