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Remote Sensing","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"[![GPLv3 License](https://img.shields.io/badge/License-GPL%20v3-yellow.svg)](https://opensource.org/licenses/)\n[![](https://www.r-pkg.org/badges/version/TreeLS)](https://cran.r-project.org/package=TreeLS)\n![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/tiagodc/TreeLS)\n![](https://cranlogs.r-pkg.org/badges/grand-total/TreeLS)\n\n# TreeLS\n\nHigh performance R functions for forest data processing based on **T**errestrial **L**aser **S**canning (but not only) point clouds.\n\n## Description\n\nThis package is a refactor of the methods described in [this paper](https://doi.org/10.1016/j.compag.2017.10.019), among many other features for 3D point cloud processing of forest environments.\n\nMost algorithms are written in C++ and wrapped in R functions through `Rcpp`. *TreeLS* is built on top of [lidR](https://github.com/Jean-Romain/lidR/), using its `LAS` 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Suggestions, ideas and references of new algorithms are always welcome - as long as they fit into TreeLS' scope.\n\n`TreeLS` is currently on v2.0.2. To install it from an official mirror, use: `install.packages(\"TreeLS\")`. To install the most recent version, check out the *Installation from source* section below.\n\n## News\n\n- August/2020: Version 2.0 is finally available! It's a major release, introducing several new functionalities, bug fixes, more robust estimators for noisy clouds and more flexible plotting. All functionalities from older versions are now available and optimized, so there should be no need to use legacy code anymore. The scope of application of TreeLS has become much wider in this version, specially due to the introduction of functions like `fastPointMetrics` and `shapeFit`, making it much easier for researchers to assess point cloud data in many contexts and develop their own methods on top of those functions. For a comprehensive list of the updates check out the [CHANGELOG](https://github.com/tiagodc/TreeLS/blob/master/CHANGELOG.md).\n\n- March/2019: `TreeLS` is finally available on CRAN and is now an official R package.\n\n\u003cimg align=\"right\" height=\"400\" src=\"https://raw.githubusercontent.com/tiagodc/Scripts/master/animations/treedt.gif\"\u003e\n\n## Main functionalities\n\n- Tree detection at plot level\n- Tree region assignment\n- Stem detection and denoising\n- Stem segmentation\n- Forest inventory\n- Fast calculation of point features\n- Research basis and other applications\n- 3D plotting and manipulation\n\n## Installation from source\n\n### Requirements\n- Rcpp compiler:\n    - on Windows: install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) for your R version - make sure to add it to your system's *path*\n    - on Mac: install Xcode\n    - on Linux: be sure to have `r-base-dev` installed\n\n### Install TreeLS latest version\n\nOn the R console, run:\n```\nremotes::install_github('tiagodc/TreeLS')\n```\n\n## Usage\n\nExample of full processing workflow from reading a point cloud file until stem segmentation of a forest plot:\n```\nlibrary(TreeLS)\n\n# open sample plot file\nfile = system.file(\"extdata\", \"pine_plot.laz\", package=\"TreeLS\")\ntls = readTLS(file)\n\n# normalize the point cloud\ntls = tlsNormalize(tls, keep_ground = F)\nx = plot(tls)\n\n# extract the tree map from a thinned point cloud\nthin = tlsSample(tls, smp.voxelize(0.02))\nmap = treeMap(thin, map.hough(min_density = 0.1), 0)\nadd_treeMap(x, map, color='yellow', size=2)\n\n# classify tree regions\ntls = treePoints(tls, map, trp.crop())\nadd_treePoints(x, tls, size=4)\nadd_treeIDs(x, tls, cex = 2, col='yellow')\n\n# classify stem points\ntls = stemPoints(tls, stm.hough())\nadd_stemPoints(x, tls, color='red', size=8)\n\n# make the plot's inventory\ninv = tlsInventory(tls, d_method=shapeFit(shape='circle', algorithm = 'irls'))\nadd_tlsInventory(x, inv)\n\n# 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