dendromatics: Automatic dendrometry in terrestrial point clouds

This package provides functionalities to implement an updated version of the algorithm presented in [CABO2018]. It detects the trees present in a terrestrial 3D point cloud from a forest plot, and compute individual tree parameters: tree height, tree location, diameters along the stem (including DBH), and stem axis.

Contents

• Installation
• Algorithm
  • 0. Height-normalization of the point cloud
  • 1. Identification of stems among user-provided horizontal stripe
  • 2. Tree individualization based on point-to-stems distances
  • 3. Computation of stem diameter at different section heights
• Functions
  • Voxelization
  • Height-normalization
  • Stem extraction
  • Tree individualization
  • Computing sections
  • Drawing LAS files
• Examples
  • Height-normalization
  • Identifying stems from a stripe
  • Individualizing trees
  • Computing sections along the stems
  • Tilt detection
• 3DFin
• CloudCompare plugin

Indices and tables

Index

Module Index

Search Page

References

[CABO2018]

Cabo, C., Ordonez, C., Lopez-Sanchez, C. A., & Armesto, J. (2018). Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. International Journal of Applied Earth Observation and Geoinformation, 69, 164–174. https://doi.org/10.1016/j.jag.2018.01.011

[ESTE1996]

Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. www.aaai.org

[PREN2021]

Prendes, C., Cabo, C., Ordonez, C., Majada, J., & Canga, E. (2021). An algorithm for the automatic parametrization of wood volume equations from Terrestrial Laser Scanning point clouds: application in Pinus pinaster. GIScience and Remote Sensing, 58(7), 1130–1150. https://doi.org/10.1080/15481603.2021.1972712

[ZHAN2016]

Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6). https://doi.org/10.3390/rs8060501