OHI Science | Citation policy
This script generates the extent of saltmarsh for each OHI region.
No updates to script or data from previous year, copied over from v2021.
Reference: Mcowen, C., Weatherdon, L., Bochove, J.-W., Sullivan, E., Blyth, S., Zockler, C., Stanwell-Smith, D., Kingston, N., Martin, C., Spalding, M., Fletcher, S., 2017. A global map of saltmarshes. BDJ 5, e11764. https://doi.org/10.3897/BDJ.5.e11764
Downloaded: 03/09/2021
Description:
Global Distribution of saltmarshes https://data.unep-wcmc.org/datasets/43 Reported at
spatial cell scale.
This dataset shows the global distribution of saltmarshes, and is composed of two subsets of point and polygon occurence data. The data were compiled by UNEP World Conservation Monitoring Centre in collaboration with many collaborators (e.g. Frederick Short of the University of New Hampshire), organisations (e.g. the OSPAR Convention for the Northeast Atlantic sea), and projects (e.g. the European project Mediterranean Sensitive Habitats “Mediseh”), across the globe (full list available in “Metadata_saltmarsh.dbf”).
Time range: 1973-2015
Convert saltmarsh shapefiles into same CRS as our regions shapefile
Prep polygon data
## filter for eez
regions_eez <- regions %>%
filter(rgn_type == "eez")
#
# ## filter for land
regions_land <- regions %>%
filter(rgn_type == "land")
#
# crs(regions_eez)
# crs(v6_saltmarsh_py_moll)
# regions_all <- regions %>%
# group_by(rgn_id) %>%
# summarise(geometry = st_union(geometry))
# mapview(head(regions_all))
# Test st_intersection on one row
sm_subset1 <- st_intersection(v6_saltmarsh_py_moll, regions[3, ])
mapview(sm_subset1)
st_area(sm_subset1$geometry)
sm_subset1$GIS_AREA_K
#### 2021 chunk the subsetting by METADATA_I ####
## get a template to start adding to instead and run a for loop, so we don't lose any work if it fails...
## this will take awhile... ~1 hour
test <- v6_saltmarsh_py_moll %>%
as.data.frame() %>%
dplyr::select(METADATA_I, GIS_AREA_K) %>%
group_by(METADATA_I) %>%
summarise(n(), area = sum(GIS_AREA_K))
saltmarsh_1 <- v6_saltmarsh_py_moll %>%
dplyr::filter(METADATA_I == 1)
sm_subset_py <- st_intersection(st_make_valid(saltmarsh_1), regions)
sum(st_area(sm_subset_py))*0.000001 # 3293.699 km2
# sm_subset_py <- st_read(file.path(dir_M, "git-annex/globalprep/hab_saltmarsh/v2021/int/saltmarsh_extent_regions_py_81.shp")) ## I had to stop the loop at metadata_I == 81 because 82 is so large... read it back in and restart since 82 will take a long time
sum(st_area(sm_subset_py))*0.000001 # 13111.01
datasets <- unique(sort(v6_saltmarsh_py_moll$METADATA_I))
n_datasets <- length(unique(sort(v6_saltmarsh_py_moll$METADATA_I)))
for(i in 2:n_datasets){ #i = 2
dataset_id <- datasets[i]
sm_data <- v6_saltmarsh_py_moll %>%
dplyr::filter(METADATA_I == dataset_id)
sm_data <- st_intersection(st_make_valid(sm_data), regions)
sm_subset_py <- rbind(sm_subset_py, sm_data)
print(i) ## what dataset are we on?
print(n_datasets) ## out of how many datasets?
print(nrow(sm_subset_py)) ## how many rows does our new dataframe have now?
}
save_incase <- sm_subset_py ## save another in our enviro just in case we accidentally over write it...
## save our progress
st_write(sm_subset_py, file.path(dir_M, "git-annex/globalprep/hab_saltmarsh/v2021/int/saltmarsh_extent_regions_py.shp"), overwrite = TRUE) ## save to mazu so we don't lose all of that work
saltmarsh_subset_py <- sf::st_read(dsn = file.path(dir_M, "git-annex/globalprep/hab_saltmarsh/v2021/int/"), layer = "saltmarsh_extent_regions_py")
## plot to see
plot(regions$geometry, col = sf.colors(12, categorical = TRUE), border = 'grey',
axes = TRUE)
plot(saltmarsh_subset_py$geometry, add = TRUE)
zoom()
## get central america coordinates in MOLL and plot to make sure it worked:
disp_win_wgs84 <- st_sfc(st_point(c(-90, 6)), st_point(c(-78, 18)),
crs = 4326) ## c(xmin,yim), c(xmax,ymax)
disp_win_wgs84
disp_win_trans <- st_transform(disp_win_wgs84, crs = '+proj=moll')
disp_win_trans
ggplot() +
geom_sf(data = regions_eez$geometry, col = sf.colors(239, categorical = TRUE)) +
geom_sf(data = regions_land$geometry, fill = sf.colors(229, categorical = TRUE)) +
geom_sf(data = saltmarsh_subset_py$geometry, col = "red") +
coord_sf(xlim = c(-8989531, -7578769),ylim = c(741349.5,2211539))
## plot global
ggplot() +
geom_sf(data = regions_eez$geometry, col = sf.colors(239, categorical = TRUE)) +
geom_sf(data = regions_land$geometry, fill = sf.colors(229, categorical = TRUE)) +
geom_sf(data = saltmarsh_subset_py$geometry, col = "red")
## calculate polygon areas
saltmarsh_area_py <- saltmarsh_subset_py %>%
mutate(extent_km2 = st_area(saltmarsh_subset_py)*0.000001)
st_geometry(saltmarsh_area_py) <- NULL
## group by and summarise to get area per each rgn_id
saltmarsh_area_py_sum <- saltmarsh_area_py %>%
group_by(rgn_id) %>%
summarise(sum_extent_km2 = as.numeric(sum(extent_km2)))
sum(saltmarsh_area_py_sum$sum_extent_km2) # 55781.7655781.76 km2 of polygons - basically perfect!!
