Mapping Burn Severity of Mediterranean-Type Vegetation Using Satellite Multispectral Data

Knowledge of the spatial distribution of burn severity immediately following a fire is needed to locate areas requiring management for environmental impacts and timber salvage, and for validation of fire risk and fire behavior models. We evaluated methods for mapping post-fire burn severity in southern California Mediterranean-type ecosystems using satellite images calibrated and validated by field-collected data. The effects of spectral transforms, temporal dimensionality, classifiers, and sensor type on the accuracy of burn severity classification were analyzed. We mapped and analyzed the distributions of five categories of burn severity or land cover for two southern California wildfires based primarily on classification of Landsat TM/ETM+ data, with IKONOS MS data also being evaluated. Map accuracy was assessed relative to field-based classification of burn severity of randomly located plots, using the Composite Burn Index approach. Maps based on the multi-temporal Kauth Thomas transform of Landsat TM/ETM+ data and maximum likelihood classifier had the highest overall accuracy (64 and 55%) and kappa values (0.51 and 0.37) for the two study areas. Forested lands were classified at a much higher level of accuracy (overall accuracy near 80%), while accurate classification of burn severity in shrublands was more challenging (overall accuracy less than 50%). The lower stature vegetation of shrublands typically experiences crown-burning fires, such that range of burn severity for shrublands is more limited.

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