Abstract:The Dutch coast is characterized by sandy beaches flanked by dunes. Understanding the morphology of the coast is essential for defense against flooding of the hinterland. Because most dramatic changes of the beach and the first dune row happen during storms, it is important to assess the state of the coast immediately after a storm. This is expensive and difficult to organize with Airborne Laser Scanning (ALS). Therefore, the performance of a Land-based Mobile Mapping System (LMMS) in mapping a stretch of sandy Dutch coast of 6 km near the municipality of Egmond is evaluated in this research. A test data set was obtained by provider Geomaat using the StreetMapper LMMS system. Both the relative quality of laser point heights and of a derived Digital Terrain model (DTM) are assessed. First, the height precision of laser points is assessed a priori by random error propagation, and a posteriori by calculating the height differences between close-by points. In the a priori case, the result is a theoretical laser point precision of around 5 cm. In the a posteriori approach it is shown that on a flat beach a relative precision of 3 mm is achieved, and that almost no internal biases exist. In the second analysis, a DTM with a grid size of 1 m is obtained using moving least squares. Each grid point height includes a quality description, which incorporates both measurement precision and terrain roughness. Although some problems remain with the scanning height of 2 m, which causes shadow-effect behind low dunes, it is concluded that a laser LMMS enables the acquisition of a high quality DTM product, which is available within two days.

Commission VI, WG VI/4 KEY WORDS: terrestrial laser scanning, joint roughness, range noise, discrete wavelet transform, stationary wavelet transform, denoising performance ABSTRACT:The precision of Terrestrial Laser Scanning (TLS) data depends mainly on the inherent random range error, which hinders extraction of small details from TLS measurements. New post processing algorithms have been developed that reduce or eliminate the noise and therefore enable modelling details at a smaller scale than one would traditionally expect. The aim of this research is to find the optimum denoising method such that the corrected TLS data provides a reliable estimation of small-scale rock joint roughness. Two wavelet-based denoising methods are considered, namely Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), in combination with different thresholding procedures. The question is, which technique provides a more accurate roughness estimates considering (i) wavelet transform (SWT or DWT), (ii) thresholding method (fixed-form or penalised low) and (iii) thresholding mode (soft or hard). The performance of denoising methods is tested by two analyses, namely method noise and method sensitivity to noise. The reference data are precise Advanced TOpometric Sensor (ATOS) measurements obtained on 20×30 cm rock joint sample, which are for the second analysis corrupted by different levels of noise. With such a controlled noise level experiments it is possible to evaluate the methods' performance for different amounts of noise, which might be present in TLS data. Qualitative visual checks of denoised surfaces and quantitative parameters such as grid height and roughness are considered in a comparative analysis of denoising methods. Results indicate that the preferred method for realistic roughness estimation is DWT with penalised low hard thresholding.

Rock joint roughness characterization is often an important aspect of rock engineering projects. Various methods have been developed to describe the topography of the joint surface, for example Joint Roughness Coefficient (JRC) correlation charts or disc-clinometer measurements. The goal of this research is to evaluate the accuracy, precision and limits of Terrestrial Laser Scanning (TLS) for making remote measurements of large-scale rock joints. In order to find the most appropriate roughness parameterization method for TLS data and to analyse the capability of TLS for roughness estimation, experiments were made with a 20 × 30 cm joint sample. The sample was scanned with TLS and compared to reference measurements made with the Advanced TOpometric Sensor (ATOS) system. Analysis of two roughness parameterization methods, virtual compass and disc-clinometer, and angular threshold method, showed that the latter is less sensitive to noise. Comparative studies of ATOS and TLS roughness parameters indicate that the TLS can adequately quantify surface irregularities with a wavelength greater than 5 mm from a distance of 10 m.

Surface roughness represents a major component of rock discontinuity shear strength. To achieve comprehensive, accurate, and efficient estimates of in situ discontinuity roughness, the traditional contact measuring methods are being replaced by advanced remote-sensing technologies. Terrestrial laser scanner (TLS) is well suited for measuring large inaccessible discontinuities; however, inherent TLS range noise strongly influences the surface details and roughness estimation. The aim of this research is to establish an optimal wavelet-denoising procedure for the TLS data acquired with different scanning configurations (range and incidence angle), and for rock discontinuities having different roughness characteristics and surface reflectivity. The conventional discrete wavelet transform and stationary wavelet transform in combination with four threshold selection methods are applied on TLS data in the direction of range measurements (range denoising) and in the direction perpendicular to the best-fit plane (surface denoising). The performance of the denoising procedures is assessed by comparing the range and surface-denoised TLS surfaces with reference surfaces acquired with the Advanced TOpometric Sensor. Comparative analyses of the roughness calculated according to the angular thresholding method (Grasselli, in Shear strength of rock joints based on quantified surface description, Ph.D. thesis. EPF Lausanne, Lausanne; Grasselli, Shear strength of rock joints based on quantified surface description, Ph.D. thesis, EPF Lausanne, Lausanne, 2001) indicate that all the denoising methods improve the roughness estimated from the TLS data appreciably; however, the level of improvement depends intrinsically on geometrical characteristics of the rock surface and scanning configuration. Range denoising has been found to provide more reliable noise estimations.