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- Title
基于分位数回归的杉木人工林地位级划分方法研究
- Authors
张 博; 陈科屹; 周 来; Saeed, Sajjad; 张雅馨; 孙玉军
- Abstract
[Objective] To optimize the efficiency of the site classes grouping strategy and improve the accuracy of site classification, site classes grouping model and to propose a site quality evaluation method based on quantile regression model. [Method] The traditional methods (standard deviation adjustment method) and quantile regression method were used to classify and evaluate the site quality of 418 pure Chinese fir (Cunninghamia lanceolata) forests at Jiangle Forest Farm in Sanming City, Fujian Province, and the results were compared. The baseline age (A0) was determined based on the high growth of the stand tree and the maturity of the Chinese fir plantations. Using standard deviation adjustment method and according to the standard age tree height value and exponential interval, the site classes curve cluster was constructed and divided into 8 levels. The quantile regression method was based on the guiding curve. According to the data distribution characteristics, 8 quantile points (0.01, 0.05, 0.15, 0.30, 0.70, 0.85,0.95, and 0.99) were specified to construct the quantile regression model, and the quantile curves were used to divide the site classes. [Result] The results showed that the quantile regression model could quickly and accurately determine the site type of the pure Chinese fir plantation, based on the principle that the sum of squares (or the absolute value of the difference) between the average stand height and the prediction stand height of each site class curves is the smallest. The evaluation results of the site quality were not significantly different from the traditional methods. [Conclusion] The quantile regression model describes, classifies, regresses, predicts and verifies the correlation between stand growth and site quality from the perspective of data. The quantile regression curve cluster, based on the guided growth model, intuitively reflect the stand high changing under the different site class, so as to comprehensively and accurately predict the productivity of Chinese fir plantations.
- Publication
Forest Research, 2021, Vol 34, Issue 4, p103
- ISSN
1001-1498
- Publication type
Article
- DOI
10.13275/j.cnki.lykxyj.2021.04.012