Large scale calculation of single trees

from ALS LiDAR data by a novel 3D segmentation technique

Image Source: National Park Bavarian Forest

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  Other features: - patented 3D technology - Platform: C / C ++, Windows or Linux:

  
  

  Features:

  
  

  • Extraction of dominating trees as well as small trees in lower forest layers
  • Tree species classification
  • Fully automatic and seamless processing of large scale areas
  • Utilizes outcomes of full waveform decomposition (i.e. intensity, pulse width)
  • Optionally: Watershed-based segmentation of canopy surface only
  • Results are stored in a shape file for each tree
  • Platform: C/C++, Windows or Linux, stand-alone application or as a library for integration
  • Patented 3D technology

  
  

  Input Data:

  
  

  • DTM (either raster or random, – ASCII)
  • LiDAR data (XYZ, optionally intensity and pulse width, ASCII or LAS 1.3) acquired from plane, helicopter or drone
  • Optionally georeferenced CIR aerial images and (true) orthophotos

  
  

  Output data:

  
  

  • Tree position
  • Tree height
  • Crown base height
  • Crown volume
  • Tree species
  • Optionally: LiDAR points of segmented trees

  
  

  Service and Product:

  
  

  The software can be

  
  

  • installed as a stand-alone tool for service calculations, or
  • provided as a library for integration in customer tools
  • Service (offline calculation) can also be provided
  • We have also experience in extracting other forest objects like dead trees (fallen, standing). If interested, please ask.

  
  

  Selected publications:

  
  

  Krzystek P 2018. The Future of Forest Mapping. Scientia. Jul 11, 2018. Earth and Environment. https://doi.org/10.26320/SCIENTIA187

  
  

  Amiri N, Polewski P, W, Heurich M, Krzystek P, Skidmore A. Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests. ISPRS Journal of Photogrammetry and Remote Sensing Volume 141, July 2018, Pages 265–274. https://doi.org/10.1016/j.isprsjprs.2018.05.006.

  
  

  Polewski, P., Yao, W., Heurich, M., Krzystek, P. and Stilla, U., 2016. Combining Active and Supervised Learning of Remote Sensing Data Within a Renyi Entropy Regularization Framework. IEEE Journal of Selected Topics in Applied Earth

  
  

  Yao, W., Krzystek, P. Heurich, M. 2012. Tree Species Classification and Estimation of Stem Volume and DBH based on Single Tree Ectraction by Exploiting Airborne Full-Waveform LiDAR Data. International Journal of Remote Sensing.

  
  

  Reitberger, J., Schnörr, Cl., Krzystek, P., Stilla, U., 2009. 3D Segmentation of single trees exploiting full waveform LIDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 64, No. 6, 561 – 574.