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首页|期刊导航|陕西林业科技|陕西省森林蓄积量空间分布特征及气候多因子耦合分析

陕西省森林蓄积量空间分布特征及气候多因子耦合分析

何斌 王博 陈龙 刘林 喜俊生 胡建辉

陕西林业科技2026,Vol.54Issue(3):1-8,8.
陕西林业科技2026,Vol.54Issue(3):1-8,8.DOI:10.3969/j.issn.1001-2117.2026.03.001

陕西省森林蓄积量空间分布特征及气候多因子耦合分析

Spatial Distribution Characteristics of Forest Stand Volume in Shaanxi Province and Coupling Analysis with Climatic Multi-factors

何斌 1王博 2陈龙 3刘林 3喜俊生 3胡建辉3

作者信息

  • 1. 中国地质调查局西安矿产资源调查中心,陕西西安 710100||秦岭—黄土高原过渡带水土要素耦合与生物资源保育野外观测研究站,陕西西安 710100||北京林业大学林学院,北京 100083
  • 2. 中国地质调查局西安矿产资源调查中心,陕西西安 710100||秦岭—黄土高原过渡带水土要素耦合与生物资源保育野外观测研究站,陕西西安 710100
  • 3. 中国地质调查局西安矿产资源调查中心,陕西西安 710100
  • 折叠

摘要

Abstract

As a core ecological barrier in China's north-south transition zone,the spatial heterogeneity of forest stock volume in Shaanxi Province directly impacts the maintenance of regional carbon sinks and the construc-tion of ecological security patterns.This study integrated field data from 3,613 fixed plots and meteorological data from 2014 in Shaanxi Province to characterize the spatial distribution characteristics of forest stock vol-ume and its multi-factor coupling with climate.The results show that the spatial pattern of forest stock vol-ume exhibits a distinct geographic gradient:"higher in the south and lower in the north",with clustering in mountainous areas and dispersion in plains.Spatial autocorrelation analysis identified five major forest regions(Qinling and Bashan Mountains)as high-value clusters(HH clusters,mean 138 m3·hm-2).In contrast,continuous cold spots(LL clusters,mean 18.63 m3·hm-2)have formed in the gully region of the Loess Plateau in the northern Shaanxi and the low mountain-hill region in the southern Shaanxi due to natural condi-tions or human activities.The study reveals that hydrothermal synergy is the dominant factor driving stock volume distribution,with a pronounced threshold effect.The Qinba Mountains demonstrate optimal produc-tivity when accumulated temperature(1 600~3 400℃)and precipitation(800~1 200 mm)are well-matched.Two machine learning models confirm the contribution hierarchy:growing degree days(GDD)>precipitation(Precip)>aridity index(AI).This study demonstrates how machine learning and traditional spatial statistics can be synergistically integrated to uncover the threshold contributions of climatic drivers.

关键词

森林蓄积量/空间异质性/水热耦合/地理加权回归/陕西省

Key words

Forest stand volume/spatial heterogeneity/hydrothermal coupling/geographically weigh-ted regression(GWR)/Shaanxi Province

分类

农业科技

引用本文复制引用

何斌,王博,陈龙,刘林,喜俊生,胡建辉..陕西省森林蓄积量空间分布特征及气候多因子耦合分析[J].陕西林业科技,2026,54(3):1-8,8.

基金项目

中国地质调查局项目(编号DD20230800209). (编号DD20230800209)

陕西林业科技

1001-2117

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