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Remote Sens. 2015, 7(3), 2431-2448; doi:10.3390/rs70302431

Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data

Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada
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Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 23 December 2014 / Revised: 11 February 2015 / Accepted: 17 February 2015 / Published: 2 March 2015
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Abstract

Forest fires are a critical natural disturbance in most of the forested ecosystems around the globe, including the Canadian boreal forest where fires are recurrent. Here, our goal was to develop a new daily-scale forest fire danger forecasting system (FFDFS) using remote sensing data and implement it over the northern part of Canadian province of Alberta during 2009–2011 fire seasons. The daily-scale FFDFS was comprised of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived four-input variables, i.e., 8-day composite of surface temperature (TS), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The TS, NMDI, and NDVI variables were calculated during i period and PW during j day and then integrated to forecast fire danger conditions in five categories (i.e., extremely high, very high, high, moderate, and low) during j + 1 day. Our findings revealed that overall 95.51% of the fires fell under “extremely high” to “moderate” danger classes. Therefore, FFDFS has potential to supplement operational meteorological-based forecasting systems in between the observed meteorological stations and remote parts of the landscape. View Full-Text
Keywords: fire spot; normalized multiband drought index; normalized difference vegetation index; operational perspective; precipitable water; surface temperature fire spot; normalized multiband drought index; normalized difference vegetation index; operational perspective; precipitable water; surface temperature
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chowdhury, E.H.; Hassan, Q.K. Development of a New Daily-Scale Forest Fire Danger Forecasting System Using Remote Sensing Data. Remote Sens. 2015, 7, 2431-2448.

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