Quantifying Early-Seral Forest Composition with Remote SensingPhotogrammetric Engineering & Remote Sensing
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AbstractSpatially 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.
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Citation InformationRayma 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
Available at: http://works.bepress.com/peter-wolter/15/