基于模拟变量和定量回归的Larix gmelinii再生树苗的高度直径模型
Le-le Lyu1, Wen-Bin Wang2, Ling-Bo Dong1
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
|October 29, 2023
概括
优化支架密度对于增强Larix gmelinii树苗高度增长至关重要. 规范树木密度指数 (SDI) 对年轻森林的高度发展产生积极影响.
科学领域:
- 林业科学 林业科学
- 生态建模 生态建模
- 量化林业是一种量化林业.
背景情况:
- 树木密度是影响森林生长和发展的关键因素.
- (Dahurian Larch) 是北极森林的一个关键物种,但其在不同密度下的再生动态需要进一步研究.
- 对树木高度的准确建模对于森林管理和产量预测至关重要.
研究的目的:
- 为了研究树密度指数 (SDI) 和Larix gmelinii树苗的高度-乳房直径之间的关系.
- 评估不同非线性树高度模型的性能,并开发一个改进的模型,结合SDI.
- 量化不同种群密度等级对树苗高度增长的影响.
主要方法:
- 从2054个Larix gmelinii树苗收集数据,这些树苗分布在Daxing'anling地区的55个固定地块上.
- 使用四分位数方法将立位密度指数 (SDI) 分类为四个类别.
- 构建和比较木材高度的模拟变量和定量回归模型,结合SDI.
- 评估了五种非线性树高度模型,其中理查德斯模型显示最合适.
主要成果:
- 理查兹模型为Larix gmelinii的高度-乳房直径提供了最好的适配,R_a^2为0.7637.
- 与基础模型相比,采用SDI的模拟变量模型显示了更好的准确性 (R_a^2增加1.3%) 和更少的错误 (RMSE,MAE,AIC).
- 量子回归分析表明,在量子 τ=0.5 的模型产生了最佳匹配统计数据.
- 与最低密度类 (SDI1) 相比,较高的SDI类 (5.6%至11.3%) 的树高度显著增加.
结论:
- 立体密度显著影响Larix gmelinii幼苗的高度增长.
- 开发的模拟变量模型,结合基于理查兹函数的SDI,可以更准确地预测树的高度.
- 调节种群密度是一种可行的林业战略,以促进Larix gmelinii再生的高度增长.
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