Estimating individual tree DBH and biomass of durable Eucalyptus using UAV LiDAR

Abstract

Fast-growing eucalyptus species, used as vineyard posts in New Zealand's Marlborough region, offer both durability and potential carbon sequestration benefits. However, the scale of carbon sequestration by these species remains unexplored. This study aimed to estimate individual tree dimensions (diameter at breast height, DBH) and above-ground biomass (AGB) for Eucalyptus globoidea and E. bosistoana using light detection and ranging (LiDAR) data acquired by an unpiloted aerial vehicle (UAV). LiDAR data were captured before destructive sampling, and 96 individual tree LiDAR metrics were extracted. Three machine learning (ML) models, including Partial Least Squares Regression (PLSR), Random Forest, and Extreme Gradient Boosting (XGBoost), were trained. Model performance was evaluated using the root mean square error and coefficient of determination (R 2 ). SHapley Additive exPlanations (SHAP) analysis was employed to explain model predictions and evaluate input variables. Results showed that among the ML models, XGBoost and PLSR demonstrated superior performance, with the former yielding the highest R 2 values for AGB (0.903) and the latter getting the highest R 2 values for DBH (0.829). SHAP analysis highlighted that LiDAR height and voxel metrics were the most important factors influencing AGB and DBH predictions. These findings demonstrate that UAV LiDAR can provide efficient and accurate AGB estimates in eucalyptus plantations, supporting the wine industry's carbon neutrality efforts. • High-density UAV LiDAR combined with destructive sampling achieved R 2 of 0.903 for AGB estimation. • PLSR outperformed complex ML algorithms in DBH estimation (R 2 = 0.829) for eucalyptus species. • SHAP analysis revealed height and voxel metrics as key predictors AGB and DBH estimation. • Species-specific models demonstrated varying performance, indicating importance of species consideration in biomass estimation.

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