Merge branch 'master' of github.com:traitecoevo/saltbush

NAMESPACE

@@ -1,6 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-export(calculate_chv)
 export(calculate_chv_nopca)
 export(calculate_cv)
 export(calculate_field_diversity)

R/calculate_field_diversity.R

@@ -22,7 +22,7 @@ calculate_field_diversity <- function(survey_data){
 
   # loop thru each unique site
   for (survey in ausplot_surveys) {
-    # Filter data for the current site
+    # filter data for the current site
     site_survey_data <- survey_data |>
       dplyr::filter(site_unique == survey)
 
@@ -48,19 +48,24 @@ calculate_field_diversity <- function(survey_data){
     inv_simpson <- vegan::diversity(community_matrix[, -1], index = 'invsimpson')
 
     subplot_diversity <- subplot_diversity |>
-      dplyr::mutate(shannon_diversity = shannon_diversity,
+      dplyr::mutate(site_location_name = substr(survey, 1, 10),
+             shannon_diversity = shannon_diversity,
              simpson_diversity = simpson_diversity,
              pielou_evenness = shannon_diversity / log(species_richness),
              exp_shannon = exp(shannon_diversity),
-             inv_simpson = inv_simpson,
-             survey = survey)
+             inv_simpson = inv_simpson)
+
+    # order columns
+    subplot_diversity <- subplot_diversity |>
+      dplyr::select(site_location_name, everything())
+
 
     # store  result for  current site
     all_site_results[[survey]] <- subplot_diversity
   }
 
   # combine into one df
-  field_diversity <- dplyr::bind_rows(all_site_results, .id = "site")
+  field_diversity <- dplyr::bind_rows(all_site_results, .id = "site_unique")
 
   return(list(
     field_diversity = field_diversity,

R/calculate_spectral_metrics.R

@@ -1,13 +1,13 @@
 #' @title Calculate Spectral Metrics
 #' @description Calculates CV, SV, and CHV from a pixel values dataframe with columns for each wavelength, `site_name`, and `aoi_id`.
-#' This help file applies to the functions `calculate_cv`, `calculate_sv`, `calculate_chv`, `calculate_chv_nopca`, and `calculate_spectral_metrics`.
+#' This help file applies to the functions `calculate_cv`, `calculate_sv`, `calculate_chv_nopca`, and `calculate_spectral_metrics`.
 #' @param pixel_values_df A data frame containing pixel values, typically obtained from the `extract_pixel_values` function.
 #' @param wavelengths A list of wavelengths that correspond to column names in `pixel_values_df`.
 #' @param rarefaction Logical; if TRUE, applies a rarefaction step that increases processing time.
 #' @param min_points Integer; minimum number of pixels per `aoi` to standardize uneven pixel numbers across sites (used if `rarefaction = TRUE`).
 #' @param n Integer; number of subset permutations if `rarefaction = TRUE`.
 #' @return A dataframe containing spectral metrics for each `aoi` within each site/raster.
-#' @aliases calculate_cv calculate_sv calculate_chv calculate_chv_nopca calculate_spectral_metrics
+#' @aliases calculate_cv calculate_sv calculate_chv_nopca calculate_spectral_metrics
 #' @export
 #' @import data.table
 #' @examples
@@ -99,20 +99,7 @@ calculate_sv <- function(pixel_values_df, wavelengths) {
 }
 
 
-# chv function
-#' @import data.table
-#' @export
-calculate_chv <- function(df, dim) {
-  CHV_df <- df |>
-    select(1:dim)
 
-  # convert to matrix
-  CHV_matrix <- as.matrix(CHV_df)
-
-  # calculate chv
-  CHV <- geometry::convhulln(CHV_matrix, option = "FA")
-  return(CHV)
-}
 
 # function to calculate chv for each aoi
 #' @import data.table
@@ -126,7 +113,7 @@ calculate_chv_nopca <- function(df,
   # convert to data.table for better performance
   setDT(df)
 
-  results <- data.table::data.table(aoi_id = double(), CHV_nopca = double())
+  results <- data.table::data.table(aoi_id = double(), CHV = double())
 
   # loop through each aoi_id
   for (aoi in unique(df$aoi_id)) {
@@ -155,7 +142,7 @@ calculate_chv_nopca <- function(df,
     }
 
     # store results in data.table
-    results <- rbind(results, data.table::data.table(aoi_id = aoi, CHV_nopca = mean_chv), fill = TRUE)
+    results <- rbind(results, data.table::data.table(aoi_id = aoi, CHV = mean_chv), fill = TRUE)
   }
 
   return(results)

