Exploring the potential of explorative point clouds in floodplain maintenance

Abstract

Water management is an integral part of Dutch history, driven by the continuous need to reduce flood risk. Because a large area of the country is located below Normaal Amsterdams Peil (NAP), there is an ongoing challenge to safely discharge all the water to the sea. Therefore, flood safety policy has become crucial to protect the Netherlands from natural hazards. An essential part of this strategy involves the Waardegedreven Onderhoudscontract Uiterwaarden (WOCU) Rijntakken project, which is responsible for managing the floodplains adjacent to the Rijntakken within the Netherlands.

The current lack of efficiency and effectiveness regarding change inspection in the large and sometimes inaccessible areas of the floodplain requires the use of remote sensing change detection to move toward a data-driven maintenance process, in particular, by using point cloud data. This is nowadays a widely used data source in a variety of fields to capture elevations and in this way extract valuable information from terrains. Despite its usage in a variety of applications, the data is often underused since the data is frequently processed directly to other data formats. This research therefore aims to reveal the potential of explorative point clouds in floodplain maintenance.

Light Detection and Ranging (LiDAR)- and multispectral data were acquired at two moments, one before and one after the summer, with a time interval of 45 days. Subsequently, these acquired datasets evolved into an explorative point cloud by adding attributes, including vegetation health, also known as Normalized Difference Vegetation Index (NDVI), and the distance between these two point clouds, the cloud-to-cloud distance. This explorative point cloud with the integrated additional information was visualised to several disciplines involved in the WOCU project. This was done in Three Dimensional (3D) by using Virtual Reality (VR). This collaborative approach revealed the potential use cases of the Red, Green, Blue (RGB), cloud-to-cloud distance, and NDVI point clouds highlighting the potential of explorative point clouds.

Potential use cases that were found are; highly detailed area modeling, vegetation overgrowth monitoring, bank erosion detection, flora status assessment, monitoring of vegetation types, digital inspection of remote sites, participation medium, and identification of atrophied ground patches. Attributes added to point clouds enhanced insights. Especially the RGB point cloud sparked excitement due to its realistic appearance. The Cloud-to-Cloud Distance (C2CD) attribute showed potential, especially for erosion detection. However, due to the short timeframe between measurements, it could not be detected. The NDVI attribute was perceived as less interesting.

The use of explorative point clouds, generated from raw LiDAR point cloud data, offers potential uses and insights for floodplain maintenance. The interdisciplinary value of explorative point clouds was clearly visible. This thesis emphasizes that underused raw LiDAR data, by making it explorative, can act as a valuable resource.