Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: A case study in Wuhan, China

Abstract: Cellular Automata (CA) are widely used to model the dynamics within complex land use and land cover (LULC) systems. Past CA model research has focused on improving the technical modeling procedures, and only a few studies have sought to improve our understanding of the nonlinear relationships that underlie LULC change. Many CA models lack the ability to simulate the detailed patch evolution of multiple land use types. This study introduces a patch-generating land use simulation (PLUS) model that integrates a land expansion analysis strategy and a CA model based on multi-type random patch seeds. These were used to understand the drivers of land expansion and to investigate the landscape dynamics in Wuhan, China. The proposed model achieved a higher simulation accuracy and more similar landscape pattern metrics to the true landscape than other CA models tested. The land expansion analysis strategy also uncovered some underlying transition rules, such as that grassland is most likely to be found where it is not strongly impacted by human activities, and that deciduous forest areas tend to grow adjacent to arterial roads. We also projected the structure of land use under different optimizing scenarios for 2035 by combining the proposed model with multi-objective programming. The results indicate that the proposed model can help policymakers to manage future land use dynamics and so to realize more sustainable land use patterns for future development. Software for PLUS has been made available at https://github.com/HPSCIL/Patch-generating_Land_Use_Simulation_Model

• The paper presents the PLUS model, a hybrid simulation using LEAS and CARS that achieves a figure of merit of 0.2642 for land use change accuracy.
• It demonstrates that the PLUS model outperforms traditional models like FLUS by producing realistic landscape patterns validated with observed metrics.
• The study provides actionable insights into drivers such as infrastructure proximity, enabling effective scenario-based forecasting for sustainable urban planning.

An In-depth Analysis of the PLUS Model for Sustainable Land Expansion

This paper presents a nuanced approach to understanding and simulating land use change, specifically focusing on sustainable land expansion through the development of the Patch-generating Land Use Simulation (PLUS) model. The research utilizes a cellular automata (CA) framework to explore complex land use and land cover (LULC) dynamics, with a focus on Wuhan, China.

Methodology and Model Design

The PLUS model integrates a Land Expansion Analysis Strategy (LEAS) with a CA model utilizing multi-type random patch seeds (CARS). The LEAS is pivotal in uncovering transition rules for land use changes by analyzing the expansion patterns of land types rather than the traditional 'from-to' transitions. This approach mitigates computational complexity and enhances the model's applicability, making it possible to handle transitions in regions with multiple land uses more effectively.

The CARS module introduces a novel mechanism for simulating patch dynamics by generating and expanding patches from random seeds. This allows for a more realistic simulation of patch-based land use changes, capturing the interactions between synthetic urban growth and natural land types. The model's feedback loop between global demands and local competition ensures a calibrated simulation process driven by realistic constraints and objectives.

Model Validation and Performance

The PLUS model demonstrates superior accuracy and landscape pattern similarity over traditional models such as the FLUS model. In terms of the figure of merit (FOM), the PLUS model achieved a value of 0.2642, highlighting its enhanced simulation capability. The landscape metrics further validate this, with several metrics closely aligning with observed patterns, indicating the model’s efficacy in generating realistic landscape simulations.

Analysis of Underlying Drivers

One of the primary contributions of the paper is the detailed analysis of LULC underlying drivers using LEAS. By focusing on growth patterns instead of specific transitions, the model identifies significant variables influencing land type expansions, such as proximity to infrastructure, which greatly impacts urban and deciduous forest growth. This analysis provides valuable insights for policymakers seeking to understand the dynamics of urban expansion and ecological transitions.

Scenario-Based Land Use Forecasting

The research applies the PLUS model to project future land use scenarios in Wuhan through 2035, utilizing multi-objective programming (MOP) to tailor land use structure to various strategic objectives. Scenarios include economic development, ecological protection, and sustainable development, each offering distinct land use configurations and benefits. The sustainable development scenario notably balances economic, ecological service value, and capacity, serving as a benchmark for regional planning towards sustainability.

Implications and Future Directions

The implications of this research are twofold. Practically, the PLUS model offers a robust framework for urban planners and policymakers to simulate and manage land use changes under different development objectives. Theoretically, it advances CA-based modeling by integrating pattern and transition dynamics within a single framework, potentially applicable to other regions and contexts.

Looking ahead, future research might explore enhancing the PLUS model to incorporate real-time data and feedback loops, as well as applying the methodology to broader geographic scopes or different ecological zones. Additionally, integration with socio-economic modeling could yield even richer insights into the implications of land use strategies.

In conclusion, this research presents a comprehensive model that not only achieves competitive accuracy in simulating land use patterns but also provides actionable insights into the drivers and implications of land expansion. This work lays a solid foundation for future exploration and application in sustainable land management.