Mapping fuel moisture codes using MODIS images and the Getis statistic over western Canada grasslands

• Published in: International journal of remote sensing 2010-01, Vol.32 (6), p.1619-1634
• Main Authors: Merzouki, A, Leblon, B
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Bulk molding compounds; Direct current; drought; Earth sciences; Earth, ocean, space; Exact sciences and technology; fire hazard; fire weather; Fires; forest fires; Fuels; fuels (fire ecology); Fundamental and applied biological sciences. Psychology; General aspects. Techniques; image analysis; Internal geophysics; land cover; Mathematical models; moderate resolution imaging spectroradiometer; MODIS; Moisture; national grasslands; national parks; Teledetection and vegetation maps
• ISSN: 1366-5901; 0143-1161; 1366-5901
• DOI: 10.1080/01431160903586773

In Canada, fire danger is predicted by the Canadian Forest Fire Danger Rating System (CFFDRS). One of its subsystems is the Canadian Fire Weather Index (FWI) system, which has two slow-drying fuel moisture codes: the drought code (DC) and the duff moisture code (DMC). Both codes are used in this study as a surrogate of dead fuel moisture. We evaluate the capability of Moderate Resolution Imaging Spectrometer (MODIS) observations to map DC and DMC. A comparison was made between 16-day composite MODIS images acquired in the 2000 to 2004 fire seasons over Grasslands National Park of Canada (GNPC), Saskatchewan, Canada and DC and DMC data interpolated from weather station records. Overall, spectral variables and DC and DMC exhibited clear seasonal trends, and were therefore influenced more by temporal factors than by land cover factors. Data were then grouped into temporal categories, by year and seasons, for stepwise multiple regression calculations. We note that the use of shortwave infrared (SWIR)-based variables slightly improved the performances of both DC and DMC models. Spectrally derived DC and DMC data showed improved spatial resolution of mapping drought over pre-burned areas compared to broadly interpolated weather station-based estimations. The spectral estimation was improved by applying the Getis statistic (Gᵢ*) to the resulting maps, showing the potential of combining the Gᵢ* statistic and MODIS data in predicting fuel moisture codes in relationship to fire occurrence.