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mapme.biodiversity is currently experiencing major changes to its user-interface and overall functionality. Please visit the announcement issue to learn more about these changes.


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.


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("", dependencies = TRUE)

Available resources and indicators

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

name description licence
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
global_surface_water_occurrence Global Surface Water - Percentage of water occurrence
global_surface_water_recurrence Global Surface Water - Percentage of water recurrence
global_surface_water_seasonality Global Surface Water - Seasonality of water occurrrence
global_surface_water_transitions Global Surface Water - Transition classes
gmw Global Mangrove Watch - Vector data of mangrove extent CC BY 4.0
nasa_firms NASA Fire Information for Resource Management System (FIRMS) - Global fire map data archive
nasa_grace NASA Gravity Recovery And Climate Experiment (GRACE) - Measurments of Earth’s mass and water changes
nasa_srtm NASA Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM)
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
worldclim_max_temperature WorldClim - Monthly maximum temperature 2000 - 2018
worldclim_min_temperature WorldClim - Monthly minimum temperature 2000 - 2018
worldclim_precipitation WorldClim - Monthly precipitation 2000 - 2018
worldpop WorldPop - Unconstrained Global Mosaics 2000 - 2020 CC-BY 4.0

Next, is a list of supported indicators.

name description
active_fire_counts Number of detected fires by NASA FIRMS
active_fire_properties Extraction of properties of fires detected by NASA FIRMS
biome Areal statistics of biomes from TEOW
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
fatalities 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_transitions Areal statistics of surface water grouped by transition class
landcover Areal statistics grouped by landcover class
mangroves_area Area covered by mangroves
population_count Statistic of population counts
precipitation_chirps Statistics of CHIRPS precipitation layer
precipitation_wc Statistics of WorldClim precipitation layer
soilproperties Statistics of SoilGrids layers
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
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

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.

## Linking to GEOS 3.12.1, GDAL 3.8.2, PROJ 9.3.1; 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 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 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.

  outdir = system.file("res", package = "mapme.biodiversity"),
  verbose = FALSE

(system.file("extdata", "sierra_de_neiba_478140_2.gpkg", package = "mapme.biodiversity") %>%
  sf::read_sf() %>%
    get_gfw_treecover(version = "GFC-2020-v1.8"),
    get_gfw_lossyear(version = "GFC-2020-v1.8"),
  ) %>%
  calc_indicators(calc_treecover_area_and_emissions(years = 2016:2017, min_size = 1, min_cover = 30)) %>%
## Simple feature collection with 2 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: 2 × 9
##   WDPAID NAME            DESIG_ENG     ISO3  assetid years emissions treecover
##    <dbl> <chr>           <chr>         <chr>   <int> <int>     <dbl>     <dbl>
## 1 478140 Sierra de Neiba National Park DOM         1  2016      2400     2360.
## 2 478140 Sierra de Neiba National Park DOM         1  2017      2839     2348.
## # ℹ 1 more variable: geom <POLYGON [°]>

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. Currently, mapme.biodiversity supports parallel processing on the asset level of the calc_indicators() function only. We also currently assume that parallel processing is done on the cores of a single machine. In future developments, we would like to support distributed processing. If you are working on a distributed use-cases, please contact the developers, e.g. via the discussion board or mail.

To process 6 assets in parallel and report a progress bar you will have to set up the following in your code:


plan(multisession, workers = 6) # set up parallel plan

  aoi <- calc_indicators(
      min_size = 1,
      min_cover = 30

plan(sequential) # close child processes

Note, that the above code uses future::multisession() as the parallel backend. This backend will resolve the calculation in multiple background R sessions. You should use that backend if you are operating on Windows, using RStudio or otherwise are not sure about which backend to use. In case you are operating on a system that allows process forking and are not using RStudio, consider using future::multicore() for more efficient parallel processing.

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