The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.

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Three-dimensional modelling using photogrammetric point clouds derived from UAV-based aerial imagery is currently a popular topic in the scientific community. In particular, the use of image-based point clouds to enhance and update LiDAR DSMs is of growing interest in forest environments, i.e. as a future forest inventory method. Thanks to very high resolution imagery acquired via low-altitude UAV optical sensor payloads, very dense and accurate photogrammetric point clouds can be reconstructed through a triangulation process by means of photogrammetry software. In order to validate the use of image-based point clouds for their potential use in operational forestry, further comparison studies with LiDAR DSMs are being carried out by various research institutions. The acquisition of UAV-based aerial imagery, with the aim of producing accurate photogrammetric point clouds, though cost-effective, is not without its challenges. Due to constraints regarding power capacity and fair weather windows, we came to develop an effective image acquisition workflow with an emphasis on precision flight planning. The aim of this paper is to explore the process of UAV-based aerial imagery acquisition for the purpose of producing photogrammetric point clouds, as well as to give an overview of the initial stages of our research. With the aid of an image acquisition workflow that is adaptable to various field conditions, technical failures and precision flight planning, we estimate that the acquisition of aerial imagery for point cloud production will become more efficient as well as more precise, and in turn influence the accuracy of the 3D-modelling of forested areas.

Remote sensing methods for forest monitoring are evolving rapidly thanks to recent advances in Unmanned Aerial Vehicle technology and digital photogrammetry. Photogrammetric point clouds allow the non-destructive derivation of individual tree parameters at a low cost. The fusion of aerial and terrestrial photogrammetry for creating full-tree point clouds is of utility for forest research, as tree volume could be assessed more economically and efficiently than by traditional methods. However, this is challenging to implement due to difficulties with co-registration and issues of occlusion. This study explores the possibility of using spherical targets typically used for Terrestrial Laser Scanning to accomplish the co-registration of UAV-based and terrestrial photogrammetric datasets. Results show a full-tree point cloud derived from UAV oblique imagery in combination with terrestrial imagery. Despite issues of noise produced from the sky in terrestrial imagery, the methodology is promising for aerial and terrestrial point cloud fusion.