Zhang Wei, Chen Guan-Mou, Dong Ling-Bo
Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China.
Ying Yong Sheng Tai Xue Bao. 2025 Aug;36(8):2270-2278. doi: 10.13287/j.1001-9332.202508.004.
Based on the survey data of 55 permanent plots of plantation in Maoershan Experimental Forest Farm of Northeast Forestry University, we divided the stand density index (SDI) of the plots into three grades by mean±standard deviation method, namely SDI Ⅰ∈(0, 695], SDI Ⅱ∈(695, 1027], and SDI Ⅲ∈(1027, ∞] trees·hm. Based on the Logistic equation, we constructed the maximum crown width prediction model of individual tree at three quantiles (0.90, 0.95, and 0.99) under different SDI grades by coupling dummy variables and quantile regression. We further used the crown projection area method to compile the stand management density number table under different SDI grades and quantified the influence of stand density on stand volume and carbon storage. The results showed that the maximum crown width models of individual tree with different SDI had significant differences, and that the maximum crown width of individual tree could be better simulated at 0.90 quantile. The independent sample test showed that compared with the Logistic basic model, the of the single-tree dummy variable quantile maximum crown model could be significantly increased by 0.20, while the root mean square error was significantly reduced by 0.15 m. Based on the established single tree dummy variable quantile maximum crown width model and crown projection area method, we compiled the stand management density number table under different SDI grades. When the target diameter of cultivation was 30 cm, compared with SDI Ⅰ, stand volume and carbon storage were overestimated by 26.16 m·hm and 10.10 t C·hm, respectively, when SDI grades were not distinguished. Similarly, compared with SDIⅡ, these values were overestimated by 15.99 m·hm and 6.12 t C·hm. However, compared with SDIⅢ, they were significantly underestimated by 85.13 m·hm and 33.04 t C·hm. Our results indicated that ignoring the differences of SDI grades could overestimate the carbon sequestration capacity of low-density stands but underestimate the actual contribution of high-density stands. Therefore, implementing stand density regulation based on SDI grades is conducive to achieving precise quality improvement of plantations.
基于东北林业大学帽儿山实验林场55块人工林固定样地的调查数据，我们采用均值±标准差法将样地的林分密度指数（SDI）分为三个等级，即SDIⅠ∈(0, 695]、SDIⅡ∈(695, 1027]、SDIⅢ∈(1027, ∞]株·hm。基于逻辑斯蒂方程，通过耦合虚拟变量和分位数回归，构建了不同SDI等级下三个分位数（0.90、0.95和0.99）处单木最大冠幅预测模型。我们进一步采用树冠投影面积法编制了不同SDI等级下的林分经营密度数表，并量化了林分密度对林分蓄积量和碳储量的影响。结果表明，不同SDI下的单木最大冠幅模型存在显著差异，在0.90分位数处能更好地模拟单木最大冠幅。独立样本检验表明，与逻辑斯蒂基本模型相比，单木虚拟变量分位数最大冠幅模型的决定系数可显著提高0.20，而均方根误差显著降低0.15 m。基于所建立的单木虚拟变量分位数最大冠幅模型和树冠投影面积法，我们编制了不同SDI等级下的林分经营密度数表。当培育目标直径为30 cm时，不区分SDI等级时，与SDIⅠ相比，林分蓄积量和碳储量分别高估了26.16 m³·hm²和10.10 t C·hm²。同样，与SDIⅡ相比，这些值分别高估了15.99 m³·hm²和6.12 t C·hm²。然而，与SDIⅢ相比，它们分别显著低估了85.13 m³·hm²和33.04 t C·hm²。我们的结果表明，忽略SDI等级差异会高估低密度林分的碳固存能力，但低估高密度林分的实际贡献。因此，基于SDI等级实施林分密度调控有利于实现人工林精准提质。
