Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China.
Agricultural Sciences Academy of Rizhao, Rizhao 276800, Shandong, China.
Ying Yong Sheng Tai Xue Bao. 2023 Sep;34(9):2355-2362. doi: 10.13287/j.1001-9332.202309.005.
Based on data collected from 2054 saplings of forest in 55 fixed plots in 2018-2019 in Cuigang Forestry Station, Daxing'anling area, we classified the stand density index (SDI) into four classes, ., Class Ⅰ (SDI<1863 plants·hm), Class Ⅱ (1863 plants·hm≤SDI<2155 plants·hm), Class Ⅲ (2155 plants·hm≤SDI<2459 plants·hm) and Class Ⅳ (SDI≥2459 plants·hm) by using the quartile method. We constructed a dummy variable model and quantile regression model for the height-breast diameter of saplings of with dummy variable method introduced SDI. The results showed that among the five selected representative non-linear tree height curve models, the Richards model fitted the best, with , RMSE and MAE of 0.7637, 0.8250 m and 0.5696 m. The dummy variable model including the SDI constructed based on the Richards model showed a 1.3% increase in compared with the base model, while RMSE, MAE, and AIC decreased by 2.1%, 1.5%, and 11.2%, respectively. When the quantile was 0.5, of quantile regression model was the maximum, and RMSE, MAE, AIC was the minimum, being 0.7612, 0.8294 m, 0.5657 m, and -767.19, respectively. Compared with SDI, sapling height in SDI-SDI was increased by 5.6%, 5.6%, and 11.3%, suggesting reasonable that regulation of stand density was conducive to increase the height growth of saplings in regeneration.
基于 2018-2019 年在大兴安岭图强林业局 55 个固定样地中采集的 2054 株幼树数据，我们采用四分位法将林分密度指数（SDI）分为 4 类，即Ⅰ类（SDI<1863 株·hm）、Ⅱ类（1863 株·hm≤SDI<2155 株·hm）、Ⅲ类（2155 株·hm≤SDI<2459 株·hm）和Ⅳ类（SDI≥2459 株·hm）。我们采用哑变量法引入 SDI，构建了幼树树高-胸径的哑变量模型和分位数回归模型。结果表明，在所选择的 5 种具有代表性的非线性树高曲线模型中， Richards 模型拟合效果最好，拟合优度为 0.7637，均方根误差和平均绝对误差分别为 0.8250 m 和 0.5696 m。基于 Richards 模型构建的包含 SDI 的哑变量模型与基础模型相比，增加了 1.3%，而均方根误差、平均绝对误差和 AIC 分别降低了 2.1%、1.5%和 11.2%。当分位数为 0.5 时，分位数回归模型的 达到最大，均方根误差、平均绝对误差和 AIC 达到最小，分别为 0.7612、0.8294 m、0.5657 m 和-767.19。与 SDI 相比，SDI-SDI 中的幼树树高增加了 5.6%、5.6%和 11.3%，表明合理的林分密度调控有利于促进更新幼树的高生长。
