updating doc

R/calculate_spectral_metrics.R

@@ -13,16 +13,17 @@
 #' @examples
 #' set.seed(123)
 #' df <- data.frame(
-#' site_name = rep(c("site_one", "site_two", "site_three", "site_four"), each = 5000),
-#' aoi_id = 1,
-#' blue = runif(20000, min = 0, max = 1),
-#' green = runif(20000, min = 0, max = 1),
-#' red = runif(20000, min = 0, max = 1),
-#' red_edge = runif(20000, min = 0, max = 1),
-#' nir = runif(20000, min = 0, max = 1))
-#' pixelvalues <- calculate_cv(df,
-#' wavelengths = c('blue','green','red','red_edge','nir'),
-#' rarefaction = TRUE, min_points = 5000, n = 999)
+#'   site_name = rep(c("site_one", "site_two", "site_three", "site_four"), each = 5000),
+#'   aoi_id = 1,
+#'   blue = runif(20000, min = 0, max = 1),
+#'   green = runif(20000, min = 0, max = 1),
+#'   red = runif(20000, min = 0, max = 1),
+#'   red_edge = runif(20000, min = 0, max = 1),
+#'   nir = runif(20000, min = 0, max = 1))
+#' spectral_metrics <- calculate_spectral_metrics(df,
+#'                                                wavelengths = c('blue','green','red','red_edge','nir'),
+#'                                                rarefaction = TRUE, min_points = 5000, n = 999)
+
 calculate_cv <- function(pixel_values_df,
                          wavelengths,
                          rarefaction = FALSE,
@@ -70,7 +71,7 @@ calculate_cv <- function(pixel_values_df,
 
 
 
-# sv function\
+# sv function
 #' @import data.table
 #' @export
 calculate_sv <- function(pixel_values_df, wavelengths) {
@@ -177,7 +178,7 @@ calculate_spectral_metrics <- function(pixel_values_df,
     # calculate metrics, pass rarefaction where needed
     cv <- calculate_cv(site_pixel_values, wavelengths = wavelengths, rarefaction = rarefaction, n = n, min_points = min_points)
     sv <- calculate_sv(site_pixel_values, wavelengths = wavelengths)
-    chv <- calculate_chv_nopca(site_pixel_values, wavelengths, rarefaction = rarefaction, min_points = min_points)
+    chv <- calculate_chv_nopca(site_pixel_values, wavelengths, rarefaction = rarefaction, n = n, min_points = min_points)
 
     results[[site]] <- list(CV = cv, SV = sv, CHV = chv)
   }

R/create_masked_raster.R

@@ -8,6 +8,15 @@
 #' @param nir_threshold_df optional - a two columned df with 'site' col (site values must match first string of input file name) and  threshold values for each file
 #' @param red_band_index layer number for red band
 #' @param nir_band_index layer number for nir band
+#' @examples
+#' input_folder <- 'inst/doc/multiband_image'
+#' output_folder <- tempdir()
+#' create_masked_raster(input_folder,
+#'                     output_dir = output_folder,
+#'                     ndvi_threshold = 0.02,
+#'                     nir_threshold = 0.04,
+#'                     red_band_index = 3,
+#'                     nir_band_index = 5)
 #' @return A masked raster image, saved in the output directory
 #' @export
 

R/extract_pixel_values.R

@@ -5,18 +5,9 @@
 #' @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
-#' set.seed(123)
-#' df <- data.frame(
-#' site_name = rep(c("site_one", "site_two", "site_three", "site_four"), each = 5000),
-#' aoi_id = 1,
-#' blue = runif(20000, min = 0, max = 1),
-#' green = runif(20000, min = 0, max = 1),
-#' red = runif(20000, min = 0, max = 1),
-#' red_edge = runif(20000, min = 0, max = 1),
-#' nir = runif(20000, min = 0, max = 1))
-#' pixelvalues <- calculate_cv(df,
-#' wavelengths = c('blue','green','red','red_edge','nir'),
-#' rarefaction = TRUE, min_points = 5000, n = 999)
+#' aoi_files <- list.files('inst/doc/fishnet', pattern = '_fishnet.shp$', full.names = TRUE)
+#' raster_files <- list.files('inst/doc/multiband_image', pattern = '.tif$', full.names = TRUE)
+#' pixelvalues <- extract_pixel_values(raster_files, aoi_files, c('blue', 'green', 'red', 'red_edge', 'nir'))
 #' @export
 
 extract_pixel_values <- function(raster_files, aoi_files, wavelength_names){

R/find_optimum_thresholds.R

@@ -6,9 +6,13 @@
 #' @param class_col name of column where object class are stored
 #' @param class_value value/s of class that should be masked - e.g. for ndvi, 'non-veg' or c('bare_ground','rocks'), for nir, 'shadow'
 #' @return a df with optimum thresholds for each site
-#' @export
 #' @examples
-#' rnorm(500)
+#' ndvi_values <- data.frame(site = rep(c("site_one", "site_two"), each = 100),
+#' point = rep(1:100, times = 2),
+#' ndvi = runif(200, min = -1, max = 1),
+#' class = sample(rep(c('veg', 'non-veg'), each = 100)))
+#' ndvi_thresholds <- find_optimum_thresholds(ndvi_values, class_col = 'class', band_or_index_col = 'ndvi', site_col = 'site', class_value = 'non-veg')
+#' @export
 
 
 # add a for loop so nir and ndvi ground truth values can be given in the same df instead of seperately :)