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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.