National forest inventories (NFI) have a long history providing data to obtain nationally representative and accurate estimates of growing stock. Today, in most NFIs additional data are collected to provide information on a range of forest ecosystem functions such as biodiversity, habitat, nutrient and carbon dynamics. An important driver of nutrient and C cycling is decomposing biomass produced by forest vegetation. Several studies have demonstrated that understory vegetation, particularly annual plant litter of the herb layer can contribute significantly to nutrient and C cycling in forests. A methodology to obtain comprehensive, consistent and nationally representative estimates of herb layer biomass on NFI plots could provide added value to NFIs by complementing the existing strong basis of biomass estimates of the tree and tall shrub layer. The study was based on data from the Swiss NFI since it covers a large environmental gradient, which extends its applicability to other NFIs. Based on data from 405 measurements in nine forest strata, a parsimonious model formulation was identified to predict total and non-ligneous herb layer biomass. Besides herb layer cover, elevation was the main statistically significant explanatory variable for biomass. The regression models accurately predicted biomass based on absolute percentage cover (for total biomass: R2 = 0.65, p = 0; for non-ligneous biomass: R2 = 0.76; p = 0) as well as on cover classes (R2 = 0.83; p = 0; and R2 = 0.79, p = 0), which are typically used in NFIs. The good performance was supported by the verification with data from repeated samples. For the 2nd, 3rd, and 4th Swiss NFI estimates of non-ligneous above-ground herb layer biomass 586.6 ± 7.7, 575.2 ± 7.6, and 586.7 ± 7.9 kg·ha− 1, respectively. The study presents a methodology to obtain herb layer biomass estimates based on a harmonized and standardized attribute available in many NFIs. The result of this study was a parsimonious model requiring only elevation data of sample plots in addition to NFI cover estimates to provide unbiased estimates at the national scale. These qualities are particularly important as they ensure accurate, consistent, and comparable results.

中文翻译：

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扩展统一的国家森林清单草本层植被覆盖率观测值，以得出综合的生物量估算值

国家森林清单（NFI）拥有悠久的历史，可提供数据来获得具有全国代表性的准确的林木蓄积估计。如今，在大多数NFI中都收集了更多数据，以提供有关森林生态系统功能范围的信息，例如生物多样性，生境，养分和碳动态。养分和碳循环的重要驱动因素是分解森林植被产生的生物量。多项研究表明，林下植被，特别是草本层的一年生凋落物，可以显着促进森林中的养分和碳循环。通过补充现有的树木和高灌木层生物量估算的强大基础，一种在NFI样地上获得草本层生物量的全面，一致和全国代表性的估算方法可以为NFI提供增值。该研究基于瑞士NFI的数据，因为它涵盖了较大的环境梯度，从而将其适用性扩展到其他NFI。基于来自9个森林地层的405个测量值的数据，确定了一个简约的模型公式来预测总和非木质草本层生物量。除草本层覆盖外，海拔高度是生物量的主要统计显着解释变量。回归模型基于绝对百分比覆盖率（对于总生物量：R2 = 0.65，p = 0；对于非木质生物量：R2 = 0.76； p = 0）以及覆盖类别（R2 = 0.83; p）准确预测生物量= 0； R2 = 0.79，p = 0），通常在NFI中使用。重复样本数据的验证支持了良好的性能。对于第二，第三 瑞士和第四次NFI对非木质地上草本层生物量的估计值分别为586.6±7.7、575.2±7.6和586.7±7.9 kg·ha-1。这项研究提出了一种基于许多NFI中可用的协调和标准化属性来获取草药层生物量估计值的方法。这项研究的结果是一个简化的模型，除了NFI覆盖率估算值之外，仅需要样本样地的海拔数据即可在全国范围内提供无偏估算值。这些质量特别重要，因为它们可以确保准确，一致和可比的结果。这项研究的结果是一个简化的模型，除了NFI覆盖率估算值之外，仅需要样本样地的海拔数据即可在全国范围内提供无偏估计值。这些质量特别重要，因为它们可以确保准确，一致和可比较的结果。这项研究的结果是一个简化的模型，除了NFI覆盖率估算值之外，仅需要样本样地的海拔数据即可在全国范围内提供无偏估算值。这些质量特别重要，因为它们可以确保准确，一致和可比较的结果。