基于β回归的Larix olgensis的茎湿度含量预测模型
Hua-Yan Cao1, Zheng Miao1, Yuan-Shuo Hao1
1Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China.
Ying yong sheng tai xue bao = The journal of applied ecology
|April 22, 2024
概括
这项研究模拟了沿着树干的树 (Larix olgensis) 的水分含量变化. 混合效应β回归模型准确地根据树木和地块因素预测树木,心木,树皮和茎的水分含量.
科学领域:
- 林业科学 林业科学
- 木材科学 木材科学 木材科学
- 生态建模 生态建模
背景情况:
- 了解木材的水分含量对于木材质量和森林管理至关重要.
- 树干中的水分含量的纵向变化会影响木材的特性和加工.
- 人工 (Larix olgensis) 种植园对于木材生产具有重要意义.
研究的目的:
- 为了研究Larix olgensis中树,心木,树皮和茎的水分含量的纵向变化模式.
- 开发和验证混合效应β回归模型,用于预测树木的水分含量.
- 确定影响树干沿着水分分布的关键因素.
主要方法:
- 构建两级混合效应β回归模型,包括情节和树效应.
- 使用两个采样方案对模型进行校准:无限制的相对高度 (方案I) 和有限的高度 (<2米) (方案II).
- 使用平均绝对百分比误差 (MAPE) 评估模型准确性,采用不同数量的样本磁盘.
主要成果:
- 树木和树干的水分含量在纵向上升;心木显示轻微下降,然后增加;树皮的水分含量增加并平衡.
- 相对高度,冠底高度,支架密度,年龄和主导高度是水分含量的关键驱动因素.
- 方案I在2-3盘 (MAPE高达7.4%) 实现了稳定的预测准确性,而方案II在1.3米和2米的盘 (MAPE高达7.1%) 上是有效的.
结论:
- 混合效应β回归模型可以准确地预测Larix olgensis的水分含量.
- 这些模型有效地考虑了情节和树木的变化,增强了预测能力.
- 这些发现支持优化采样策略,用于森林库存和木材质量评估中的水分含量评估.
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