摘要
【目的】目前关于估计单株树木叶片数量方法的研究较少,本研究结合嵌套式回归的原理,介绍一种通过目测确定单株树木叶片数量的方法,为叶生物量和叶面积的研究奠定理论基础,为估计群落尺度的叶生物量和叶面积指数提供更加方便快捷的手段。【方法】本方法的原理是:(1)将枝条划分为枝轴,枝轴为任意级别枝条去掉分枝后的主轴;(2)确定单个枝轴上的叶片数量;(3)通过枝条的分枝关系建立枝条容量-枝轴数量查找表,确定枝条和枝轴的数量关系;(4)将枝条所包含的所有枝轴上的叶片累加得到枝条的叶片数量;(5)计算单株树木的叶片数量。采用该方法对长白山北坡假色槭的单株叶片数量进行估计。【结果】在所测量的25株树中,容量为1至容量为7的枝条上的平均枝轴数分别为1.0、3.0、8.9、32.9、105.0、323.0、1015.3,枝轴上的平均叶片数为2.9片,通过计算25株样树的单株叶片数量并绘制散点图,散点大致呈指数型分布,符合树木的一般生长规律。建立了预测假色槭叶片数量的最适方程,即y=261.60DBH 1.65,根据检验结果,使用该方法预测的假色槭叶片数量偏大15.58%。【结论】本方法正确、可靠,具有工作量小、效率高的特点,整个估计过程中无破坏性取样。若结合单片叶的面积、干质量等指标,可以使群落尺度的叶生物量和叶面积指数的计算更加快捷。
[Objective]There is currently limited research on methods for estimating the number of leaves on individual tree.Combined with the principle of nested regression,this study introduced a method to visually estimate the amount of individual-tree leaves.The aim of this study was to provide the theoretical basis for the study of leaf biomass and leaf area,and to provide a more convenient and efficient method for estimating the leaf biomass and leaf area index in the community.[Method]The principle of this method was:(1)dividing the branches into branch axes,which are the main axes of any level of branch after removing branches;(2)determining the amount of leaves on individual main branch;(3)establishing a lookup table for securing the total amount of main branches of the individual branch based on the hierarchy structure;(4)obtaining the total amount of leaves by summing the numbers of all main branches;(5)calculating the leaf amount of individual trees,and estimating the number of leaves on individual trees on the north of Changbai Mountain with this method.[Result]Among the 44 trees measured,the number of main branches with branch capacity of 1 to 7 was:1.0,3.0,8.9,32.9,105.0,323.0 and 1015.3,the average number of leaves on the main branch was 2.9.The minimum DBH of the measured trees was 5.4 cm,and the number of leaves was 3038;the maximum DBH was 28.3 cm,and the number of leaves was 62783.Calculating the number of leaves on 25 individual trees and drawing a scatter plot,the points are roughly distributed exponentially,conform to the growth pattern of trees.The optimal equation for predicting the number of leaves was y=261.60 DBH 1.65,according to the test,the number of leaves predicted by this method was 15.58%larger.[Conclusion]This method is correct,reliable,and has the characteristics of low workload and high efficiency.There is no destructive sampling throughout the entire estimation process.If combined with indicators such as single leaf area and dry mass,the calculation of community scale leaf biomass and leaf area index can be faster.
作者
胡博
刘琪璟
徐振招
秦立厚
郑东升
Hu Bo;Liu Qijing;Xu Zhenzhao;Qin Lihou;Zheng Dongsheng(School of Forestry,Beijing Forestry University,Beijing 100083,China)
出处
《北京林业大学学报》
CAS
CSCD
北大核心
2023年第12期120-126,共7页
Journal of Beijing Forestry University
基金
科技部基础资源调查专项(2019FY101602)。
关键词
单木叶片数量
目测
无损估计
嵌套式回归
number of leaves on individual tree
ocular estimation
non-destructive estimation
nested regression
