Visser Fleur, Buis Kerst, Verschoren Veerle, Meire Patrick
Institute of Science and the Environment, University of Worcester, Henwick Grove, Worcester WR2 6AJ, UK.
Department of Biology, Ecosystem Management Research Group, University of Antwerp, Universiteitsplein 1C, Wilrijk B-2610, Belgium.
Sensors (Basel). 2015 Sep 30;15(10):25287-312. doi: 10.3390/s151025287.
UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R²-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R²-values up to 0.77) corresponded with the OBRA findings. A 10% error was achieved under sub-optimal data collection conditions, which indicates that the method could be suitable for many SAV mapping applications.
无人机和其他低空遥感平台已被证明是用于河流系统遥感的非常有用的工具。目前，消费级相机仍是用于此目的最常用传感器。特别是，利用光在水中的强烈衰减，正致力于从用此类相机收集的光学图像数据中获取河流水深。尚未有研究将此方法应用于绘制水生植被的淹没深度，水生植被的反射特性与河床基质有很大不同。因此，本研究探讨了利用光学图像数据绘制浅水清澈溪流中水生植被（SAV）淹没深度的可能性。我们首先将Legleiter等人（2009年）的最佳波段比率分析方法（OBRA）应用于清澈溪流中三种大型植物物种的光谱特征数据集。结果表明，对于每个物种，特定波长的比率与深度密切相关。对所有物种的综合评估得出了同样强的相关性，表明植被光谱变化的影响相对于深度变化引起的光谱变化是次要的。在包括825至925纳米近红外（NIR）区域的一个波段和可见光区域的一个波段的组合中发现了最强的相关性（不同物种的R²值范围为0.67至0.90）。目前，高空间和光谱分辨率的数据通常无法直接用于将OBRA结果应用于SAV深度测绘的图像数据。相反，采用了一种新颖的低成本数据采集方法，使用近红外敏感数码单反相机获取六波段高空间分辨率图像合成数据。利用SAV淹没深度的现场数据集开发回归模型，以便从图像像素值绘制淹没深度。提供最佳性能模型（R²值高达0.77）的波段（组合）与OBRA的结果一致。在次优数据收集条件下实现了10%的误差，这表明该方法可能适用于许多SAV测绘应用。
