sgsR - structurally guided sampling

sgsR is designed to implement structurally guided sampling approaches for enhanced forest inventories. The package was designed to function using rasterized airborne laser scanning (ALS; Lidar) metrics to allow for stratification of forested areas based on structure.

If you aren’t working with ALS data, any remote sensing data sets in a raster format (e.g. optical satellite imagery, climate data, drone-based products) can be used as inputs to help guide your environmental sampling needs.

sgsR is being actively developed, so you may encounter bugs. If that happens, please report your issue here by providing a reproducible example.

Installation :computer:

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)

Example usage :bar_chart:

#--- 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

Vignettes :books:

Check out the online documentation to see how sgsR functions and how you might use it for your work!

Vignettes include:

• Package fundamentals - vignette("sgsR")

• Sampling algorithms - vignette("sampling")

• Stratification algorithms - vignette("stratification")

• Calculate algorithms - vignette("calculating")

Collaborators :woman: :man:

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

Funding :raised_hands:

Development of sgsR was made possible thanks to the financial support of the Canadian Wood Fibre Centre’s Forest Innovation Program.