Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata

Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes, such as in biological diversity and the carbon cycle. Both natural cycles and human activities can be better examined with more accurate sourc...

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Published inEarth's future Vol. 7; no. 9; pp. 1102 - 1116
Main Authors Cao, Min, Zhu, Yanhui, Quan, Jinling, Zhou, Sheng, Lü, Guonian, Chen, Min, Huang, Mengxue
Format Journal Article
LanguageEnglish
Published Bognor Regis John Wiley & Sons, Inc 01.09.2019
Wiley
Subjects
Agricultural production
Biodiversity
Carbon cycle
Cellular automata
cellular automata (CA)
Climate change
Computer simulation
Emission standards
Environmental changes
Geopolitics
global change assessment model (GCAM)
Heterogeneity
HyperText Markup Language
Land cover
Land use
Land use and land cover change (LUCC)
Modelling
Socioeconomic factors
Spatial data
Spatial heterogeneity
Spatial resolution
spatial sequential modeling
Temporal resolution
China
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Abstract Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes, such as in biological diversity and the carbon cycle. Both natural cycles and human activities can be better examined with more accurate sources of land use data with higher spatial resolution. More importantly, it is crucial to consider spatial heterogeneity to simulate future changes in LUCC. In this paper, a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) is proposed. This modeling strategy is designed to sequentially spatialize global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100. The GCAM model is employed to predict the land use and land cover area demands for 283 world regions, which are divided by intersecting 32 geopolitical and socioeconomic regions and 18 agroecological zones. The spatialization rules of CA is trained separately for each world region to distinguish global land use and land cover types. The different spatialization rules and trends in land use and land cover demand for each of the 283 regions reflect the spatial heterogeneity in the GCAM‐CA model. We implement and validate the model for the simulation from 2000 to 2010. Next, the model is used to simulate three future scenarios, REF, G26, and G45, demonstrating that the GCAM‐CA model is effective for future global‐scale simulation of LUCCs. GCAM‐CA is freely available at the open geographic modeling and simulation platform (OpenGMS, http://geomodeling.njnu.edu.cn/GCAM‐CA.jsp). Key Points In this paper, we propose a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) We use this strategy to sequentially spatialize and predict global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100
AbstractList Abstract Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes, such as in biological diversity and the carbon cycle. Both natural cycles and human activities can be better examined with more accurate sources of land use data with higher spatial resolution. More importantly, it is crucial to consider spatial heterogeneity to simulate future changes in LUCC. In this paper, a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) is proposed. This modeling strategy is designed to sequentially spatialize global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100. The GCAM model is employed to predict the land use and land cover area demands for 283 world regions, which are divided by intersecting 32 geopolitical and socioeconomic regions and 18 agroecological zones. The spatialization rules of CA is trained separately for each world region to distinguish global land use and land cover types. The different spatialization rules and trends in land use and land cover demand for each of the 283 regions reflect the spatial heterogeneity in the GCAM‐CA model. We implement and validate the model for the simulation from 2000 to 2010. Next, the model is used to simulate three future scenarios, REF, G26, and G45, demonstrating that the GCAM‐CA model is effective for future global‐scale simulation of LUCCs. GCAM‐CA is freely available at the open geographic modeling and simulation platform (OpenGMS, http://geomodeling.njnu.edu.cn/GCAM‐CA.jsp ). Key Points In this paper, we propose a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) We use this strategy to sequentially spatialize and predict global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100
Abstract Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes, such as in biological diversity and the carbon cycle. Both natural cycles and human activities can be better examined with more accurate sources of land use data with higher spatial resolution. More importantly, it is crucial to consider spatial heterogeneity to simulate future changes in LUCC. In this paper, a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) is proposed. This modeling strategy is designed to sequentially spatialize global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100. The GCAM model is employed to predict the land use and land cover area demands for 283 world regions, which are divided by intersecting 32 geopolitical and socioeconomic regions and 18 agroecological zones. The spatialization rules of CA is trained separately for each world region to distinguish global land use and land cover types. The different spatialization rules and trends in land use and land cover demand for each of the 283 regions reflect the spatial heterogeneity in the GCAM‐CA model. We implement and validate the model for the simulation from 2000 to 2010. Next, the model is used to simulate three future scenarios, REF, G26, and G45, demonstrating that the GCAM‐CA model is effective for future global‐scale simulation of LUCCs. GCAM‐CA is freely available at the open geographic modeling and simulation platform (OpenGMS, http://geomodeling.njnu.edu.cn/GCAM‐CA.jsp).
Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes, such as in biological diversity and the carbon cycle. Both natural cycles and human activities can be better examined with more accurate sources of land use data with higher spatial resolution. More importantly, it is crucial to consider spatial heterogeneity to simulate future changes in LUCC. In this paper, a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) is proposed. This modeling strategy is designed to sequentially spatialize global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100. The GCAM model is employed to predict the land use and land cover area demands for 283 world regions, which are divided by intersecting 32 geopolitical and socioeconomic regions and 18 agroecological zones. The spatialization rules of CA is trained separately for each world region to distinguish global land use and land cover types. The different spatialization rules and trends in land use and land cover demand for each of the 283 regions reflect the spatial heterogeneity in the GCAM‐CA model. We implement and validate the model for the simulation from 2000 to 2010. Next, the model is used to simulate three future scenarios, REF, G26, and G45, demonstrating that the GCAM‐CA model is effective for future global‐scale simulation of LUCCs. GCAM‐CA is freely available at the open geographic modeling and simulation platform (OpenGMS, http://geomodeling.njnu.edu.cn/GCAM‐CA.jsp).
Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes, such as in biological diversity and the carbon cycle. Both natural cycles and human activities can be better examined with more accurate sources of land use data with higher spatial resolution. More importantly, it is crucial to consider spatial heterogeneity to simulate future changes in LUCC. In this paper, a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) is proposed. This modeling strategy is designed to sequentially spatialize global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100. The GCAM model is employed to predict the land use and land cover area demands for 283 world regions, which are divided by intersecting 32 geopolitical and socioeconomic regions and 18 agroecological zones. The spatialization rules of CA is trained separately for each world region to distinguish global land use and land cover types. The different spatialization rules and trends in land use and land cover demand for each of the 283 regions reflect the spatial heterogeneity in the GCAM‐CA model. We implement and validate the model for the simulation from 2000 to 2010. Next, the model is used to simulate three future scenarios, REF, G26, and G45, demonstrating that the GCAM‐CA model is effective for future global‐scale simulation of LUCCs. GCAM‐CA is freely available at the open geographic modeling and simulation platform (OpenGMS, http://geomodeling.njnu.edu.cn/GCAM‐CA.jsp). Key Points In this paper, we propose a modeling strategy (hereinafter referred to as GCAM‐CA) that combines a global change assessment model (GCAM) with cellular automata (CA) We use this strategy to sequentially spatialize and predict global LUCCs with 1‐km spatial resolution and 5‐year temporal resolution from 2010 to 2100
Author Huang, Mengxue
Zhou, Sheng
Zhu, Yanhui
Chen, Min
Cao, Min
Quan, Jinling
Lü, Guonian
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  organization: Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
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SSID ssj0000970357
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Snippet Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental changes,...
Abstract Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental...
Abstract Characterizing land use and land cover change (LUCC) is critical for understanding the interaction between human activities and global environmental...
SourceID doaj
proquest
crossref
wiley
SourceType Open Website
Aggregation Database
Publisher
StartPage 1102
SubjectTerms Agricultural production
Biodiversity
Carbon cycle
Cellular automata
cellular automata (CA)
Climate change
Computer simulation
Emission standards
Environmental changes
Geopolitics
global change assessment model (GCAM)
Heterogeneity
HyperText Markup Language
Land cover
Land use
Land use and land cover change (LUCC)
Modelling
Socioeconomic factors
Spatial data
Spatial heterogeneity
Spatial resolution
spatial sequential modeling
Temporal resolution
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Title Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata
URI https://onlinelibrary.wiley.com/doi/abs/10.1029%2F2019EF001228
https://www.proquest.com/docview/2306732183
https://doaj.org/article/56c561053b2646f69d5f3a48f93490f1
Volume 7
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