sum(v6_saltmarsh_py_moll$GIS_AREA_K) # 55667.35 - a little less than the area above, but that is OK... the small difference is probably due to reprojecting
## save this
write.csv(saltmarsh_area_py_sum, "int/saltmarsh_py_area.csv", row.names = FALSE)Prep points data
## read in points data
v6_saltmarsh_pts <- sf::st_read(dsn = file.path(dir_wcmc, "14_001_WCMC027_Saltmarsh_v6/01_Data"), layer = "WCMC027_Saltmarshes_Pt_v6")
## transform pts data to have same crs as regions eez
v6_saltmarsh_pts <- st_transform(v6_saltmarsh_pts, crs = "+proj=moll +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs")
#### 2021 chunk the subsetting by datasetid ####
## get a template to start adding to instead and run a for loop, so we don't lose any work if it fails or we need to stop it for some reason...
test <- v6_saltmarsh_pts %>%
as.data.frame() %>%
dplyr::select(METADATA_I) %>%
group_by(METADATA_I) %>%
summarise(n())
saltmarsh_pts_0 <- v6_saltmarsh_pts %>%
dplyr::filter(METADATA_I == 0)
sm_subset_pts <- st_intersection(st_make_valid(saltmarsh_pts_0), regions)
datasets <- unique(sort(v6_saltmarsh_pts$METADATA_I))
n_datasets <- length(unique(sort(v6_saltmarsh_pts$METADATA_I)))
for(i in 2:n_datasets){ #i = 2 ## Have the start the loop with 2, since we've already completed i = 1 (METADATA_I == 0) above to make our template sf
dataset_id <- datasets[i]
sm_data <- v6_saltmarsh_pts %>%
dplyr::filter(METADATA_I == dataset_id)
sm_data <- st_intersection(st_make_valid(sm_data), regions)
sm_subset_pts <- rbind(sm_subset_pts, sm_data)
print(i) ## what dataset are we on?
print(n_datasets) ## out of how many datasets?
print(nrow(sm_subset_pts)) ## how many rows does our new dataframe have now? (should end up with less than ~17000)
}
## save the points subset
st_write(sm_subset_pts, file.path(dir_M, "git-annex/globalprep/hab_saltmarsh/v2021/int/saltmarsh_extent_regions_pts.shp"), overwrite = TRUE)
## plot to make sure it works
ggplot() +
geom_sf(data = regions_eez$geometry, col = sf.colors(239, categorical = TRUE)) +
geom_sf(data = regions_land$geometry, fill = sf.colors(229, categorical = TRUE)) +
geom_sf(data = sm_subset_pts$geometry, col = "red")
#### Now we will calculate a proxy area for each region which has points. We will do this by counting the points in each rgn_id, figuring out the median size of each polygon in those countries from our polygon dataset, and assigning that median value to each point. If there is no median value for a specific country, we will gapfill with the global median.
