The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 989–994, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-989-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 989–994, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-989-2022
 
30 May 2022
30 May 2022

EVALUATION OF AZURE KINECT DERIVED POINT CLOUDS TO DETERMINE THE PRESENCE OF MICROHABITATS ON SINGLE TREES BASED ON THE SWISS STANDARD PARAMETERS

C. R. Fol, A. Murtiyoso, and V. C. Griess C. R. Fol et al.
  • Forest Resources Management, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zurich, Switzerland

Keywords: Point Cloud, Azure Kinect, Close-range Photogrammetry, Microhabitat Detection, DBH, Bark Texture

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.