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

Remote Sensing

Ehsan H. Chowdhury²  Quazi K. Hassan¹  Ioannis Gitas² 

[1] Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada; E-Mail

关键词: fire spot;    normalized multiband drought index;    normalized difference vegetation index;    operational perspective;    precipitable water;    surface temperature;   

DOI  :  10.3390/rs70302431

来源: mdpi

【 摘 要 】

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 (T_S), normalized difference vegetation index (NDVI), and normalized multiband drought index (NMDI); and daily precipitable water (PW). The T_S, 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.