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

Xun Liang, Qingfeng Guan+4 more|arXiv (Cornell University)|Oct 22, 2020

Land Use and Ecosystem Services参考文献 12被引用 40

一句话总结

本文提出 PLUS 模型，一种生成斑块的土地利用仿真，将土地扩张分析与基于多类型随机斑块种子的元胞自动机结合，用以研究武汉土地扩张的驱动因素并投射 2035 年情景。

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

研究动机与目标

• 促进对土地利用与覆盖变化（LULCC）中非线性驱动因素的理解。
• 开发一个生成斑块的 LULC 仿真框架（PLUS），以改进斑块级动态。
• 评估模型输出与真实景观格局的一致性，并与其他 CA 模型进行比较。
• 在多目标优化下探索面向政策相关的未来土地利用情景。

提出的方法

• 将土地扩张分析策略与基于多类型随机斑块种子的元胞自动机模型相结合。
• 对照真实景观格局评估仿真精度，并与替代的 CA 模型进行比较。
• 分析过渡规则，例如草地与人类活动的关联以及靠近干道的阔叶林生长。
• 将 PLUS 与多目标规划相结合，在不同优化情景下投射 2035 的土地利用结构。

实验结果

研究问题

• RQ1 PLUS 框架在武汉识别出的 LULC 变化的关键非线性驱动因素是什么？
• RQ2 PLUS 模型在模拟斑块级土地利用动态方面与其他 CA 模型相比如何？
• RQ3 在不同优化情景下，2035 年会出现哪些未来土地利用模式？
• RQ4 可以从 PLUS 模拟中推断出哪些景观过渡规则？

主要发现

• 与所测试的其他 CA 模型相比，PLUS 实现了更高的仿真精度和更接近真实景观的格局度量。
• 土地扩张分析显示，草地倾向于出现在人类影响较弱的地区，而阔叶林则倾向于在干道邻近区域生长。
• 将该模型与多目标规划结合时，支持投射 2035 的土地利用结构。
• 结果提供可用于指导可持续土地利用规划的政策相关见解。