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应用生态学报 ›› 2024, Vol. 35 ›› Issue (3): 587-596.doi: 10.13287/j.1001-9332.202403.001

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基于beta回归的长白落叶松树干含水率预测模型

曹华燕, 苗铮, 郝元朔, 董利虎*   

  1. 东北林业大学林学院, 森林生态系统可持续经营教育部重点实验室, 哈尔滨 150040
  • 收稿日期:2023-11-14 修回日期:2024-01-13 出版日期:2024-03-18 发布日期:2024-06-18
  • 通讯作者: *E-mail: donglihu2006@163.com
  • 作者简介:曹华燕, 女, 1998年生, 硕士研究生。主要从事林分生长与收获模型研究。E-mail: 1092817560@qq.com
  • 基金资助:
    国家自然科学基金项目(31971649)、黑龙江省自然科学基金优秀青年项目(YQ2022C005)、中央高校基本科研业务费专项资金项目(2572020DR03)和黑龙江头雁创新团队计划项目(森林资源高效培育技术研发团队)

Stem moisture content prediction model for Larix olgensis based on beta regression.

CAO Huayan, MIAO Zheng, HAO Yuanshuo, DONG Lihu*   

  1. Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China
  • Received:2023-11-14 Revised:2024-01-13 Online:2024-03-18 Published:2024-06-18

摘要: 为探究人工长白落叶松边材、心材、树皮、树干含水率沿树干的纵向变化规律,本研究结合样地、样木效应,构建了基于beta回归的含水率混合效应模型,采用不限定相对高度(方案Ⅰ)和限定高度在2 m以下(方案Ⅱ)2种抽样方式对模型进行校正。结果表明: 边材、树干含水率沿树干向上逐渐增加;心材含水率沿树干向上先略减后增大;树皮含水率沿树干向上先增大后趋于平缓,然后再增加。相对高度、活冠高、林分每公顷胸高断面积、年龄和林分优势高是显著影响长白落叶松木材含水率的因子。方案Ⅰ下,随机抽取2~3个圆盘的含水率测量值来校准模型可以得到稳定的预测精度,树干含水率的平均绝对误差百分比(MAPE)可达7.2%(随机抽取2个),边材、心材、树皮含水率的MAPE可达7.4%、10.5%、10.5%(随机抽取3个);方案Ⅱ下,抽取1.3和2 m圆盘的含水率测量值校准模型最适宜,边材、心材、树皮和树干含水率的MAPE分别达到7.8%、11.0%、10.4%和7.1%。所有beta混合效应回归模型的预测精度都明显优于基础模型。包含样地、样木效应的两水平beta混合效应回归模型可以很好地预测长白落叶松各部位的含水率。

关键词: 长白落叶松, 木材含水率, beta回归模型, 边材, 心材, 树皮, 树干

Abstract: To investigate the longitudinal variation patterns of sapwood, heartwood, bark and stem moisture content along the trunk of artificial Larix olgensis, we constructed mixed effect models of moisture content based on beta regression by combining the effects of sampling plot and sample trees. We used two sampling schemes to calibrate the model, without limiting the relative height (Scheme Ⅰ) and with a limiting height of less than 2 m (Scheme II). The results showed that sapwood and stem moisture content increased gradually along the trunk, heartwood moisture content decreased slightly and then increased along the trunk, and bark moisture content increased along the trunk and then levelled off before increasing. Relative height, height to crown base, stand area at breast height per hectare, age, and stand dominant height were main factors driving moisture content of L. olgensis. Scheme Ⅰ showed the stable prediction accuracy when randomly sampling moisture content measurements from 2-3 discs to calibrate the model, with the mean absolute percentage error (MAPE) of up to 7.2% for stem moisture content (randomly selected 2 discs), and the MAPE of up to 7.4%, 10.5% and 10.5% for sapwood, heartwood and bark moisture content (randomly selected 3 discs), respectively. Scheme Ⅱ was appropriate when sampling moisture content measurements from discs of 1.3 and 2 m height and the MAPE of sapwood, heartwood, bark and stem moisture content reached 7.8%, 11.0%, 10.4% and 7.1%, respectively. The prediction accuracies of all mixed effect beta regression models were better than the base model. The two-level mixed effect beta regression models, considering both plot effect and tree effect, would be suitable for predicting moisture content of each part of L. olgensis well.

Key words: Larix olgensis, wood moisture content, beta regression model, sapwood, heartwood, bark, stem