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

Remote Sensing

Michael C. Stambaugh³  Lyndia D. Hammer²  Ralph Godfrey¹  Ioannis Gitas⁴  Josef Kellndorfer⁴ 

[1]Wichita Mountains Wildlife Refuge, U.S. Fish and Wildlife Service, 32 Refuge Headquarters, Indiahoma, OK 73552, USA

[2] E-Mail:

[3]Lomakatsi Restoration Project, PO Box 3084, Ashland, OR 97520, USA

[4] E-Mail:

[5]Department of Forestry, 203 ABNR Building, University of Missouri-Columbia, Columbia, MO 65211, USA

[6]id="af1-remotesensing-07-10501">1Department of Forestry, 203 ABNR Building, University of Missouri-Columbia, Columbia, MO 65211, U

关键词: CBI;    drought;    fire history;    Landsat;    dNBR;    RdNBR;    remote sensing;   

DOI  :  10.3390/rs70810501

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.