黄龙山林区白皮松天然次生林生长规律研究

【目的】为实现陕西省白皮松天然次生林的合理经营,建立符合其生长规律的模型,为科学抚育较大树龄的天然次生林提供决策依据。【方法】以陕西省黄龙山林区白皮松天然次生林为研究对象,选择标准木进行树干解析,采用人工神经网络(ANN)和3种常见的理论函数建立了胸径、树高、材积生长模型,并绘制生长曲线图对林区内白皮松天然次生林生长规律进行分析。【结果】①采用人工神经网络建模技术构建的胸径生长量模型、树高生长量模型、材积生长量模型优于3种传统模型。②所建神经网络模型在拟合生长缓慢的白皮松生长过程方面具有较好的应用推广能力。③白皮松胸径速生期为30~60 a,胸径连年生长量在120 a达到最大值;20~30 a为树高生长的速生期,树高连年生长量在30 a达到最大值;白皮松材积生长速生期为110~130 a,材积连年生长量在130 a达到最大值。在135 a时,黄龙山林区白皮松还未达到数量成熟龄。【结论】所建神经网络模型能为黄龙山林区白皮松古树研究奠定基础,生长规律的研究可以为不同阶段白皮松经营提供参考。

The growth law for natural secondary forests of Pinus bungeana in the Huanglong Mountain forest region

【Objective】In order to realize the reasonable management of Pinus bungeana natural secondary forests in Shaanxi Province,the growth model which accords with the growth law of P. bungeana was established to provide bases for management decisions and achieve a reasonable management of oak natural secondary forests of P. bungeana with the older tree age.【Method】This study was conducted in a natural secondary forest of P. bungeana in the Huanglong Mountain forest region, Shaanxi Province. Standard trees were chosen for a trunk analysis, and diameter at breast height (DBH), tree height, and volume growth models were established based on data from the analysis of tree trunks with artificial neural network(ANN)and three common models. Growth curves were drawn to study the growth law of natural secondary forests of P. bungeana. 【Result】The DBH ANN growth model, tree height ANN growth model, and volume ANN growth model were better than the three commonly used models. The ANN model showed a good application and promotion ability in fitting the growth process of P. bungeana, which grows slowly. The rapid DBH, tree height and volume growth periods were 30-60, 20-30 and 110-130 a, respectively. The maximum current annual increment for DBH, tree height and volume was 120, 90 and 130 a, respectively. P. bungeana has not reached the age of quantitative maturity in 135 a. 【Conclusion】The ANN models could lay the foundation for ancient tree research in the Huanglong Mountain forest region. The growth law research can provide a reference and support for the management of different stages of P. bungeana forest.