Institute for Water and River Basin Management, Department of Aquatic Environmental Engineering, Karlsruhe Institute of Technology, Gotthard-Franz-Str. 3, 76131 Karlsruhe, Germany.
Norwegian Institute for Nature Research, FRAM-High North Research Centre for Climate and the Environment, P.O. Box 6606 Langnes, NO-9296 Tromsø, Norway.
Sensors (Basel). 2020 Apr 8;20(7):2102. doi: 10.3390/s20072102.
In this study, we focused on three species that have proven to be vulnerable to winter stress: and Our objective was to determine plant traits suitable for monitoring plant stress as well as trait shifts during spring. To this end, we used a combination of active and passive handheld normalized difference vegetation index (NDVI) sensors, RGB indices derived from ordinary cameras, an optical chlorophyll and flavonol sensor (Dualex), and common plant traits that are sensitive to winter stress, i.e. height, specific leaf area (SLA). Our results indicate that NDVI is a good predictor for plant stress, as it correlates well with height ( = 0.70, < 0.001) and chlorophyll content ( = 0.63, < 0.001). NDVI is also related to soil depth ( = 0.45, < 0.001) as well as to plant stress levels based on observations in the field ( = -0.60, < 0.001). Flavonol content and SLA remained relatively stable during spring. Our results confirm a multi-method approach using NDVI data from the Sentinel-2 satellite and active near-remote sensing devices to determine the contribution of understory vegetation to the total ecosystem greenness. We identified low soil depth to be the major stressor for understory vegetation in the studied plots. The RGB indices were good proxies to detect plant stress (e.g. Channel G%: = -0.77, < 0.001) and showed high correlation with NDVI ( = 0.75, < 0.001). Ordinary cameras and modified cameras with the infrared filter removed were found to perform equally well.
在这项研究中，我们专注于三种已被证明易受冬季胁迫的物种： 和 。我们的目标是确定适合监测植物胁迫以及春季植物特征变化的特征。为此，我们使用了主动和被动手持式归一化差异植被指数（NDVI）传感器、普通相机衍生的 RGB 指数、光学叶绿素和类黄酮传感器（Dualex）以及对冬季胁迫敏感的常见植物特征（如高度、比叶面积[SLA]）相结合的方法。我们的结果表明，NDVI 是植物胁迫的良好预测指标，因为它与高度（ = 0.70， < 0.001）和叶绿素含量（ = 0.63， < 0.001）高度相关。NDVI 还与土壤深度（ = 0.45， < 0.001）以及基于野外观察的植物胁迫水平（ = -0.60， < 0.001）相关。类黄酮含量和 SLA 在春季相对稳定。我们的结果证实了使用 Sentinel-2 卫星和主动近遥感设备的 NDVI 数据的多方法方法，以确定林下植被对总生态系统绿色度的贡献。我们确定低土壤深度是研究区域林下植被的主要胁迫源。RGB 指数是检测植物胁迫的良好指标（例如通道 G%： = -0.77， < 0.001），与 NDVI 高度相关（ = 0.75， < 0.001）。发现普通相机和移除红外滤镜的改装相机同样有效。