sm_subset_pts <- st_read(file.path(dir_M, "git-annex/globalprep/hab_saltmarsh/v2021/int/saltmarsh_extent_regions_pts.shp"))
saltmarsh_subset_py <- sf::st_read(dsn = file.path(dir_M, "git-annex/globalprep/hab_saltmarsh/v2021/int/"), layer = "saltmarsh_extent_regions_py")
saltmarsh_area_py <- saltmarsh_subset_py %>%
mutate(extent_km2 = st_area(saltmarsh_subset_py)*0.000001)
st_geometry(saltmarsh_area_py) <- NULL
## get a count of the points in each region
saltmarsh_points_area <- sm_subset_pts %>%
group_by(rgn_id) %>%
summarise(count_points = n())
## get rid of geometry column to make this a df
st_geometry(saltmarsh_points_area) <- NULL
## filter for the point regions
test <- saltmarsh_area_py %>%
filter(rgn_id %in% saltmarsh_points_area$rgn_id) %>%
group_by(rgn_id) %>%
summarise(mean_km2 = mean(extent_km2),
median_km2 = median(extent_km2),
count_polygons = n())
mean(saltmarsh_area_py$extent_km2) # 14.2555
global_median_km2 <- as.numeric(median(saltmarsh_area_py$extent_km2)) # 0.1121728
## we will use the lower of the global values
## now multiply the count of points by the median area of points in these locations to get our extent in km2, for those that are still NA after (the regions which dont have polygons, and only have points), we will give them the global median size
saltmarsh_points_area_sum <- saltmarsh_points_area %>%
left_join(test, by = "rgn_id") %>%
mutate(extent_km2 = median_km2*count_points) %>%
mutate(extent_km2 = ifelse(is.na(extent_km2), global_median_km2*count_points, extent_km2)) %>%
dplyr::select(rgn_id, sum_extent_km2_pts = extent_km2)
sum(saltmarsh_points_area_sum$sum_extent_km2_pts) # 5928.334 km2
## save this data
write.csv(saltmarsh_points_area_sum, "int/saltmarsh_points_area.csv", row.names = FALSE)Combine points and polygon area estimates into one dataset
saltmarsh_points_area_sum <- read_csv("int/saltmarsh_points_area.csv")
saltmarsh_area_py_sum <- read_csv("int/saltmarsh_py_area.csv")
## finally, combine our points areas and polygon areas into one dataset and save
saltmarsh_area_final <- saltmarsh_area_py_sum %>%
full_join(saltmarsh_points_area_sum, by = "rgn_id") %>%
mutate(sum_extent_km2 = replace_na(sum_extent_km2, 0),
sum_extent_km2_pts = replace_na(sum_extent_km2_pts, 0)) %>% ## make all of the NAs --> 0 so that we can sum
mutate(extent_km2_final = sum_extent_km2 + sum_extent_km2_pts,
habitat = "saltmarsh",
year = 2017) %>% ## the latest raw data update is year = 2017 (when this dataset was published)
dplyr::select(rgn_id, year, habitat, km2 = extent_km2_final) %>%
filter(rgn_id < 255)
write.csv(saltmarsh_area_final, "data/habitat_extent_saltmarsh_updated.csv", row.names = FALSE)
region_data()
## lets make a gapfilling file
saltmarsh_area_final_gf <- saltmarsh_area_final %>%
dplyr::select(rgn_id, habitat) %>%
mutate(variable = "extent", gap_fill = 0) %>%
full_join(rgns_eez, by = "rgn_id") %>%
dplyr::select(rgn_id, habitat, variable, gap_fill) %>%
mutate(habitat = "saltmarsh", variable = "extent")
write.csv(saltmarsh_area_final_gf, "data/extent_saltmarsh_gf.csv", row.names = FALSE)v6_saltmarsh_pts <- sf::st_read(dsn = file.path(dir_wcmc, "14_001_WCMC027_Saltmarsh_v6/01_Data"), layer = "WCMC027_Saltmarshes_Pt_v6")
v6_saltmarsh_py <- sf::st_read(dsn = file.path(dir_wcmc, "14_001_WCMC027_Saltmarsh_v6/01_Data"), layer = "WCMC027_Saltmarshes_Py_v6")
saltmarsh_area_final <- read_csv("data/habitat_extent_saltmarsh_updated.csv")
habitat_extent_saltmarsh_old <- read_csv("~/github/ohiprep_v2021/globalprep/hab_saltmarsh/v2012/data/habitat_extent_saltmarsh_updated.csv")
compare_habitat_extent <- saltmarsh_area_final %>%
left_join(habitat_extent_saltmarsh_old, by = "rgn_id") %>%
mutate(km2.y = ifelse(
km2.x >0 & is.na(km2.y) ,0, #assign 0 values to old data km2 that have new data so that we can properly graph these differences.
km2.y
)) %>%
mutate(difference = km2.x - km2.y)
ggplot(compare_habitat_extent, aes(x = km2.y, y = km2.x)) +
geom_point() +
geom_abline() +
labs(title = "saltmarsh Habitat version old vs version 7", x = "old extent", y= "new extent") +
theme_bw()
filter(compare_habitat_extent, km2.y == 0)
sum(st_area(v6_saltmarsh_py)) #55667303187 [m^2]
55667303187*0.000001
# 55667.3 total area before eez intersection, not including points
sum(saltmarsh_area_final$km2)
#61710.1 total area after eez intersection, including the gapfilled points - the points account for the difference
sum(habitat_extent_saltmarsh_old$km2)
#37454.98 total area for the old extent data
sum(v6_saltmarsh_py_moll$GIS_AREA_K) # 55667.35
## check saudi arabia:
USA_polygons <- v6_saltmarsh_py %>%
filter(ISO3 == "USA")
sum(USA_polygons$GIS_AREA_K) # 18815.19 - ok... matches our estimate nearly perfectly
sum(st_area(USA_polygons))*0.000001 # 18815.19 km2