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 landuse 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 parallelization in cloud-infrastructures such as AWS or 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. It’s 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")

To install the development version, use the following command:

remotes::install_github("https://github.com/mapme-initiative/mapme.biodiversity")

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.

To list all available resources and indicators run:

library(mapme.biodiversity)
library(sf)

## Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE

resources <- names(available_resources())
indicators <- names(available_indicators())
cat(sprintf("Supported resources:\n- %s\n\nSupported indicators:\n- %s",
            paste(resources, collapse = "\n- "),
            paste(indicators, collapse = "\n- ")))

## Supported resources:
## - chirps
## - esalandcover
## - gfw_emissions
## - gfw_lossyear
## - gfw_treecover
## - gmw
## - nasa_firms
## - nasa_grace
## - nasa_srtm
## - nelson_et_al
## - soilgrids
## - teow
## - worldclim_max_temperature
## - worldclim_min_temperature
## - worldclim_precipitation
## - worldpop
## 
## Supported indicators:
## - active_fire_counts
## - active_fire_properties
## - biome
## - drought_indicator
## - ecoregion
## - elevation
## - landcover
## - mangroves_area
## - population_count
## - precipitation_chirps
## - precipitation_wc
## - soilproperties
## - temperature_max_wc
## - temperature_min_wc
## - traveltime
## - treecover_area
## - treecover_area_and_emissions
## - treecoverloss_emissions
## - tri

Once you have decided on an indicator you are interested in, you can initialize a biodiversity portfolio by using an sf-object that only contains geometries of type POLYGON via the init_portfolio() function call. This will set some important information that is needed further down the processing chain. We can then request the download of a resource that is required to calculate specific indicators. Once the indicator has been calculated for individually for all assets in a portfolio, the data is returned as a nested list column to the original object.

(system.file("extdata", "sierra_de_neiba_478140_2.gpkg", package = "mapme.biodiversity") %>%
    sf::read_sf() %>%
    init_portfolio(
      years = 2016:2017,
      outdir = system.file("res", package = "mapme.biodiversity"),
      tmpdir = system.file("tmp", package = "mapme.biodiversity"),
      add_resources = FALSE,
      cores = 1,
      verbose = FALSE
    ) %>%
    get_resources(
      resources = c("gfw_treecover", "gfw_lossyear", "gfw_emissions"),
      vers_treecover = "GFC-2020-v1.8", vers_lossyear = "GFC-2020-v1.8"
    ) %>%
    calc_indicators("treecover_area_and_emissions", min_size = 1, min_cover = 30) %>%
    tidyr::unnest(treecover_area_and_emissions))

## 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      2832     2357.
## 2 478140 Sierra de Neiba National Park DOM         1  2017      3468     2345.
## # … with 1 more variable: geom <POLYGON [°]>

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