基于云模型的白马雪山和博格达山林线与树线分布高度及影响因素的比较分析  

Comparison and analysis of timberline and treeline distribution height and influencing factors of Baima Snow Mountain and Bogda Mountain based on cloud model

作　　者：穆豪祥 韩芳 唐秀美[2] 王志勇 王哲[1] MU Haoxiang;HAN Fang;TANG Xiumei;WANG Zhiyong;WANG Zhe(School of Civil Engineering and Geomatics,Shandong University of Technology,Zibo 255000,Shandong,China;Research Center of Information Technology,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China)

机构地区：[1]山东理工大学建筑工程与空间信息学院,淄博255000 [2]北京市农林科学院信息技术研究中心,北京100097

出　　处：《地理研究》2023年第7期1941-1956,共16页Geographical Research

基　　金：国家自然科学基金项目(41401111);山东省自然科学基金项目(ZR2021MD080);山东省农业科技资金项目(2019LY006)。

摘　　要：林线作为重要的地理和生态界线,备受国内外学者的关注。然而林线和树线之间的过渡区内不同类型的植被斑块交错分布,呈现一定的随机性,导致林线和树线的分布界线也具有一定的模糊性。目前大多数研究将林线/树线简化为连续变化的曲线,难以表达和分析林线与树线的模糊性和过渡区内不同类型植被斑块分布的随机性。本研究采用复合高程信息的NDVI数据提取白马雪山和博格达山林线与树线数据点,构建林线与树线分布高度云模型,定量分析林线和树线分布的不确定性,在此基础上比较白马雪山与博格达山林线与树线影响因素的差异。主要结论:①构建了白马雪山和博格达山林线/树线分布高度云模型,以林线、树线分布高度云模型用期望(Ex)、熵(En)、超熵(He)3个数字特征来表达林线、树线分布的整体特性。②博格达山林线与树线分布高度云模型的熵(林线410.71 m、树线597.32 m)和超熵(林线66.22 m、树线280.86 m)大于白马雪山(熵:林线182.33 m、树线193.96 m;超熵:林线56.26 m、树线65.86 m),即博格达山林线与树线分布的不确定性高于白马雪山。③干燥度是白马雪山林线与树线分布高度贡献率最高的影响因素(50.26%、44.11%),其次是7月均温(12.76%、17.93%)和积雪效应(23.97%、11.48%),而博格达山林线与树线分布高度贡献率最高的影响因素是7月均温(48.15%、60.59%),其次是干燥度(28.57%、17.67%)。两地林线和树线分布的主导因素明显差异。本研究以白马雪山和博格达山林线与树线分布高度云模型的数字特征,表达林线和树线分布的模糊性和随机性,并比较分析两地林线与树线影响因素的差异,为精细分析垂直带分布的复杂性、定量分析垂直带影响因子的尺度变化和空间分异,提供了新的角度和方法。As an important geographical and ecological boundary,the timberline has attracted extensive attention from scholars at home and abroad.At present,most studies simplify the timberline/treeline to a continuous change curve,which is difficult to express and analyze the fuzziness of timberline and treeline and the randomness of distribution of different vegetation patches in the transition zone.In this study,the NDVI data of composite elevation information were used to extract the sampling points of timberline and treeline in Baima Snow Mountain and Bogda Mountain,and the cloud model of distribution height of timberline and treeline was constructed to quantitatively analyze the uncertainty of timberline and treeline distribution,and the differences of influencing factors of timberline and treeline between Baima Snow Mountain and Bogda Mountain were compared and analyzed.The main conclusions were as follows:(1)The cloud model of the distribution height of timberline/treeline in Baima Snow Mountain and Bogda Mountain was constructed,and the overall characteristics of the distribution of timberline/treeline were characterized by the expected(Ex),entropy(En)and hyper entropy(He)using the cloud model of the distribution height of timberline/treeline.(2)The entropy(timberline 410.71 m,treeline 597.32 m)and hyper entropy(timberline 66.22 m,treeline 280.86 m)of the cloud model of distribution height of Bogda Mountain timberline and treeline are greater than those of Baima Snow Mountain(entropy:timberline 182.33 m,treeline 193.96 m;hyper entropy:timberline 56.26 m,treeline 65.86 m),that is,the uncertainty of distribution of Bogda Mountain timberline and treeline is much higher than that of Baima Snow Mountain.(3)The dryness is the largest contributing factor(50.26%,44.11%)to the distribution height of timberline and treeline in Baima Snow Mountain,followed by the average temperature in July(12.76%,17.93%)and snow effect(23.97%,11.48%),while the largest contributing factor of timberline and treeline distribution height in Bogd

关 键 词：林线 树线 云模型 影响因素 白马雪山 博格达山 

分 类 号：S718.5[农业科学—林学]