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Crosswalk data for CGLS based on Lumbierres et al. (2021)

Source: R/crosswalk_lumb_cgls_data.R
crosswalk_lumb_cgls_data.Rd

The International Union for Conservation of Nature (IUCN) Red List of Threatened Species provides a habitat classification scheme for different habitat types. To map the spatial location of each of these habitat classes, Lumbierres et al. (2021) developed a global habitat classification dataset using the Copernicus Global Land Service Land Cover (CGLS-LC100) dataset (Buchhorn et al., 2019; Buchhorn et al., 20200). This dataset provides a crosswalk table to associate the values in the raster dataset with IUCN habitat classes.

Usage

data(crosswalk_lumb_cgls_data)

Format

A data frame (tibble::tibble()) object with 159 rows and 2 columns. Each row corresponds to a different IUCN habitat class. It has the following columns:

code

The character code for a given IUCN habitat class.

value

The numeric value assigned to grid cells in the raster data that contain the IUCN habitat class (see get_lumb_cgls_habitat_data()).

Source

The data were derived from Lumbierres et al. (2021).

References

Buchhorn M, Smets B, Bertels L, Lesiv M, Tsendbazar N-E, Herold M, and Fritz SA (2019) Copernicus Global Land Service: Land Cover 100m: Epoch 2015: Globe. Dataset of the global component of the Copernicus Land Monitoring Service. doi:10.5281/zenodo.3243508

Buchhorn M, Smets B, Bertels L, de Roo B, Lesiv M, Tsendbazar N-E, Linlin L, and Tarko A (2020) Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015–2019: Product User Manual. Geneve: Zenodo. doi:10.5281/zenodo.3606295

Lumbierres M, Dahal PR, Di Marco M, Butchart SHM, Donald PF, and Rondinini C (2021) Translating habitat class to land cover to map area of habitat of terrestrial vertebrates. Conservation Biology, 36, e13851. doi:10.1111/cobi.13851

See also

A preprocessed version of the habitat classification data can be imported using get_lumb_cgls_habitat_data().

Examples

# load data
data(crosswalk_lumb_cgls_data)

# print data
print(crosswalk_lumb_cgls_data)
#> # A tibble: 159 × 2
#>    code  value
#>    <chr> <int>
#>  1 1       100
#>  2 1.1     100
#>  3 1.2     100
#>  4 1.3     100
#>  5 1.4     100
#>  6 1.5     100
#>  7 1.6     100
#>  8 1.7     100
#>  9 1.8     100
#> 10 1.9     100
#> # ℹ 149 more rows