Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment

Abstract

Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land-water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). The general processing steps involve data filtering, water surface detection and refraction correction. Specifically, the procedure of water surface detection and modelling, solely using green laser lidar data, has not previously been described in detail for tidal environments. The aim of this study was to fill this gap of knowledge by developing a step-by-step procedure for making a digital water surface model (DWSM) using the green laser lidar data. The detailed description of the processing procedure augments its reliability, makes it user-friendly and repeatable. A DEM was obtained from the processed topobathymetric lidar data collected in spring 2014 from the Knudedyb tidal inlet system in the Danish Wadden Sea. The vertical accuracy of the lidar data is determined to ±8 cm at a 95 % confidence level, and the horizontal accuracy is determined as the mean error to ±10 cm. The lidar technique is found capable of detecting features with a size of less than 1 m 2 . The derived high-resolution DEM was applied for detection and classification of geomorphometric and morphological features within the natural environment of the study area. Initially, the bathymetric position index (BPI) and the slope of the DEM were used to make a continuous classification of the geomorphometry. Subsequently, stage (or elevation in relation to tidal range) and a combination of statistical neighbourhood analyses (moving average and standard deviation) with varying window sizes, combined with the DEM slope, were used to classify the study area into six specific types of morphological features (i.e. subtidal channel, intertidal flat, intertidal creek, linear bar, swash bar and beach dune). The developed classification method is adapted and applied to a specific case, but it can also be implemented in other cases and environments.

Ecohydraulic Modelling to Support Fish Habitat Restoration Measures

“…Whereas conventional methods for mapping bathymetry can provide accurate measurements, they can have limitations due to restricted accessibility, safety precautions and time required [13,14] to fully cover the interested areas. In the other hand, ALB data requires post-processing, including filtering and removal noise and false echoes, water surface detection and correction for the refraction [15]. ALB surveys are affected by environmental conditions such as floods, rain and snow and by water turbidity, since dissolved and suspended organic material affect negatively the river bottom reflection [16].…”

“…These ground points were already filtered by AHM who removed the raw data noise originated from the laser being scattered by birds, clouds, dust and other particles. The filtering process involved both automatic and manual filtering (see [15] for more details). In addition, vegetation was also removed from the point cloud by AHM in the pre-processing step.…”

The application of data innovations to geomorphological impact analyses in coastal areas: An East Anglia, UK, case study

“…However, the spatial resolution and accuracy of data obtainable by these methods is lower than that possible using MBES. Another alternative, is bathymetric Lidar (Andersen et al, 2017;Eisemann et al, 2018), this is can provide higher resolution data than satellite or X-band radar, yet is limited by meteorological and ocean conditions. The use of remote sensing data offers significant accuracy improvements over the more manual and traditional methods required to analyse historic analogue datasets, which also rely on the skills of an individual, and can lead potentially to high levels of uncertainty (Burningham and French, 2011).…”

A method for estimating sediment budgets of washover deposits using digital terrain models

“…Based on the classification methodology of Weiss (2001), Wright et al (2005) developed a geomorphometric classification tool, that is the Benthic Terrain Modeler (BTM) for classifying benthic terrain using bathymetry data. Several classification schemes using the BTM have since been established (e.g., Andersen et al, 2017; Diesing et al, 2009; Lundblad et al, 2006; Verfaillie et al, 2007). Such classifications use threshold‐values of surface derivatives to convey interpretation of morphometric classes (Drăguţ & Eisank, 2011), that is land‐surface segmentation.…”