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沿海水杉防护林带三维结构参数模型

张雷1,张鹏1,王华2,周新华3,虞木奎1,吴统贵1*#br#   

  1. (1国家林业局华东沿海防护林生态系统定位观测研究站, 中国林业科学研究院沿海防护林研究中心, 中国林业科学研究院亚热带林业研究所, 杭州 311400;2东台市林业中心, 江苏东台 224200; 3Campbell Scientific, Inc., Logan, USA UT 84321)
  • 出版日期:2017-04-10 发布日期:2017-04-10

Threedimension structure model of the costal Metasequoia glyptostroboides shelterbelts.

ZHANG Lei1, ZHANG Peng1, WANG Hua2, ZHOU Xin-hua3, YU Mu-kui1, WU Tong-gui1*#br#   

  1. (1 East China Coastal Forest Ecosystem Longterm Research Station, State Forestry Administration of China; Costal Forest Research Center, Research Institute of Subtropical Forestry, CAF, Hangzhou 311400, China; 2 Dongtai City Forestry Center, Dongtai 224200, Jiangsu, China; 3 Campbell Scientific, Inc., Logan, USA UT 84321).
  • Online:2017-04-10 Published:2017-04-10

摘要: 为精准量化水杉林带三维结构,有效提高防风效应数值模拟的精度,对水杉标准木树干、枝条和叶片的表面积和体积进行测量,建立了水杉林带的三维结构参数模型。结果表明: 所选水杉林带的表面积密度的变化范围为0.0012~3.4857 m2·m-3,体积密度的变化范围为0.000002~0.012397 m3·m-3;水杉林带的树冠形状、树木生长状况等与林带结构密切相关;林带结构具有一定的空间异质性,枝条和叶片的表面积和体积主要集中在冠层中部,而树干则集中在下部;林带内各组分表面积和体积组成有很大区别,树干的体积较大(占总体积的75.28%),而表面积较小(占总面积的5.57%);叶片的表面积较大(占总表面积的78.39%),而体积很小(占总体积的3.87%);表面积/体积比为叶片(20.23)>枝条(0.77)>树干(0.07);与以往研究相比,模型可以全面反映林带的结构特征,更接近林带的真实结构。同时,林带表面积和体积对气流的影响作用有一定差异,利用林带的表面积密度和体积密度共同作为林带的结构参数,可以反映水杉林带各组分对气流影响的差异,更好地表现林带的空气动力学特征。

关键词: 农田, 分区, 风险识别, 序贯指示模拟, 土壤重金属

Abstract: In order to clarify the structure features of Metasequoia glyptostroboides shelterbelts and improve the prediction accuracy of windbreak effect, we measured the vegetative surface area and volume of the trunks, branches and leaves and established the threedimensional structure model of M. glyptostroboides stands. The results showed that the vegetative surface area density ranged from 0.0012 to 3.4857 m2·m-3, and the cubic density ranged from 0.000002 to 0.012397 m3·m-3. Moreover, the crown shape and growth status were closely related with the shelterbelt structure. And the structure was heterogeneous in space: the surface and volume of branches and leaves reached a maximum value at the middle of the crown, but for the trunk, they gathered at the lower part. Meanwhile, the vegetative surface area density changed with the shapes of trunks, braches and leaves. The trunk had big volume (accounting for 75.28% of the total volume) but less vegetative surface area (accounting for 5.57% of the total surface area), the leaves had big surface area (accounting for 78.39% of the total surface area) but less volume (accounting for 3.87% of the total volume). The ratio of surface to volume was ranked in the order of leaves (20.23) > branches (0.77) > trunks (0.07). Compared with previous studies, our model could describe the structure much more comprehensively, which was much closer to the real structure of the shelterbelts. Additionally, owing to the different influences of wind, each component had different aerodynamic effects. Thus, using both vegetative surface area density and cubic density to parameterize the shelterbelts structure could reflect the different effects of each component on wind and better exhibit the aerodynamic effect of the shelterbelts.

Key words: sequential indicator simulation method, soil heavy metals, zoning, farmland, risk identification