Title: Small area estimation of growing stock timber volume, basal area, mean stem diameter, and stem density for mountain forests in Austria Show Chinese title

Title: 奥地利山地森林生长木材体积、断面积、平均树干直径和林分密度的小区域估计

Abstract: Regression models were evaluated to estimate stand-level growing stock volume (GSV), quadratic mean diameter (QMD), basal area (BA), and stem density (N) in the Brixen im Thale forest district of Austria. Field measurements for GSV, QMD, and BA were collected on 146 inventory plots using a handheld mobile personal laser scanning system. Predictor variables were derived from airborne laser scanning (ALS)-derived normalized digital surface and terrain models. The objective was to generate stand-level estimates and associated uncertainty for GSV, QMD, BA, and N across 824 stands. A unit-level small area estimation framework was used to generate stand-level posterior predictive distributions by aggregating predictions from finer spatial scales. Both univariate and multivariate models, with and without spatially varying intercepts, were considered. Predictive performance was assessed via spatially blocked cross-validation, focusing on bias, accuracy, and precision. Despite exploratory analysis suggesting advantages of complex multivariate spatial models, simpler univariate spatial -- and in some cases, non-spatial -- models exhibited comparable predictive performance. Show Chinese abstract

Abstract: 回归模型被评估用于估计奥地利Brixen im Thale森林区的林分水平生长材积(GSV)、断面平均直径(QMD)、胸高断面积(BA)和树干密度(N)。GSV、QMD和BA的实地测量数据通过手持式移动个人激光扫描系统在146个样方中收集。预测变量来源于机载激光扫描(ALS)生成的归一化数字表面和地形模型。目的是在824个林分中生成GSV、QMD、BA和N的林分水平估计值及其相关不确定性。使用单元水平的小区域估计框架，通过聚合更细空间尺度的预测来生成林分水平的后验预测分布。考虑了单变量和多变量模型，包括具有和不具有空间变化截距的模型。通过空间块交叉验证评估预测性能，重点在于偏差、准确性和精确性。尽管探索性分析表明复杂多变量空间模型具有优势，但简单的单变量空间模型——在某些情况下甚至非空间模型——表现出相当的预测性能。