Interpolate and Map Tree-Ring Data — CFS

This function performs inverse distance weighting (IDW) interpolation of tree-ring data across a spatial grid, either for all species combined or by individual species. It generates yearly interpolated raster maps over a user-defined extent or the extent of the input data.

CFS_mapping(
  data,
  year.span = c(1801, 2017),
  extent.lim = NULL,
  grid.step = 0.1,
  by.spc = FALSE
)

Arguments

data: input in wide format.
year.span: Numeric vector of length 2 giving the range of years to include.
extent.lim: Optional numeric vector defining the spatial extent (c(xmin, xmax, ymin, ymax)). If NULL, the extent is determined from the input data.
grid.step: Numeric value specifying the grid spacing in degrees.
by.spc: Logical; if TRUE, maps are generated by species; if FALSE, all species are combined.

Value

An object of class cfs_map, a list of interpolated raster layers by species and year.

Examples


# \donttest{
# loading processed data
dt.samples_trt <- readRDS(system.file("extdata", "dt.samples_trt.rds", package = "growthTrendR"))
cols.meta = c("uid_tree", "uid_site", "longitude", "latitude", "species")
dt.mapping <- dt.samples_trt$tr_all_wide[
  , c(..cols.meta, as.character(1991:1995)), with = FALSE]
results_mapping <- CFS_mapping(dt.mapping, year.span = c(1991,1993))
#> [inverse distance weighted interpolation]
#> [inverse distance weighted interpolation]
#> [inverse distance weighted interpolation]
# }