1. Forests provide multiple services, and in the face of global change adaptive management strategies are needed, which inevitably must be based on models. However, most locally accurate forest models are tied to the stand scale and cannot readily be applied across large areas. Empirical data for model initialisation are often not available at large spatial scales. National Forest Inventories (NFIs) provide spatially representative tree and stand samples, but their samples are typically small, that is, only a few trees are measured per plot, and they are truncated, that is, not each tree has the same probability of being observed. To overcome these issues, we develop and apply a methodology to derive stand descriptions from small sample data, taking the Swiss NFI as a case study.
2. We extended the traditional Weibull function to (multi‐)truncated unimodal and bimodal forms that are suitable for the representation of samples from survey designs with multiple callipering thresholds. Subsequently, we applied these functions in an extended parameter prediction method to derive stand diameter distributions from representative samples. Additionally, we predicted species compositions using a multinomial logistic regression model and assigned them to the diameter distributions of the stands.
3. The diameter distribution of 9.1% of the Swiss NFI samples was better described by a bimodal than a unimodal Weibull function. The uni‐ and bimodal diameter model in combination with the model to determine species composition can be used to predict stand descriptions from single small samples or entire forest types in the target area. Thereby, the bimodal form is suitable for capturing stand structures with distinct under‐ and overstorey. In Switzerland, the diameter distributions of stands are typically positively skewed.
4. Our method can be applied to any large‐scale dataset (e.g. NFI) and allows to generate initial conditions in terms of spatially representative stands. These, in turn, are suitable for forest stand simulators, which allows for developing adaptive forest management strategies at large scales, by simulating realistic and site‐specific stand development while still reflecting detailed management measures. Furthermore, stand descriptions can be used to assess tree species diversity, regeneration and harvest potentials.

中文翻译：

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从小型森林样本到广义的单峰和双峰林分描述

1. 森林提供多种服务，面对全球变化，需要自适应管理策略，这不可避免地必须基于模型。但是，大多数本地准确的森林模型都与林分规模有关，因此无法轻松地在大面积上应用。用于模型初始化的经验数据通常在较大的空间范围内不可用。国家森林清单（NFI）提供了具有空间代表性的树木和林分样本，但是它们的样本通常很小，也就是说，每个样地只测量了几棵树，而它们被截断了，也就是说，并不是每棵树都有相同的概率被砍伐。观察到的。为了克服这些问题，我们以瑞士NFI为案例研究，开发并应用了一种方法来从小样本数据中获取展位描述。
2. 我们将传统的Weibull函数扩展为（多）截断的单峰和双峰形式，适用于表示具有多个callipering阈值的调查设计中的样本。随后，我们将这些函数应用到扩展参数预测方法中，以从代表性样本中得出林分直径分布。此外，我们使用多项逻辑回归模型预测物种组成，并将其分配给林分的直径分布。
3. 通过双峰比单峰威布尔函数更好地描述了9.1％的瑞士NFI样品的直径分布。单峰和双峰直径模型与确定物种组成的模型相结合，可用于根据目标区域中的单个小样本或整个森林类型预测林分描述。因此，双峰形式适用于捕获下层和下层明显的林分结构。在瑞士，看台的直径分布通常呈正偏。
4. 我们的方法可以应用于任何大规模数据集（例如NFI），并可以根据空间代表性林分生成初始条件。这些反过来又适用于林分模拟器，通过模拟现实的和特定地点的林分发展，同时仍能反映详细的管理措施，从而允许大规模开发自适应森林管理策略。此外，林分描述可用于评估树木的多样性，再生和收获潜力。