Download and process Global Simulation Land Use/Cover (GEOSOS) 1 km variables

This function downloads, processes, and extracts variables from the Global Land-Use and Land-Cover Change Product (2010-2100). The dataset provides global 1 km resolution rasters based on different IPCC scenarios.

Usage

geososlandcover(x, vars, scenario = "A1B", year = 2010, discover = TRUE, ...)

Arguments

x: The output from `par_set()` defining the area or locations for extraction, the reference system, and the buffer. Leave this empty and use `par_set()` to define parameters for download.
vars: Character vector of one or more variables to download and process.
scenario: Character. The IPCC scenario: "A1B", "A2", "B1", or "B2". Ignored if year is 2010.
year: Numeric or character. The year of the product: 2010, 2050, or 2100.
discover: Logical. Kept for compatibility (currently unused).
...: Additional arguments (currently unused).

Value

If `par_set()` contained a raster/polygon/points with buffer: a `SpatRaster` stack of processed variables. If `par_set()` contained spatial points or data.frame of points without buffer: a `data.frame` of x, y, and extracted values.

Details

Available variables (working synonyms in parentheses):

Land Cover Classification

1 - "landcover" (Categorical: 1=Water, 2=Forest, 3=Grassland, 4=Farmland, 5=Urban, 6=Barren) ("lc", "cover", "land cover", "land cover class", "land use", "lulc", "classes")

Simulation Parameters

Years: 2010, 2050, 2100 (Note: 2010 is the MODIS baseline).
Scenarios: "A1B", "A2", "B1", "B2" (Ignored if year is 2010).

Citation:
Li X, Chen G, Liu X, Liang X, Wang S, Chen Y, Pei F, Xu X (2017). "A new global land-use and land-cover change product at a 1-km resolution for 2010 to 2100 based on human–environment interactions." Annals of the American Association of Geographers 107(5), 1040–1059. https://doi.org/10.1080/24694452.2017.1303357

Examples

# \donttest{
# Example 1: Download land cover for Italy in 2050 under scenario A1B
processed <- par_set(country = "Italy", crs = 3035) %>% 
  geososlandcover(vars = c("land cover"), year = 2050, scenario = "A1B")
  
# Example 2: Extract baseline (2010) values for specific points
points_df <- data.frame(ID = 1:2, x = c(12, 13), y = c(42, 43))
extracted <- par_set(data = points_df, crs = 4326) %>%
  geososlandcover(vars = "lc", year = 2010)
# }