Abstract. In the last few years, a number of low-cost 3D scanning sensors have been developed to reconstruct the real-world environment. These sensors were primarily designed for indoor use, making them highly unpredictable in terms of their performance and accuracy when used outdoors. The Azure Kinect belongs to this category of low-cost 3D scanners and has been successfully employed in outdoor applications. In addition, this sensor possesses features such as portability and live visualization during data acquisition that makes it extremely interesting in the field of forestry. In the context of forest inventory, these advantages would allow to facilitate the task of tree parameters acquisition in an efficient manner. In this paper, a protocol was established for the acquisition of 3D data in forests using the Azure Kinect. A comparison of the resulting point cloud was performed against photogrammetry. Results demonstrated that the Azure Kinect point cloud was of suitable quality for extracting tree parameters such as diameter at breast height (DBH, with a standard deviation of 2.2cm). Furthermore, the quality of the visual and geometric information of the point cloud was evaluated in terms of its feasibility to identify microhabitats. Microhabitats represent valuable information on forest biodiversity and are included in Swiss forest inventory measurements. In total, five different microhabitats were identified in the Azure Kinect Point cloud. The measurements were therefore comparable to sensors such as terrestrial laser scanning and photogrammetry. Therefore, we argue that the Azure Kinect point cloud can efficiently identify certain types of microhabitats and this study presents a first approach of its application in forest inventories.

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Abstract. Forest digitisation is one of the next major challenges to be tackled in the forestry domain. As a consequence of tremendous advances in 3D scanning technologies, broad areas of forest can be mapped in 3D dramatically faster than 20 years ago. Consequently, capturing 3D forest point clouds with the use of 3D sensing technologies – such as lidar – is becoming predominant in the field of forestry. However, the processing of 3D point clouds to bring semantics to the 3D forestry data – e.g. by linking them with ecological values – has not seen similar advancements. Therefore, in this paper we consider a novel approach based on the use of VR (Virtual reality) as a potential solution for deriving biodiversity from 3D point clouds acquired in the field. That is, we developed a VR labelling application to visualise forest point clouds and to perform the segmentation of several biodiversity components on tree stems e.g., mosses, lichens and bark pockets. Furthermore, the VR segmented point cloud was analysed with standard accuracy and precision metrics. Namely, the proposed VR application managed to achieve an IoU (Intersection over Union) rate value of 98.74% for the segmentation of bark pockets and resp. 93.71% for the moss and lichen classes. These encouraging results reinforce the potential for the proposed VR labelling method for other purposes in the future, for example for AI (Artificial Intelligence) training dataset creation.