OHI Science | Citation policy
This script generates the extent of coral for each OHI region.
Creating an actual script to calculate this. This has not been updated since 2012. Updating the data with newest version (version 4).
Downloaded: 07/25/2019
Description:
Global Distribution of Coral Reefs https://data.unep-wcmc.org/datasets/1 Reported at spatial cell scale.
This dataset shows the global distribution of coral reefs in tropical and subtropical regions. It is the most comprehensive global dataset of warm-water coral reefs to date, acting as a foundation baseline map for future, more detailed, work. This dataset was compiled from a number of sources by UNEP World Conservation Monitoring Centre (UNEP-WCMC) and the WorldFish Centre, in collaboration with WRI (World Resources Institute) and TNC (The Nature Conservancy). Data sources include the Millennium Coral Reef Mapping Project (IMaRS-USF and IRD 2005, IMaRS-USF 2005) and the World Atlas of Coral Reefs (Spalding et al. 2001).
Time range: 1954-2018
Reclassify the coral extent data into a mask of 1 or NA, and then compute zonal statistics for the count of cells within an OHI region that have coral and then convert into km2.
## This file makes it easier to process data for the OHI global assessment
## by creating the following objects:
##
## * dir_M = identifies correct file path to Mazu (internal server) based on your operating system
## * mollCRS = the crs code for the mollweide coordinate reference system we use in the global assessment
## * regions_shape() = function to load global shapefile for land/eez/high seas/antarctica regions
## * ohi_rasters() = function to load two rasters: global eez regions and ocean region
## * region_data() = function to load 2 dataframes describing global regions
## * rgn_syns() = function to load dataframe of region synonyms (used to convert country names to OHI regions)
## * low_pop() = function to load dataframe of regions with low and no human population
## * UNgeorgn = function to load dataframe of UN geopolitical designations used to gapfill missing data## loads 2 rasters: zones and ocean
## zones = raster cells with OHI region ID values, see rgns_all.csv to link IDs with names
## ocean = raster with ocean cells identified as 1, otherwise 0Convert seagrass shapefiles into same CRS as our region zones raster
Fasterize/rasterize: Where there is seagrass assign a value of 1 and NA otherwise
Stack rasters and adjust
Calculate zonal stats with zones raster and new combined seagrass. Convert to km^2 and save int/output files
Data Check
Justification for using ~0.9 km2 for average seagrass polygon area