An R package for processing global biodiversity data • mapme.biodiversity

About

Biodiversity areas, especially primary forests, provide multiple ecosystem services for the local population and the planet as a whole. The rapid expansion of human land use into natural ecosystems and the impacts of the global climate crisis put natural ecosystems and the global biodiversity under threat.

The mapme.biodiversity package helps to analyse a number of biodiversity related indicators and biodiversity threats based on freely available geodata-sources such as the Global Forest Watch. It supports computational efficient routines and heavy parallel computing in cloud-infrastructures such as AWS or Microsoft Azure using in the statistical programming language R. The package allows for the analysis of global biodiversity portfolios with a thousand or millions of AOIs which is normally only possible on dedicated platforms such as the Google Earth Engine. It provides the possibility to e.g. analyse the World Database of Protected Areas (WDPA) for a number of relevant indicators. The primary use case of this package is to support scientific analysis and data science for individuals and organizations who seek to preserve the planet biodiversity. Its development is funded by the German Development Bank KfW.

Installation

The package and its dependencies can be installed from CRAN via:

install.packages("mapme.biodiversity", dependencies = TRUE)

To install the development version, use the following command:

remotes::install_github("https://github.com/mapme-initiative/mapme.biodiversity", dependencies = TRUE)

Available resources and indicators

Below is a list of the resources currently supported by mapme.biodiversity.

name	description	licence
accessibility_2000	Accessibility data for the year 2000 from the Global Accessibility Map project	See JRC data policy: https://joint-research-centre.ec.europa.eu/jrc-mission-statement-work-programme/data-policy_en
acled	Armed Conflict Location & Event Data (ACLED)	Visit acleddata.com
biodiversity_intactness_index	Biodiversity Intactness Index	CC-BY-4.0
chelsa	Climatologies at High resolution for the Earth Land Surface Areas (CHELSA)	Unknown - Must cite!
chirps	Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)	CC - unknown
esalandcover	Copernicus Land Monitoring Service (CLMS) 100 meter land cover product	CC-BY 4.0
fritz_et_al	Drivers of deforestation in the tropics	CC-BY 4.0
gfw_emissions	Global Forest Watch - CO2 Emssions caused by forest cover loss	CC-BY 4.0
gfw_lossyear	Global Forest Watch - Year of forest cover loss occurence	CC-BY 4.0
gfw_treecover	Global Forest Watch - Percentage of canopy closure in 2000	CC-BY 4.0
global_surface_water_change	Global Surface Water - Change of water occurrence intensity	https://www.copernicus.eu/en/access-data
global_surface_water_occurrence	Global Surface Water - Percentage of water occurrence	https://www.copernicus.eu/en/access-data
global_surface_water_recurrence	Global Surface Water - Percentage of water recurrence	https://www.copernicus.eu/en/access-data
global_surface_water_seasonality	Global Surface Water - Seasonality of water occurrrence	https://www.copernicus.eu/en/access-data
global_surface_water_transitions	Global Surface Water - Transition classes	https://www.copernicus.eu/en/access-data
gmw	Global Mangrove Watch - Vector data of mangrove extent	CC BY 4.0
gsw_time_series	Global Surface Water - Yearly Time Series	https://global-surface-water.appspot.com/download
humanfootprint	Time series on human pressures on natural ecosystems.	CC BY 4.0
ipbes_biomes	Global Assessment Report on Biodiversity and Ecosystem Services division of the earth’s surface into biomes and anthromes.	CC 4.0
irr_carbon	Amount of carbon irrecoverably lost by a typical land use conversion event until mid-century.	CC NC 4.0
iucn	IUCN Species Richness Raser Dataset	https://www.iucnredlist.org/terms/terms-of-use
key_biodiversity_areas	Key Biodiversity Areas	https://www.keybiodiversityareas.org/termsofservice
man_carbon	Amount of carbon that is manageable by humans.	CC NC 4.0
mcd64a1	MODIS Burned Area Monthly Product (Aqua and Terra)	https://lpdaac.usgs.gov/data/data-citation-and-policies/
nasa_grace	NASA Gravity Recovery And Climate Experiment (GRACE) - Measurments of Earth’s mass and water changes	https://nasagrace.unl.edu/About.aspx
nasa_srtm	NASA Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM)	https://lpdaac.usgs.gov/data/data-citation-and-policies/
nelson_et_al	Global maps of traveltime to cities	CC-BY 4.0
soilgrids	ISRIC - Modelled global soil property layers	CC-BY 4.0
teow	Terrestrial Ecosystems of the World (TEOW) from WWF-US	unknown
ucdp_ged	UCDP Georeferenced Event Dataset (UCDP GED)	CC-BY 4.0
vul_carbon	Amount of carbon that is vulnerable to a typical land use conversion event.	CC NC 4.0
worldclim_max_temperature	WorldClim - Monthly maximum temperature 1960 - 2021	https://www.worldclim.org/about.html
worldclim_min_temperature	WorldClim - Monthly minimum temperature 1960 - 2021	https://www.worldclim.org/about.html
worldclim_precipitation	WorldClim - Monthly precipitation 1960 - 2021	https://www.worldclim.org/about.html
worldpop	WorldPop - Unconstrained Global Mosaics 2000 - 2020	CC-BY 4.0

