This package contains functions and algorithms to extend the lidR package (versions >= 4.0.0). These functions or algorithms are not yet or will not be included in the lidR package either because they are:

• :microscope: Experimental and not supported by a peer-reviewed and accessible publications.
• :zap: Non suitable for lidR usually because they are not sufficiently efficient.
• :warning: Not tested enought and I'm not sure they are sufficiently robust.
• :octocat: Require extra packages available on github but not on CRAN

This package will NOT be submitted on CRAN and must be installed from github. It depends on lidR (>= 4.0.0) and should be seen as a laboratory with more or less interesting content inside.

Features

Lake delineation :microscope:

Lake delineation from point cloud using delineate_lakes()

Powerline segmentation :microscope: :warning:

Powerline segmentation from point cloud using find_transmissiontowers(), classify_transmissiontowers(), classify_wires(), track_wires()

Various tree detection/segmentation from peer-reviewed papers :zap:

• ptree(): Vega, C., Hamrouni, a., El Mokhtari, S., Morel, J., Bock, J., Renaud, J.-P., … Durrieu, S. (2014). PTrees: A point-based approach to forest tree extraction from lidar data. International Journal of Applied Earth Observation and Geoinformation, 33, 98–108. https://doi.org/10.1016/j.jag.2014.05.001
• hamraz2016(): Hamraz, H., Contreras, M. A., & Zhang, J. (2016). A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation, 52, 532–541. https://doi.org/10.1016/j.cageo.2017.02.017
• LayerStacking(): Ayrey, E., Fraver, S., Kershaw, J. A., Kenefic, L. S., Hayes, D., Weiskittel, A. R., & Roth, B. E. (2017). Layer Stacking: A Novel Algorithm for Individual Forest Tree Segmentation from LiDAR Point Clouds. Canadian Journal of Remote Sensing, 43(1), 16–27. https://doi.org/10.1080/07038992.2017.1252907
• multichm(): Eysn, L., Hollaus, M., Lindberg, E., Berger, F., Monnet, J. M., Dalponte, M., … Pfeifer, N. (2015). A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the Alpine Space. Forests, 6(5), 1721–1747. https://doi.org/10.3390/f6051721

Parameter free tree detection :microscope:

lmfauto() is a fast algorithm for individual tree detection with 0 parameters designed to process thousands of square kilometres without supervision.

Installation

remotes::install_github("Jean-Romain/lidRplugins")

To install the package from github make sure you have a working development environment.

• Windows: Install Rtools.exe.
• Mac: Install Xcode from the Mac App Store.
• Linux: Install the R development package, usually called r-devel or r-base-dev