Article(id=1322924153468236127, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924153027834207, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.06.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749398400000, receivedDateStr=2025-06-09, revisedDate=1755273600000, revisedDateStr=2025-08-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339296928, onlineDateStr=2026-10-30, pubDate=1782316800000, pubDateStr=2026-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339296928, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339296928, creator=13701087609, updateTime=1793339296928, updator=13701087609, issue=Issue{id=1322924153027834207, tenantId=1146029695717560320, journalId=1322258771877134531, year='2026', volume='46', issue='6', pageStart='1', pageEnd='230', issueExtLink='null', onlineDate='null', pubDate='1782316800000', pubDateStr='2026-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793339296824, creator='13701087609', updateTime=1793344680557, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322946734422360158, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924153027834207, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322946734422360159, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924153027834207, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=18, endPage=24, ext={EN=ArticleExt(id=1322924153648591200, articleId=1322924153468236127, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=Study on diameter structure distribution of coniferous and broad-leaved mixed forests based on finite mixed Weibull model, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】 A diameter distribution model was constructed to fit the diameter class structure of a mixed forest of spruce and fir trees.
【Method】 Taking a 1 hm2 coniferous and broad-leaved mixed plots in the Jingouling forest farm of Jilin region as the research object, a finite mixed Weibull model was used for fitting the diameter distribution of each plots, and the fitting results were compared with the Gamma model and Weibull model. The results indicated that for most of the mixed forests of spruce and fir in the region, they belong to a unimodal distribution, and from the perspective of shape parameters, they belong to a positively skewed mountainous distribution.
【Result】 The RMSE (root mean square error) and MAE (mean absolute error) of the finite mixed Weibull model are significantly smaller than those of the Weibull model and Gamma model, indicating a clear advantage; The RMSE error of the finite mixed Weibull distribution decreased by 29.38% and 31.73% respectively compared to the Weibull distribution and Gamma distribution, and the MAE decreased by 28.27% and 36.23% respectively. Regardless of unimodal or multimodal distribution, the finite mixed Weibull model is superior to the Weibull model. From the composition of tree species in various plots, the advantage of the limited mixed Weibull model is greater in plots with a higher proportion of coniferous trees.
【Conclusion】 It indicates that more finite mixed Weibull models can be used to replace the Weibull model when fitting the diameter distribution of spruce and fir mixed forests.
, authors=Hong GUO, Xiao HE, authorsList=Hong GUO, Xiao HE, authorCompany=null, correspAuthors=Xiao 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=1322924154063827298, articleId=1322924153468236127, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=基于限定混合Weibull模型的针阔混交林直径分布规律, columnId=1322924153761837409, journalTitle=中南林业科技大学学报, columnName=林学, runingTitle=null, highlight=null, articleAbstract=
【目的】 构建天然云冷杉针阔混交林的直径分布模型,明确针阔混交林的径级结构,量化直径多样性。
【方法】 以吉林省汪清林业局金沟岭林场12块天然云冷杉针阔混交1 hm2大样地为研究对象,使用限定混合Weibull模型、Gamma模型、Weibull模型拟合各样地的直径分布,通过均方根误差(RMSE)和平均绝对误差(MAE)等指标评价直径分布模型的拟合效果。
【结果】 对于该地区的大部分针阔混交林,直径分布属于单峰分布,从形状参数来看,属于正偏山状分布。3种方法相比,限定混合Weibull模型的RMSE和MAE明显小于Weibull模型和Gamma模型,优势明显;限定混合Weibull分布相比Weibull分布和Gamma分布的RMSE误差分别减少了29.38%、31.73%,MAE分别减少了28.27%、36.23%。无论单峰分布、多峰分布、S状分布,限定混合Weibull模型都优于Weibull模型。从各样地树种组成来看,在针叶树比例较高的样地中,限定混合Weibull模型优势更大。
【结论】 总体来说,在拟合多树种的混交林直径分布时,可以更多地选用限定混合Weibull模型,从而能更加精准地量化天然针阔混交林的直径多样性。
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