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

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).

Predicting land use/cover changes and its association to agricultural production on the slopes of Mount Kilimanjaro, Tanzania

“…The results in this study are consistent with the global trend reported by Cao et al (2019b) showing increases in agricultural land from 2010 to 2100 in all three scenarios used. The report further shows shrinking in the areas under forest, grassland and shrubland.…”

“…Food production and ecological systems are chiefly affected by changes in cultivated land (Cao et al 2019b). In this study, despite the expansion in agricultural land, it is not reflected in the production of the selected staple food crops (Figure 8).…”

Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China

“…CA models are spatially explicit and land use change decisions are made based on the state of the neighbouring cells . CA models are often used for the spatial allocation of land use and land cover at a high spatial resolution (Cao et al, 2019) and may be used in combination with other models, such as ABM (e.g. Charif et al, 2017;Mustafa et al, 2017;Troost et al, 2015;Vermeiren et al, 2016).…”

Spatio-temporal modelling for the evaluation of an altered Indian saline Ramsar site and its drivers for ecosystem management and restoration

“…Cellular-based models consider both spatial and temporal components of LULC dynamics [33]. They are easy to standardize and competent to simulate wetland complexes [34]. These have been extensively used for past LULC trend analyses.…”