Improving AGB estimations by integrating tree height and crown radius from multisource remote sensing

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Accurate forest above ground biomass (AGB) estimation is vital for ecological functions and carbon stock assessment.
• Directly measuring diameter at breast height (DBH) using remote sensing is challenging.
• Alternative features from remote sensing data are needed for precise AGB estimation.

Purpose of the Study:

• To demonstrate the feasibility of estimating AGB using crown radius (R) and tree height (H) extracted from multi-source remote sensing (RS) data.
• To compare the performance of allometric growth equations using H, R, or both for AGB estimation in different forest types.
• To establish a reliable method for high-precision, global-scale forest AGB estimation.

Main Methods:

• Utilized airborne laser scanning (ALS) and terrestrial laser scanning (TLS) to generate point clouds for extracting tree height (H) and crown radius (R).
• Applied nine allometric growth equations to fit coniferous (Larix principis-rupprechtii) and broadleaf (Fraxinus chinensis, Sophora japonica) forests.
• Compared model performance using H alone, R alone, and combined H+R features for AGB estimation.

Main Results:

• The quadratic polynomial model incorporating both H and R provided the best AGB estimation across all vegetation types.
• In mixed tall and short coniferous forests, the H+R model achieved R² of 0.9282 and RMSE of 25.30 kg (rRMSE 17.31%).
• Models using combined H and R significantly outperformed models using only H or R.

Conclusions:

• Estimating forest AGB using crown radius and tree height derived from high-resolution remote sensing data is feasible and accurate.
• The combined H+R approach offers a robust method for precise AGB quantification, crucial for ecological and carbon stock assessments.
• This methodology supports high-precision, global-scale forest AGB estimation, overcoming limitations of traditional DBH measurements.