Spatial Sequential Modeling and Predication of Global Land Use and Land Cover Changes by Integrating a Global Change Assessment Model and Cellular Automata. Issue 9 (17th September 2019)

• Record Type: Journal Article
• Authors: Cao, Min; Zhu, Yanhui; Quan, Jinling; Zhou, Sheng; Lü, Guonian; Chen, Min; Huang, Mengxue
• Is Part Of: Earth's future. Volume 7:Issue 9(2019)
• Page Start: 1102
• Page End: 1116
• Publication Date: 2019-09-17
• Subjects: Land use and land cover change (LUCC) -- global change assessment model (GCAM) -- cellular automata (CA) -- spatial sequential modeling; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1029/2019EF001228
• Languages: English
• ISSNs: 2328-4277

Abstract

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