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    基于PLUS模型的福建省闽清县撂荒地时空演变特征及其关键驱动因子

    Spatiotemporal evolution characteristics and key driving factors of abandoned farmland in Minqing county, Fujian Province based on the PLUS model

    • 摘要: 【目的】闽清县是东南丘陵典型山区县,耕地撂荒直接关系区域粮食安全与土地可持续利用。系统分析耕地撂荒驱动机制,为防治山区耕地被撂荒提供依据。【方法】基于2000—2025年的土地利用遥感影像,借助Google Earth Engine (GEE)平台、ArcGIS和基于斑块生成土地利用变化模拟模型(Patch-generating Land use Simulation Model,PLUS模型)等工具,厘清驱动因子、识别撂荒易发区。【结果】1)时序上撂荒耕地呈四个阶段:2002—2008年大幅回落、全域零散分布;2009—2018年周期波动,2010年后向中心收紧主要聚集在白樟镇、云龙乡、白中镇、坂东镇;2019—2023年撂荒规模低位震荡,撂荒聚集区形成,边缘乡镇零星新增撂荒地块;2024年阶段性反弹。2)驱动因子共线分析显示,各因子VIF值介于1.10~2.03,无共线干扰,R2=0.46,整体拟合良好;驱动因子排序为坡度>DEM>到二级道路距离>到主干道距离>人口。3)基于贡献度前五位构建撂荒易发指数(ASI),易发区域分布于池园镇、上莲乡和省璜镇等边缘乡镇。【结论】闽清县耕地撂荒以自然、交通、人口为主要驱动因子,GDP、降水等因素为辅助;现有管控政策需分区施策,全方位遏制撂荒。实现了县域撂荒耕地精准监测,揭示了自然与社会经济因素交互驱动的内在机制,为山区耕地撂荒治理提供了技术路径及治理思路。

       

      Abstract: Objective Minqing county, a typical mountainous county in the hilly region of Southeast China, faces significant challenges of cultivated land abandonment that threaten regional food security and sustainable land use. This study systematically analyzes the driving mechanisms of farmland abandonment to support targeted prevention strategies. Methods Based on land-use remote sensing data from 2000 to 2025, this research integrates the Google Earth Engine (GEE) platform, ArcGIS, and the Patch generating Land Use Simulation (PLUS) model to identify driving factors and delineate high risk abandonment zones. Results 1) The temporal evolution of abandoned cultivated land can be divided into four stages: the area of abandoned land decreased sharply from 2002 to 2008 with scattered abandoned patches distributed across the whole county; the abandoned land area fluctuated periodically during 2009–2018, and the gravity of abandoned land shrank to central river valleys after 2010, mainly concentrated in Baizhang town, Yunlong township, Baizhong town and Bandong town; the scale of abandoned land fluctuated at a low level from 2019 to 2023, with a stable agglomeration core of abandoned land formed in central areas, and only sporadic newly abandoned plots emerged in marginal townships; a periodic rebound of abandoned land area occurred in 2024. 2) Collinearity diagnostics indicate no multicollinearity among factors (VIF: 1.18–2.03, R²=0.46). The contribution ranking of driving factors is: slope gradient (0.16) > DEM (0.15) > distance to secondary roads (0.13) > distance to trunk roads (0.12) > population (0.11). 3) Based on the top five contributing factors, the Abandonment Susceptibility Index (ASI) was constructed based on the top five contributing factors, identifying high-susceptibility areas predominantly distributed in peripheral townships such as Chiyuan town, Shanglian township, and Shenghuang township. Conclusions Cultivated land abandonment in Minqing county is primarily driven by the combined effects of natural topography, transportation accessibility, and population dynamics, with GDP and precipitation playing secondary roles. Current blanket land use control policies are insufficient to resolve persistent marginal farmland abandonment; therefore, differentiated zoning strategies are required to effectively curb abandonment. This study achieves long term precise monitoring at the county scale, reveals the interactive mechanisms between natural and socio economic factors, and provides both a technical framework and targeted governance strategies for cultivated land protection in mountainous regions

       

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