Gong, P, Pu, RL, Li, ZQ, Scarborough, J, Clinton, N, Levien, LM (2006). "An integrated approach to wildland fire mapping of California, USA using NOAA/AVHRR data". PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 72(2), 139-150.
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 Notional Oceanic and Atmospheric Administration's (NOAA) satellite. The approach consists of two parts: active fire detection and burnt area mopping. 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 worm 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.