Modeling a single-tree biomass equation by seemingly unrelated regression and dummy variables with Larix kaempferi

Abstract

Developing generalized single-tree biomass models suitable for forest biomass estimation is an effective way to provide scientific approaches. To simplify biomass modeling and improve the accuracy of model estimation for better understanding of biomass, carbon stocks, and dynamics in large-scale forests and to precisely estimate tree biomass, this study used stem, bark, needle, branch, and root biomass of Larix kaempferi of 161 sample trees in Gansu, Hubei, and Liaoning Provinces to generalize single-tree biomass equations suitable for different organs and regions using seemingly unrelated regression and dummy variable modeling methods. Results showed that the generalized biomass equations not only solved compatibility problems with different components but also increased accuracy with an average increase of 0.28%-0.44% in R2, decreased 0.40%-6.61% in the root mean square error (ERMS), and decreased 1.63%-6.61% in the mean abosolute bias (BMA). Effects due to region increased accuracy more than effects due to developmental stages. When both region and developmental stages were added to the dummy variable model, it was more accurate and produced the best equation. Therefore, we suggest that both regional and developmental stages be considered as dummy variables to establish generalized biomass equations in order to solve the compatibility problem with different components as well as for overcoming problems of generalizing with different regions.