Next, is a list of supported indicators.

name	description
biodiversity_intactness_index	Averaged biodiversity intactness index.
biome	Areal statistics of biomes from TEOW
burned_area	Monthly burned area detected by MODIS satellites
deforestation_drivers	Areal statistics of deforestation drivers
drought_indicator	Relative wetness statistics based on NASA GRACE
ecoregion	Areal statstics of ecoregions based on TEOW
elevation	Statistics of elevation based on NASA SRTM
exposed_population_acled	Number of people exposed to conflicts based on ACLED
exposed_population_ucdp	Number of people exposed to conflicts based on UCDP GED
fatalities_acled	Number of fatalities by event type based on ACLED.
fatalities_ucdp	Number of fatalities by group of conflict based on UCDP GED
gsw_change	Statistics of the surface water change layer by JRC
gsw_occurrence	Areal statistic of surface water based on occurrence threshold
gsw_recurrence	Areal statistic of surface water based on reccurence threshold
gsw_seasonality	Areal statistic of surface water by seasonality
gsw_time_series	Global Surface Water - Yearly Time Series area estimation of water classes.
gsw_transitions	Areal statistics of surface water grouped by transition class
humanfootprint	Statistics of the human footprint data set per polygon.
ipbes_biomes	Area distibution of IBPES biomes within a polygon.
irr_carbon	Statistics of irrecoverable carbon per polygon.
key_biodiversity_areas	Area estimation of intersection with key biodiversity areas.
landcover	Areal statistics grouped by landcover class
man_carbon	Statistics of manageable carbon per polygon.
mangroves_area	Area covered by mangroves
population_count	Statistic of population counts
precipitation_chelsa	Statistics of CHELSA precipitation layer
precipitation_chirps	Statistics of CHIRPS precipitation layer
precipitation_wc	Statistics of WorldClim precipitation layer
slope	Statistics of slope based on NASA SRTM
soilproperties	Statistics of SoilGrids layers
species_richness	Species richness statistics based on user-specified raster files.
temperature_max_wc	Statistics of WorldClim maximum temperature layer
temperature_min_wc	Statistics of WorldClim minimum temperature layer
traveltime	Statistics of traveltime to the clostes city grouped by city category
traveltime_2000	Statistics of traveltime to the clostest city in 2000
treecover_area	Area of forest cover by year
treecover_area_and_emissions	Area of forest cover and greenhouse gas emssions caused by forest loss by year
treecoverloss_emissions	Greenouse gas emissions cause by forest loss by year
tri	Statistics of terrain rudgedness index based on NASA SRTM DEM
vul_carbon	Statistics of vulnerable carbon per polygon.

Usage example

mapme.biodiversity works by constructing a portfolio from an sf object. Specific raster and vector resource matching the spatio-temporal extent of the portfolio are made available locally. Once all required resources are available, indicators can be calculated individually for each asset in the portfolio.

library(mapme.biodiversity)
library(sf)

## Linking to GEOS 3.13.0, GDAL 3.9.2, PROJ 9.5.0; sf_use_s2() is TRUE

Once you have decided on an indicator you are interested in, you can start by making the required resource available for your portfolio. Using mapme_options() you can set an output directory, control the maximum size of polygons before they are chunked into smaller parts, and control the verbosity of the package.

