Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands
AbstractBurn severity metrics and classification have yet to be tested for many eastern U.S. deciduous vegetation types, but, if suitable, would be valuable for documenting and monitoring landscape-scale restoration projects that employ prescribed fire treatments. Here we present a performance analysis of the Composite Burn Index (CBI) and its relationship to spectral data (differenced Normalized Burn Ratio (dNBR) and its relative form (RdNBR)) across an oak woodland - grassland landscape in southwestern Oklahoma, USA. Correlation and regression analyses were used to compare CBI strata, assess models describing burn severity, and determine thresholds for burn severity classes. Confusion matrices were used to assess burn severity classification accuracy. Our findings suggest that dNBR and RdNBR, thresholded using total CBI, can produce an accurate burn severity map in oak woodlands, particularly from an initial assessment period. Lower accuracies occurred for burn severity classifications of grasslands and raises questions related to definitions and detection of burn severity for grasslands, particularly in transition to more densely treed structures such as savannas and woodlands. View Full-Text
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Stambaugh, M.C.; Hammer, L.D.; Godfrey, R. Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands. Remote Sens. 2015, 7, 10501-10522.
Stambaugh MC, Hammer LD, Godfrey R. Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands. Remote Sensing. 2015; 7(8):10501-10522.Chicago/Turabian Style
Stambaugh, Michael C.; Hammer, Lyndia D.; Godfrey, Ralph. 2015. "Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands." Remote Sens. 7, no. 8: 10501-10522.