林分空间结构参数N元分布及其诠释——以小陇山锐齿栎天然混交林为例
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摘要
【目的】以甘肃省小陇山锐齿栎天然混交林为例,采用林分空间结构参数N元分布全面、系统地揭示林分空间结构特征,以实现森林结构信息的精确描述和直观表达,为森林结构精准调控和重建提供基础参考信息。【方法】对70 m×70 m标准地内的林木进行每木定位和调查,采用Winkelmass计算每株林木的混交度(M)、角尺度(W)、大小比数(U)和密集度(C),借助Excel透视表统计N元分布相对频率,并采用R 3.4.3、Origin 2015绘制N元分布图。【结果】锐齿栎天然混交林整体及林分内大多数林木呈随机分布、混交良好、较为密集;林分整体中庸且各大小比数等级林木均接近20%;二元分布、三元分布和四元分布中该林分结构最突出特点表现为:不管结构参数如何组合,林分均表现出不同结构组合下大多数林木呈随机分布或混交良好。【结论】结构参数N元分布借助分布频率表达优势从不同层次和角度全面系统地描述了林分结构特征信息,且不同分布之间优势互补,实现了林分结构从"点→线→面→体→超体"的精准详尽解译;双X横坐标或双Y纵坐标的3D图满足了多元分布结构信息直观展现需求;N元分布为森林结构精准调控和林分结构重建提供了先决信息。
[Objective] To accurately describe and intuitively express forest structure information, and provide basic information for forest structure regulation and reconstruction, N-variate distributions were used to comprehensively and systematically characterize the spatial structure of the Quercus aliena var.acuteserrata natural mixed forest in Xiaolong Mountains, Gansu Province of northwestern China.[Method] Each tree in the 70 m × 70 m permanent sample plot was located and surveyed. The mingling(M), uniform angle index(W), neighborhood comparison(U) and crowding(C) of each tree were calculated by the Winkelmass software. The relative frequency of N-variate distributions was counted by the Excel Pivot Tables, and their corresponding multivariate diagrams were graphed by the R 3.4.3 and Origin 2015.[Result] In the Quercus aliena var. acuteserrata natural mixed forest, the whole forest and most trees were randomly distributed, well mixed, relatively dense. The whole forest was moderately differentiated and each neighborhood comparison degree occupied near 20% trees. Among the bivariate distribution, trivariate distribution and quadrivariate distribution, the most prominent characteristics of the spatial structure was as follows: no matter how the structural parameters were combined, most trees were randomly distributed or well mixed under different structural combinations. [Conclusion] The N-variate distribution comprehensively and systematically describes the spatial structure characteristics from different levels and angles. Different distributions make their respective advantages complementary to each other and realize the stepwise interpretation process from point to hyperploid. 3D figures with double X horizontal coordinates or double Y vertical coordinates intuitively graph the structure information of the multivariate distribution. The N-variate distributions provide prior information for forest structure regulation and reconstruction.
[Objective] To accurately describe and intuitively express forest structure information, and provide basic information for forest structure regulation and reconstruction, N-variate distributions were used to comprehensively and systematically characterize the spatial structure of the Quercus aliena var.acuteserrata natural mixed forest in Xiaolong Mountains, Gansu Province of northwestern China.[Method] Each tree in the 70 m × 70 m permanent sample plot was located and surveyed. The mingling(M), uniform angle index(W), neighborhood comparison(U) and crowding(C) of each tree were calculated by the Winkelmass software. The relative frequency of N-variate distributions was counted by the Excel Pivot Tables, and their corresponding multivariate diagrams were graphed by the R 3.4.3 and Origin 2015.[Result] In the Quercus aliena var. acuteserrata natural mixed forest, the whole forest and most trees were randomly distributed, well mixed, relatively dense. The whole forest was moderately differentiated and each neighborhood comparison degree occupied near 20% trees. Among the bivariate distribution, trivariate distribution and quadrivariate distribution, the most prominent characteristics of the spatial structure was as follows: no matter how the structural parameters were combined, most trees were randomly distributed or well mixed under different structural combinations. [Conclusion] The N-variate distribution comprehensively and systematically describes the spatial structure characteristics from different levels and angles. Different distributions make their respective advantages complementary to each other and realize the stepwise interpretation process from point to hyperploid. 3D figures with double X horizontal coordinates or double Y vertical coordinates intuitively graph the structure information of the multivariate distribution. The N-variate distributions provide prior information for forest structure regulation and reconstruction.
引文
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