Using LiDAR derivatives to estimate sediment grain size on beaches in False Bay

Abstract

Coastal environments form where the land, sea and atmosphere meet and interact in unique and dynamic ways. A state of dynamic equilibrium is ideally maintained by the natural ocean processes of wind, waves and tides. Human interference, leading to global and climate change in particular, continues to have a big impact on coastal environments. The vulnerability of the sandy coast is dependent on the physical characteristics such as orientation/exposure, beach slope, and sand grain size. These characteristics are also dependent on each other; for calm beaches, flatter slopes are prevalent with a finer grain size; and conversely with steeper beaches which are usually exposed to higher wave energy, grain size tends to be larger. Knowledge about one of these parameters (beach slope, grain size, wave energy) can therefore theoretically be used as a proxy to predict the other factors. This information would be of particular interest for coastal protection and disaster risk management. Field assessments and surveys are, however, expensive and often impossible in a country such as South Africa with a long and largely inaccessible coast. Remote sensing technologies and LiDAR (light detection and ranging) in particular, hold much potential for the assessment of relevant physical beach parameters. The intensity characteristic of LiDAR scanning essentially represents the amount of energy reflected from the target of the scan and is affected by the composition and roughness of the surface. The work that will be presented coupled field measurements of slope and grain size to LiDAR derived slope and intensity to determine whether these can be used as a proxy to predict beach grain size. This would provide a valuable tool for the spatial assessment of beach vulnerability on a national scale. The initial findings of this study will be presented at this conference.