Geospatial covariate data layers Covariates / Distance to ESA-CCI-LC inland water per country (for the whole 2000-2012) / Armenia 100m. Covariates

Armenia 100m. Covariates
Distance to ESA-CCI-LC inland water, Armenia

The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The value of each grid cell represents the distance (in kilometres) from its centre to the nearest inland waterbody

Data Source: Lamarche, C., Santoro, M., Bontemps, S., d'Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P. and Arino, O., 2017. 'Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community'. Remote Sensing, 9(1), 36 (https://doi.org/10.3390/rs9010036) – ESA (European Space Agency) CCI (Climate Change Initiative) Land Cover project led by UCLouvain, 2017: Water Bodies v4.0 (http://maps.elie.ucl.ac.be/CCI/viewer/)

Methodology: The geodesic distance from each grid cell centre to the nearest inland waterbody has been calculated using the haversine formula and a global gridded inland waterbody dataset to avoid edge effects at the country boundaries. The global gridded inland waterbody datasets have been created following the description in Lloyd, C. T., H. Chamberlain, D. Kerr, G. Yetman, L. Pistolesi, F. R. Stevens, A. E. Gaughan, J. J. Nieves, G. Hornby, K. MacManus, P. Sinha, M. Bondarenko, A. Sorichetta, and A. J. Tatem, 2019. 'Global Spatio-temporally Harmonised Datasets for Producing High-resolution Gridded Population Distribution Datasets'. Big Earth Data (https://doi.org/10.1080/20964471.2019.1625151).

Region : Armenia
DOI : 10.5258/SOTON/WP00644
Date of production : 2018-11-02
Recommended citation

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00644

Data Files :
WorldPop datasets are available under the Creative Commons Attribution 4.0 International License. This means that you are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially, provided attribution is included (appropriate credit and a link to the licence).