R/create_masked_raster.R

@@ -1,5 +1,5 @@
 #' @title Create Masked Raster
-#' @description Creates a masked raster, non-vegetation pixels using input ndvi & nir threshold values
+#' @description Creates a masked raster, removes non-vegetation pixels using input ndvi & nir threshold values
 #' @param input can be a directory containing multiple tif/envi files, string of files, or single file.
 #' @param output_dir an output directory for the masked raster/s to be saved
 #' @param ndvi_threshold NDVI threshold (values beneath this contain non-veg pixels and should be masked)

R/extract_pixel_values.R

@@ -2,18 +2,16 @@
 #' @description Extracts pixel values from each layer of multiband image and creates pixel value df
 #' @param raster_files directory of input raster files
 #' @param aoi_files area of interest file - shapefile containing one or more site polygons for each raster
-#' @param wavelength_names wavelength names for each band, must match order of stacked layers
 #' @return a df with pixel values for each of the image layers
 #' @examples
-#' aoi_files <- list.files('inst/extdata/fishnet',
-#'    pattern = '_fishnet.shp$', full.names = TRUE)
+#' aoi_files <- list.files('inst/extdata/aoi',
+#'    pattern = 'image_aoi.shp$', full.names = TRUE)
 #' raster_files <- list.files('inst/extdata/multiband_image',
 #'    pattern = '.tif$', full.names = TRUE)
-#' pixelvalues <- extract_pixel_values(raster_files, aoi_files,
-#'    c('blue', 'green', 'red', 'red_edge', 'nir'))
+#' pixelvalues <- extract_pixel_values(raster_files, aoi_files)
 #' @export
 
-extract_pixel_values <- function(raster_files, aoi_files, wavelength_names){
+extract_pixel_values <- function(raster_files, aoi_files){
 
   all_pixel_values_list <- list()
 
@@ -32,9 +30,6 @@ extract_pixel_values <- function(raster_files, aoi_files, wavelength_names){
     # read in raster file
     raster_data <- raster::stack(raster_file)
 
-    # apply names - should be saved in wavelength order as per sect 1 of this script
-    names(raster_data) <- wavelength_names
-
     # create empty list
     pixel_values_list <- list()
 

R/saltbush-package.R

@@ -14,7 +14,6 @@
 #'
 #' * [calculate_cv]
 #' * [calculate_sv]
-#' * [calculate_chv]
 #' * [calculate_chv_nopca]
 #' * [calculate_spectral_metrics]
 #' * [create_masked_raster]

README.Rmd

@@ -34,12 +34,47 @@ library(saltbush)
  remotes::install_github("traitecoevo/saltbush")
 ```
 
+## Usage
+
+## Spectral metrics
+
 ### Spectral diversity metrics:
 
 + co-efficient of variance (CV)
 + spectral variance (SV)
 + convex hull volume (CHV)
 
+1. List raster files and area of interest files
+
+```{r}
+raster_files <- list.files(system.file("extdata/example", package = "saltbush"),
+    pattern = '.tif$', full.names = TRUE)
+
+aoi_files <- list.files(system.file("extdata/aoi", package = "saltbush"),
+    pattern = 'NSABHC0009_aoi.shp$', full.names = TRUE)
+
+
+```
+
+2. Extract pixel values
+
+```{r}
+pixel_values <- extract_pixel_values(raster_files, 
+                                     aoi_files)
+
+head(pixel_values)  
+```
+
+3. Calculate spectral metrics
+```{r}
+metrics <- calculate_spectral_metrics(pixel_values, masked = F, wavelengths = colnames(pixel_values[, 2:6]), rarefaction = F)
+
+head(metrics)
+
+```
+
+## Taxonomic metrics
+
 ### Taxonomic diversity metrics:
 
 + species richness
@@ -49,17 +84,15 @@ library(saltbush)
 + exponential shannon's index
 + inverse simpson's index
 
-## Usage
-
 1. Download example plot data from AusPlots. The `veg.PI` part extracts the point intercept data from the AusPlots data structure.
 
 ```{r}
 my.data <- ausplotsR::get_ausplots(my.Plot_IDs=c("SATFLB0004", "QDAMGD0022", "NTASTU0002"), veg.PI=TRUE)$veg.PI
 ```
 
-2. Calculate diversity from the point intercepts using different diversity metrics
+2. Calculate diversity from the point intercepts using different diversity metrics. The output is a list which includes taxonomic metrics, and also community matrices for checks.
 
 ```{r}
 field_diversity <- calculate_field_diversity(my.data)
-field_diversity
+field_diversity$field_diversity
 ```