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Title: Methods for estimating aboveground biomass and its components for five Pacific Northwest tree species

Author: Poudel, Krishna P.; Hailemariam, Temesgen.;

Date: 2015

Source: In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 19-24.

Publication Series: General Technical Report (GTR)

   Note: This article is part of a larger document. View the larger document

Description: Performance of three groups of methods to estimate total and/or component aboveground biomass was evaluated using the data collected from destructively sampled trees in different parts of Oregon. First group of methods used analytical approach to estimate total and component biomass using existing equations, and produced biased estimates for our dataset. The second group used a system of equations fitted with seemingly unrelated regression (SUR), and was superior to group I methods. The third group of methods predicted the proportions of biomass in each component using beta, Dirichlet, and multinomial logistic regression (MLR). The MLR approach produced smaller root mean squared error (RMSE) compared to the SUR approaches except for grand fir branch biomass while the beta and Dirichlet regressions provided smaller RMSE compared to the SUR approaches for 85 percent of the species-component combinations.

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Poudel, Krishna P.; Hailemariam, Temesgen. 2015. Methods for estimating aboveground biomass and its components for five Pacific Northwest tree species. In: Stanton, Sharon M.; Christensen, Glenn A., comps. 2015. Pushing boundaries: new directions in inventory techniques and applications: Forest Inventory and Analysis (FIA) symposium 2015. 2015 December 8–10; Portland, Oregon. Gen. Tech. Rep. PNW-GTR-931. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station: 19-24.

 


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