Vegetation biomass is commonly measured through destructive sampling, but this method is time-consuming and is not applicable for certain studies. Therefore, it is necessary to find reliable methods to estimate vegetation biomass indirectly. Quantification of early-seral vegetation biomass in reforested stands in the United States Pacific Northwest (PNW) is important as competition between the vegetation community and planted conifer seedlings can have important consequences on seedling performance. The goal of this study was to develop models to indirectly estimate early-seral vegetation biomass using vegetation cover, height, or a combination of the two for different growth habits (ferns, forbs, graminoids, brambles, and shrubs) and environments (wet and dry) in reforested timber stands in Western Oregon, USA. Six different linear and non-linear regression models were tested using cover or the product of cover and height as the only predicting variable, and two additional models tested the use of cover and height as independent variables. The models were developed for six different growth habits and two different environments. Generalized models tested the combination of all growth habits (total) and sites (pooled data set). Power models were used to estimate early-seral vegetation biomass for most of the growth habits, at both sites, and for the pooled data set. Furthermore, when power models were preferred, most of the growth habits used vegetation cover and height separately as predicting variables. Selecting generalized models for predicting early-seral vegetation biomass across different growth habits and environments is a good option and does not involve an important trade-off by losing accuracy and/or precision. The presented models offer an efficient and non-destructive method for foresters and scientists to estimate vegetation biomass from simple field or aerial measurement of cover and height. Depending on the objectives and availability of input data, users may select which model to apply.

中文翻译：

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地被植物——早期浆叶植被的生物量函数

植被生物量通常通过破坏性采样来测量，但这种方法耗时且不适用于某些研究。因此，有必要寻找可靠的方法来间接估计植被生物量。量化美国太平洋西北部 (PNW) 重新造林林分的早期浆叶植被生物量非常重要，因为植被群落和种植的针叶树幼苗之间的竞争会对幼苗性能产生重要影响。本研究的目标是开发模型，使用植被覆盖度、高度或两者的组合来间接估计不同生长习性（蕨类植物、杂草、禾本科植物、荆棘和灌木）和环境（潮湿和干燥）在美国俄勒冈州西部的重新造林的木材林中。使用覆盖率或覆盖率和高度的乘积作为唯一预测变量测试了六个不同的线性和非线性回归模型，另外两个模型测试了使用覆盖率和高度作为自变量的情况。这些模型是针对六种不同的生长习惯和两种不同的环境开发的。广义模型测试了所有生长习惯（总计）和站点（汇集数据集）的组合。功率模型用于估计两个地点和汇总数据集的大多数生长习性的早期血清植被生物量。此外，当首选幂模型时，大多数生长习性分别使用植被覆盖度和高度作为预测变量。选择通用模型来预测不同生长习性和环境中的早期浆叶植被生物量是一个不错的选择，并且不会因失去准确性和/或精度而涉及重要的权衡。所提出的模型为林业工作者和科学家提供了一种有效且无损的方法，可以通过简单的野外或空中覆盖和高度测量来估计植被生物量。根据输入数据的目标和可用性，用户可以选择要应用的模型。