Article(id=1322924372134080992, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924314793750542, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.10.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757433600000, receivedDateStr=2025-09-10, revisedDate=1763308800000, revisedDateStr=2025-11-17, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339349062, onlineDateStr=2026-10-30, pubDate=1792857600000, pubDateStr=2026-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339349062, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339349062, creator=13701087609, updateTime=1793339349062, updator=13701087609, issue=Issue{id=1322924314793750542, tenantId=1146029695717560320, journalId=1322258771877134531, year='2026', volume='46', issue='10', pageStart='1', pageEnd='256', issueExtLink='null', onlineDate='null', pubDate='1792857600000', pubDateStr='2026-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793339335392, creator='13701087609', updateTime=1793344604640, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322946415693017274, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924314793750542, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322946415693017275, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924314793750542, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=133, endPage=145, ext={EN=ArticleExt(id=1322924372322824673, articleId=1322924372134080992, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=Spatio-temporal evolution and prediction of ecological quality in ecological protection redline areas using a machine learning algorithm: a case study of the Poyang lake basin, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】 This study aims to investigate the long-term and effective protection mechanisms for ecological protection redline areas, which are crucial for maintaining ecological security patterns, ensuring ecosystem functionality, and supporting sustainable socioeconomic development.
【Method】 The research took the ecological protection redline areas in the Poyang lake basin as its study object. It constructed a dynamic ecological quality monitoring system based on the "matrix-stress-wildness" framework by integrating multi-source remote sensing data and ground monitoring data. The study introduced the XGBoost machine learning algorithm to adaptively adjust weight thresholds and establish a modified ecological quality index. Furthermore, it employed Theil-Sen trend analysis, Mann-Kendall test, and long short-term memory (LSTM) neural network to systematically reveal and predict the spatiotemporal evolution patterns and future development trends of ecological quality within the ecological protection redline areas of the Poyang lake basin.
【Result】 The ecological quality within the ecological protection redline areas of the Poyang lake basin showed distinct phased characteristics temporally. During the rapid change period, spatial heterogeneity of ecological quality significantly intensified, with widening disparities among different land use types. In the stable adjustment period, the overall fluctuation range of ecological quality tended to moderate, with restorative improvements observed in some areas, reflecting the positive effects of ecological protection policies. Spatially, regions with lower ecological quality are mainly concentrated in the northwestern mountainous and hilly areas, the central basins characterized by concentrated agriculture and urbanization, and the northern lake areas affected by significant human activities. In contrast, areas with higher ecological quality are primarily distributed in the southern part of the basin, which features high natural vegetation coverage and minimal human disturbance. LSTM model predictions indicated that future ecological quality is primarily classified as "poor" and "medium," with "good" quality areas covering only 10 600 km2 and concentrated in the Ganjiang river source reserve. Typical ecologically vulnerable areas continue to face multiple anthropogenic and natural pressures.
【Conclusion】 The monitoring and assessment system established in this study effectively identifies the spatiotemporal evolution characteristics of ecological quality, providing data support and theoretical references for precise management and policy formulation in ecological protection redline areas.
, authors=Wujun Xu
1, 2, Jiuheng Xu
1, 3, Xiaojian Wei
4, Jiexia He
4, authorsList=Wujun Xu, Jiuheng Xu, Xiaojian Wei, Jiexia He, authorCompany=null, correspAuthors=Jiexia He, 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=1322924372687729122, articleId=1322924372134080992, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=基于机器学习算法的生态保护红线范围生态质量时空演变与预测——以鄱阳湖流域为例, columnId=1322924188427760267, journalTitle=中南林业科技大学学报, columnName=生态学, runingTitle=null, highlight=null, articleAbstract=
【目的】 研究生态保护红线区域长期、有效的保护机制对维护生态安全格局、保障生态系统功能和支撑经济社会可持续发展具有重要意义。
