文献类型：期刊文献

英文题名：Telescope method for characterizing the spatial structure of a pine-oak mixed forest in the Xiaolong Mountains, China

作者：Zhang, Ganggang[1] Hui, Gangying[1] Zhang, Gongqiao[1] Zhao, Zhonghua[1] Hu, Yanbo[1]

通信作者：Hui, GY[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, Key Lab Tree Breeding & Cultivat State Forestry A, Beijing 100091, Peoples R China

年份：2019

卷号：34

期号：8

起止页码：751-762

外文期刊名：SCANDINAVIAN JOURNAL OF FOREST RESEARCH

收录：;EI(收录号：20194507649104);Scopus(收录号：2-s2.0-85074608110);WOS:【SCI-EXPANDED(收录号:WOS:000493888800001)】；

基金：This work was supported by the National Key Research and Development Program of China [2016YFD0600203]; the Surface project of National Natural Science Foundation [31670640]; and the National Key Research and Development Program of China (2017YFC050400501).

语种：英文

外文关键词：Spatial structure; quadrivariate distribution; telescope method; vertical projection dimension reduction; marginal probability distribution function

摘要：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.