评估LiDAR数据源、预测变量分辨率与贝叶斯变更支持模型中的空间随机效应对森林清查的影响
Evaluating LiDAR Data Sources, Predictor Resolution, and Spatial Random Effects in Bayesian Change-of-Support Models for Forest Inventory
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中文总结 AI 辅助
本研究比较ULS与ALS数据结合贝叶斯变更支持模型估算混交林蓄积量，发现ULS模型预测误差更低（RMSPE降低13.8%），且及时高分辨率LiDAR数据比复杂空间模型更有价值。
中文摘要 AI 辅助
森林管理者需要及时的林分尺度信息用于经营规划。基于模型的估计将稀疏的野外样地数据与遥感辅助数据相结合，以估算小面积单元的林分蓄积量（GSV）。这在混交林和结构异质性森林中尤为重要，因为及时的结构信息可以在气候变化及相关干扰压力下支持管理决策。无人机激光雷达（ULS）提供了灵活获取高分辨率LiDAR数据的途径，但其相对于传统机载激光雷达（ALS）在基于模型推断中的优势尚未得到充分理解。我们使用贝叶斯变更支持模型，比较了公共ALS数据和新采集的ULS数据，以估算德国东北部一片混交林的GSV。我们评估了分布型LiDAR指标和空间随机效应的影响。ULS模型始终优于ALS模型，交叉验证的均方根预测误差（RMSPEs）分别为68.5 m³/ha和79.5 m³/ha，预测误差降低了13.8%。分布型指标对ULS模型的益处大于ALS模型，使RMSPE最多降低10.1%，而空间效应带来的改进较小，但计算成本更高。ULS模型在林分尺度潜在均值的预测不确定性上也更低。这一优势可能部分归因于与野外数据在时间上更接近，以及更精细的预测变量信息。这些发现表明，及时且信息丰富的LiDAR数据可能比日益复杂的空间模型结构对林分尺度GSV估算更有益。在及时获取和高分辨率冠层表征在操作上可行的情况下，ULS具有前景。需要进行与时间匹配的ALS和ULS数据的受控比较，以区分平台效应与时间不匹配的影响。
英文摘要
Forest managers need timely stand-level information to support operational planning, particularly in mixed-species, structurally heterogeneous forests facing climate-related disturbance. Model-based estimation combines sparse field data with remotely sensed predictors to estimate growing stock volume (GSV) for small areas. While uncrewed aerial vehicle laser scanning (ULS) offers flexible, high-resolution LiDAR acquisition, its advantages over conventional airborne laser scanning (ALS) remain unclear. We compared publicly available ALS and newly acquired ULS data using Bayesian change-of-support models to estimate GSV in a mixed-species forest in north-eastern Germany. We evaluated distributional LiDAR metrics and spatial random effects. ULS consistently outperformed ALS, achieving cross-validated RMSPEs of 68.5 m^3/ha and 79.5 m3/ha, respectively-a 13.8 % reduction in prediction error. Distributional metrics improved ULS models more strongly, reducing RMSPE by up to 10.1 %; spatial effects provided only minor gains at substantially higher computational cost. ULS also produced lower uncertainty in latent stand-mean GSV estimates. The ULS advantage may reflect both finer-scale canopy information and closer temporal alignment with field measurements. Timely, information rich LiDAR may therefore be more valuable for stand-level GSV estimation than increasingly complex spatial models. Temporally matched ALS-ULS comparisons are needed to isolate platform effects.
发表机构
- BOKU University（维也纳农业大学）
- Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。
