Three-dimensional (3D) data from airborne laser scanning (ALS) and, more recently, digital aerial photogrammetry (DAP) have been successfully used to model forest attributes. While multi-temporal, wall-to-wall ALS data is not usually available, aerial imagery is regularly acquired in many regions. Thus, the combination of ALS and DAP data provide a sufficient temporal resolution to properly monitor forests. However, field data is needed to fit new forest attribute models for each 3D data acquisition, which is not always affordable. In this study, we examined whether transferability of growing stock volume (GSV) models may provide an improvement in the efficiency of forest inventories updating. We used two available ALS datasets acquired with different characteristics in 2009 and 2010, respectively, generated two DAP point clouds from imagery collected in 2010 and 2017, and utilized field data from two ground surveys conducted in 2009 and 2016-2017. We first analyzed the stability of point cloud derived metrics. Then, Support Vector Regression models based on the most stable metrics were fitted to assess model transferability by applying them to other datasets in four different cases: (1) ALS-ALS, (2) DAP-DAP temporal, (3) ALS-DAP and (4) ALS-DAP temporal. Some metrics were found to be enough stable in each case, so they could be used interchangeably between datasets. The application of models to other datasets resulted in unbiased predictions with relative root mean square error differences ranging from -8.27% to 14.59%. Results demonstrated that 3D-based GSV models may be transferable between point clouds of the same type as well as point clouds acquired using different technologies such as ALS and DAP, suggesting that DAP data may be used as a cost-efficient source of information for updating ALS-assisted forest inventories.

来自机载激光扫描（ALS）的三维（3D）数据以及最近的数字航空摄影测量（DAP）已成功地用于对森林属性进行建模。尽管通常无法获得多时相的ALS数据，但在许多地区都定期获取了航空影像。因此，ALS和DAP数据的组合提供了足够的时间分辨率，可以正确地监视森林。但是，对于每个3D数据采集来说，都需要现场数据来适应新的森林属性模型，但这并不总是可以承受的。在这项研究中，我们检查了生长种群数量（GSV）模型的可转移性是否可以提高森林清单更新的效率。我们分别使用了2009年和2010年获得的两个具有不同特征的ALS数据集，根据2010年和2017年收集的图像生成了两个DAP点云，并利用了2009年和2016-2017年进行的两次地面调查的现场数据。我们首先分析了点云派生指标的稳定性。然后，基于最稳定指标的支持向量回归模型适合于通过在四种不同情况下将其应用于其他数据集来评估模型的可移植性：（1）ALS-ALS，（2）DAP-DAP时间，（3）ALS-DAP （4）ALS-DAP时间。发现某些指标在每种情况下都足够稳定，因此可以在数据集之间互换使用。将模型应用于其他数据集可产生无偏预测，相对均方根误差在-8.27％至14.59％之间。