Produce diversity maps from a set of features

Jean-Baptiste Féret, Florian de Boissieu

2025-01-24

Once spectral features are computed from optical data, they can be used as input variables in biodivMapR.

biodivMapR v2 provides a simplified procedure compared to biodivMapR v1. A unique function named biodivMapR_full runs the full workflow and produces a limited set of outputs.

Produce diversity maps from SPCA file

SPCA was previously computed and the relevant spectral features were identified based on visual interpretation. This selection of components will be used here to produce diversity maps with biodivMapR. The SPCA file is a unique raster file stacking all components. biodivMapR needs the path for the stacked data as well as the list of selected features. The mask previously computed is also used.

# 1- define biodivMapR output directory 
output_dir_PCA2 <- file.path('./biodivMapR/SPCA')
dir.create(output_dir_PCA2, showWarnings = F, recursive = T)

# 2- define parameters for biodivMapR
window_size <- 10         # window side size for computation of spectral diversity
nbclusters <- 50          # nb of clusters (spectral species)

# 3- define path for intermediate variables to be saved
# - information related to kmeans clustering defining spectral species
Kmeans_info_save <- file.path(output_dir_PCA2,'Kmeans_info.RData')
# - information related to beta diversity mapping (BC dissimilarity + PCoA)
Beta_info_save <- file.path(output_dir_PCA2,'Beta_info.RData')

# 4- adjust parameters for multithread & computational efficiency
maxRows <- 1000           # nb of lines processed at once (adjust based on RAM available)
nbCPU <- 4                # nb of threads for parallel processing

# 5- apply biodivMapR 
selectedPC <- c(1,5,6)
ab_info_SPCA <- biodivMapR_full(input_raster_path = PCA_Output$PCA_Files$PCA, 
                                input_mask_path = mask_path_PCA,
                                output_dir = output_dir_PCA2, 
                                SelectBands = selectedPC, 
                                window_size = window_size, 
                                nbclusters = nbclusters, 
                                Kmeans_info_save = Kmeans_info_save,
                                Beta_info_save = Beta_info_save, 
                                maxRows = maxRows, nbCPU = nbCPU)

Here, ab_info_SPCA includes two lists:

• Kmeans_info, which gathers all information related to the k-means clustering: centroid, min and max values for each feature

• Beta_info, which gathers all information related to the computation of the beta diversity

These lists are saved as Rdata files corresponding to the files defined by Kmeans_info_save and Beta_info_save. These files can then be used for an independent process with biodivMapR_full. The path for these files can then be provided for input variables Kmeans_info_read, Beta_info_read.

biodivMapR will not recompute this information, if user provides a valid path for Kmeans_info and Beta_info. This option is interesting when adjusting Kmeans_info and Beta_info for a region including multiple tiles, and processing each tile independently.

$\alpha$ and $\beta$ diversity maps produced from the analysis of selected PCs are displayed below.

Produce diversity maps from spectral indices with biodivMapR

Spectral indices computed previously will be used here to produce diversity maps with biodivMapR. Each spectral index was saved as an individual raster file. biodivMapR needs a list of paths for input features and corresponding names. The mask previously computed based on interquartile ranges of spectral indices is also used.

# 1- define biodivMapR output directory 
output_dir_SI2 <- file.path('./biodivMapR/SpectralIndices')
dir.create(output_dir, showWarnings = F, recursive = T)

# 2- define parameters for biodivMapR
window_size <- 10         # window side size for computation of spectral diversity
nbclusters <- 50          # nb of clusters (spectral species)

# 3- define path for intermediate variables to be saved
# - information related to kmeans clustering defining spectral species
Kmeans_info_save <- file.path(output_dir_SI2,'Kmeans_info.RData')
# - information related to beta diversity mapping (BC dissimilarity + PCoA)
Beta_info_save <- file.path(output_dir_SI2,'Beta_info.RData')

# 4- adjust parameters for multithread & computational efficiency
maxRows <- 1000           # nb of lines processed at once (adjust based on RAM available)
nbCPU <- 4                # nb of threads for parallel processing

# 4- apply biodivMapR 
ab_info_SI <- biodivMapR_full(input_raster_path = SI_path, 
                              input_mask_path = mask_path_SI,
                              output_dir = output_dir_SI2, 
                              window_size = window_size,
                              nbclusters = nbclusters, 
                              Kmeans_info_save = Kmeans_info_save,
                              Beta_info_save = Beta_info_save, 
                              maxRows = maxRows, nbCPU = nbCPU)

The validation can then be performed if ground information is available Validation is described in this tutorial.