An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

Abstract

Accurate large-scale biomass prediction is crucial for assessing forest carbon storage and dynamics. It can also inform sustainable forest management practices and climate change mitigation efforts. However, stand-level biomass models are still scarce worldwide. Our study aims to introduce the generalized additive model (GAM) as a convenient and efficient approach for forest biomass estimation. Data from 311 sample plots of three conifer species in northeastern China were used to evaluate the performance of the GAM model and compare it with traditional nonlinear seemingly unrelated regression (NSUR) models in predicting stand biomass, including total, aboveground, and component biomass. The results indicated that the goodness of fit of GAM was better than that of NSUR in two model systems. In the majority of cases, the scatter plots and prediction performance revealed that the stand total and component biomass models utilizing GAM outperformed those based on NSUR. Disregarding heteroscedasticity and requiring fewer statistical assumptions provide additional support for the replacement of NSUR-based models with GAM-based models. This study implies that the GAM approach has greater potential for developing a system of stand biomass models.