sgsR
A structurally guided sampling toolbox for LiDAR-based forest inventories.
https://github.com/tgoodbody/sgsr
Category: Biosphere
Sub Category: Forest Remote Sensing
Last synced: about 5 hours ago
JSON representation
Repository metadata
- Host: GitHub
- URL: https://github.com/tgoodbody/sgsr
- Owner: tgoodbody
- License: gpl-3.0
- Created: 2021-04-08T21:20:44.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2025-03-03T00:47:02.000Z (about 2 months ago)
- Last Synced: 2025-04-17T22:43:47.832Z (13 days ago)
- Language: R
- Homepage: https://tgoodbody.github.io/sgsR/
- Size: 1.15 GB
- Stars: 46
- Watchers: 2
- Forks: 9
- Open Issues: 0
- Releases: 10
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
README.Rmd
--- output: github_document --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/" ) ``` # sgsR - structurally guided sampling [](https://github.com/tgoodbody/sgsR/actions) [](https://app.codecov.io/gh/tgoodbody/sgsR?branch=main) [](https://CRAN.R-project.org/package=sgsR) ## Installation :computer: {.unnumbered} Install the stable version of [`sgsR`from CRAN](https://cran.r-project.org/package=sgsR) with: ``` r install.packages("sgsR") library(sgsR) ``` Install the most recent development version of [`sgsR` from Github](https://github.com/tgoodbody/sgsR) with: ``` r install.packages("devtools") devtools::install_github("https://github.com/tgoodbody/sgsR") library(sgsR) ``` ## Citing `sgsR` in literature Open access publication: [sgsR: a structurally guided sampling toolbox for LiDAR-based forest inventories](https://doi.org/10.1093/forestry/cpac055) To cite `sgsR` use `citation()` from within R with: ```{r} print(citation("sgsR"), bibtex = TRUE) ``` ## Overview `sgsR` provides a collection of stratification and sampling algorithms that use auxiliary information for allocating sample units over an areal sampling frame. ALS metrics, like those derived from the [`lidR` package](https://cran.r-project.org/package=lidR) are the intended inputs. Other remotely sensed or auxiliary data can also be used (e.g. optical satellite imagery, climate data, drone-based products). `sgsR` is being actively developed, so you may encounter bugs. If that happens, [please report your issue here](https://github.com/tgoodbody/sgsR/issues) by providing a reproducible example. ## Example usage :bar_chart: {.unnumbered} ``` r #--- Load mraster files ---# r <- system.file("extdata", "mraster.tif", package = "sgsR") #--- load the mraster using the terra package ---# mraster <- terra::rast(r) #--- apply quantiles algorithm to mraster ---# sraster <- strat_quantiles(mraster = mraster$zq90, # use mraster as input for stratification nStrata = 4) # produce 4 strata #--- apply stratified sampling ---# existing <- sample_strat(sraster = sraster, # use sraster as input for sampling nSamp = 200, # request 200 samples mindist = 100, # samples must be 100 m apart plot = TRUE) # plot output ``` ## Resources & Vignettes :books: {.unnumbered} Check out [the package documentation](https://tgoodbody.github.io/sgsR/index.html) to see how you can use `sgsR` functions for your work. `sgsR` was presented at the ForestSAT 2022 Conference in Berlin. [Slides for the presentation can be found here.](https://tgoodbody.github.io/sgsR-ForestSAT2022/) ## Collaborators :woman: :man: {.unnumbered} We are thankful for continued collaboration with academic, private industry, and government institutions to help improve `sgsR`. Special thanks to to: ```{r,echo=FALSE, results = 'asis'} library(knitr) names <- c("Martin Queinnec", "Joanne C. White", "Piotr Tompalski", "Andrew T. Hudak", "Ruben Valbuena", "Antoine LeBoeuf", "Ian Sinclair", "Grant McCartney", "Jean-Francois Prieur", "Murray Woods") aff <- c("University of British Columbia", "Canadian Forest Service", "Canadian Forest Service", "United States Forest Service", "Swedish University of Agricultural Sciences", "Ministère des Forêts, de la Faune et des Parcs", "Ministry of Northern Development, Mines, Natural Resources and Forestry", "Forsite Consultants Ltd.", "Université de Sherbrooke", " (Retired) Ministry of Northern Development, Mines, Natural Resources and Forestry") urls <- c( "https://www.researchgate.net/profile/Martin-Queinnec", "https://scholar.google.ca/citations?user=bqjk4skAAAAJ&hl=en/", "https://scholar.google.ca/citations?user=RtYdz0cAAAAJ&hl=en/", "https://www.fs.usda.gov/research/about/people/ahudak/", "https://scholar.google.com/citations?user=Nx336TQAAAAJ&hl=en/", "https://scholar.google.com/citations?user=wGsKOK8AAAAJ&hl=en/", "https://ca.linkedin.com/in/ian-sinclair-984929a4/", "https://www.signalhire.com/profiles/grant-mccartney%27s-email/99719223/", "https://www.researchgate.net/scientific-contributions/Jean-Francois-Prieur-2142960944", "https://www.researchgate.net/profile/Murray-Woods" ) df <- data.frame(Collaborator = names, Affiliation = aff) df$Collaborator <- paste0("[", df$Collaborator, "](", urls, ")") kable(df) ``` ## Funding :raised_hands: {.unnumbered} Development of `sgsR` was made possible thanks to the financial support of the Canadian Wood Fibre Centre's Forest Innovation Program.
