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    王涛, 董灵波, 刘兆刚, 张凌宇, 陈莹. 大兴安岭天然次生林林木补植空间优化[J]. 北京林业大学学报, 2019, 41(5): 127-136. DOI: 10.13332/j.1000-1522.20190025
    引用本文: 王涛, 董灵波, 刘兆刚, 张凌宇, 陈莹. 大兴安岭天然次生林林木补植空间优化[J]. 北京林业大学学报, 2019, 41(5): 127-136. DOI: 10.13332/j.1000-1522.20190025
    Wang Tao, Dong Lingbo, Liu Zhaogang, Zhang Lingyu, Chen Ying. Optimization of replanting space of natural secondary forest in Daxing’anling Mountains of northeastern China[J]. Journal of Beijing Forestry University, 2019, 41(5): 127-136. DOI: 10.13332/j.1000-1522.20190025
    Citation: Wang Tao, Dong Lingbo, Liu Zhaogang, Zhang Lingyu, Chen Ying. Optimization of replanting space of natural secondary forest in Daxing’anling Mountains of northeastern China[J]. Journal of Beijing Forestry University, 2019, 41(5): 127-136. DOI: 10.13332/j.1000-1522.20190025

    大兴安岭天然次生林林木补植空间优化

    Optimization of replanting space of natural secondary forest in Daxing’anling Mountains of northeastern China

    • 摘要:
      目的为优化林分空间结构,提高现有林分质量,加速森林生态系统恢复,加速大兴安岭地区森林群落向顶级群落演替,确定天然次生林林下补植树种和补植位置,为大兴安岭地区的森林经营提供理论支持和方法。
      方法本文以大兴安岭地区白桦纯林、落叶松白桦混交及针叶混交3种典型的林分类型为例,在次生林原有天然更新的基础上,利用Voronoi图进行空间单元确定和Voronoi结点边缘校正,并使用反距离插值使林木空间结构参数可视化,预测林分中未含林木区域的空间结构信息,将其作为补植依据,使用熵权法确定各空间结构参数权重,将各空间结构参数插值后图像进行加权叠加,以林分非空间结构为约束条件,优化林分空间结构为目标,探讨次生林林下补植树种和位置。
      结果(1)选取兴安落叶松为补植树种,各林型幼树补植数量分别为660、1 970、315。(2)补植后白桦纯林样地中白桦幼树幼苗混交度增加为0.52,其他树种幼树幼苗混交度增加为0.51;落叶松白桦混交林中白桦幼树幼苗混交度增加为0.84,其他树种幼树幼苗混交度增加为0.70;针叶混交林中白桦幼树幼苗增加为0.78,其他树种幼树幼苗混交度增加为0.50。(3)补植后各林型样地林下幼树幼苗Voronoi图多边形边数标准差分别为1.31、1.41、1.36,均处于随机分布状态。白桦纯林、落叶松白桦混交林及针叶混交林中落叶松幼树幼苗Voronoi图多边形边数标准差分别为1.27、1.40、1.37,均呈随机分布。
      结论采伐和补植是两种相反的空间优化方式,将林木空间结构参数进行反距离插值,可以预测林分中无林木区域的空间结构参数值,插值后的图像像元大小可以设定为补植幼树所需要的林地面积大小,依据林分空间结构的优化目标和补植数量的调控目标,提取样地未含林木区域空间参数值,进而确定补植的树种和位置。

       

      Abstract:
      ObjectiveThis paper aims to optimize the spatial structure of stand, improve the quality of existing stands, accelerate the restoration of forest ecosystems, accelerate the succession of forest communities to top communities in the Daxing’anling Mountains of northeastern China, and determine the replanting species and replanting sites of natural secondary forests.
      MethodTaking three typical forest types of birch, birch-larch mixed forest and larch coniferous mixed forest in the Daxing’anling area as an example, based on the natural regeneration of the secondary forest, the Voronoi diagram was used to determine the spatial unit and correct the edge by Voronoi nodal. The inverse distance weighted method was used to visualize the spatial structure parameters of the forest, and the spatial structure information of the forest area was not included in the forest stand and it was used as the basis for replanting. The entropy method was used to determine the weight of each spatial structure parameter, and the image interpolated by spatial structure parameters was weighted superimposed. With the non-spatial structure as the constraint and the optimization of the stand structure, we discussed the species and location of replanting trees under canopy of secondary forest, and provide theoretical support and methods for forest management in the Daxing'anling Mountains.
      Result(1) Larix gmelinii was selected as replanting tree species. The number of replanting trees of each forest type was 660, 1 970, 315, respectively. (2) After the replanting, the mingling degree of birch seedings and saplings in the pure birch forest increased to 0.52, and that of other tree species seedings and saplings increased to 0.51. The mingling degree of birch seedings and saplings in larch and birch mixed forest increased to 0.84, and that of other tree species seedings and saplings increased to 0.70; the mingling degree of birch species seedings and saplings in larch mixed coniferous forest increased to 0.78, and the mingling degree of other tree species seedings and saplings increased to 0.50. (3) After replanting, the coefficients of variation of the Voronoi diagram polygons of forest seedings and saplings under canopy of each forest type were 1.31, 1.41, 1.36, and they were all in random distribution state. The variation coefficient of the Voronoi diagram polygon of larch tree seedings and saplings in the pure birch forest, larch and birch mixed forest and larch mixed coniferous forest was 1.27, 1.40 and 1.37, respectively, they were also all in random distribution state.
      ConclusionHarvesting and replanting are two opposite spatial optimization methods. The inverse distance weight of the spatial structure parameters of trees can predict the spatial structure parameters of non-forest areas in forests. The interpolated image pixel size can be set to the forest area for replanting trees. According to the optimization goal of stand spatial structure and the regulation goal of replanting quantity, extracting the spatial structure parameter value of forest area that does not contain tree, we can determine the species and location of the replanted forest under the canopy.

       

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