Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution

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Abstract

A fine global future land use/land cover (LULC) is critical for demonstrating the geographic heterogeneity of earth system dynamics and human-earth interaction. In this study, we produced a 1 km global future LULC dataset that takes into account future climate and socio-economic changes as well as the impact of simulated results of the former year on temporally adjacent periods. By incorporating the variations in climatic and socio-economic factors, we differentiated LULC suitability probabilities for historical and future periods across representative SSP-RCP scenarios. Then, by using an improved cellular automata model-PLUS to simulate the patch-level changes of various land classes, we iteratively downscaled water-basin-level LULC demands in various future scenarios to a spatial resolution of 1 km. Our dataset achieves a high degree of simulation accuracy (Kappa = 0.94, OA = 0.97, FoM = 0.10) and precisely captures the spatial-temporal heterogeneity of global LULC changes under the combined effects of climate change and socio-economic development. This robust and fine-scale LULC dataset provides valuable spatially-explicit information essential for earth system modeling and intricate dynamics between anthropogenic activities and the environment.

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Present and future Köppen-Geiger climate classification maps at 1-km resolution

Hylke E. Beck, Niklaus E. Zimmermann, Tim McVicar … (2018)

We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980–2016) and for projected future conditions (2071–2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen.

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Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

Ian Harris, Timothy Osborn, Phil Jones … (2020)

CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901–2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.

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Greening of the Earth and its drivers

Zaichun Zhu, Shilong Piao, Ranga Myneni … (2016)

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Author and article information

Contributors

Changxiu Cheng:

ORCID: http://orcid.org/0000-0003-2988-8915

chengcx@bnu.edu.cn

Xudong Wu:

ORCID: http://orcid.org/0000-0002-0752-0282

wuxudong@bjfu.edu.cn

Journal

Journal ID (nlm-ta): Sci Data

Journal ID (iso-abbrev): Sci Data

Title: Scientific Data

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2052-4463

Publication date (Electronic): 28 October 2023

Publication date PMC-release: 28 October 2023

Publication date Collection: 2023

Volume: 10

Electronic Location Identifier: 748

Affiliations

[1 ]State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, ( https://ror.org/022k4wk35) Beijing, 100875 PR China

[2 ]GRID grid.458451.9, ISNI 0000 0004 0644 4980, National Tibetan Plateau Data Center, ; Beijing, 100101 PR China

[3 ]School of Soil and Water Conservation, Beijing Forestry University, ( https://ror.org/04xv2pc41) Beijing, 100083 PR China

[4 ]Research Department of Complexity Science, Potsdam Institute for Climate Impact Research, ( https://ror.org/03e8s1d88) Potsdam, 14473 Germany

Author information

Tianyuan Zhang http://orcid.org/0000-0002-2337-6209

Changxiu Cheng http://orcid.org/0000-0003-2988-8915

Xudong Wu http://orcid.org/0000-0002-0752-0282

Article

Publisher ID: 2637

DOI: 10.1038/s41597-023-02637-7

PMC ID: 10613310

PubMed ID: 37898602

SO-VID: d975e967-ce38-44e9-a642-3954bd8b9fde

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 12 July 2023

Date accepted : 12 October 2023

Funding

Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);

Award ID: 71904003

Award ID: 42041007

Award Recipient : Changxiu Cheng Xudong Wu

Funded by: FundRef https://doi.org/10.13039/100005156, Alexander von Humboldt-Stiftung (Alexander von Humboldt Foundation);

Funded by: Young Talent Promotion Project of China Association for Science and Technology (Grant No. 2020-2022QNRC002)

Funded by: Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA23100303)

Custom metadata

Keywords: environmental sciences,climate sciences

Data availability:

Keywords: environmental sciences, climate sciences

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Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution

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