In tgoodbody/sgsR: Structurally Guided Sampling

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/"
)

sgsR - structurally guided sampling

Installation :computer: {.unnumbered}

Install the stable version of sgsRfrom CRAN with:

install.packages("sgsR")
library(sgsR)

Install the most recent development version of sgsR from Github with:

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

To cite sgsR use citation() from within R with:

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 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 by providing a reproducible example.

Example usage :bar_chart: {.unnumbered}

#--- 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 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.

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:

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)