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Article(id=1322924656944096004, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924574345670676, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.07.015, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1750694400000, receivedDateStr=2025-06-24, revisedDate=1757865600000, revisedDateStr=2025-09-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339416966, onlineDateStr=2026-10-30, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339416966, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339416966, creator=13701087609, updateTime=1793339416966, updator=13701087609, issue=Issue{id=1322924574345670676, tenantId=1146029695717560320, journalId=1322258771877134531, year='2026', volume='46', issue='7', pageStart='1', pageEnd='234', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793339397274, creator='13701087609', updateTime=1793344662547, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322946659029746602, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924574345670676, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322946659029746603, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924574345670676, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=153, endPage=166, ext={EN=ArticleExt(id=1322924658521154309, articleId=1322924656944096004, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=Driving influence mechanism of spatiotemporal evolution of land use and multi-scenario simulation in Changsha City based on the PLUS model, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】

By analyzing the quantitative changes in land use types, identifying driving mechanisms, and examining land cover transition patterns along with spatial proximity relationships in Changsha from 2000 to 2020, this study aims to reveal the underlying trends of land-use transformation in the city.

【Method】

Using a PLUS model-based mask extraction approach on raster datasets, this study examined the correlations and underlying mechanisms of various driving factors influencing land use change patterns in Changsha from 2000 to 2020. The land-use expansion module employs adaptive relationships to simulate spatial evolution, enabling analysis of dynamic processes within the study area. Based on the land-use spatiotemporal change data of Changsha from 2000 to 2020, a linear regression model was fitted to investigate the temporal and quantitative relationships among different land use types. The optimal fitting line was obtained by minimizing the sum of squared residuals between observed values and model predictions. The 12 selected driving factors were normalized to generate raster spatial distribution maps, contribution rates, and root mean square errors. Using the random forest algorithm, development probabilities for various land types were calculated. Based on three research objectives-natural development, ecological benefit maximization, and urban economic growth-three future scenarios (natural development, ecological conservation, and urban expansion) were established to simulate future land changes.

【Result】

Changes in land use categories showed a high correlation with time sequences, with linear regression coefficients averaging over 0.85. The main driving factors and their contributions vary among different land categories. The expansion area of urban land was mainly affected by elevation. The contribution rate of elevation reached 0.16 during the period from 2000 to 2010, and the RMSE value was 0.23. During the period from 2010 to 2020, the contribution rate of elevation decreased to 0.15, and the RMSE value was 0.24. Future land use simulations under the ND (Natural development), EP (Ecological protection), and US (Urban spread) scenarios predict urban expansion areas of 145.22 km2, 46.93 km2, and 317.64 km2, respectively, highlighting the significant role of government policies and measures in influencing the spatiotemporal evolution of urban land use.

【Conclusion】

From 2000 to 2010, urban expansion demonstrated a steady growth trend in both scale and duration, while the decline rates of arable land and forest land accelerated during the same period. From 2010 to 2020, the expansion rate of construction land slowed down and stabilized, effectively mitigating the reduction trends of ecological lands such as arable land and forest land. In the influence mechanism, elevation is the primary factor; during this period, number of soil types exhibited the lowest impact intensity. The temporal variation patterns of these driving factors align closely with Changsha's actual development trajectory: extensive and rapid urban expansion from 2000 to 2010, followed by controlled expansion scale and slower growth rates from 2010 to 2020.

, authors=Jifeng Jiang1, Jiyun She1, Zheli Zeng2, Tao Li1, authorsList=Jifeng Jiang, Jiyun She, Zheli Zeng, Tao Li, authorCompany=null, correspAuthors=Jiyun She, authorNote=null, correspAuthorsNote=null, copyrightStatement=© 2026 Journal of Central South University of Forestry & Technology, copyrightOwner=Journal of Central South University of Forestry & Technology, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322924658898641670, articleId=1322924656944096004, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=基于PLUS模型的长沙市土地利用时空演变驱动影响机制与多情景模拟, columnId=1322924188427760267, journalTitle=中南林业科技大学学报, columnName=生态学, runingTitle=null, highlight=null, articleAbstract=
【目的】

通过对长沙市2000—2020年土地利用类型数量变化、驱动影响机制、覆被转移类型和空间邻域分析，揭示长沙市的用地变化规律。

【方法】

基于PLUS模型掩膜提取栅格数据集，研究长沙市2000—2020年不同驱动因子对土地利用类型变化过程的相关性和影响机制。该模型的用地扩张模块通过自适应算法模拟空间演变，从而分析研究区域的动态过程。基于长沙市2000—2020年各土地利用时空变化数据，通过线性回归模型拟合研究时间和各类型数量的响应关系，通过最小化观测值与模型预测值的残差平方和，求得最佳拟合直线；同时，对选取的12个驱动因子归一化处理，得到栅格空间分布图、贡献率大小及均方根误差。基于随机森林算法得到各土地利用类型的发展概率，结合自然发展、生态效益最大化和城市经济发展3类研究目标，共设置自然发展情景、生态保护情景和城市扩张情景3种未来情景进行模拟分析未来土地变化。

