基于PLUS模型的粤港澳大湾区土地利用演变的多情景模拟研究

闫欣; 赵梓妤; 付炎芳; 丁琼; 凌博华; 黄港亚; 吴希文

doi:10.12052/gdutxb.250132

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基于PLUS模型的粤港澳大湾区土地利用演变的多情景模拟研究

环境科学与工程 | 更新时间：2026-08-04

- 基于PLUS模型的粤港澳大湾区土地利用演变的多情景模拟研究
- Predicting Land Use Changes in the Guangdong-Hong Kong-Macao Greater Bay Area Under Alternative Scenarios Using the PLUS Model
- 广东工业大学学报 2026年43卷第4期页码：47-57
- 作者机构：
  
  广东工业大学土木与交通工程学院,广东,广州,510006
- 作者简介：
  
  闫欣(2001–) ，女，硕士研究生，主要研究方向为遥感图像分析方法及其应用, E-mail：2112309039@mail2.gdut.edu.cn
  吴希文(1983–)，男，教授，博士生导师，主要研究方向为遥感图像分析方法及其应用，E-mail：hayman.ng@gdut.edu.cn
- 基金信息：
  
  广东省“珠江人才计划”引进创新创业团队项目(2019ZT08L213)
- DOI：10.12052/gdutxb.250132
  中图分类号： TP79;P237
- 纸质出版：2026
- 稿件说明：
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闫欣, 赵梓妤, 付炎芳, 等. 基于PLUS模型的粤港澳大湾区土地利用演变的多情景模拟研究[J]. 广东工业大学学报, 2026, 43(4): 47-57.
闫欣, 赵梓妤, 付炎芳, 等. 基于PLUS模型的粤港澳大湾区土地利用演变的多情景模拟研究[J]. 广东工业大学学报, 2026, 43(4): 47-57. DOI： 10.12052/gdutxb.250132.

DOI： 10.12052/gdutxb.250132

摘要

在全球气候变化背景下，研究未来气候变化情景下的土地利用变化可揭示城市发展的多模态路径，为优化土地资源再分配和可持续发展政策提供科学依据。本文基于粤港澳大湾区2010~2020年土地利用演变分析，耦合CMIP6提供的共享社会经济路径(Shared Socioeconomic Pathways

SSPs)和代表性浓度路径(Representative Concentration Pathways

RCPs)情景框架，利用Patch-generating Land Use Simulation (PLUS) 模型对2030~2050年土地利用格局进行预测。结果表明： PLUS模型对2020年土地利用的模拟总体精度为87.76%，Kappa指数为0.816，满足土地利用变化模拟的要求。2030~2050年间城市建设用地将持续增加，不同情景下的增长速率依次为SSP5.85 SSP2.45 SSP1.19；耕地、林地和水域面积将持续减少，其中在SSP5.85情景下耕地和林地减缩幅度最大。在SSP1.19情景下耕地−林地−水域系统维持较高稳定性，空间格局契合“山水林田湖草沙生命共同体”的生态理念，较好地平衡城市发展与生态保护需求。本文研究可为粤港澳大湾区国土空间优化和韧性发展的土地资源配置提供科学参考。

Abstract

Investigating land use change under future climate change scenarios is essential for revealing multimodal trajectories of urban development

which provides a scientific foundation for optimizing land resource redistribution and formulating sustainable development policies. This study analyzes the historical land use evolution in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2010 to 2020 and couples the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) framework from CMIP6. Using the patch-generating land use simulation (PLUS) model

land use patterns in the GBA for the period 2030~2050 under multiple scenarios are simulated. The simulation results for 2020 achieve an overall accuracy of 87.76% and a Kappa coefficient of 0.816

indicating reliable performance for projecting future changes. The findings reveal a continuous expansion of built-up land between 2030 and 2050 across all scenarios

with the growth rate ranked as SSP5.85 SSP2.45 SSP1.19. In contrast

cultivated land

forest land

and water bodies are projected to decline

with the most substantial reductions occurring under the SSP5.85 scenario. Notably

the SSP1.19 scenario maintains relatively high stability in the structure of cultivated land

forest land

and water bodies. Its spatial configuration aligns with the concept of ‘mountains

rivers

forests

farmlands

lakes

grasslands

and deserts as a community of life’ effectively balancing urban development needs with ecological conservation. Therefore

the land resource configuration under SSP1.19 offers a scientific basis for optimizing territorial spatial planning and supporting resilient development strategies in the GBA.

