Country-level Land Cover - categories and transitions

Country-level Land Cover Categories

Country-level Land Cover Categories (CLCC) has information about land cover categories areas (in km²) on a country level. You can download the data files for different years from 1992 to 2015.

Country-level Land Cover Transitions

Country-level Land Cover Transitions (CLCT) has information about transitions (net changes) of the land cover categories areas (in km²) on a country level over five-year periods. Positive values indicate an increase of the given land cover category area and negative values indicate a decrease of the given land cover category area. You can download the data files for different five-year periods between 1992 to 2015.

Country-level Land Cover Gross Changes

Country-level Land Cover Gross Changes has information about gross changes of the land cover categories areas (in km²) on a country level over five-year periods.

Methods

Information about the methods and code used to generate the Country-level Land Cover Transitions data.

Feedback

We appreciate feedback regarding these datasets, such as suggestions, discovery of errors, difficulties in using the data, and format preferences. Please submit comments to the GitHub issue tracker or contact Jakub Nowosad directly.

References

• Center for International Earth Science Information Network (CIESIN), Columbia University, (2018). Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4TD9VDP
• ESA, 2017. European Space Agency Land Cover CCI Product User Guide Version 2.0. Tech. rep. https://maps.elie.ucl.ac.be/CCI/viewer/
• Netzel P., Nowosad J., Jasiewicz J., Niesterowicz J, Stepinski T., (2018). GeoPAT 2: user’s manual. Zenodo. http://doi.org/10.5281/zenodo.1291123
• GDAL/OGR contributors (2018). GDAL/OGR Geospatial Data Abstraction software Library. Open Source Geospatial Foundation. URL http://gdal.org
• R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
• Jonathan Asher Greenberg and Matteo Mattiuzzi (2018). gdalUtils: Wrappers for the Geospatial Data Abstraction Library (GDAL) Utilities. R package version 2.0.1.14. https://CRAN.R-project.org/package=gdalUtils
• Robert J. Hijmans (2019). raster: Geographic Data Analysis and Modeling. R package version 2.9-2. https://www.rspatial.org/
• Pebesma, E., 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1), 439-446, https://doi.org/10.32614/RJ-2018-009
• Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2019). dplyr: A Grammar of Data Manipulation. R package version 0.8.0.1. https://CRAN.R-project.org/package=dplyr
• Lionel Henry and Hadley Wickham (2019). purrr: Functional Programming Tools. R package version 0.3.2. https://CRAN.R-project.org/package=purrr
• Jakub Nowosad (2018). rgeopat2: Additional Functions for ‘GeoPAT’ 2. R package version 0.2.6. https://CRAN.R-project.org/package=rgeopat2
• Maximillian H.K. Hesselbarth, Marco Sciaini, Jakub Nowosad and Sebastian Hanss (2019). landscapemetrics: Landscape Metrics for Categorical Map Patterns. R package version 1.1. https://r-spatialecology.github.io/landscapemetrics/