【结果】

土地利用类型的变化与时间序列呈现出高度相关性，线性回归系数平均超过0.85。不同土地类型的主要驱动因子及其贡献各不相同。城市用地扩张区域主要受高程影响。2000—2010年高程因子贡献率达0.16，RMSE为0.23；2010—2020年高程因子贡献率下降为0.15，RMSE为0.24。在自然发展、生态保护和城市扩张情景下对未来土地利用的模拟预测中，城市扩张面积分别为145.22、46.93、317.64 km2，突出了政府政策措施在影响城市土地利用时空演变中的重要作用。

【结论】

2000—2010年城市扩张规模随时间呈稳定增长态势，同期耕地和林地缩减速率加快；2010—2020年建设用地扩张速率有所放缓且趋于稳定，耕地和林地等生态用地的缩减态势得到了有效缓解。影响机制中高程为主要影响因子，该时段影响率最小的为土种类型数量；12个驱动因子的时序变化特征，与长沙市2000—2010年城市扩张范围广、速率快，2010—2020年扩张规模受控、增速放缓的实际发展规律基本吻合。

, authors=蒋纪峰1, 佘济云1, 曾哲礼2, 李涛1, authorsList=蒋纪峰, 佘济云, 曾哲礼, 李涛, authorCompany=null, correspAuthors=佘济云, authorNote=

蒋纪峰（），硕士研究生。

, correspAuthorsNote=
佘济云（），教授，博士研究生导师。
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蒋纪峰（），硕士研究生。

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蒋纪峰（），硕士研究生。

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基于PLUS模型的长沙市土地利用时空演变驱动影响机制与多情景模拟
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蒋纪峰 1 , 佘济云 1 , 曾哲礼 2 , 李涛 1
中南林业科技大学学报 | 生态学 2026,46(7): 153-166
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中南林业科技大学学报 |生态学 2026 , 46 (7) : 153 -166
基于PLUS模型的长沙市土地利用时空演变驱动影响机制与多情景模拟
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蒋纪峰1 , 佘济云1 , 曾哲礼2, 李涛1
作者信息
  • 1.中南林业科技大学　林学院，湖南　长沙　410004
  • 2.国家林业和草原局西南调查规划院，云南　昆明　650032
通讯作者:
佘济云（），教授，博士研究生导师。
作者简介:

蒋纪峰（），硕士研究生。

Driving influence mechanism of spatiotemporal evolution of land use and multi-scenario simulation in Changsha City based on the PLUS model
Jifeng Jiang1 , Jiyun She1 , Zheli Zeng2, Tao Li1
Affiliations
  • 1.School of Forestry, Central South University of Forestry & Technology, Changsha 410004, Hunan, China
  • 2.Southwest Surveying and Planning Institute of the National Forestry and Grassland Administration, Kunming 650032, Yunnan, China
出版时间: 2026-07-25 doi: 10.14067/j.cnki.1673-923x.2026.07.015
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摘要
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【目的】

通过对长沙市2000—2020年土地利用类型数量变化、驱动影响机制、覆被转移类型和空间邻域分析，揭示长沙市的用地变化规律。

【方法】

基于PLUS模型掩膜提取栅格数据集，研究长沙市2000—2020年不同驱动因子对土地利用类型变化过程的相关性和影响机制。该模型的用地扩张模块通过自适应算法模拟空间演变，从而分析研究区域的动态过程。基于长沙市2000—2020年各土地利用时空变化数据，通过线性回归模型拟合研究时间和各类型数量的响应关系，通过最小化观测值与模型预测值的残差平方和，求得最佳拟合直线；同时，对选取的12个驱动因子归一化处理，得到栅格空间分布图、贡献率大小及均方根误差。基于随机森林算法得到各土地利用类型的发展概率，结合自然发展、生态效益最大化和城市经济发展3类研究目标，共设置自然发展情景、生态保护情景和城市扩张情景3种未来情景进行模拟分析未来土地变化。

【结果】

土地利用类型的变化与时间序列呈现出高度相关性，线性回归系数平均超过0.85。不同土地类型的主要驱动因子及其贡献各不相同。城市用地扩张区域主要受高程影响。2000—2010年高程因子贡献率达0.16，RMSE为0.23；2010—2020年高程因子贡献率下降为0.15，RMSE为0.24。在自然发展、生态保护和城市扩张情景下对未来土地利用的模拟预测中，城市扩张面积分别为145.22、46.93、317.64 km2，突出了政府政策措施在影响城市土地利用时空演变中的重要作用。

