Yan Jiang

This study develops an integrated multi-algorithm spatial simulation framework for large-scale urban expansion analysis by combining a Back Propagation Artificial Neural Network (BP-ANN), the Future Land Use Simulation (FLUS) model, Cellular Automata (CA), the standard deviational ellipse, Neighborhood Aggregation Analysis, and landscape metrics within a unified data flow. Using the 2020 land use pattern as the baseline, BP-ANN estimates land use suitability probabilities from multiple driving factors; FLUS-CA allocates land under SSP1–SSP5 through adaptive inertia, neighborhood effects, and conversion costs; and spatial statistics and landscape metrics diagnose expansion magnitude, local aggregation, direction, built-up morphology, and cross-country differences. Urban built-up areas expand continuously under all scenarios, increasing by 38.92–49.33% by 2050 relative to 2020. Areas of high local aggregation intensity exhibit pronounced spatial differentiation, while the regional pattern maintains a northwest–southeast orientation, with scenario-dependent differences in centroid location and spatial dispersion. Edge density generally declines as built-up land increases, indicating greater built-up contiguity at the regional scale; this pattern should not be interpreted as evidence of reduced ecological landscape fragmentation. New built-up land encroaches on farmland and coastal ecological spaces and displays marked cross-country heterogeneity. The framework connects driving-factor identification, scenario configuration, spatial allocation, and pattern diagnosis, providing methodological support for cross-national multi-scenario simulation, urban planning, ecological conservation, and transboundary spatial collaborative governance in ASEAN.

As a core component of terrestrial ecosystems, forests play an irreplaceable ecological role in carbon sequestration, biodiversity conservation, and global climate regulation. Home to key global forest belts including the Congo Basin, the African continent’s forest changes directly shape regional ecological balance and sustainable development while profoundly affecting global ecological security and climate dynamics. Based on the Shared Socioeconomic Pathways (SSPs), a unified narrative framework for global socioeconomic and environmental change scenarios, this study couples techniques such as the Future Land Use Simulation (FLUS) model, dynamic degree analysis, transition matrix, K-means clustering analysis, and patch fragmentation analysis. This work aims to answer two key questions: (1) What are the spatiotemporal characteristics and dominant drivers of African woodland changes under different SSPs? (2) How do spatial clustering and fragmentation patterns vary across scenarios? It systematically predicts and analyzes the spatiotemporal characteristics, driving mechanisms, and fragmentation change patterns of African woodlands in 2030, 2050, and 2070 under five scenarios (SSP1-SSP5) with 2020 as the baseline. These five official IPCC SSP frameworks represent five distinctly divergent socioeconomic development trajectories ranging from sustainable to fossil-fuel-driven development, which are the core differentiated scenarios recommended by IPCC; full inclusion facilitates systematic comparison of varied forest feedback features across Africa’s diversified national development backgrounds. The research results show that understory forests in the SSP5 (Fossil Fuel-dominated Development) scenario exhibit a stable growth trend, with the total area transferred in significantly exceeding the area transferred out from 2020 to 2070, resulting in a net increase of 143,513 km². This growth occurs because high-income economies under this scenario invest heavily in ecological restoration and forest protection, offsetting carbon-intensive development impacts. The core forest density continues to increase and is distributed in contiguous areas; the SSP4 (uneven development) scenario regarding forest degradation is the most severe, with the dynamic rate expected to drop to −0.05% between 2050 and 2070, and a net transfer of −265,581 km². Forest fragmentation is highest, and the core density area is gradually shrinking. Cluster analysis shows that forest area remains relatively stable in most African countries, with stable countries accounting for as much as 95.49% under scenario SSP5. Regions with woodland expansion are mainly distributed in North Africa and localized parts of Southern Africa. After refinement using independent tree-density evidence, woodland expansion in South Africa is shown to be more limited and spatially heterogeneous; these newly expanded woodlands are mostly artificial plantations and alien invasive tree stands rather than native natural woodlands, mainly occurring in eastern and southeastern areas rather than in arid western regions. The spatiotemporal transfer process exhibits significant periodic differentiation, with 2030–2050 being a critical transitional period for forest change, and the differentiation effect between scenarios intensifying. Fragmentation analysis indicates that scenario SSP3 (regional rivalry, with moderate population growth and weak policy constraints) has the best forest integration and the lowest degree of fragmentation, while scenario SSP4 is most strongly affected by human activities and has the highest risk of patch fragmentation. These findings can provide a scientific basis for African countries to formulate differentiated forest protection policies and optimize ecological restoration plans, while also offering theoretical insights for continental-scale forest ecological management.