Simulating tropical forest change using a cellular automata-agent based model

Traditional Land Use and Cover Change (LUCC) studies have predominantly focused on attribute changes (e.g., forest area, forest change rates, forest types) with limited consideration of the spatial features embedded in dynamic processes. However, LUCC varies across spatial and temporal dimensions in the real world, and these spatial dynamic features are essential in LUCC modeling. Here, we used the spatiotemporal features of LUCC to reproduce and forecast tropical forest changes from 1979 to 2100 in the Guanacaste region using a proposed Cellular Automata-Agent Based Model (CA-AB). The pilot model was validated against historical forest change scenarios. Additionally, this model was used to forecast future scenarios under different assumptions: current trend scenarios, economy-development-driven scenarios, and ecology-protection-driven scenarios. Our results demonstrated that historical simulations for the second period (1997–2015) were more accurate than those for the first period (1979–1997), likely because the extent of forest change (both loss and gain) was greater in the first period, increasing the likelihood of forest changes in random locations. In addition, the simulations of the CA-AB model more closely match the actual forest cover in the second period compared to the CA model alone. This improvement is primarily due to the inclusion of spatial and temporal disparities, particularly those driven by agents' decision behaviors. Besides, simulated forest areas are largest under ecology-protection-driven scenarios, followed by current trend scenarios, with economy-development-driven scenarios having the smallest forest areas. Notably, all three scenarios share a common feature: forest cover concentrates in the peripheral areas of the Guanacaste region, while it declines in the central area over time due to land resource limitations, population growth, and other potential factors.