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Add 2024 GEBCO
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@sean-rohan-NOAA
sean-rohan-NOAA committed Dec 3, 2024
1 parent ec1945a commit 0327a63
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4 changes: 2 additions & 2 deletions analysis/efh_bathymetry_2028/01_invert_gebco_elevation.R
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Expand Up @@ -4,11 +4,11 @@ dir.create(here::here("analysis", "efh_bathymetry_2028", "data", "efh_bathy"), r
dir.create(here::here("analysis", "efh_bathymetry_2028", "output", "efh_bathy"), recursive = TRUE)

# Transform GEBCO Z coordinates to match Alaska bathymetry (negative is up)
gebco <- terra::rast(here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2023_n67.0_s54.0_w-175.0_e-154.0.tif"))
gebco <- terra::rast(here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2024_n67.0_s54.0_w-175.0_e-154.0.tif"))
gebco <- gebco * -1

map_layers <- akgfmaps::get_base_layers(select.region = "sebs", set.crs = crs(gebco, proj = TRUE))

gebco <- terra::mask(gebco, map_layers$akland[2,], inverse = TRUE)

terra::writeRaster(gebco, filename = here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2023_inverse.tif"))
terra::writeRaster(gebco, filename = here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2024_inverse.tif"))
243 changes: 200 additions & 43 deletions analysis/efh_bathymetry_2028/02_make_efh_bathymetry.R
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@@ -1,60 +1,194 @@
# Generate bathymetry raster for EFH

library(akgfmaps) # Version 4.0: install_github("afsc-gap-products/akgfmaps@shp2025")
library(arcgisbinding) # Version 1.0.1.306: Install for a specific R version through ArcGIS Pro
# Software versions ----
# R version: 4.4.1
# Python 3.9
# ArcGIS Pro Version 13.1.0.41833
# arcgisbinding version 1.0.1.311
# Reticulate version 1.35.0

library(akgfmaps) # Version 4.0
library(arcgisbinding) # Version 1.0.1.311: Installed for ArcGIS Pro 13.1.0.41833
library(reticulate) # Version 1.35.0
library(sf)
library(terra)

# Setup directory
dir.create(here::here("analysis", "efh_bathymetry_2028", "data", "efh_bathy"), recursive = TRUE)
dir.create(here::here("analysis", "efh_bathymetry_2028", "output", "efh_bathy"), recursive = TRUE)
# Alaska Albers Equal Area
aea_wkt <- 'PROJCS["NAD_1983_Albers",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-154.0],PARAMETER["Standard_Parallel_1",55.0],PARAMETER["Standard_Parallel_2",65.0],PARAMETER["Latitude_Of_Origin",50.0],UNIT["Meter",1.0]]'

# Define ArcGIS license
# Get akgfmaps layers for masking
map_layers <-
akgfmaps::get_base_layers(
select.region = "ebs",
set.crs = aea_wkt,
high.resolution.coast = TRUE # Use the highest resolution coastline
)

# Setup directories for bathymetry data and output
dir.create(
here::here("analysis", "efh_bathymetry_2028", "data", "efh_bathy"),
recursive = TRUE,
showWarnings = FALSE
)

dir.create(
here::here("analysis", "efh_bathymetry_2028", "output", "efh_bathy"),
recursive = TRUE,
showWarnings = FALSE
)

# Define ArcGIS Pro license ----
arcgisbinding::arc.check_product()

# Set environment - Python 3.9 with arcpy already installed
# Set Python environment - Python 3.9 with arcpy already installed
# Command line install: conda install arcpy=3.1 -c esri
reticulate::use_condaenv("C:\\Users\\sean.rohan\\.conda\\envs\\py39")

# Load arcpy library
ARCPY <- reticulate::import("arcpy")

# Set input file paths - these should be entered in order of quality, with the best or most recent raster first.
# Remove eastern portion of AI grid that extends beyond interpolated area
ai_grid <-
terra::rast(
here::here("analysis", "efh_bathymetry_2028", "data", "ai_grid_100m")
)

unimak_bounds <-
sf::st_polygon(
list(
matrix(
c(-167, 40,
-167, 54.2,
-177, 53.5,
-179, 60,
-150, 60,
-150, 40,
-167, 40),
ncol = 2,
byrow = TRUE
)
)
) |>
sf::st_sfc(crs = "NAD83") |>
sf::st_as_sf() |>
sf::st_transform(crs = aea_wkt)

ai_grid <-
terra::mask(
x = ai_grid,
mask = unimak_bounds,
inverse = TRUE
) |>
terra::trim()

terra::writeRaster(
x = ai_grid,
filename = here::here("analysis", "efh_bathymetry_2028", "data", "ai_100m_masked.tif"),
overwrite = TRUE
)