A portfolio is represented by an sf-object. It is required for the object to only contain geometries of type POLYGON and MULTIPOLYGON as assets. We can request the download of a resource for the spatial extent of our portfolio by using the get_resources() function. We simply supply our portfolio and one or more resource functions. Once the resources were made available, we can query the calculation of an indicator by using the calc_indicators() function. This function also expects the portfolio as input and one or more indicator functions. Once the indicator has been calculated for all assets in a portfolio, the data is returned as a nested list column to the original portfolio object. The output of each indicator is standardized to common format, consisting of a tibble with columns datetime, variable, unit, and value. We can transform the the data to long format by using portfolio_long().

mapme_options(
  outdir = system.file("res", package = "mapme.biodiversity"),
  chunk_size = 1e6, # in ha
  verbose = FALSE
)

aoi <- system.file("extdata", "sierra_de_neiba_478140_2.gpkg", package = "mapme.biodiversity") %>%
  sf::read_sf() %>%
  get_resources(
    get_gfw_treecover(version = "GFC-2023-v1.11"),
    get_gfw_lossyear(version = "GFC-2023-v1.11"),
    get_gfw_emissions()
  ) %>%
  calc_indicators(calc_treecover_area_and_emissions(years = 2016:2017, min_size = 1, min_cover = 30)) %>%
  portfolio_long()

aoi

## Simple feature collection with 4 features and 8 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -71.80933 ymin: 18.57668 xmax: -71.33201 ymax: 18.69931
## Geodetic CRS:  WGS 84
## # A tibble: 4 × 9
##   WDPAID ISO3  assetid indicator        datetime            variable unit  value
##    <dbl> <chr>   <int> <chr>            <dttm>              <chr>    <chr> <dbl>
## 1 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 emissio… Mg    4296.
## 2 478140 DOM         1 treecover_area_… 2016-01-01 00:00:00 treecov… ha    2370.
## 3 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 emissio… Mg    4970.
## 4 478140 DOM         1 treecover_area_… 2017-01-01 00:00:00 treecov… ha    2358.
## # ℹ 1 more variable: geom <POLYGON [°]>

Using cloud storages

mapme.biodiversity leverages GDAL’s capabilities for data I/O. For users of this package, that means that integrating a cloud storage is as easy as setting up a configuration file and changing the outdir argument in mapme_options(). While you could also decide to use environment variables, we recommend to set up a GDAL config file. You can find GDAL’s documentation on this topic here.

Suppose that we want to use an AWS S3 bucket that we control to write resource data to. Let’s assume this bucket is already set up and we wish to refer to it in our R code as mapme-data. The GDAL configuration file should look something like this:

[credentials]

[.mapme-data]
path=/vsis3/mapme-data
AWS_SECRET_ACCESS_KEY=<your-access-key>
AWS_ACCESS_KEY_ID=<your-access-id>

The connection will be handled based on GDAL’s virtual file system. You can find documentation on specific options for your cloud provider here.

Ideally, you would also set the following in the .Renviron file in your user’s home directory to ensure that GDAL is aware of this configuration when an R session is started:

GDAL_CONFIG_FILE = "<path-to-your-config-file>"

Then, in your scripts set the outdir option to the value specified with the path variable in the configuration file:

mapme_options(outdir = "/vsis3/mapme-data")

A note on parallel computing

mapme.biodiversity follows the parallel computing paradigm of the {future} package. That means that you as a user are in the control if and how you would like to set up parallel processing. Since {mapme.biodiversity} v0.9, we apply pre-chunking to all assets in the portfolio. That means that assets are split up into components of roughly the size of chunk_size. These components can than be iterated over in parallel to speed up processing. Indicator values will be aggregated automatically.

library(future)
plan(cluster, workers = 6)

As another example, with the code below one would apply parallel processing of 2 assets, with each having 4 workers available to process chunks, thus requiring a total of 8 available cores on the host machine. Be sure to not request more workers than available on your machine.

library(progressr)

plan(cluster, workers = 2)

with_progress({
  aoi <- calc_indicators(
    aoi,
    calc_treecover_area_and_emissions(
      min_size = 1,
      min_cover = 30
    )
  )
})

plan(sequential) # close child processes

Head over to the online documentation find more detailed information about the package.