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

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 t...

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Autores principales: Zhang, Tianyuan, Cheng, Changxiu, Wu, Xudong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613310/
https://www.ncbi.nlm.nih.gov/pubmed/37898602
http://dx.doi.org/10.1038/s41597-023-02637-7
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author Zhang, Tianyuan
Cheng, Changxiu
Wu, Xudong
author_facet Zhang, Tianyuan
Cheng, Changxiu
Wu, Xudong
author_sort Zhang, Tianyuan
collection PubMed
description 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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spelling pubmed-106133102023-10-30 Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution Zhang, Tianyuan Cheng, Changxiu Wu, Xudong Sci Data Data Descriptor 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. Nature Publishing Group UK 2023-10-28 /pmc/articles/PMC10613310/ /pubmed/37898602 http://dx.doi.org/10.1038/s41597-023-02637-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Zhang, Tianyuan
Cheng, Changxiu
Wu, Xudong
Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution
title Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution
title_full Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution
title_fullStr Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution
title_full_unstemmed Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution
title_short Mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution
title_sort mapping the spatial heterogeneity of global land use and land cover from 2020 to 2100 at a 1 km resolution
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613310/
https://www.ncbi.nlm.nih.gov/pubmed/37898602
http://dx.doi.org/10.1038/s41597-023-02637-7
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