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

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Author
Chowdhury, Ehsan
Hassan, Quazi
Accessioned
2015-03-29T17:24:59Z
Available
2015-03-29T17:24:59Z
Issued
2015-03-02
Subject
Remote sensing
Fire danger
Forecasting
Type
journal article
Metadata
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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.
Citation
Chowdhury, E.H.; Hassan, Q.K. 2015. Development of a new daily-scale forest fire danger forecasting system using remote sensing data. Remote Sensing 7(3), 2431-2448.
Department
Geomatics Engineering
Faculty
Schulich School of Engineering
Institution
University of Calgary
Publisher
MDPI
Doi
http://dx.doi.org/10.11575/PRISM/10037
Uri
http://hdl.handle.net/1880/50394
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