Forest floor vegetation plays a crucial role in ecosystem processes of temperate and boreal forests. Remote sensing offers a valuable tool to characterize the forest floor through reflectance spectra. While passive optical airborne and satellite data have been used to map spectral properties of forest understory, these sensors are limited by cloud cover, especially in high latitudes. To date, LiDAR and SAR have not been explored for this application even though their data are less dependent on illumination conditions and provide information on tree canopy structure and tree distribution which is connected to forest floor properties. We investigated active remote sensing techniques to establish links between forest structure and spectral properties of forest floor across European temperate, hemiboreal and boreal forest ecosystems. First, in the exploratory part, the research question was : Which forest structure metrics are connected to the spectral properties of the forest floor? Next, our predictive part focused on: What is the potential of (1) terrestrial laser scanning (TLS) data, (2) airborne laser scanning data, (3) satellite-borne SAR data, and (4) these data sources combined to predict forest floor spectral properties? Our results revealed that nine forest structure metrics were potentially associated with forest floor reflectance. We identified TLS-derived clumping index and SAR-derived VV backscatter coefficient and VH/VV ratio as significantly connected to forest floor reflectance in certain Sentinel-2 spectral bands. Overall, the active remote sensors achieved the best predictions for forest floor reflectance in red-edge, near-infrared and shortwave infrared regions. Using data from all three sensors together to predict the forest floor spectra yielded better results than using any of the sensors alone. When data from a single sensor were used, the highest prediction accuracies for forest floor reflectance in the red-edge and near-infrared regions were achieved with SAR data, and in the shortwave infrared region with either SAR or TLS data. In the future, the accuracy of predicting forest floor characteristics in temperate and boreal forests could benefit from a synergy of passive and active technologies.

中文翻译：

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探索 SAR 和地面和机载 LiDAR 在预测温带和北方森林森林地面光谱特性方面的潜力


森林地面植被在温带和北方森林的生态系统过程中起着至关重要的作用。遥感提供了一种有价值的工具，可以通过反射光谱来描述森林地面的特征。虽然被动光学机载和卫星数据已被用于绘制森林林下层的光谱特性，但这些传感器受到云量的限制，尤其是在高纬度地区。迄今为止，尚未探索 LiDAR 和 SAR 用于此应用，尽管它们的数据不太依赖于照明条件，并提供有关树冠结构和树木分布的信息，这些信息与森林地面特性有关。我们研究了主动遥感技术，以建立欧洲温带、半北和北方森林生态系统的森林结构和森林地面光谱特性之间的联系。首先，在探索部分，研究问题是：哪些森林结构指标与森林地面的光谱特性有关？接下来，我们的预测部分侧重于：（1） 地面激光扫描 （TLS） 数据，（2） 机载激光扫描数据，（3） 卫星 SAR 数据，以及 （4） 这些数据源结合起来预测森林地面光谱特性的潜力是什么？我们的结果表明，9 个森林结构指标可能与森林地面反射率相关。我们确定了 TLS 衍生的聚集指数和 SAR 衍生的 VV 背散射系数和 VH/VV 比率与某些 Sentinel-2 光谱波段的森林地面反射率显着相关。总体而言，主动遥感器对红边、近红外和短波红外区域的森林地面反射率进行了最佳预测。 同时使用来自所有三个传感器的数据来预测森林地面光谱比单独使用任何传感器产生更好的结果。当使用来自单个传感器的数据时，使用 SAR 数据实现红边和近红外区域森林地面反射率的最高预测精度，而在短波红外区域使用 SAR 或 TLS 数据实现最高预测精度。未来，预测温带和北方森林森林地面特征的准确性可能会受益于被动和主动技术的协同作用。