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

M Ahmed,Quazi Hassan,Masoud Abdollahi,Anil Gupta

doi:10.3390/rs11182101

M Ahmed, Quazi Hassan + Show 2 more

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https://doi.org/10.3390/rs11182101

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Abstract

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.

Highlights

Forest fires are natural disturbances in many forest ecosystems around the world, including the forested regions in Canada
The results demonstrated that about 49% and 39% of forecasted forest fires fell under very high and high classes, respectively, which indicated that about 88% fires fell under these two fire danger (FD) classes
We proposed a simple but effective four-day scale forest fire danger forecasting system (FFDFS) primarily using remote sensing (RS)-based input variables

Summary

Introduction

Forest fires are natural disturbances in many forest ecosystems around the world, including the forested regions in Canada. The FWI is related to the relative forest fire potential, whereas the actual fire behavior is predicted by FBP [11]. The inherent problem of deriving the spatial dynamics from weather observations has already been identified in literature, as employing interpolation methods for deriving the spatial extent of weather dynamics from station-based weather observations is problematic [16]. To overcome such issue, satellite-based remote sensing (RS) technology would be an alternative approach to study the forest fire danger conditions [16,17,18]

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