[Additive aboveground biomass equations based on different predictors for natural Tilia Linn]

Area of Science:

• Forestry Science
• Ecology
• Biometrics

Background:

• Accurate forest biomass quantification is essential for ecological research, including carbon storage, climate change, and forest productivity.
• Biomass models provide a robust approach for estimating forest biomass, serving as foundational data for various ecological studies.
• Tilia Linn. species in the Xiaoxing'an Mountains and Zhangguangcai ranges are key components of forest ecosystems.

Purpose of the Study:

• To develop and validate additive systems of individual tree biomass equations for natural Tilia Linn. in China.
• To assess the impact of incorporating tree diameter at breast height (D), height (H), and crown factor on biomass model performance.
• To provide precise biomass estimation tools for ecological research and forest management.

Main Methods:

• Developed three additive systems of individual tree biomass equations based on D only, D and H, and best model selection.
• Employed nonlinear seemingly unrelated regression for parameter estimation and addressed heteroscedasticity with unique weight functions.
• Validated models using the Jackknifing technique to ensure reliability and accuracy.

Main Results:

• All three additive systems demonstrated good fit and prediction capabilities for Tilia Linn. biomass (R_a² > 0.84, MPE < 8.5%).
• Models incorporating total tree height and crown factor significantly improved fitting performance and prediction precision.
• Stem and aboveground biomass equations outperformed branch, foliage, and crown equations; the best model system yielded superior results.

Conclusions:

• Additive systems of biomass equations provide accurate estimations for Tilia Linn. individual tree biomass.
• Incorporating total tree height and crown factor is essential for enhancing biomass model accuracy and precision.
• The developed models are valuable tools for ecological research, carbon stock assessment, and sustainable forest management.