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1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100
In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China’s future LULC following...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960815/ https://www.ncbi.nlm.nih.gov/pubmed/35347153 http://dx.doi.org/10.1038/s41597-022-01204-w |
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author | Luo, Meng Hu, Guohua Chen, Guangzhao Liu, Xiaojuan Hou, Haiyan Li, Xia |
author_facet | Luo, Meng Hu, Guohua Chen, Guangzhao Liu, Xiaojuan Hou, Haiyan Li, Xia |
author_sort | Luo, Meng |
collection | PubMed |
description | In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China’s future LULC following the Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs). To supplements existing datasets with a high spatial resolution, comprehensive pathway coverage, and delicate account for urban land change, here we present a 1-km gridded LULC dataset for China under 24 comprehensive SSP-RCP scenarios covering 2020–2100 at 10-year intervals. Our approach is to integrate the Global Change Analysis Model (GCAM) and Future Land Use Simulation (FLUS) model. This dataset shows good performance compared to remotely sensed CCI-LC data and is generally spatio-temporally consistent with the Land Use Harmonization version-2 dataset. This new dataset (available at 10.6084/m9.figshare.14776128.v1) provides a valuable alternative for multi-scenario-based research with high spatial resolution, such as earth system modeling, ecosystem services, and carbon neutrality. |
format | Online Article Text |
id | pubmed-8960815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89608152022-04-12 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100 Luo, Meng Hu, Guohua Chen, Guangzhao Liu, Xiaojuan Hou, Haiyan Li, Xia Sci Data Data Descriptor In the past decades, China has undergone dramatic land use/land cover (LULC) changes. Such changes are expected to continue and profoundly affect our environment. To navigate future uncertainties toward sustainability, increasing efforts have been invested in projecting China’s future LULC following the Shared Socioeconomic Pathways (SSPs) and/or Representative Concentration Pathways (RCPs). To supplements existing datasets with a high spatial resolution, comprehensive pathway coverage, and delicate account for urban land change, here we present a 1-km gridded LULC dataset for China under 24 comprehensive SSP-RCP scenarios covering 2020–2100 at 10-year intervals. Our approach is to integrate the Global Change Analysis Model (GCAM) and Future Land Use Simulation (FLUS) model. This dataset shows good performance compared to remotely sensed CCI-LC data and is generally spatio-temporally consistent with the Land Use Harmonization version-2 dataset. This new dataset (available at 10.6084/m9.figshare.14776128.v1) provides a valuable alternative for multi-scenario-based research with high spatial resolution, such as earth system modeling, ecosystem services, and carbon neutrality. Nature Publishing Group UK 2022-03-28 /pmc/articles/PMC8960815/ /pubmed/35347153 http://dx.doi.org/10.1038/s41597-022-01204-w Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Luo, Meng Hu, Guohua Chen, Guangzhao Liu, Xiaojuan Hou, Haiyan Li, Xia 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100 |
title | 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100 |
title_full | 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100 |
title_fullStr | 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100 |
title_full_unstemmed | 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100 |
title_short | 1 km land use/land cover change of China under comprehensive socioeconomic and climate scenarios for 2020–2100 |
title_sort | 1 km land use/land cover change of china under comprehensive socioeconomic and climate scenarios for 2020–2100 |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960815/ https://www.ncbi.nlm.nih.gov/pubmed/35347153 http://dx.doi.org/10.1038/s41597-022-01204-w |
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