Knowledge about the forest canopy stratification is of essential importance for forest management and planning. Collecting structural information (e.g. natural regeneration) still depends on cost and labour intensive forest inventories with a coarse spatio-temporal resolution. Remote sensing partly overcomes these limitations and particularly active sensors of type light detection and ranging (LiDAR) have proven their great potential of separating forest strata. The applicability of LiDAR metrics for the differentiation of the spruce dominated forest strata in Central Germany has not been tested yet. Additionally, studying the potential of Sentinel-2 metrics for the classification of forest strata is lacking too.

In this study, we investigated the capabilities of six different classification approaches for the differentiation of five forest strata that are typical for the study region. Reference data were derived from forest inventory measurements surveyed on a dense 200 × 200 m grid. The six classification approaches were trained with fused and un-fused LiDAR and Sentinel-2 inferred metrics. The classification results were compared using the overall mean accuracy, sensitivity and specificity via receivers operating characteristics of multi-class problems. We were interested in the classification abilities of Sentinel-2 metrics due to the obvious advantages of Sentinel-2 based metrics (free of charge, high spatio-temporal coverage). We assumed that the canopy structure determines the reflection on stand level and thus might facilitate the classification of different canopy strata. Beforehand, it was important to examine the influence of distinctly imbalanced and collinear reference data on the classification results.

We found that the Random Forest classifier most accurately separated the five forest strata with a mean overall accuracy of 83.3% (Kappa = 76.2%). These values were achieved from balanced training data and the classification capability was confirmed by classification results from an independent test data set. Fused predictors of active (LiDAR) and passive (Sentinel-2) remote sensing revealed no substantial improvement in the classification accuracy due to the dominant role of LiDAR metrics. Herein, we identified that especially the height variability, top height, portion of LiDAR-returns between 2 m and 10 m and the standard deviation of the return number between the 25th and 50th height percentile, predominately contributed to the classification accuracy. Classification results purely based on Sentinel-2 metrics revealed a rather small overall mean accuracy of 54.7%. The metrics (e.g. median, variance, entropy) were derived from Sentinel-2 indices, covering the visible and near to short infrared spectrum. Variable importance computations unraveled a detectable but minor contribution of MSI, TCG, NDVI to the classification result. Finally, our data driven observations illustrated serious drawbacks associated to data imbalance, collinearity and autocorrelation and presented practical guidance to cope with these issues.

关于森林冠层分层的知识对于森林管理和规划至关重要。收集结构信息（例如自然更新）仍然依赖于成本和劳动密集型森林清单，时空分辨率粗略。遥感部分克服了这些限制，特别是光探测和测距（LiDAR）类型的主动传感器已经证明了它们在分离森林地层方面的巨大潜力。LiDAR 指标对德国中部以云杉为主的森林地层差异化的适用性尚未经过测试。此外，还缺乏研究 Sentinel-2 指标对​​森林地层分类的潜力。

在这项研究中，我们调查了六种不同分类方法对研究区域典型的五个森林地层的分化能力。参考数据来自在密集的 200 × 200 m 网格上调查的森林库存测量值。六种分类方法使用融合和未融合的 LiDAR 和 Sentinel-2 推断指标进行训练。通过多类问题的接收器操作特性，使用总体平均准确度、灵敏度和特异性来比较分类结果。由于基于 Sentinel-2 的指标的明显优势（免费、高时空覆盖），我们对 Sentinel-2 指标的分类能力很感兴趣。我们假设冠层结构决定了林分水平的反映，因此可能有助于不同冠层的分类。事先，重要的是检查明显不平衡和共线的参考数据对分类结果的影响。

我们发现随机森林分类器最准确地分离了五个森林层，平均总体准确率为 83.3%（Kappa = 76.2%）。这些值是从平衡的训练数据中获得的，并且分类能力由独立测试数据集的分类结果确认。主动 (LiDAR) 和被动 (Sentinel-2) 遥感的融合预测器显示，由于 LiDAR 指标的主导作用，分类精度没有显着提高。在这里，我们发现，特别是高度变化、顶部高度、2 m 到 10 m 之间的 LiDAR 回波部分以及第 25 和第 50 个高度百分位数之间的回波数的标准偏差，主要有助于分类精度。纯粹基于 Sentinel-2 指标的分类结果显示，总体平均准确度相当小，为 54.7%。指标（例如中值、方差、熵）来自 Sentinel-2 指数，涵盖可见光和近至短红外光谱。变量重要性计算揭示了 MSI、TCG、NDVI 对分类结果的可检测但很小的贡献。最后，我们的数据驱动观察说明了与数据不平衡、共线性和自相关相关的严重缺陷，并为应对这些问题提供了实用指导。NDVI 对分类结果。最后，我们的数据驱动观察说明了与数据不平衡、共线性和自相关相关的严重缺陷，并为应对这些问题提供了实用指导。NDVI 对分类结果。最后，我们的数据驱动观察说明了与数据不平衡、共线性和自相关相关的严重缺陷，并为应对这些问题提供了实用指导。