Xiao Zhou, Xuan Zhang, Ram P. Sharma, Fengying Guan
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引用次数: 0
Key message
A
nonlinear mixed effects canopy density model developed with
predictor variables describing stand characteristics and soil nutrients
provides a high prediction accuracy.
Abstract
Canopy density (CD) of a forest stand is an indicator of describing tree vitality, tree growth, competition status, environmental condition, and climate change. CD is substantially influenced by several site and environmental factors, such as soil, terrain, and stand factors. However, the CD prediction model developed utilizing the important site and environmental factors is still lacking. This study developed a nonlinear mixed-effects (NLME) CD model using data from 259 sample plots distributed across the moso bamboo (Phyllostachys edulis) forests in the eight provinces of southern China. Like fast-growing tree species, moso bamboo has high rates of growth and carbon sequestration, accumulating large amount of biomass in a short period. The CD model includes the effects of factors describing characteristics of terrains, soil and forest stands. The results showed that elevation significantly affected CD. The NLME CD model developed with the significant predictors included are: stand density (N), dominant height (DH), base area (BA), total nitrogen (TN), and soil organic carbon (SOC). When modeling random effects at the provincial level, the fitting accuracy of the model was significantly improved. Among several strategies used in calibrating NLME CD model or estimating random effects, an increased accuracy was obtained with increasing number of sample plots. However, using many sample plots per province to calibrate NLME CD model may increase the inventory costs with a little gain in the accuracy. Using the two medium BA-plots in each province, slope, and elevation or two largest BA-plots at the different slope-aspect could provide a compromise between measurement cost, model use efficiency, and prediction accuracy. NLME CD model can reduce measurement requirements in the field and support forest managers for more effective bamboo forest management strategies.
摘要林分冠层密度（CD）是描述树木活力、树木生长、竞争状况、环境条件和气候变化的指标。林冠密度受多种地点和环境因素（如土壤、地形和林分因素）的重大影响。然而，利用重要的地点和环境因素开发的 CD 预测模型仍然缺乏。本研究利用分布在中国南方八省毛竹林中的 259 个样地的数据，建立了非线性混合效应（NLME）毛竹林干旱区预测模型。与速生树种一样，毛竹具有高生长率和固碳能力，能在短期内积累大量生物量。CD模型包括描述地形、土壤和林分特征的因子的影响。结果表明，海拔对 CD 有明显影响。在建立的 NLME CD 模型中，重要的预测因子包括：林分密度（N）、优势高度（DH）、基部面积（BA）、全氮（TN）和土壤有机碳（SOC）。在省一级建立随机效应模型时，模型的拟合精度明显提高。在校准 NLME CD 模型或估算随机效应的几种策略中，随着样地数量的增加，模型拟合精度也随之提高。然而，在每个省使用多个样地校准无损检测模型可能会增加库存成本，而精度却提高不多。在每个省份、坡度和海拔高度上使用两个中等BA样地，或在不同坡度-海拔高度上使用两个最大BA样地，可以在测量成本、模型使用效率和预测精度之间取得折中。NLME CD 模型可以降低野外测量要求，为森林管理者制定更有效的竹林管理策略提供支持。
期刊介绍：
Trees - Structure and Function publishes original articles on the physiology, biochemistry, functional anatomy, structure and ecology of trees and other woody plants. Also presented are articles concerned with pathology and technological problems, when they contribute to the basic understanding of structure and function of trees. In addition to original articles and short communications, the journal publishes reviews on selected topics concerning the structure and function of trees.
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