Article(id=1322927144556429423, tenantId=1146029695717560320, journalId=1322258771877134531, issueId=1322924574345670676, articleNumber=null, orderNo=null, doi=10.14067/j.cnki.1673-923x.2026.07.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1769443200000, receivedDateStr=2026-01-27, revisedDate=1774886400000, revisedDateStr=2026-03-31, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340010060, onlineDateStr=2026-10-30, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340010060, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340010060, creator=13701087609, updateTime=1793340010060, 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=111, endPage=120, ext={EN=ArticleExt(id=1322927144753561712, articleId=1322927144556429423, tenantId=1146029695717560320, journalId=1322258771877134531, language=EN, title=Influencing factors and prediction models for fuel loading in Chinese fir forests of Hunan Province, columnId=null, journalTitle=Journal of Central South University of Forestry & Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=
【Objective】 This study aims to systematically elucidate the distribution patterns and main influencing factors of fuel loads in three fuel types (herb layer, litter layer, and humus layer), construct high-precision prediction models for fuel load, and to accurately identify high-fire-risk areas in Chinese fir forest regions and develop differentiated forest fire management strategies.
【Method】 Based on 702 standard sample plots of Chinese fir forests in Hunan Province, surveyed from 2020 to 2022. Fifteen factors, including aspect, slope position, slope gradient, and canopy closure were selected. One-way analysis of variance was used to examine the distribution characteristics of fuel loads among the three fuel types, and Pearson correlation analysis was applied to investigate the correlations between 15 factors and 3 types of fuel loading. Prediction models for fuel load, including linear regression, random forest, gradient boosting, and XGBoost, were developed using Python 3.8.
【Result】 1) The fuel loads in the litter layer (0.615–21.110 t/hm2) and humus layer (0–44.210 t/hm2) of Cunninghamia lanceolata forests in Hunan Province exhibited extremely wide ranges. The mean fuel loads, in descending order, were: humus layer (11.647 t/hm2), litter layer (6.407 t/hm2), and herb layer (0.638 t/hm2), with significant differences among the three (P<0.01); 2) The fuel load of the herb layer was primarily driven positively by herb cover (r=0.673) and significantly suppressed by canopy closure (r=-0.290) and stand density (r=-0.240). For the litter layer, litter thickness was the core influencing factor (r=0.657), while elevation (r=0.098) and slope position (r=-0.119) indirectly regulated its accumulation by affecting decomposition rates. The fuel load of the humus layer was significantly positively correlated with humus thickness (r=0.780), elevation (r=0.167), and total growing stock (r=0.180); 3) The gradient boosting model performed best for the herb layer (R2=0.682 6) and litter layer (R2=0.751 8), while the XGBoost model achieved the best performance for predicting the humus layer fuel load (R2=0.665 5). The machine learning models (random forest, gradient boosting, and XGBoost) significantly outperformed the traditional linear regression model and demonstrated good generalization capability.
【Conclusion】 1) The extremely high fuel loads in the litter and humus layers of some C. lanceolata forest stands in Hunan Province pose a significant risk for forest fire occurrence and rapid spread; 2) Canopy closure has opposite effects on the herb layer (highly significant negative correlation) and the litter layer (highly significant positive correlation); therefore, when implementing thinning operations, managers should balance the fire risks of both layers, and promptly remove logging residues after thinning to avoid a sharp increase in litter input. In addition, litter decomposition is slower and accumulation is greater at high-elevation upper slope positions; thus, prescribed burning or mechanical removal of thick litter layers should be carried out before the dry winter-spring season. Moreover, some stands have developed deep humus layers, and areas with thick humus accumulation should be prioritized for ground-fire prevention and regularly inspected; 3) The gradient boosting and XGBoost prediction models exhibit high accuracy and strong stability, providing technical support for dynamic fuel monitoring, precise fire-risk zoning, and scientific fire prevention decision-making in C. lanceolata forest regions.
, authors=Min Zhang, Zhengping Zhao, Xuewu Yan, Ping Deng, Suying Zhang, Ying Wu, Kun Peng, authorsList=Min Zhang, Zhengping Zhao, Xuewu Yan, Ping Deng, Suying Zhang, Ying Wu, Kun Peng, authorCompany=null, correspAuthors=Zhengping Zhao, Xuewu Yan, 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=1322927145152020593, articleId=1322927144556429423, tenantId=1146029695717560320, journalId=1322258771877134531, language=CN, title=湖南省杉木林可燃物载量影响因子及预测模型, columnId=1322924153761837409, journalTitle=中南林业科技大学学报, columnName=林学, runingTitle=null, highlight=null, articleAbstract=
【目的】 系统阐明草本层、枯落物层与腐殖质层3类可燃物载量的分布格局、主要影响因子，并构建高精度可燃物载量预测模型，以期精准识别杉木Cunninghamia lanceolata林高火险区域并制定差异化的林火管理对策。
【方法】 本研究以2020—2022年获取的湖南省702个杉木林标准地为研究对象，选取坡向、坡位、坡度、郁闭度等15个因子，使用单因素方差分析3种类型可燃物载量分布特征，运用Pearson相关性分析探究15个因子和3类可燃物载量相关性，采用Python 3.8语言建立线性回归、随机森林、梯度提升和XGBoost共4种可燃物载量预测模型。
【结果】 1）湖南省杉木林枯落物层（0.615～21.110 t/hm2）和腐殖质层（0～44.210 t/hm2）可燃物载量变幅极大，可燃物载量均值由大到小依次为腐殖质层（11.647 t/hm2）、枯落物层（6.407 t/hm2）、草本层（0.638 t/hm2），三者之间存在显著性差异（P<0.01）。2）草本层可燃物载量主要由草本盖度（r=0.673）正向驱动，受郁闭度（r=-0.290）、林分密度（r=-0.240）显著抑制；枯落物层可燃物载量以枯落物厚度为核心影响因子（r=0.657），海拔（r=0.098）与坡位（r=-0.119）通过影响分解速率间接调控其积累；腐殖质层可燃物载量与腐殖质厚度（r=0.780）、海拔（r=0.167）及总蓄积量（r=0.180）显著正相关。3）梯度提升模型在草本层（R2=0.682 6）和枯落物层（R2=0.751 8）表现最优，XGBoost模型在腐殖质层预测中效果最好（R2=0.665 5）。机器学习模型（随机森林、梯度提升和XGBoost）显著优于传统线性回归模型，且具有良好的泛化能力。
【结论】 1）湖南省杉木林部分林分枯落物层和腐殖质层可燃物载量极大，森林火灾发生和急速蔓延的风险不容忽视。2）郁闭度对草本层（极显著负相关）与枯落物层（极显著正相关）具有相反效应，林分间伐经营时需权衡二者风险，并在间伐后及时清理采伐剩余物，避免枯落物输入量激增；枯落物层在高海拔上坡位分解慢、累积多，需在冬、春干旱季节前对厚层枯落物实施计划烧除或机械清理；部分林分形成了深厚的腐殖质层，应将深厚腐殖质聚集区作为地下火防控重点，定期进行排查。3）梯度提升和XGBoost预测模型精度高、稳定性强，可为杉木林区可燃物动态监测、火险精准区划及科学防火决策提供技术支撑。
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