Sample block mean mapped forest cover over a region of interest

This function samples the block mean mapped forest cover over a given polygon. It can use either a custom forest cover mask provided as input or download and use Global Forest Change (GFC) tree cover tiles (Hansen et al., 2013).

Usage

sampleTreeCover(
  roi,
  thresholds,
  forest_mask = NULL,
  weighted_mean = FALSE,
  gfc_folder = "data/GFC",
  gfc_dataset_year = "latest"
)

Arguments

roi: An sf or SpatVector object representing the Region of Interest.
thresholds: Numeric vector of tree cover thresholds percentages (e.g., c(10, 20, 30)) to calculate forest cover percentages.
forest_mask: A SpatRaster object with a custom forest cover mask. If NULL, Hansen GFC tree cover tiles will be downloaded and used.
weighted_mean: Logical, if TRUE the weighted mean is calculated considering the approximate fraction of each cell that is covered by the roi (default is FALSE).
gfc_folder: Character string specifying the directory to download GFC data.
gfc_dataset_year: Numeric value describing which version of the Hansen data to use: any year in the 2018-2023 range or "latest" (default).

Value

Numeric value representing the mean forest cover for the polygon.

References

M. C. Hansen et al., High-Resolution Global Maps of 21st-Century Forest Cover Change. Science342,850-853(2013). DOI:10.1126/science.1244693

Examples

# Load required libraries
library(sf)

# Define a region of interest (ROI) in the Daintree forest
roi_daintree <- st_polygon(list(rbind(c(145.3833, -16.2500), c(145.3933, -16.2500),
                                      c(145.3933, -16.2400), c(145.3833, -16.2400),
                                      c(145.3833, -16.2500))))
roi_sf_daintree <- st_sfc(roi_daintree, crs = 4326)

# Example 1: Calculate forest cover (unweighted)
if (FALSE) { # \dontrun{
sampleTreeCover(roi_sf_daintree, thresholds = c(10, 20, 30))
} # }

# Example 2: Calculate forest cover (weighted)
if (FALSE) { # \dontrun{
sampleTreeCover(roi_sf_daintree, thresholds = c(10, 20, 30), weighted_mean = TRUE)
} # }