Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1163-1168, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1163-1168, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1163-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

VALIDATING A WORKFLOW FOR TREE INVENTORY UPDATING WITH 3D POINT CLOUDS OBTAINED BY MOBILE LASER SCANNING

J. Wang1,2 and R. Lindenbergh1 J. Wang and R. Lindenbergh
  • 1Dept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, Post Box 5048, 2628CN Delft, The Netherlands
  • 2Regional Innovation Center Europe, Fugro. 2263 HW Leidschendam, The Netherlands

Keywords: Tree inventory, Mobile Laser Scanning, Point Cloud Data, Tree classification

Abstract. Urban trees are an important component of our environment and ecosystem. Trees are able to combat climate change, clean the air and cool the streets and city. Tree inventory and monitoring are of great interest for biomass estimation and change monitoring. Conventionally, parameters of trees are manually measured and documented in situ, which is not efficient regarding labour and costs. Light Detection And Ranging (LiDAR) has become a well-established surveying technique for the acquisition of geo-spatial information. Combined with automatic point cloud processing techniques, this in principle enables the efficient extraction of geometric tree parameters. In recent years, studies have investigated to what extend it is possible to perform tree inventories using laser scanning point clouds. Give the availability of a city of Delft Open data tree repository, we are now able to present, validate and extend a workflow to automatically obtain tree data from tree location until tree species. The results of a test over 47 trees show that the proposed methods in the workflow are able to individual urban trees. The tree species classification results based on the extracted tree parameters show that only one tree was wrongly classified using k-means clustering.