WhiteboxTools R Frontend.
This repository is related to the whitebox R package for geospatial analysis, which is an R frontend of a stand-alone executable command-line program called WhiteboxTools.
- Authors: Dr. John Lindsay (https://jblindsay.github.io/ghrg/index.html)
- Contributors: Dr. Qiusheng Wu (https://wetlands.io | https://LidarBlog.com)
- GitHub repo: https://github.com/giswqs/whiteboxR
- CRAN link: https://cran.r-project.org/package=whitebox
- WhiteboxTools: https://github.com/jblindsay/whitebox-tools
- User Manual: https://jblindsay.github.io/wbt_book
- Free software: MIT license
The whitebox R package is built on WhiteboxTools, an advanced geospatial data analysis platform developed by Prof. John Lindsay (webpage; jblindsay) at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. LiDAR point clouds can be interrogated (LidarInfo, LidarHistogram), segmented, tiled and joined, analyized for outliers, interpolated to rasters (DEMs, intensity images), and ground-points can be classified or filtered. WhiteboxTools is not a cartographic or spatial data visualization package; instead it is meant to serve as an analytical backend for other data visualization software, mainly GIS. Suggested citation: Lindsay, J. B. (2016). Whitebox GAT: A case study in geomorphometric analysis. Computers & Geosciences, 95, 75-84. doi:10.1016/j.cageo.2016.07.003.
There are three ways to install the whitebox R package.
Note: whitebox has been temporarily taken down from CRAN due to policy violation (Downloads of WhiteboxTools binary as part of package installation). An updated version will be released once I figure out how to solve this issue. For the time being, you can install whitebox from R-Forge or GitHub.
whitebox is now available on CRAN, so you can install it with:
whitebox is also available on R-Forge, so you can install it with:
You can alternatively install the development version of whitebox from GitHub as follows:
if (!require(devtools)) install.packages('devtools') devtools::install_github("giswqs/whiteboxR")
You’ll also need to make sure your machine is able to build packages from source. See Package Development Prerequisites for the tools needed for your operating system.
library(whitebox) # Prints the whitebox-tools help...a listing of available commands print(wbt_help()) # Prints the whitebox-tools license print(wbt_license()) # Prints the whitebox-tools version print(wbt_version()) # Prints the toolbox for a specific tool. print(wbt_toolbox()) # List all available tools in whitebox-tools print(wbt_list_tools()) # Lists tools with 'lidar' in tool name or description. print(wbt_list_tools("lidar")) # Prints the help for a specific tool. print(wbt_tool_help("lidar_info")) # Retrieves the tool parameter descriptions for a specific tool. print(wbt_tool_parameters("slope")) # View the source code for a specific tool on the source code repository. print(wbt_view_code("breach_depressions"))
How to run tools?
Tool names in the whitebox R package can be called using the snake_case (e.g. lidar_info). See below for an example. If you are interested in using the WhiteboxTools command-line program, check WhiteboxTools Usage.
library(whitebox) # Set input raster DEM file dem <- system.file("extdata", "DEM.tif", package="whitebox") # Run tools wbt_feature_preserving_smoothing(dem, "./smoothed.tif", filter=9, verbose_mode = TRUE) wbt_breach_depressions("./smoothed.tif", "./breached.tif") wbt_d_inf_flow_accumulation(dem, "./flow_accum.tif")
The WhiteboxTools library currently contains more than 443 tools, which are each grouped based on their main function into one of the following categories: Data Tools, GIS Analysis, Hydrological Analysis, Image Analysis, LiDAR Analysis, Mathematical and Statistical Analysis, Stream Network Analysis, and Terrain Analysis. For a listing of available tools, complete with documentation and usage details, please see the WhiteboxTools User Manual.
If you would like to contribute to the project as a developer, follow these instructions to get started:
- Fork the whiteboxR repository (https://github.com/giswqs/whiteboxR)
- Create your feature branch (git checkout -b my-new-feature)
- Commit your changes (git commit -am 'Add some feature')
- Push to the branch (git push origin my-new-feature)
- Create a new Pull Request
Unless explicitly stated otherwise, any contribution intentionally submitted for inclusion in the work shall be licensed as the MIT license without any additional terms or conditions.
The WhiteboxR package is distributed under the MIT license, a permissive open-source (free software) license.
whiteboxR is distributed as is and without warranty of suitability for application. If you encounter flaws with the software (i.e. bugs) please report the issue. Providing a detailed description of the conditions under which the bug occurred will help to identify the bug. Use the Issues tracker on GitHub to report issues with the software and to request feature enchancements. Please do not email Dr. Lindsay directly with bugs.