ForestTools

Detect and segment individual tree from remotely sensed data

https://github.com/andrew-plowright/foresttools

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 33 DOI reference(s) in README
  • Academic publication links
    Links to: biorxiv.org, researchgate.net, sciencedirect.com, springer.com, wiley.com, nature.com, plos.org, mdpi.com, ieee.org
  • Committers with academic emails
    3 of 6 committers (50.0%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (13.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

Detect and segment individual tree from remotely sensed data

Basic Info
  • Host: GitHub
  • Owner: andrew-plowright
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 18.8 MB
Statistics
  • Stars: 78
  • Watchers: 10
  • Forks: 24
  • Open Issues: 2
  • Releases: 1
Created about 9 years ago · Last pushed 7 months ago
Metadata Files
Readme Changelog

README.md

ForestTools

license R-CMD-check

The ForestTools R package offers functions to analyze remote sensing forest data. Please consult the NEWS.md file for updates.

To get started, consult the canopy analysis tutorial. For a quick guide on generating spatial statistics from ForestTools outputs, consult the spatial statistics tutorial

To cite the package use citation("ForestTools") from within R.

Plowright A. (2023). ForestTools: Tools for Analyzing Remote Sensing Forest Data. R package version 1.0.2, https://github.com/andrew-plowright/ForestTools.

Features

Detect and segment trees

Individual trees can be detected and delineated using a combination of the variable window filter (vwf) and marker-controlled watershed segmentation (mcws) algorithms, both of which are applied to a rasterized canopy height model (CHM). CHMs are typically derived from aerial LiDAR or photogrammetric point clouds.

image info

Compute textural metrics

Grey-level co-occurrence matrices (GLCMs) and their associated statistics can be computed for individual trees using a single-band image and a segment raster (which can be produced using mcws). These metrics can be used as predictors for tree classification.

References

This library implements techniques developed in the following studies:

Research

The following is a non-exhaustive list of studies that use the ForestTools library. Several of these papers discuss topics such as algorithm parameterization, and may be informative for users of this library.

📈 LiDAR Applications in Forest Inventories
Check out this ArcGIS StoryMap showcasing a forest inventory analysis in Kisatchie National Forest (Louisiana, USA) using the tree detection and segmentation algorithms implemented in ForestTools.

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Owner

  • Name: Andrew Plowright
  • Login: andrew-plowright
  • Kind: user
  • Location: Vancouver, BC
  • Company: Canada Centre for Mapping and Earth Observation

🛰 Remote sensing, geospatial analysis, natural resource management 🏞

GitHub Events

Total
  • Issues event: 3
  • Watch event: 12
  • Issue comment event: 15
  • Push event: 8
  • Pull request event: 2
  • Fork event: 2
Last Year
  • Issues event: 3
  • Watch event: 12
  • Issue comment event: 15
  • Push event: 8
  • Pull request event: 2
  • Fork event: 2

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 126
  • Total Committers: 6
  • Avg Commits per committer: 21.0
  • Development Distribution Score (DDS): 0.349
Past Year
  • Commits: 8
  • Committers: 2
  • Avg Commits per committer: 4.0
  • Development Distribution Score (DDS): 0.125
Top Committers
Name Email Commits
Andrew Plowright a****t@a****a 82
Andrew Plowright a****t@a****a 34
Andrew Plowright p****w@g****m 7
jbmsamba j****i@t****m 1
Michael Koontz m****z@g****m 1
Alex a****h@u****u 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 29
  • Total pull requests: 6
  • Average time to close issues: about 2 months
  • Average time to close pull requests: about 14 hours
  • Total issue authors: 25
  • Total pull request authors: 4
  • Average comments per issue: 3.86
  • Average comments per pull request: 2.0
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 2
  • Average time to close issues: about 2 months
  • Average time to close pull requests: about 18 hours
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 2.5
  • Average comments per pull request: 4.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • andrew-plowright (3)
  • azh2 (2)
  • dpessi (2)
  • rhijmans (1)
  • iserecgis (1)
  • fsecanho (1)
  • chugom (1)
  • totor2027 (1)
  • ns-1m (1)
  • fellicecity (1)
  • nealresearch (1)
  • j-arren (1)
  • Tobias1234 (1)
  • kylenessen (1)
  • ailich (1)
Pull Request Authors
  • andrew-plowright (2)
  • zmpeg (1)
  • mikoontz (1)
  • ailich (1)
Top Labels
Issue Labels
enhancement (1)
Pull Request Labels

Packages

  • Total packages: 3
  • Total downloads:
    • cran 413 last-month
  • Total docker downloads: 606
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 2
    (may contain duplicates)
  • Total versions: 15
  • Total maintainers: 1
proxy.golang.org: github.com/andrew-plowright/ForestTools
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.4%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago
proxy.golang.org: github.com/andrew-plowright/foresttools
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.4%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago
cran.r-project.org: ForestTools

Tools for Analyzing Remote Sensing Forest Data

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 2
  • Downloads: 413 Last month
  • Docker Downloads: 606
Rankings
Forks count: 4.1%
Stargazers count: 8.0%
Docker downloads count: 12.5%
Average: 14.9%
Downloads: 17.6%
Dependent repos count: 19.5%
Dependent packages count: 27.9%
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
  • R >= 4.0 depends
  • APfun * imports
  • Rcpp * imports
  • doParallel * imports
  • foreach * imports
  • imager * imports
  • methods * imports
  • parallel * imports
  • plyr * imports
  • progress * imports
  • raster * imports
  • rgeos * imports
  • sp * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests
.github/workflows/R-CMD-check.yaml actions
  • actions/checkout v3 composite
  • r-lib/actions/check-r-package v2 composite
  • r-lib/actions/setup-pandoc v2 composite
  • r-lib/actions/setup-r v2 composite
  • r-lib/actions/setup-r-dependencies v2 composite
Dockerfile docker
  • achubaty/r-spatial-base latest build