An Integrated Approach to Wildland Fire Mapping of California, USA Using NOAA/AVHRR Data

To map wildland fires for emission estimation in California, this paper presents an integrated approach to wildfire mapping using daily data of the Advanced Very High Resolution Radiometer (AVHRR) on board a National Oceanic and Atmospheric Administration’s (NOAA) satellite. The approach consists of two parts: active fire detection and burnt area mapping. In active fire detection, we combined the strengths of a fixed multi-channel threshold algorithm and an adaptive-threshold contextual algorithm and modified the fire detection algorithm developed by the Canada Center for Remote Sensing (CCRS) for fire detection in boreal forest ecosystems. We added a contextual test, which considers the radiometric difference between a fire pixel and its surrounding pixels, and a sun glint elimination test to the CCRS algorithm. This can effectively remove false alarms caused by highly reflective clouds and surfaces and by warm backgrounds. In burnt area mapping, we adopted and modified the Hotspot and NDVI Differencing Synergy (HANDS) algorithm, which combines the strengths of hotspot detection and multi-temporal NDVI differencing. We modified the HANDS procedure in three ways: normalizing post-fire NDVI to pre-fire NDVI by multiplying an NDVI ratio coefficient, calculating mean and standard deviation of NDVI decrease of land-cover types separately, and adding a new iteration procedure for confirming potential burnt pixels. When the integrated method was applied to the mapping of wildland fires in California during the 1999 fire season, it produced comparable results. Most of the wildfires mapped were found to be correct, especially for those in forested ecosystems. Validation was based both on limited ground truth from the California Department of Forestry and Fire Protection and on interpreted burnt areas from Landsat 7 TM scenes. An Integrated Approach to Wildland Fire Mapping of California, USA Using NOAA/AVHRR Data Peng Gong, Ruiliang Pu, Zhanqing Li, James Scarborough, Nicolas Clinton, and Lisa M. Levien Introduction Wildfires and prescribed burning occur frequently in California. They produce hazardous emissions leading to episodic increases in particulate matter (PM2.5) and visibility reducing particles. Smoke from such burning, if not properly managed, can have significant health impacts on exposed populations. A reliable estimation of burnt areas caused by biomass burning in California, therefore, is a key for emission estimation. Ground-based measurements of fire perimeters after-fire events or regular overpasses for fire mapping during the burning using an airplane with an onboard sensor may not meet the requirement for emission estimation at a regional or continental scale. Remote sensing is the most efficient and economic means for monitoring fires over large areas on a routine basis despite its various limitations (Li et al., 2000a, b; Justice et al., 1993). The type of sensor most widely used for long-term, large-scale fire monitoring is the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration’s (NOAA) polar orbiting satellites (Stroppiana et al., 2000; Li et al., 1997; Justice et al., 1996; Kennedy et al., 1994; Kaufman et al., 1990; Flannigan and Vonder Haar, 1986). AVHRR (onboard the NOAA-14 satellite and earlier) data are available at a nominal spatial resolution of 1.1 km in five channels: the visible, near-infrared (IR), mid-IR, and two thermal-IR portions of the spectrum. Such spectral resolution offers considerable benefits to fire monitoring (Harris, 1996). Channels 1 and 2 provide data capable of detecting, monitoring, and measuring smoke emissions (Khazenie and Richardson, 1993; Kaufman et al., 1990), but contain no thermal information. Channel 3 is extremely sensitive to sub-pixel hot spots, making it the most important channel for fire detection (Rauste et al., 1997; Pozo et al., 1997; Franca et al., 1995; Setzer and Pereira, 1991; Muirhead and Cracknell, 1985) though it has a low temperature saturation point ( 321 k) (most existing algorithms concentrate on the third channel, hoping to overcome this disadvantage). Channels 4 and 5 are far less sensitive to sub-pixel hotspots, but they can frequently help detect fires when combined with other channels (Flasse and Ceccato, 1996; Justice et al., 1996). In addition, the AVHRR onboard post-NOAA-14 satellites also include a 1.65 m short wave infrared (SWIR) PHOTOGRAMMETRIC ENGINEER ING & REMOTE SENS ING Feb r ua r y 2006 139 Peng Gong and Ruiliang Pu are with the State Key Lab of Remote Sensing Science, IRSA, Box 9718, Beijing, China, 100101 ([email protected]). Peng Gong, Ruiliang Pu, James Scarborough, and Nicolas Clinton are with the Center for Assessment and Monitoring of Forest and Environmental Resources, 137 Mulford Hall, University of California, Berkeley, CA 94720. Zhanqing Li is with the Department of Meteorology and Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742-2465. Lisa M. Levien is with the USDA Forest Service, Forest Health Protection, 1920 20th Street, Sacramento, CA 95814. Photogrammetric Engineering & Remote Sensing Vol. 72, No. 2, February 2006, pp. 139–150. 0099-1112/06/7202–0139/$3.00/0 © 2006 American Society for Photogrammetry and Remote Sensing 03-085.qxd 1/18/06 5:16 AM Page 139