Development of a UAV based topographic sensor for monitoring dynamic coastal environments

尽管我们的沿海环境在保护内陆免受海洋洪水侵袭以及与之相关的栖息地和经济价值方面受到重视，但由于成本高昂，重复监测主要限于年度时间尺度。然而，对管理沿海沉积物资源的影响是如此之大，准确的、高时空分辨率的数据对于支持地貌理解驱动维护决策至关重要。定期提供的高分辨率地形数据将改变我们目前对大尺度海滩动力学的理解，通过提供第一次~每天的高分辨率测量数据，我们将发现沉积物正在四处移动。该项目将与我们的CASE合作伙伴佩文西海防有限公司(见CASE合作伙伴部分)合作，开发一种新型的、小尺寸的移动激光扫描传感器(MLS)，该传感器将被集成到无人机(无人机)中。传感器和平台将能够在几个小时内收集佩文西海岸的适当高分辨率(高垂直精度和每平方米100s点)数据，提高对沉积物动力学的理解。机载图像处理将使特征(例如瓦片)跟踪的发展成为可能，从而努力开发能够监测动态环境特征的智能系统。除了这项研究对监测大片动态地貌的直接影响外，对于那些开发用于完全自主飞行的感知和避免系统的人来说，这将是非常有兴趣的，并对智能特征跟踪的进一步完善具有重大意义。

Despite the importance placed upon our coastal environments in terms of providing inland protection from marine flooding and the habitat and economic value associated with them, due to high costs, repeat monitoring is largely limited to annual timescales. Yet the implications for managing the coastal sediment resource are such that accurate, high spatio-temporal resolution data is essential to underpin the geomorphological understanding driving maintenance decisions. The regular delivery of high resolution topographic data will transform our current understanding of large scale beach dynamics, by providing the very first ~daily measurements of high the sediment is moving around.In conjunction with our CASE partner, Pevensey Coastal Defence Ltd (see CASE partner section), this project will develop a novel, small form factor, mobile laser scanning sensor (MLS) which will be integrated into an UAV (Unmanned Aerial Vehicle). The sensor and platform will be able to collect suitably high resolution (high vertical accuracy and 100s points per m2) data along the Pevensey coast in a matter of hours, improving the understanding of sediment dynamics. On board image processing will enable the development of feature (e.g. shingle spits) tracking in an effort to develop 'smart' systems which can monitor dynamic environmental features.As well as the immediate impact of this research for monitoring large areas of dynamic land forms, it will be of great interest to those developing sense and avoid systems for full autonomous flights and in further refinement of smart feature tracking.