Abstract
Forest fire is one of the major natural hazards/disasters in Canada and many ecosystems across the world. Here, our objective was to enhance the performance of an existing solely remote sensing-based forest fire danger forecasting system (FFDFS), and its implementation over the northern region of the Canadian province of Alberta. The modified FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daily surface temperature (Ts) and precipitable water (PW), and 8-day composite of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), where we assumed that cloud-contaminant pixels would reduce the risk of fire occurrences. In addition, we generated ignition cause-specific static fire danger (SFD) maps derived using the historical human- and lightning-caused fires during the period 1961–2014. Upon incorporating different combinations of the generated SFD maps with the modified FFDFS, we evaluated their performances against actual fire spots during the 2009–2011 fire seasons. Our findings revealed that our proposed modifications were quite effective and the modified FFDFS captured almost the same amount of fires as the original FFDFS, i.e., about 77% of the detected fires on an average in the top three fire danger classes of extremely high, very high, and high categories, where about 50% of the study area fell under low and moderate danger classes. Additionally, we observed that the combination of modified FFDFS and human-caused SFD map (road buffer) demonstrated the most effective results in fire detection, i.e., 82% of detected fires on an average in the top three fire danger classes, where about 46% of the study area fell under the moderate and low danger categories. We believe that our developments would be helpful to manage the forest fire in order to reduce its overall impact.
Highlights
Forest fires are an integral part of ecosystems across the globe, including those in the Canadian boreal region
In order to determine the best combination of remote sensing-based data and static fire danger (SFD) maps in forecasting forest fire danger conditions, we evaluated the remote sensing-based daily fire danger maps, and enhanced ones based on the SFD maps by overlaying the ground-based forest fire spots during the period 2009–2011
Upon evaluating the daily fire danger maps, we compared those with the results reported by Chowdhury and Hassan [8] in order to find the best system in forecasting forest fire danger conditions
Summary
Forest fires are an integral part of ecosystems across the globe, including those in the Canadian boreal region. In order to quantify the balance between negative and positive impacts, it is extremely important to have an efficient system to manage forest fires. In this context, one of the critical steps to understanding the pre-fire conditions is the need of formulating a forest fire danger forecasting system, which would be possible by using remote sensing-based approaches. We can divide the remote sensing-based approaches broadly into two categories, such as monitoring systems that determine the fire danger conditions during and/or after fire occurrences [5,6,7]; and forecasting systems that predict the fire danger conditions before fire occurrences [1,8,9].
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