# Fix major depth errors in 2024 GEBCO bathymetry (unreasonably large depths in the EBS inner domain)

gebco_inverse <- terra::rast(here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2024_inverse.tif"))

gebco_mask <- gebco_inverse
gebco_mask[!is.na(values(gebco_mask))] <- 1

gebco_ebs_shelf_10_20 <-
terra::mask(
x = gebco_inverse,
mask = sf::st_transform(
dplyr::filter(
map_layers$survey.strata,
Stratum %in% c(10, 20)),
crs = terra::crs(gebco_inverse)
)
)

gebco_ebs_shelf_10_20[values(gebco_ebs_shelf_10_20) < 100] <- NA

gebco_ebs_shelf_10_20[!is.na(values(gebco_ebs_shelf_10_20))] <- -999

gebco_ebs_shelf_10_20 <-
as.polygons(gebco_ebs_shelf_10_20) |>
sf::st_as_sf() |>
sf::st_buffer(dist = 5000)

gebco_inverse1 <-
terra::mask(
x = gebco_inverse,
mask = gebco_ebs_shelf_10_20,
inverse = TRUE
)

gebco_inverse2 <-
terra::focal(
gebco_inverse1,
w = 7,
fun = mean,
na.policy = "only",
na.rm = TRUE
)

for(ii in 1:5) {
gebco_inverse2 <-
terra::focal(
gebco_inverse2,
w = 7,
fun = mean,
na.policy = "only",
na.rm = TRUE
)
}

plot(gebco_inverse2 * gebco_mask)

terra::writeRaster(
gebco_inverse2,
filename = here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2024_inverse_filled.tif")
)


# Set input file paths for bathymetry rasters files. These should be entered in order of quality,
# with the highest quality raster first, second highest quality second, etc. The order of these
# rasters determines which layer gets used in the mosaic process.
in_paths <- c(
here::here("analysis", "efh_bathymetry_2028", "data", "goa_bathy"),
here::here("analysis", "efh_bathymetry_2028", "data", "ai_grid_100m", "ai_grid_100m"),
here::here("analysis", "efh_bathymetry_2028", "data", "ai_100m_masked.tif"),
here::here("analysis", "efh_bathymetry_2028", "data", "Norton_Bathymetry", "norton_final2"),
here::here("analysis", "efh_bathymetry_2028", "data", "bs_bathy"),
here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2023_inverse.tif")
here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2024_inverse_filled.tif")
)

# Set output locations
out_dir_path <- here::here("analysis", "efh_bathymetry_2028", "data")

filename_mosaic <- "efh_mosaic_100m.tif"
filename_mosaic_resample <- "efh_mosaic_resample_100m.tif"
filename_tin <- "efh_tin_100m"
filename_masked <- "efh_masked_100m.tif"
filename_raw <- "efh_raw_100m.tif"
filename_output_100m <- "efh_bathy_100m.tif"
filename_output_1km <- "efh_bathy_1km.tif"

bathy_depth_range <- c(0, 2000)

# Alaska Albers Equal Area
aea_wkt <- 'PROJCS["NAD_1983_Albers",GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",-154.0],PARAMETER["Standard_Parallel_1",55.0],PARAMETER["Standard_Parallel_2",65.0],PARAMETER["Latitude_Of_Origin",50.0],UNIT["Meter",1.0]]'


# Get akgfmaps layers for masking
map_layers <- akgfmaps::get_base_layers(select.region = "ebs", set.crs = aea_wkt)
# Set valid depth range for pixels to be used for interpolation, in meters
bathy_depth_range <- c(0, 12000)

# Write land mask shapefile so it can be used by arcpy
sf::st_write(obj = map_layers$akland,
dsn = here::here("analysis",
"efh_bathymetry_2028",
"data",
"akland_temp_mask.shp"),
append = FALSE
sf::st_write(
obj = map_layers$akland,
dsn = here::here("analysis",
"efh_bathymetry_2028",
"data",
"akland_temp_mask.shp"),
append = FALSE
)