Owner metadata
- Name: Tristan Goodbody
- Login: tgoodbody
- Email:
- Kind: user
- Description:
- Website:
- Location: Vancouver, Canada
- Twitter: GoodbodyT
- Company:
- Icon url: https://avatars.githubusercontent.com/u/60200460?u=d7b10752f026cb179f4bee973053fb7fa7677a77&v=4
- Repositories: 2
- Last ynced at: 2023-03-04T15:10:04.581Z
- Profile URL: https://github.com/tgoodbody
GitHub Events
Total
- Watch event: 2
- Issue comment event: 1
- Push event: 2
- Pull request event: 2
Last Year
- Watch event: 2
- Issue comment event: 1
- Push event: 2
- Pull request event: 2
Committers metadata
Last synced: 1 day ago
Total Commits: 555
Total Committers: 6
Avg Commits per committer: 92.5
Development Distribution Score (DDS): 0.029
Commits in past year: 1
Committers in past year: 1
Avg Commits per committer in past year: 1.0
Development Distribution Score (DDS) in past year: 0.0
Name | Commits | |
---|---|---|
Tristan Goodbody | 6****y | 539 |
evadChoi | d****i@m****a | 6 |
Nic | s****s@h****t | 4 |
Jean-Romain | j****1@u****a | 4 |
rhijmans | r****s@g****m | 1 |
Teun van den Brand | t****d@g****m | 1 |
Committer domains:
- ulaval.ca: 1
- hotmail.it: 1
- mail.ubc.ca: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 22
Total pull requests: 14
Average time to close issues: 21 days
Average time to close pull requests: 2 days
Total issue authors: 7
Total pull request authors: 6
Average comments per issue: 1.05
Average comments per pull request: 0.21
Merged pull request: 13
Bot issues: 0
Bot pull requests: 0
Past year issues: 2
Past year pull requests: 1
Past year average time to close issues: about 4 hours
Past year average time to close pull requests: about 1 month
Past year issue authors: 1
Past year pull request authors: 1
Past year average comments per issue: 1.0
Past year average comments per pull request: 1.0
Past year merged pull request: 1
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- tgoodbody (13)
- spono (2)
- jfprieur (2)
- rhijmans (2)
- ttrotto (1)
- lukasjarron (1)
- rymac17 (1)
Top Pull Request Authors
- tgoodbody (8)
- Jean-Romain (2)
- evadChoi (1)
- rhijmans (1)
- teunbrand (1)
- spono (1)
Top Issue Labels
- bug (11)
- enhancement (4)
- invalid (2)
Top Pull Request Labels
Package metadata
- Total packages: 1
-
Total downloads:
- cran: 255 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 10
- Total maintainers: 1
cran.r-project.org: sgsR
Structurally Guided Sampling
- Homepage: https://github.com/tgoodbody/sgsR
- Documentation: http://cran.r-project.org/web/packages/sgsR/sgsR.pdf
- Licenses: GPL (≥ 3)
- Latest release: 1.4.5 (published about 1 year ago)
- Last Synced: 2024-11-30T23:38:12.162Z (5 months ago)
- Versions: 10
- Dependent Packages: 0
- Dependent Repositories: 1
- Downloads: 255 Last month
-
Rankings:
- Stargazers count: 7.754%
- Forks count: 8.693%
- Dependent repos count: 23.802%
- Average: 24.726%
- Dependent packages count: 28.64%
- Downloads: 54.741%
- Maintainers (1)
Dependencies
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- R >= 3.5.0 depends
- methods * depends
- BalancedSampling * imports
- SamplingBigData * imports
- clhs * imports
- dplyr * imports
- ggplot2 * imports
- sf * imports
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- RANN * suggests
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- entropy * suggests
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- rmarkdown * suggests
- roxygen2 * suggests
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- testthat >= 3.0.0 suggests
Score: 11.16947695061237