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Open AccessFeature PaperArticle

An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data

1
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
2
NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
3
Alberta Environment and Parks, 2938 11 Street NE, Calgary, AB T2E 7L7, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 923; https://doi.org/10.3390/rs10060923
Received: 20 May 2018 / Revised: 2 June 2018 / Accepted: 11 June 2018 / Published: 12 June 2018
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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. View Full-Text
Keywords: human-caused static fire danger map; lightning-caused static fire danger map; normalized difference vegetation index; normalized difference water index; precipitable water; surface temperature human-caused static fire danger map; lightning-caused static fire danger map; normalized difference vegetation index; normalized difference water index; precipitable water; surface temperature
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MDPI and ACS Style

Abdollahi, M.; Islam, T.; Gupta, A.; Hassan, Q.K. An Advanced Forest Fire Danger Forecasting System: Integration of Remote Sensing and Historical Sources of Ignition Data. Remote Sens. 2018, 10, 923.

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