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开发用于宽叶混合森林的立基面积模型的添加系统

Xijuan Zeng¹, Dongzhi Wang¹, Dongyan Zhang²

¹College of Forestry, Hebei Agricultural University, Baoding 071001, China.

Plants (Basel, Switzerland)

|July 13, 2024

概括

在混合森林中,准确预测森林基面积 (SBA) 是至关重要的. 这项研究开发了一种使用非线性回归的多个物种的附加SBA模型,提高了森林管理的预测准确性.

科学领域:

林业科学林业科学
生态生态学生态生态学
量化林业量化林业是什么

背景情况:

标准基础面积 (SBA) 对于预测森林生长和产量至关重要.
由于复杂的结构,在多种混合森林中准确预测SBA具有挑战性.
在SBA模型中的附加性对于可靠的森林管理预测至关重要.

研究的目的:

为多个物种的宽叶混合森林构建一个附加立基面积 (SBA) 预测模型.
为了比较不同建模技术的有效性,以实现SBA加值.
通过结合关键森林位变量来提高SBA预测的准确性.

主要方法:

非线性最小平方回归 (NLS) 被用来构建基本的SBA模型.
在多个物种的附加建模中,应用了比例调整 (AP) 和非线性看似无关回归 (NSUR).
理查兹和科尔夫模型被认为是个体物种 (Populus davidiana和Betula platyphylla) 的最佳模型.

主要成果:

理查兹 (M6) 和科尔夫 (M1) 模型对分别P. davidiana和B. platyphylla的SBA预测是最佳的.
开发的SBA模型整合了现场质量,站立密度指数和年龄,大大提高了预测准确性.
非线性看似无关回归 (NSUR) 证明比比例调整 (AP) 更有效,在混合森林中实现SBA附加性.

结论:

该研究成功开发了一种准确的,添加性SBA预测模型,用于混合森林.
在多种森林中,NSUR是构建附加SBA模型的推方法.
这些发现为优化混合森林中树结构和SBA预测提供了科学基础.

关键词:

贝图拉 (Betula platyphylla) 是一个白类植物.人类达维迪亚种群 (Populus davidiana) 是一个大卫种群.按比例进行调整.宽叶混合森林宽叶混合森林非线性看似无关的回归.站立基底面积模型的模型.

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