inst/doc/Copernicus_Digital_Elevation_Models.R

In CopernicusDEM: Copernicus Digital Elevation Models

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(fig.width = 6,
                      fig.height = 6, 
                      fig.align = "center",
                      warning = FALSE, 
                      message = FALSE,
                      echo = TRUE,
                      eval = FALSE)

## -----------------------------------------------------------------------------
# 
# # We disable s2
# sf::sf_use_s2(use_s2 = FALSE)
# 
# # We load the .csv files
# files = c(system.file('vignette_data/Alberta_Wolves.csv', package = "CopernicusDEM"),
#           system.file('vignette_data/Mountain_caribou.csv', package = "CopernicusDEM"))
# 
# 
# taxon_data = mapv_data = list()
# 
# 
# for (FILE in files) {
# 
#   cat(glue::glue("Processing of the '{basename(FILE)}' file ..."), '\n')
# 
#   dtbl = data.table::fread(FILE, header = TRUE, stringsAsFactors = FALSE)
#   cols = c('location-long', 'location-lat', 'timestamp', 'individual-local-identifier',
#            'individual-taxon-canonical-name')
# 
#   dtbl_subs = dtbl[, ..cols]
#   colnames(dtbl_subs) = c('longitude', 'latitude', 'timestamp', 'individual_local_identifier',
#                           'individual-taxon-canonical-name')
# 
#   taxon_data[[unique(dtbl_subs$`individual-taxon-canonical-name`)]] = dtbl_subs
# 
#   dtbl_subs_sf = sf::st_as_sf(dtbl_subs, coords = c("longitude", "latitude"), crs = 4326)
# 
#   sf_rst_ext = fitbitViz::extend_AOI_buffer(dat_gps_tcx = dtbl_subs_sf,
#                                             buffer_in_meters = 250,
#                                             CRS = 4326,
#                                             verbose = TRUE)
# 
#   #................................................................
#   # Download the Copernicus DEM 30m elevation data because it has
#   # a better resolution, it takes a bit longer to download because
#   # the .tif file size is bigger
#   #...............................................................
# 
#   dem_dir = tempdir()
#   print(dem_dir)
# 
#   sfc_obj = sf_rst_ext$sfc_obj |>
#     sf::st_make_valid()
# 
#   dem30 = CopernicusDEM::aoi_geom_save_tif_matches(sf_or_file = sfc_obj,
#                                                    dir_save_tifs = dem_dir,
#                                                    resolution = 30,
#                                                    crs_value = 4326,
#                                                    threads = parallel::detectCores(),
#                                                    verbose = TRUE)
# 
#   TIF = list.files(dem_dir, pattern = '.tif', full.names = TRUE)
# 
#   if (length(TIF) > 1) {
# 
#     #....................................................
#     # create a .VRT file if I have more than 1 .tif files
#     #....................................................
# 
#     file_out = file.path(dem_dir, 'VRT_mosaic_FILE.vrt')
# 
#     vrt_dem30 = CopernicusDEM::create_VRT_from_dir(dir_tifs = dem_dir,
#                                                    output_path_VRT = file_out,
#                                                    verbose = TRUE)
#   }
# 
#   if (length(TIF) == 1) {
# 
#     #..................................................
#     # if I have a single .tif file keep the first index
#     #..................................................
# 
#     file_out = TIF[1]
#   }
# 
#   # Crop the DEM raster
#   raysh_rst = fitbitViz::crop_DEM(tif_or_vrt_dem_file = file_out,
#                                   sf_buffer_obj = sfc_obj,
#                                   verbose = TRUE)
# 
#   # Downsample the raster to make the visualization feasible
#   raysh_rst_downsample = terra::aggregate(x = raysh_rst,
#                                           fact = 5,
#                                           fun = mean,
#                                           cores = parallel::detectCores())
# 
#   # create the Elevation OpenTopoMap
#   mp_elev = mapview::mapview(x = raysh_rst_downsample,
#                              col.regions = grDevices::terrain.colors(10),
#                              layer.name = 'Elevation',
#                              map.types = 'OpenTopoMap',
#                              legend = TRUE)
# 
#   # get the unique colors of the identifier
#   unq_color_ids = length(unique(dtbl_subs_sf$individual_local_identifier))
#   print(unq_color_ids)
#   set.seed(seed = 3)
#   colors_ids = sample(x = colors(distinct = TRUE),
#                       size = unq_color_ids,
#                       replace = FALSE)
# 
#   # convert the identifier to a factor
#   dtbl_subs_sf$individual_local_identifier = as.factor(dtbl_subs_sf$individual_local_identifier)
# 
#   # visualize the identifier
#   mp_ids = mapview::mapview(dtbl_subs_sf,
#                             zcol = 'individual_local_identifier',
#                             layer.name = 'identifier',
#                             col.regions = grDevices::colorRampPalette(colors = colors_ids, space = "Lab"),
#                             legend = ifelse(unq_color_ids > 10, FALSE, TRUE))
# 
#   # combine both mapview objects
#   mp_both = mp_elev + mp_ids
# 
#   mapv_data[[unique(dtbl_subs$`individual-taxon-canonical-name`)]] = mp_both
# }
# 

## -----------------------------------------------------------------------------
# 
# #.............................................
# # create the 'mapview' object of both datasets
# #.............................................
# 
# dtbl_all = rbind(taxon_data$`Canis lupus`, taxon_data$`Rangifer tarandus`)
# 
# # see the number of observations for each animal
# table(dtbl_all$`individual-taxon-canonical-name`)
# 
# # create an 'sf' object of both data.tables
# dat_gps_tcx = sf::st_as_sf(dtbl_all, coords = c("longitude", "latitude"), crs = 4326)
# 
# lft = mapview::mapview(x = dat_gps_tcx,
#                                 zcol = 'individual-taxon-canonical-name',
#                                 map.types = 'OpenTopoMap',
#                                 layer.name = 'taxon-canonical-name',
#                                 legend = TRUE)
# lft
# 

## -----------------------------------------------------------------------------
# 
# mapv_data$`Rangifer tarandus`                           # caribou
# 

## -----------------------------------------------------------------------------
# 
# mapv_data$`Canis lupus`                                 # wolves
#