-brightgreen.svg?style=flat){kind=icon}
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:
To install the development version, use the following command:
{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:
## Linking to GEOS 3.11.1, GDAL 3.8.2, PROJ 9.1.1; sf_use_s2() is TRUEresources <- 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
## - fritz_et_al
## - gfw_emissions
## - gfw_lossyear
## - gfw_treecover
## - gmw
## - nasa_firms
## - nasa_grace
## - nasa_srtm
## - nelson_et_al
## - soilgrids
## - teow
## - ucdp_ged
## - worldclim_max_temperature
## - worldclim_min_temperature
## - worldclim_precipitation
## - worldpop
##
## Supported indicators:
## - active_fire_counts
## - active_fire_properties
## - biome
## - deforestation_drivers
## - drought_indicator
## - ecoregion
## - elevation
## - fatalities
## - 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
## - triOnce 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"),
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.
## # ℹ 1 more variable: geom <POLYGON [°]>{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:
library(future)
library(progressr)
plan(multisession, workers = 6) # set up parallel plan
with_progress({
portfolio <- calc_indicators(
portfolio,
"treecover_area_and_emissions",
min_size = 1,
min_cover = 30
)
})
plan(sequential) # close child processesNote, 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.