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  • 标题:A voxel-based technique to estimate the volume of trees from terrestrial laser scanner data
  • 本地全文:下载
  • 作者:A. Bienert ; C. Hess ; H.-G. Maas
  • 期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 印刷版ISSN:2194-9042
  • 电子版ISSN:2194-9050
  • 出版年度:2014
  • 卷号:XL-5
  • 页码:101-106
  • DOI:10.5194/isprsarchives-XL-5-101-2014
  • 出版社:Copernicus Publications
  • 摘要:The precise determination of the volume of standing trees is very important for ecological and economical considerations in forestry. If terrestrial laser scanner data are available, a simple approach for volume determination is given by allocating points into a voxel structure and subsequently counting the filled voxels. Generally, this method will overestimate the volume. The paper presents an improved algorithm to estimate the wood volume of trees using a voxel-based method which will correct for the overestimation. After voxel space transformation, each voxel which contains points is reduced to the volume of its surrounding bounding box. In a next step, occluded (inner stem) voxels are identified by a neighbourhood analysis sweeping in the X and Y direction of each filled voxel. Finally, the wood volume of the tree is composed by the sum of the bounding box volumes of the outer voxels and the volume of all occluded inner voxels. Scan data sets from several young Norway maple trees ( Acer platanoides ) were used to analyse the algorithm. Therefore, the scanned trees as well as their representing point clouds were separated in different components (stem, branches) to make a meaningful comparison. Two reference measurements were performed for validation: A direct wood volume measurement by placing the tree components into a water tank, and a frustum calculation of small trunk segments by measuring the radii along the trunk. Overall, the results show slightly underestimated volumes (–0.3% for a probe of 13 trees) with a RMSE of 11.6% for the individual tree volume calculated with the new approach.
  • 关键词:Terrestrial Laser Scanning; Point Cloud; Tree; Volume Estimation; Voxel
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