【方法】 以鄱阳湖流域生态保护红线区为对象，通过融合多源遥感数据及地面监测数据，构建“基质性-胁迫风险-野性”生态质量动态监测体系；引入XGBoost机器学习算法对权重阈值进行自适应调整，建立修正生态质量指数；采用Theil-Sen趋势分析、Mann-Kendall检验、长短期记忆神经网络等方法，系统揭示并预测鄱阳湖流域生态保护红线范围生态环境质量的时空演变规律及未来发展趋势。
【结果】 鄱阳湖流域生态保护红线范围生态质量在时序上呈现出明显的阶段性变化特征。其中，快速变化期内生态质量空间异质性显著增强，各类用地类型间差异扩大；而在稳定调整期，生态质量总体波动幅度趋缓，部分区域出现恢复性改善，反映出生态保护政策的积极成效。空间分布上，生态质量较低的区域主要集中在西北部山地丘陵区、中部农业与城镇集中分布的盆地以及北部受人类活动影响较大的湖区周边；而生态质量较高的区域主要分布于流域南部的自然植被覆盖度高、人类干扰较小的山地与森林地带。未来生态质量以“较差”和“中等”等级为主，“良好”等级区域仅1.06万km2，集中于赣江源保护区，而典型生态脆弱区仍面临多重人为与自然压力。
【结论】 本研究建立的监测与评估体系可有效识别生态质量的时空演变特征，为生态保护红线区域的精准管理和政策制定提供数据支持与理论参考。
, authors=徐武军
1, 2, 许久恒
1, 3, 危小建
4, 何杰霞
4, authorsList=徐武军, 许久恒, 危小建, 何杰霞, authorCompany=null, correspAuthors=何杰霞, authorNote=
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1.Field Scientific Observation and Research Station for Ecological Protection Red Line in Poyang Lake Basin of the Ministry of Natural Resources, Nanchang 330025, Jiangxi, China
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1.Field Scientific Observation and Research Station for Ecological Protection Red Line in Poyang Lake Basin of the Ministry of Natural Resources, Nanchang 330025, Jiangxi, China
3.Jiangxi Jiulingshan National Nature Reserve, Yichun 330600, Jiangxi, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322924375422415354, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, authorId=1322924375229477366, language=CN, stringName=许久恒, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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1.自然资源部鄱阳湖流域生态保护红线野外科学观测研究站，江西 南昌 330025
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4.a. School of Surveying and Geoinformation Engineering; b. Jiangxi Key Laboratory of Watershed Ecological Process and Information; c. Nanchang Key Laboratory of Landscape Process and Territorial Spatial Ecological Restoration, East China University of Technology, Nanchang 330013, Jiangxi, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322924375678267903, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, authorId=1322924375506301436, language=CN, stringName=危小建, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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4.东华理工大学 a. 测绘与空间信息工程学院；b. 江西省流域生态过程与信息重点实验室；c. 南昌市景观过程与国土空间生态修复重点实验室，江西 南昌 330013, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1322924374881350124, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, xref=4., ext=[AuthorCompanyExt(id=1322924374889738733, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, companyId=1322924374881350124, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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4.东华理工大学 a. 测绘与空间信息工程学院；b. 江西省流域生态过程与信息重点实验室；c. 南昌市景观过程与国土空间生态修复重点实验室，江西 南昌 330013)])]), Author(id=1322924375753765377, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, orderNo=3, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=hejiexia2002@126.com, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1322924375854428675, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, authorId=1322924375753765377, language=EN, stringName=Jiexia He, firstName=Jiexia, middleName=null, lastName=He, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=
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4.东华理工大学 a. 测绘与空间信息工程学院；b. 江西省流域生态过程与信息重点实验室；c. 南昌市景观过程与国土空间生态修复重点实验室，江西 南昌 330013, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1322924374881350124, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, xref=4., ext=[AuthorCompanyExt(id=1322924374889738733, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, companyId=1322924374881350124, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
4.a. School of Surveying and Geoinformation Engineering; b. Jiangxi Key Laboratory of Watershed Ecological Process and Information; c. Nanchang Key Laboratory of Landscape Process and Territorial Spatial Ecological Restoration, East China University of Technology, Nanchang 330013, Jiangxi, China), AuthorCompanyExt(id=1322924374898127342, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, companyId=1322924374881350124, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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46(8): 1755-1768., articleTitle=Ecological safety early warning and pattern construction based on deep learning models: a case study of the Poyang lake basin, refAbstract=null)], funds=[Fund(id=1322924379872571921, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, awardId=JXZSZ2025003, language=CN, fundingSource=自然资源部鄱阳湖流域生态保护红线野外科学观测研究站开放课题基金资助(JXZSZ2025003), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322924373027467747, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, xref=1., ext=[AuthorCompanyExt(id=1322924373035856356, tenantId=1146029695717560320, journalId=1322258771877134531, articleId=1322924372134080992, companyId=1322924373027467747, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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