Evaluation of Azure Kinect Derived Point Clouds to Determine the Presence of Microhabitats on Single Trees Based on the Swiss Standard Parameters

Abstract

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.

Low-Cost Mapping of Forest Under-Storey Vegetation Using Spherical Photogrammetry

“…While parallel developments in aerial lidar acquisition have also seen significant improvements, especially in miniaturisation (Corte et al, 2020), assessing under-storey vegetation is used to answer very specific questions, which may only be addressed using data obtained via terrestrial techniques. This includes, for example, the monitoring of tree growth (Brolly et al, 2013) or tree microhabitats (Fol et al, 2022), for both of which the level of detail provided by aerial lidar is often insufficient simply due to occlusions in forests.…”

“…Indeed, the costs for lidar data acquisition has remained high in recent years thus often requiring researchers to choose between quality and budget in many financially constrained projects. A possible solution to this problem is the use of alternative 3D mapping techniques such as close-range photogrammetry and depth cameras (Fol et al, 2022). However, these solutions present users with another problem: notably the strictly convergent nature of photogrammetric acquisition networks.…”

Feasibility Study of Using Virtual Reality for Interactive and Immersive Semantic Segmentation of Single Tree Stems

“…The most widespread forest 3D model data are point clouds acquired by TLS or airborne lidar. However, thanks to progress made in low-cost 3D scanning technology, the source of forest 3D point clouds is likely to progressively diversify (Mokroš et al, 2018, Fol et al, 2022, Kükenbrink et al, 2022. Nevertheless, point clouds are certainly going to remain the preferred 3D format in the forestry field for the time being due to its heterogeneous and complex environment which is quite difficult to model using other 3D representations, such as meshes or geometric primitives.…”