Evaluating LiDAR 数据 (Data) Sources, Predictor Resolution, and Spatial Random Effects in 贝叶斯 (Bayesian) Change-of-Support 模型 (Model)s for Forest Inventory

Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory

Forest managers require timely stand-level information for operational planning. Model-based estimation combines sparse field samples with remotely sensed auxiliary data to estimate growing stock volume (GSV) for small-area units. This is particularly relevant in mixed-species and structurally heterogeneous forests, where timely structural information can support management decisions under climate change and associated disturbance pressures. Uncrewed aerial vehicle laser scanning (ULS) provides flexible access to high-resolution LiDAR data, but its benefits over conventional airborne laser scanning (ALS) for model-based inference remain insufficiently understood. We compared public ALS and new ULS data using Bayesian change-of-support models to estimate GSV in a mixed-species forest in north-eastern Germany. We assessed the effects of distributional LiDAR metrics and spatial random effects. ULS models consistently outperformed ALS models, with cross-validated root mean squared prediction errors (RMSPEs) of 68.5 m^3/ha and 79.5 m^3/ha , respectively, a 13.8 % reduction in prediction error. Distributional metrics benefited ULS models more than ALS models, reducing RMSPE by up to 10.1 %, whereas spatial effects yielded minor improvements at greater computational cost. ULS models also exhibited lower stand-level predictive uncertainty for latent stand-mean GSV. This advantage may partly reflect closer temporal alignment with field data and finer-scale predictor information. These findings suggest that timely, information-rich LiDAR data may be more beneficial for stand-level GSV estimation than increasingly complex spatial model structures. ULS is promising where timely acquisition and high-resolution canopy characterization are operationally feasible. Controlled comparisons with temporally matched ALS and ULS data are needed to distinguish platform effects from temporal mismatch.