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| Citation: |
Hu Bo, Liu Qijing, Xu Zhenzhao, Qin Lihou, Zheng Dongsheng. A simple method for estimating the amount of leaves of Acer pseudosieboldianum[J]. Journal of Beijing Forestry University, 2023, 45(12): 120-126. DOI: 10.12171/j.1000-1522.20210518
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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.
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.
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 3 038; the maximum DBH was 28.3 cm, and the number of leaves was 62 783. 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 DBH1.65, according to the test, the number of leaves predicted by this method was 15.58% larger.
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.
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