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HILDA+ Global Land Use Change between 1960 and 2019

Winkler, Karina ORCID iD icon; Fuchs, Richard ORCID iD icon; Rounsevell, Mark D. A. ORCID iD icon; Herold, Martin

Abstract:

HILDA+ (HIstoric Land Dynamics Assessment+) is a global dataset on annual land use/cover change between 1960-2019 at 1 km spatial resolution. It is based on a data-driven reconstruction approach and integrates multiple open data streams (from high-resolution remote sensing, long-term land use reconstructions and statistics). It covers six generic land use/cover categories: 1: Urban areas, 2: Cropland, 3: Pasture/rangeland, 4: Forest, 5: Unmanaged grass/shrubland, 6: Sparse/no vegetation.HILDA+ contains annual land use/cover states and transitions, which we provide in different versions/ZIP folders:1) vGLOB-1.0_geotiff: land use/cover states and transition layers as single geotiffs from 1960 to 2019. 2) vGLOB-1.0-f_netcdf: land use/cover states and transition layers as two large netCDF files with extened time span from 1899 to 2019 (including forest class sub-division and dynamics). 3) hildap_vGLOB-1.0_change-layers: Additionally, HILDA+ change layers (change frequency; forest, cropland and pasture/rangeland change 1960-2019) are provided as geotiffs. 4) hildap_vGLOB-1.0_uncertainty: Uncertainty layers are provided as geotiffs in 3 ZIP folders (part1-3), containing annual datasets of uncertainty information: number of input datasets, deviation and average area fraction per land use/cover category.Note that maps were reprojected from original equal-area World Eckert IV (EPSG:54012; 1 km) to WGS 84 (EPSG: 4326; 0.01 degree) by using nearest neighbour resampling.


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Originalveröffentlichung
DOI: 10.1594/PANGAEA.921846
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Atmosphärische Umweltforschung (IMKIFU)
Publikationstyp Forschungsdaten
Publikationsdatum 20.08.2020
Identifikator KITopen-ID: 1000185969
Lizenz Creative Commons Namensnennung 4.0 International
Schlagwörter global, Land cover, land use, land use change, remote sensing, Binary Object, Binary Object (Media Type), Binary Object (File Size)
Art der Forschungsdaten Dataset
Nachgewiesen in OpenAlex
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