Telescope method for characterizing the spatial structure of a pine-oak mixed forest in the Xiaolong Mountains, China
Telescope method for characterizing the spatial structure of a pine-oak mixed forest in the Xiaolong Mountains, China
复制标题
望远镜法表征小龙山松栎混交林空间结构
DOI：
10.1080/02827581.2019.1680729
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发表时间：
2019-11
影响因子：
1.8
通讯作者：
Hu Yanbo
中科院分区：
文献类型：
--
作者：
Zhang Ganggang;Hui Gangying;Zhang Gongqiao;Zhao Zhonghua;Hu Yanbo
摘要 森林结构很大程度上由最近邻相互作用决定，并揭示了林分现状和发展潜力的详细解释。需要一种基于结构的方法来分析最近邻树群的关系，从而全面、系统地表征森林空间异质性。天然栎类混交林。以中国小龙山的尖叶松和油松为例，演示了望远镜法（即N变量分布）。对每棵胸径 ≥ 5 cm的树木进行调查和定位，并利用Excel数据透视表和Winkelmass软件，灵活结合统一角度指数、混合度、优势度和拥挤度四个结构参数计算N变量分布的频率。望远镜方法可以系统地解释不同分辨率下的森林结构特征。尤其是四元分布提供了最详细、最全面的空间结构信息。基于垂直投影降维和边际概率分布函数，连续递归过程很好地揭示了不同分布之间内在的定量关系。这有利于灵活优化重建森林结构，有效选择砍伐树木。
ABSTRACT Forest structure is largely determined by nearest neighbor interactions, and reveals detailed interpretations of the current situation and development potential of the stand. A structure-based method is needed to analyze the relationship of nearest neighbor tree groups and therefore to characterize the forest spatial heterogeneity in a comprehensively and systematically way. A natural mixed forest of Quercus aliena var. acutiserrata and Pinus tabulaeformis in the Xiaolong Mountains, China was taken as an example to demonstrate the telescope method (i.e. N-variate distributions). Each tree with DBH ≥ 5 cm was investigated and located, and the frequencies of N-variate distributions flexibly combined with four structure parameters, uniform angle index, mingling, dominance and crowding were calculated using Excel pivot tables and the Winkelmass software. The telescope method could systematically interpret the forest structural characteristics at different resolutions. Especially, the quadrivariate distribution provides the most detailed and comprehensive spatial structure information. Based on the vertical projection dimension reduction and marginal probability distribution function, the continuous recursive process well revealed the inherently quantitative relationships among different distributions. This could be conducive to flexibly optimizing and reconstructing forest structure, and effectively selecting the trees to be removed.
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