Install the stable version ofsgsRfromCRAN with:
install.packages("sgsR")library(sgsR)Install the most recent development version ofsgsR fromGithub with:
install.packages("devtools")devtools::install_github("https://github.com/tgoodbody/sgsR")library(sgsR)sgsR inliteratureOpen access publication:sgsR: a structurallyguided sampling toolbox for LiDAR-based forest inventories
To citesgsR usecitation() from within Rwith:
print(citation("sgsR"),bibtex =TRUE)#> To cite package 'sgsR' in publications use:#>#> Goodbody, TRH., Coops, NC., Queinnec, M., White, JC., Tompalski, P.,#> Hudak, AT., Auty, D., Valbuena, R., LeBoeuf, A., Sinclair, I.,#> McCartney, G., Prieur, J-F., Woods, ME. (2023). sgsR: a structurally#> guided sampling toolbox for LiDAR-based forest inventories. Forestry:#> An International Journal of Forest Research.#> 10.1093/forestry/cpac055.#>#> A BibTeX entry for LaTeX users is#>#> @Manual{,#> title = {sgsR: a structurally guided sampling toolbox for LiDAR-based forest inventories.},#> author = {Tristan R.H. Goodbody and Nicholas C. Coops and Martin Queinnec and Joanne C. White and Piotr Tompalski and Andrew T. Hudak and David Auty and Ruben Valbuena and Antoine LeBoeuf and Ian Sinclair and Grant McCartney and Jean-Francois Prieur and Murray E. Woods},#> journal = {Forestry: An International Journal of Forest Research},#> year = {2023},#> doi = {10.1093/forestry/cpac055},#> }#>#> Tristan RH Goodbody, Nicholas C Coops and Martin Queinnec (2025).#> Structurally Guided Sampling. R package version 1.4.6.#> https://cran.r-project.org/package=sgsR.#>#> A BibTeX entry for LaTeX users is#>#> @Manual{,#> title = {Structurally Guided Sampling},#> author = {Tristan RH Goodbody and Nicholas C Coops and Martin Queinnec},#> year = {2025},#> note = {R package version 1.4.6},#> url = {https://cran.r-project.org/package=sgsR},#> }sgsR provides a collection of stratification andsampling algorithms that use auxiliary information for allocating sampleunits over an areal sampling frame. ALS metrics, like those derived fromthelidRpackage are the intended inputs.
Other remotely sensed or auxiliary data can also be used(e.g. optical satellite imagery, climate data, drone-basedproducts).
sgsR is being actively developed, so you may encounterbugs. If that happens,please report your issuehere by providing a reproducible example.
#--- 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 stratificationnStrata =4)# produce 4 strata#--- apply stratified sampling ---#existing<-sample_strat(sraster = sraster,# use sraster as input for samplingnSamp =200,# request 200 samplesmindist =100,# samples must be 100 m apartplot =TRUE)# plot outputCheck outthepackage documentation to see how you can usesgsRfunctions for your work.
sgsR was presented at the ForestSAT 2022 Conference inBerlin.Slidesfor the presentation can be found here.
We are thankful for continued collaboration with academic, privateindustry, and government institutions to help improvesgsR.Special thanks to to:
| Collaborator | Affiliation |
|---|---|
| Martin Queinnec | University of British Columbia |
| Joanne C. White | Canadian Forest Service |
| Piotr Tompalski | Canadian Forest Service |
| Andrew T. Hudak | United States Forest Service |
| Ruben Valbuena | Swedish University of AgriculturalSciences |
| Antoine LeBoeuf | Ministère des Forêts, de la Faune et desParcs |
| Ian Sinclair | Ministry of Northern Development, Mines,Natural Resources and Forestry |
| Grant McCartney | Forsite Consultants Ltd. |
| Jean-Francois Prieur | Université de Sherbrooke |
| Murray Woods | (Retired) Ministry of NorthernDevelopment, Mines, Natural Resources and Forestry |
Development ofsgsR was made possible thanks to thefinancial support of the Canadian Wood Fibre Centre’s Forest InnovationProgram.