In mapme-initiative/mapme.biodiversity: Efficient Monitoring of Global Biodiversity Portfolios

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Introduction

In the following we will demonstrate an idealized workflow based on a subset of the Global Forest Watch (GFW) data set that is delivered together with this package. You can follow along the code snippets below to reproduce the results. Please note that to reduce the time it takes to process this vignette, we will not download any resources from the internet. In a real use case, thus processing time might substantially increase because resources have to be downloaded and real portfolios might be larger than the one created in this example.

This vignette assumes that you have already followed the steps in Installation and have familiarized yourself with the terminology used in the package. If you are unfamiliar with the terminology used here, please head over to the Terminology article to learn about the most important concepts.

The idealized workflow for using {mapme.biodiversity} consists of the following steps:

• prepare your sf-object containing only geometries of type 'POLYGON' or 'MULTIPOLYGON'
• decide which indicator(s) you wish to calculate and make the required resource(s) available
• conduct your indicator calculation, which adds a nested list column to your portfolio object
• continue your analysis in R or decide to export your results to a spatial data format to use it with other geospatial software

Getting started

First, we will load the {mapme.biodiversity} and the {sf} package for handling spatial vector data. For tabular data handling, we will also load the {dplyr} and {tidyr} packages. Then, we will read an internal GeoPackage which includes part of the geometry of a protected area in the Dominican Republic from the WDPA database.

library(mapme.biodiversity)
library(sf)
library(dplyr)
library(tidyr)

aoi_path <- system.file("extdata", "gfw_sample.gpkg", package = "mapme.biodiversity")
aoi <- st_read(aoi_path, quiet = TRUE)
aoi

aoi <- st_simplify(aoi, preserveTopology = TRUE, dTolerance = 500)

Setting standard option

We use the mapme_options() function and set some arguments, such as the output directory, that are important to govern the subsequent processing. For this, we create a temporary directory. Internally, to save time on downloading when building this vignette, we copied already existing files to that output location (code not shown here).

outdir <- file.path(tempdir(), "mapme-resources")
mapme.biodiversity:::.copy_resource_dir(outdir)

outdir <- file.path(tempdir(), "mapme-resources")
dir.create(outdir, showWarnings = FALSE)

mapme_options(
  outdir = outdir,
  verbose = TRUE
)

The outdir argument points towards a directory on the local file system of your machine. All downloaded resources will be written to respective directories nested within outdir.

Once you request a specific resource for your portfolio, only those files will be downloaded that are missing to match its spatio-temporal extent. This behaviour is beneficial, e.g. in case you share the outdir between different projects to ensure that only resources matching your current portfolio are returned.

The verbose logical controls whether or not the package will print informative messages during the calculations. Note, that even if set to FALSE, the package will inform users about any potential errors or warnings.

Getting the right resources

You can check which indicators are available via the available_indicators() function:

available_indicators()
available_indicators("treecover_area")

Say, we are interested in the treecover_area indicator. We can learn more about this indicator and its required resources by using either of the commands below or, if you are viewing the online version, head over to the treecover_area documentation.

?treecover_area
help(treecover_area)

By inspecting the help page we learned that this indicator requires the gfw_treecover and gfw_lossyear resources and it requires to specify three extra arguments: the years for which to calculate treecover, the minimum size of patches to be considered as forest and the minimum canopy coverage of a single pixel to be considered as forested.

With that information at hand, we can start to retrieve the required resource. We can learn about all available resources using the available_resources() function:

available_resources()
available_resources("gfw_treecover")

For the purpose of this vignette, we are going to download both, the gfw_treecover and gfw_lossyear resources. We can get more detailed information about a given resource, by using either of the commands below to open up the help page. If you are viewing the online version of this documentation, you can simply head over to the gfw_treecover resource documentation.

?gfw_treecover
help(gfw_treecover)
?gfw_lossyear
help(gfw_lossyear)

We can now make the required resources available for our portfolio. We will use a common interface that is used for all resources, called get_resources(). We have to specify our portfolio object and supply one or more resource functions with their respective arguments. This will then download the matching resources to the output directory specified earlier.

aoi <- get_resources(
  x = aoi,
  get_gfw_treecover(version = "GFC-2023-v1.11"),
  get_gfw_lossyear(version = "GFC-2023-v1.11")
)

Calculate specific indicators

The next step consists of calculating specific indicators. Note that each indicator requires one or more resources that were made available via the get_resources() function explained above. You will have to re-run this function in every new R session, but note that data that is already available will not be re-downloaded.

Here, we are going to calculate the treecover_area indicator which is based on the resources from GFW. Since the resources have been made available in the previous step, we can continue requesting the calculation of our desired indicator. Note the command below would issue an error in case a required resource has not been made available via get_resources() beforehand.

aoi <- calc_indicators(
  aoi,
  calc_treecover_area(years = 2000:2023, min_size = 1, min_cover = 30)
)

Now let's take a look at the results. In addition to the metadata we are already familiar with, we see that there is an additional column called treecover_area which contains a tibble.

aoi

The indicator is represented as a nested-list column in our sf-object that is named alike the requested indicator. For our single asset, this column contains a tibble with r nrow(aoi$treecover_area[[1]]) rows and four columns. Let's have a closer look at this object

aoi$treecover_area

The tibble follows a standard output format, which is the same for all indicators. Each indicator is represented as a tibble with the four columns datetime, variable, unit, and value. In case of the treecover_area indicator, the variable is called treecover and is expressed in ha.

Let's quickly visualize the results:

mapme_options(verbose = FALSE)
data <- portfolio_long(aoi)

plot(value ~ datetime, data,
  main = "Treecover over time",
  xlab = "Year", ylab = "ha",
  pch = 16, col = "steelblue"
)

If you wish to change the layout of an portfolio, you can use portfolio_long() and portfolio_wide() (see the respective online tutorial). Especially for large portfolios, it is usually a good idea to keep the geometry information in a separated variable to keep the size of the data object relatively small.

geoms <- st_geometry(aoi)
portfolio_long(aoi, drop_geoms = TRUE)

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

Exporting an portfolio object

You can use the write_portfolio() function to save a processed portfolio object to disk as a GeoPackage. This allows sharing your data with contributors who might not be using R, but any other geospatial software. Simply point towards a non-existing file on your local disk to write the portfolio. You can use read_portfolio() to read back a GeoPackage written in such a way into R:

dsn <- tempfile(fileext = ".gpkg")
write_portfolio(x = aoi, dsn = dsn, quiet = TRUE)
from_disk <- read_portfolio(dsn, quiet = TRUE)
from_disk

file.remove(dsn)

mapme-initiative/mapme.biodiversity documentation built on April 5, 2025, 12:47 p.m.