Skip to contents

TreeCompR

Hexsticker R Package for calculating distance-dependent or point cloud-based individual tree competition indices

This R package is designed for the quantitative analysis of individual tree competition within forest ecosystems. It accepts inputs such as LiDAR point clouds (as dataframe with xyz columns or las/laz files) of forest plots and individual target trees, or inventory tables (including tree ID, x, y, dbh and/or h or other size parameters). Through the use of various competition indices, it enables the assessment and measurement of competition between trees, providing valuable insights for forest ecology, inventories, and forest management strategies. This package facilitates a systematic approach to understanding interactions and resource competition among trees and enables informed decision making in forest management.

Installation

You can install the development version of TreeCompR from GitHub with:

# install.packages("devtools")
devtools::install_github("juliarieder/TreeCompR", dependencies = TRUE)

# or with vignettes on your computer
devtools::install_github("juliarieder/TreeCompR", 
            dependencies = TRUE, build_vignettes = TRUE)

Overview

TreeCompR can read point clouds or inventory tables and quantifies tree competition in different ways:

  • Functions for point-cloud based methods
    • compete_pc() quantifies tree competition from point clouds using the cone or cylinder method (see Metz et al. 2013; Seidel et al. 2015)
    • read_pc() reads 3D point clouds from .las/.laz files, .ply files and text-only file formats such as .txt or .csv, and converts LAS objects and data.frames to a TreeCompR compatible format
    • tree_pos() computes the base position and height of target tree point clouds
  • Functions for inventory-based methods
    • compete_inv() quantifies size- and distance-dependent competition using inventory data based on a series of published indices
    • read_inv() reads and validates inventory tables from data.frames, or reads tabular data directly from text-only file sources such as .txt or .csv
    • define_target() can be used to define for which trees within a plot the competition should be quantified in compete_inv()
    • plot_target() check and validate the tree position(s) and the surrounding trees after defining them with define_target()

Get started

Have a look at some examples to check out what our package can do on Get started.

Workflows and Tutorials

  • Inventory-based approach Overview on inventory based competition indices
    • ALS workflow learn how to pre-process and use ALS data (airborne laser scanning) to derive size-distance-based competition indices
    • TLS workflow learn how to pre-process and use TLS/MLS data (terrestrial or mobile laser scanning) to derive size-distance-based competition indices
  • Point-cloud-based approach Overview on quantifying competition directly from point clouds from ground-based laser scans and how to pre-process the data for the cone or cylinder method