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Quantifying Early-Seral Forest Composition with Remote Sensing
Photogrammetric Engineering & Remote Sensing
  • Rayma a. Cooley, United States Forest Service
  • Peter T. Wolter, Iowa State University
  • Brian R. Sturtevant, United States Forest Service
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Spatially explicit modeling of recovering forest structure within two years following wildfire disturbance has not been attempted, yet such knowledge is critical for determining successional pathways. We used remote sensing and field data, along with digital climate and terrain data, to model and map early-seral aspen structure and vegetation species richness following wildfire. Richness was the strongest model (RMSE = 2.47 species, Adj. R2 = 0.60), followed by aspen stem diameter, basal area (BA), height, density, and percent cover (Adj. R2 range = 0.22 to 0.53). Effects of pre-fire aspen BA and fire severity on post-fire aspen structure and richness were analyzed. Post-fire recovery attributes were not significantly related to fire severity, while all but percent cover and richness were sensitive to pre-fire aspen BA (Adj. R2 range = 0.12 to 0.33, p <0.001). This remote mapping capability will enable improved prediction of future forest composition and structure, and associated carbon stocks.

This is an article from Photogrammetric Engineering & Remote Sensing 82 (2016): 853, doi: 10.14358/PERS.82.11.853. Posted with permission.

Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.
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Rayma a. Cooley, Peter T. Wolter and Brian R. Sturtevant. "Quantifying Early-Seral Forest Composition with Remote Sensing" Photogrammetric Engineering & Remote Sensing Vol. 82 Iss. 11 (2016) p. 853 - 863
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