【结论】

2000—2010年城市扩张规模随时间呈稳定增长态势，同期耕地和林地缩减速率加快；2010—2020年建设用地扩张速率有所放缓且趋于稳定，耕地和林地等生态用地的缩减态势得到了有效缓解。影响机制中高程为主要影响因子，该时段影响率最小的为土种类型数量；12个驱动因子的时序变化特征，与长沙市2000—2010年城市扩张范围广、速率快，2010—2020年扩张规模受控、增速放缓的实际发展规律基本吻合。

土地利用  /  PLUS模型  /  线性回归模型  /  驱动因子  /  长沙市
【Objective】

By analyzing the quantitative changes in land use types, identifying driving mechanisms, and examining land cover transition patterns along with spatial proximity relationships in Changsha from 2000 to 2020, this study aims to reveal the underlying trends of land-use transformation in the city.

【Method】

Using a PLUS model-based mask extraction approach on raster datasets, this study examined the correlations and underlying mechanisms of various driving factors influencing land use change patterns in Changsha from 2000 to 2020. The land-use expansion module employs adaptive relationships to simulate spatial evolution, enabling analysis of dynamic processes within the study area. Based on the land-use spatiotemporal change data of Changsha from 2000 to 2020, a linear regression model was fitted to investigate the temporal and quantitative relationships among different land use types. The optimal fitting line was obtained by minimizing the sum of squared residuals between observed values and model predictions. The 12 selected driving factors were normalized to generate raster spatial distribution maps, contribution rates, and root mean square errors. Using the random forest algorithm, development probabilities for various land types were calculated. Based on three research objectives-natural development, ecological benefit maximization, and urban economic growth-three future scenarios (natural development, ecological conservation, and urban expansion) were established to simulate future land changes.

【Result】

Changes in land use categories showed a high correlation with time sequences, with linear regression coefficients averaging over 0.85. The main driving factors and their contributions vary among different land categories. The expansion area of urban land was mainly affected by elevation. The contribution rate of elevation reached 0.16 during the period from 2000 to 2010, and the RMSE value was 0.23. During the period from 2010 to 2020, the contribution rate of elevation decreased to 0.15, and the RMSE value was 0.24. Future land use simulations under the ND (Natural development), EP (Ecological protection), and US (Urban spread) scenarios predict urban expansion areas of 145.22 km2, 46.93 km2, and 317.64 km2, respectively, highlighting the significant role of government policies and measures in influencing the spatiotemporal evolution of urban land use.

【Conclusion】

From 2000 to 2010, urban expansion demonstrated a steady growth trend in both scale and duration, while the decline rates of arable land and forest land accelerated during the same period. From 2010 to 2020, the expansion rate of construction land slowed down and stabilized, effectively mitigating the reduction trends of ecological lands such as arable land and forest land. In the influence mechanism, elevation is the primary factor; during this period, number of soil types exhibited the lowest impact intensity. The temporal variation patterns of these driving factors align closely with Changsha's actual development trajectory: extensive and rapid urban expansion from 2000 to 2010, followed by controlled expansion scale and slower growth rates from 2010 to 2020.

land use  /  PLUS model  /  linear regression model  /  driving factors  /  Changsha City
蒋纪峰, 佘济云, 曾哲礼, 李涛. 基于PLUS模型的长沙市土地利用时空演变驱动影响机制与多情景模拟. 中南林业科技大学学报, 2026 , 46 (7) : 153 -166 . DOI: 10.14067/j.cnki.1673-923x.2026.07.015
Jifeng Jiang, Jiyun She, Zheli Zeng, Tao Li. Driving influence mechanism of spatiotemporal evolution of land use and multi-scenario simulation in Changsha City based on the PLUS model[J]. Journal of Central South University of Forestry & Technology, 2026 , 46 (7) : 153 -166 . DOI: 10.14067/j.cnki.1673-923x.2026.07.015
基金
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  • 湖南省自然科学基金项目(2022JJ40875)
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2026年第46卷第7期
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doi: 10.14067/j.cnki.1673-923x.2026.07.015
  • 接收时间：2025-06-24
  • 首发时间：2026-10-30
  • 出版时间：2026-07-25
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  • 收稿日期：2025-06-24
  • 修回日期：2025-09-15
基金
湖南省自然科学基金项目(2022JJ40875)
作者信息
    1.中南林业科技大学　林学院，湖南　长沙　410004
    2.国家林业和草原局西南调查规划院，云南　昆明　650032

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佘济云（），教授，博士研究生导师。
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