# Make 32-bit float raster mosaic, where values are set in the order of paths in in_path
# Make 32-bit float raster mosaic, where values are set in the order of paths in in_path
# This creates a 100 x 100 m resolution raster from the input files using a mosaic method where
# all of the rasters are used to fill in the coverage. In areas where layers overlap, values from the
# first layer in in_paths are used ahead of the second layer, the second layer ahead of the third,
# etc.
ARCPY$MosaicToNewRaster_management(
input_rasters = paste(in_paths, collapse = ";"),
output_location = out_dir_path,
Expand All @@ -75,48 +209,71 @@ out_raster <- ARCPY$sa$ExtractByMask(
"data",
"akland_temp_mask.shp"),
extraction_area = "OUTSIDE",
analysis_extent = NULL
analysis_extent = here::here(out_dir_path, filename_mosaic)
)

if(file.exists(here::here("analysis", "efh_bathymetry_2028", "data", filename_masked))) {
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", filename_masked))
ARCPY$Delete_management(
here::here("analysis", "efh_bathymetry_2028", "data", filename_masked)
)
}

out_raster$save(here::here("analysis", "efh_bathymetry_2028", "data", filename_masked))

# Remove pixels that are outside of a specified depth range
out_raster <- ARCPY$sa$SetNull(
in_conditional_raster = out_raster,
in_false_raster_or_constant = here::here("analysis", "efh_bathymetry_2028", "data", filename_masked),
where_clause = paste0("VALUE < ", bathy_depth_range[1], " Or VALUE >", bathy_depth_range[2])
)
out_raster <-
ARCPY$sa$SetNull(
in_conditional_raster = out_raster,
in_false_raster_or_constant = here::here("analysis", "efh_bathymetry_2028", "data", filename_masked),
where_clause = paste0("VALUE < ", bathy_depth_range[1], " Or VALUE >", bathy_depth_range[2])
)

# Remove duplicate file
if(file.exists(here::here("analysis", "efh_bathymetry_2028", "data", filename_raw ))) {
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", filename_raw))
ARCPY$Delete_management(
here::here("analysis", "efh_bathymetry_2028", "data", filename_raw)
)
}

out_raster$save(here::here("analysis", "efh_bathymetry_2028", "data", filename_raw))
out_raster$save(
here::here("analysis", "efh_bathymetry_2028", "data", filename_raw)
)

# Trim blank space and rename the depth band
efh_bathy_trim <- terra::rast(here::here("analysis", "efh_bathymetry_2028", "data", filename_raw)) |>
# Trim blank space outside of the polygons and rename the depth band in the raster
efh_bathy_trim <- terra::rast(
here::here("analysis", "efh_bathymetry_2028", "data", filename_raw)
) |>
terra::trim()

names(efh_bathy_trim) <- "depth"

terra::writeRaster(x = efh_bathy_trim,
filename = here::here("analysis", "efh_bathymetry_2028", "output", "efh_bathy", filename_output_100m),
overwrite = TRUE)
terra::writeRaster(
x = efh_bathy_trim,
filename = here::here("analysis", "efh_bathymetry_2028", "output", "efh_bathy", filename_output_100m),
overwrite = TRUE
)

# Resample the 100 m resolution grid to 1 km
efh_1km <- terra::aggregate(efh_bathy_trim, fact = 10, fun = "mean")
efh_1km <-
terra::aggregate(
efh_bathy_trim,
fact = 10,
fun = "mean"
)

terra::writeRaster(x = efh_1km,
filename = here::here("analysis", "efh_bathymetry_2028", "output", "efh_bathy", filename_output_1km),
overwrite = TRUE)
terra::writeRaster(
x = efh_1km,
filename = here::here("analysis", "efh_bathymetry_2028", "output", "efh_bathy", filename_output_1km),
overwrite = TRUE
)

# Remove intermediate files
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", filename_raw))
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", filename_masked))
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", filename_mosaic))
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", "akland_temp_mask"))
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", here::here("analysis", "efh_bathymetry_2028", "data", "ai_100m_masked.tif")))
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", filename_tin))
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", "ai_100m_masked.tif"))
ARCPY$Delete_management(here::here("analysis", "efh_bathymetry_2028", "data", "gebco_2024_inverse_filled.tif"))

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