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Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imageryAuthor(s): Michael C. Wimberly; Matthew J. Reilly
Source: Remote Sensing of Environment. 108: 189-197.
Publication Series: Miscellaneous Publication
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DescriptionRelatively little is known about the disturbance ecology of large wildfires in the southern Appalachians. The occurrence of a 4000-ha wildfire in the Linville Gorge Wilderness area in western North Carolina has provided a rare opportunity to study a large fire with a range of severities. The objectives of this study were to 1) assess the potential for using multi-temporal Landsat imagery to map fire severity in the southern Appalachians, 2) examine the influences of topography and forest community type on the spatial pattern of fire severity; and 3) examine the relationship between predicted fire severity and changes in species richness. A non-linear regression equation predicted a field-based composite burn index (CBI) as a function of change in the Normalized Burn Ratio (dNBR) with an R2 of 0.71. Fire severity was highest on drier landforms located on upper hillslopes, ridges, and on southwest aspects, and was higher in pine communities than in other forest types. Predicted CBI was positively correlated with changes in species richness and with the post-fire cover of pine seedlings (Pinus virginiana, P. rigida, and P. pungens), suggesting that burn severity maps can be used to predict community-level fire effects across large landscapes. Despite the relatively large size of this fire for the southern Appalachians, severity was strongly linked to topographic variability and pre-fire vegetation, and spatial variation in fire severity was correlated with changes in species richness. Thus, the Linville Gorge fire appears to have generally reinforced the ecological constraints imposed by underlying environmental gradients.
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CitationWimberly, Michael C.; Reilly, Matthew J. 2007. Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery. Remote Sensing of Environment. 108: 189-197.
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