Volume XXXVIII-5/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-5/W12, 79-84, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-79-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-5/W12, 79-84, 2011
https://doi.org/10.5194/isprsarchives-XXXVIII-5-W12-79-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Sep 2012

03 Sep 2012

APPROXIMATION OF VOLUME AND BRANCH SIZE DISTRIBUTION OF TREES FROM LASER SCANNER DATA

P. Raumonen1, S. Kaasalainen2, M. Kaasalainen1, and H. Kaartinen2 P. Raumonen et al.
  • 1Department of Mathematics, Tampere University of Technology P.O. Box 553, FI-33101 Tampere, Finland
  • 2Remote Sensing and Photogrammetry, Finnish Geodetic Institute P.O.Box 15, FI-02431 Masala, Finland

Keywords: LIDAR, point cloud, trees, classification, size approximation, carbon cycle

Abstract. This paper presents an approach for automatically approximating the above-ground volume and branch size distribution of trees from dense terrestrial laser scanner produced point clouds. The approach is based on the assumption that the point cloud is a sample of a surface in 3D space and the surface is locally like a cylinder. The point cloud is covered with small neighborhoods which conform to the surface. Then the neighborhoods are characterized geometrically and these characterizations are used to classify the points into trunk, branch, and other points. Finally, proper subsets are determined for cylinder fitting using geometric characterizations of the subsets.