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Remote Sens. 2015, 7(8), 10501-10522; doi:10.3390/rs70810501

Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands

1
Department of Forestry, 203 ABNR Building, University of Missouri-Columbia, Columbia, MO 65211, USA
2
Lomakatsi Restoration Project, PO Box 3084, Ashland, OR 97520, USA
3
Wichita Mountains Wildlife Refuge, U.S. Fish and Wildlife Service, 32 Refuge Headquarters, Indiahoma, OK 73552, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas, Josef Kellndorfer and Prasad S. Thenkabail
Received: 1 April 2015 / Revised: 30 July 2015 / Accepted: 12 August 2015 / Published: 17 August 2015
View Full-Text   |   Download PDF [1372 KB, uploaded 17 August 2015]   |  

Abstract

Burn 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
Keywords: CBI; drought; fire history; Landsat; dNBR; RdNBR; remote sensing CBI; drought; fire history; Landsat; dNBR; RdNBR; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

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.

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