An active-fire based burned area mapping algorithm for the MODIS sensor

• Published in: Remote sensing of environment Vol. 113; no. 2; pp. 408 - 420
• Main Authors: Giglio, Louis, Loboda, Tatiana, Roy, David P., Quayle, Brad, Justice, Christopher O.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 16-02-2009; Elsevier
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Biomass burning; Burn scar; Burned area; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fire; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; MODIS; Teledetection and vegetation maps; Russian Federation; Eurasia; United States; Southern Africa; Africa; Siberia; Russia, Siberia; USA; Burn scar; Burned area; Biomass burning; MODIS; Fire; algorithms; Vegetation index; textures; accuracy; measurement sensor; North America; trees; evaluation; dynamics; automation; imagery; high resolution; Threshold; biomass; maps; Space remote sensing; Landsat; cartography; fires; methodology
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2008.10.006

We present an automated method for mapping burned areas using 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) imagery coupled with 1-km MODIS active fire observations. The algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive vegetation index and a measure of temporal texture. Cumulative active fire maps are used to guide the selection of burned and unburned training samples. An accuracy assessment for three geographically diverse regions (central Siberia, the western United States, and southern Africa) was performed using high resolution burned area maps derived from Landsat imagery. Mapped burned areas were accurate to within approximately 10% in all regions except the high-tree-cover sub-region of southern Africa, where the MODIS burn maps underestimated the area burned by 41%. We estimate the minimum detectable burn size for reliable detection by our algorithm to be on the order of 120 ha.