maps alpha diversity indicators based on prior selection of PCs

map_alpha_div(
  Input_Image_File = FALSE,
  Input_Mask_File = FALSE,
  Output_Dir = "",
  window_size = 10,
  TypePCA = "SPCA",
  nbclusters = 50,
  MinSun = 0.25,
  pcelim = 0.02,
  Index_Alpha = "Shannon",
  FullRes = FALSE,
  LowRes = TRUE,
  MapSTD = TRUE,
  nbCPU = 1,
  MaxRAM = 0.25,
  ClassifMap = FALSE
)

Arguments

Input_Image_File: character. Path and name of the image to be processed.
Input_Mask_File: character. Path and name of the mask corresponding to the image to be processed.
Output_Dir: character. Output directory.
window_size: numeric. Size of spatial units (in pixels) to compute diversity.
TypePCA: character. Type of PCA (PCA, SPCA, NLPCA...).
nbclusters: numeric. Number of clusters defined in k-Means.
MinSun: numeric. Minimum proportion of sunlit pixels required to consider plot.
pcelim: numeric. Minimum contribution (in %) required for a spectral species.
Index_Alpha: character. Either 'Shannon', 'Simpson' or 'Fisher'.
FullRes: boolean. Full resolution.
LowRes: boolean. Low resolution.
MapSTD: boolean. map of standard deviation of the alpha diversity map (over repetitions)
nbCPU: numeric. Number of CPUs to use in parallel.
MaxRAM: numeric. MaxRAM maximum size of chunk in GB to limit RAM allocation when reading image file.
ClassifMap: character. If FALSE, perform standard biodivMapR based on SpectralSpecies. else corresponds to path for a classification map.

Value

None