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https://github.com/mapme-initiative/mapme.biodiversity

Efficient analysis of spatial biodiversity datasets for global portfolios
https://github.com/mapme-initiative/mapme.biodiversity

environment eo gis mapme spatial sustainability

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Efficient analysis of spatial biodiversity datasets for global portfolios

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README 

        
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output: github_document

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# 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:



```{r install-cran, eval = FALSE}

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

```



To install the development version, use the following command:



```{r install-devel, eval = FALSE}

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`.



```{r available_resources, echo = FALSE}

knitr::kable(mapme.biodiversity::available_resources()[, c("name", "description", "licence")])

```



Next, is a list of supported indicators.



```{r available_indicators, echo = FALSE}

knitr::kable(mapme.biodiversity::available_indicators()[, c("name", "description")])

```



## 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.



```{r resources}

library(mapme.biodiversity)

library(sf)

```



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()`.



```{r calculation}

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

```



## 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](https://gdal.org/en/latest/user/configoptions.html#gdal-configuration-file).



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:



```{ini}

[credentials]



[.mapme-data]

path=/vsis3/mapme-data

AWS_SECRET_ACCESS_KEY=

AWS_ACCESS_KEY_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](https://gdal.org/en/latest/user/virtual_file_systems.html#network-based-file-systems).



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:



```{ini}

GDAL_CONFIG_FILE = ""

```



Then, in your scripts set the `outdir` option to the value specified with

the `path` variable in the configuration file:



```{r s3-outdir, eval = FALSE}

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

```



## A note on parallel computing



`{mapme.biodiversity}` follows the parallel computing paradigm of the

[`{future}`](https://cran.r-project.org/package=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.



```{r parallel-1, eval = FALSE}

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.



```{r parallel, eval = FALSE}

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](https://mapme-initiative.github.io/mapme.biodiversity/index.html)

find more detailed information about the package.