## Installation 💻

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)

## 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 📊

#--- 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 📚

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 👩👨

We are thankful for continued collaboration with academic, private industry, and government institutions to help improve sgsR. Special thanks to to:

Collaborator Affiliation
Martin Queinnec University of British Columbia
Joanne C. White Canadian Forest Service
Development of sgsR was made possible thanks to the financial support of the Canadian Wood Fibre Centre’s Forest Innovation Program.