TemporalModelR (version 0.2.0)

scale_rasters: Scale Environmental Rasters Using Species Occurrence Data

Description

Preprocessing function that standardizes raster files using precomputed mean and standard deviation values from species occurrence data. Supports both temporally static and dynamic rasters.

Usage

scale_rasters(input_dir, output_dir, scaling_params_file,
              variable_patterns, time_cols = NULL, output_suffix = "_Scaled",
              overwrite = FALSE, verbose = TRUE)

Value

Invisibly returns a list containing:

• n_scaled: Integer. Total number of variables successfully scaled (static plus dynamic).

• output_dir: Character. Path to the directory containing scaled rasters.

Arguments

input_dir

Character. Directory containing aligned input .tif raster files, typically the output of raster_align.

output_dir

Character. Directory where scaled rasters will be saved.

scaling_params_file

Character. Path to CSV file with columns: variable, mean, sd. Typically generated by temporally_explicit_extraction.

variable_patterns

Named character vector mapping clean variable names to raster filename patterns. For time-varying variables include the time placeholder in the pattern (e.g. "forest_cover" = "forest_cover_YEAR"); for static variables omit it (e.g. "elevation" = "elevation"). Time placeholders must match entries in time_cols.

time_cols

Character vector of time placeholders for dynamic variables. Default is NULL.

output_suffix

Character. Suffix to append to output raster filenames. Default is "_Scaled".

overwrite

Logical. If TRUE, overwrites existing output files. If FALSE (default), existing files are skipped.

verbose

Logical. If TRUE (default), prints progress messages during processing. Includes per-variable scaling progress.

Details

Applies scaling to rasters via z-score transformation: (value - mean) / sd as is calculated during temporally_explicit_extraction. Scaled rasters are written to output_dir.

Scaled rasters should use the same scaling parameters derived from species occurrence data to ensure consistent standardization between training data and prediction layers.

See Also

Preprocessing: temporally_explicit_extraction, raster_align

Examples

aln_dir     <- system.file("extdata/rasters_aligned",
                           package = "TemporalModelR")

params_file <- system.file(
  "extdata/points/extracted_seasonal_Scaling_Parameters.csv",
  package = "TemporalModelR"
)

out_dir <- file.path(tempdir(), "scaled")

scale_rasters(
  input_dir           = aln_dir,
  output_dir          = out_dir,
  scaling_params_file = params_file,
  variable_patterns   = c(
    "elevation"    = "elevation",
    "forest_cover" = "forest_cover_YEAR",
    "prseas"       = "prseas_YEAR_SEASON"
  ),
  time_cols           = c("year", "season"),
  overwrite           = TRUE,
  verbose             = FALSE
)