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Conference paper2013Peer reviewedOpen access

Detection of Lying Tree Stems from Airborne Laser Scanning Data Using a Line Template Matching Algorithm

Lindberg, Eva; Hollaus, Markus; Mücke, Werner; Fransson, Johan; Pfeifer, Norbert

Abstract

Dead wood is an important habitat characteristic in forests. However, dead wood lying on the ground below a canopy is difficult to detect from remotely sensed data. Data from airborne laser scanning include measurement of surfaces below the canopy, thus offering the potential to model objects on the ground. This paper describes a new line template matching algorithm for detecting lines along the ground. The line template matching is done directly to the laser point cloud and results in a raster showing the support of the line in each raster cell. Line elements are vectorized based on the raster to represent lying tree stems. The results have been validated versus field-measured lying tree stems. The number of detected lines was 845, of which 268 could be automatically linked to the 651 field-measured stems. The line template matching produced a raster which visually showed linear elements in areas where lying tree stems where present, but the result is difficult to compare with the field measurements due to positioning errors. The study area contained big piles of storm-felled trees in some places, which made it an unusually complex test site. Longer line structures such as ditches and roads also resulted in detected lines and further analysis is needed to avoid this, for example by specifically detecting longer lines and removing them.

Keywords

3D modelling, Point cloud processing, Line detection, ALS data

Published in

International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
2013, Volume: II-5/W2, pages: 169-174
Publisher: ISPRS - International Society for Photogrammetry and Remote Sensing

Conference

ISPRS Workshop Laser Scanning 2013

      SLU Authors

      • Associated SLU-program

      Remningstorp

      UKÄ Subject classification

      Remote Sensing
      Computer Vision and Robotics (Autonomous Systems)
      Forest Science

      Publication identifier

      DOI: https://doi.org/10.5194/isprsannals-II-5-W2-169-2013

      Permanent link to this page (URI)

      https://res.slu.se/id/publ/50701