Tomsett Christopher, Leyland Julian
School of Geography and Environmental Science, University of Southampton, Highfield, Southampton SO17 1BJ, UK.
Sensors (Basel). 2021 Nov 19;21(22):7719. doi: 10.3390/s21227719.
While Uncrewed Aerial Vehicle (UAV) systems and camera sensors are routinely deployed in conjunction with Structure from Motion (SfM) techniques to derive 3D models of fluvial systems, in the presence of vegetation these techniques are subject to large errors. This is because of the high structural complexity of vegetation and inability of processing techniques to identify bare earth points in vegetated areas. Furthermore, for eco-geomorphic applications where characterization of the vegetation is an important aim when collecting fluvial survey data, the issues are compounded, and an alternative survey method is required. Laser Scanning techniques have been shown to be a suitable technique for discretizing both bare earth and vegetation, owing to the high spatial density of collected data and the ability of some systems to deliver dual (e.g., first and last) returns. Herein we detail the development and testing of a UAV mounted LiDAR and Multispectral camera system and processing workflow, with application to a specific river field location and reference to eco-hydraulic research generally. We show that the system and data processing workflow has the ability to detect bare earth, vegetation structure and NDVI type outputs which are superior to SfM outputs alone, and which are shown to be more accurate and repeatable, with a level of detection of under 0.1 m. These characteristics of the developed sensor package and workflows offer great potential for future eco-geomorphic research.
虽然无人驾驶飞行器（UAV）系统和相机传感器通常与运动结构（SfM）技术结合使用，以获取河流系统的三维模型，但在植被存在的情况下，这些技术会产生较大误差。这是因为植被的结构复杂性高，且处理技术无法识别植被区域中的裸地点。此外，对于生态地貌应用而言，在收集河流调查数据时，植被特征化是一个重要目标，这些问题会更加复杂，因此需要一种替代调查方法。激光扫描技术已被证明是一种适用于离散裸地和植被的技术，这得益于所收集数据的高空间密度以及一些系统提供双回波（例如，首次和末次）的能力。在此，我们详细介绍了一种搭载在无人机上的激光雷达和多光谱相机系统及其处理流程的开发与测试，并将其应用于一个特定的河流实地位置，同时总体上参考了生态水力学研究。我们表明，该系统和数据处理流程能够检测裸地、植被结构以及归一化植被指数（NDVI）类型的输出，这些输出优于单独的SfM输出，并且显示出更高的准确性和可重复性，检测精度可达0.1米以下。所开发的传感器套件和工作流程的这些特性为未来的生态地貌研究提供了巨大潜力。