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references

HONG C, BURNEY J A, PONGRATZ J, et al . Global and regional drivers of land-use emissions in 1961−2017[J ] . Nature, 2021, 589(7843): 55 4-561.

MARQUES A, MARTINS I S, KASTNER T, et al . Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth[J ] . Nature Ecology Evolution, 2019, 3(4): 628-637.

NEWBOLD T, HUDSON L N, HILL S L, et al . Global effects of land use on local terrestrial biodiversity[J ] . Nature, 2015, 520(7545): 45-50.

LIAO W, LIU X, LI D, et al . Stronger contributions of urbanization to heat wave trends in wet climates[J ] . Geophysical Research Letters, 2018, 45(20): 11-310.

BONAN G B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests[J]. Science, 2008, 320(5882): 1444-1449.

CHEN P H, ZHONG K W. The impact of urban land use changes on the urban heat island effect: a case study of Nanjing[J]. Science of Surveying and Mapping, 2024, 49(7): 173-183.

FINDELL K L, SHEVLIAKOVA E, MILLY P C, et al . Modeled impact of anthropogenic land cover change on climate[J ] . Journal of Climate, 2007, 20(14): 3621-3634.

XU X Y, ZHOU H. Research on land use optimization based on grey prediction and linear programming in the Pearl River Delta of China[J]. Land Resources Herald, 2024, 21(2): 79-85.

LIANG X, LIU X, LI X, et al . Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method[J ] . Landscape and urban planning, 2018, 177: 47-63.

LIANG X, GUAN Q, CLARKE K C, et al . Understanding the drivers of sustainable land expansion using a patch-generating land use simulation (PLUS) model: a case study in Wuhan, China[J ] . Computers, Environment and Urban Systems, 2021, 85: 101569.

AI X, ZHENG X, ZHANG Y, et al . Climate and land use changes impact the trajectories of ecosystem service bundles in an urban agglomeration: intricate interaction trends and driver identification under SSP-RCP scenarios[J ] . Science of The Total Environment, 2024, 944: 173828.

HURTT G C, CHINI L, SAHAJPAL R, et al . Harmonization of global land-use change and management for the period 850-2100 (LUH2) for CMIP6[J ] . Geoscientific Model Development Discussions, 2020, 13(11): 5425-5464.

CHEN X, WANG Z, YAN G H, et al . Enhanced urban growth modelling: incorporating regional development heterogeneity and noise reduction in a cellular automata model-a case study of Zhengzhou, China[J ] . Sustainable Cities and Society, 2023, 99: 104959.

LIN W, SUN Y, NIJHUIS S, et al . Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study[J ] . Science of the Total Environment, 2020, 739: 139899.

FU Q, HOU Y, WANG B, et al . Scenario analysis of ecosystem service changes and interactions in a mountain-oasis-desert system: a case study in Altay Prefecture, China[J ] . Scientific Reports, 2018, 8(1): 12939.

ZHANG S, YANG P, XIA J, et al . Land use/land cover prediction and analysis of the middle reaches of the Yangtze River under different scenarios[J ] . Science of The Total Environment, 2022, 833: 155238.

YANG Z W, CHEN Y B, WU Z F, et al . The coupling between construction land expansion and urban heat island expansion in Guangdong- Hong Kong- Macao Greater Bay[J ] . Journal of Geoinformation Science, 2018, 20(11): 1592-1603.

CHEN Z Y, WU Y W, LIN G T, et al . Spatial relationship between eco-environment quality an d land use change in Guangdong Province-Hong Kong-Macao, China Based on remote sensing-based ecological index[J ] . Environmental Science, 2024, 45(11): 6433-6447.

ZENG Z Y, WU Z, ZHU L L, et al . Impact of rapid urbanization on habitat quality and its factors in Guangdong-Hong Kong-Macao Greater Bay Area[J ] . Ecological Science, 2025, 44(1): 40-50.

HUANG S X, CAI Y N, LIANG F T. Comprehensive control of natural ecological space in the new era: a case study of Guangdong-Hong Kong-Macao Greater Bay Area[J]. Acta Ecologica Sinica, 2021, 41(23): 9196-9206.

ZHAO L J, WANG Y, LUAN S, et al . Evolution of water resources and changes of ecological environment pattern in Guangdong-Hong Kong-Macao Greater Bay Area in the past 40 years[J ] . China Geology, 2025, 52(1): 215-231.

ZHAO L L, XIA J, YANG F, et al . Current situation, framework, problems and prospect of water ecological restoration in Guangdong-Hong Kong Macao Greater Bay Area[J ] . Acta Ecologica Sinica, 2021, 41(12): 5054-5065.

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