Andreas Roncat

Landslide Displacement Monitoring Using 3D Range Flow on Airborne and Terrestrial LiDAR Data

An active landslide in Doren, Austria, has been studied by multitemporal airborne and terrestrial laser scanning from 2003 to 2012. To evaluate the changes, we have determined the 3D motion using the range flow algorithm, an established method in computer vision, but not yet used for studying landslides. The generated digital terrain models are the input for motion estimation; the range flow algorithm has been combined with the coarse-to-fine resolution concept and robust adjustment to be able to determine the various motions over the landslide. The algorithm yields fully automatic dense 3D motion vectors for the whole time series of the available data. We present reliability measures for determining the accuracy of the estimated motion vectors, based on the standard deviation of components. The differential motion pattern is mapped by the algorithm: parts of the landslide show displacements up to 10 m, whereas some parts do not change for several years. The results have also been compared to pointwise reference data acquired by independent geodetic measurements; reference data are in good agreement in most of the cases with the results of range flow algorithm; only some special points (e.g., reflectors fixed on trees) show considerably differing motions.

Regularizing method for the determination of the backscatter cross section in lidar data

The retrieval of the backscatter cross section in lidar data is of great interest in remote sensing. For the numerical calculation of the backscatter cross section, a deconvolution has to be performed; its determination is therefore an ill-posed problem. Most of the common techniques, such as the well-known method of Gaussian decomposition, make implicit assumptions on both the emitted laser pulse and the scatterers. It is well understood that a land surface is quite complicated, and in many cases it cannot be composed of pure Gaussian function combinations. Therefore the assumption of Gaussian decomposition of waveforms may be invalid sometimes. In such cases an inversion method might be a natural choice. We propose a regularizing method with a posteriori choice of the regularizing parameter for solving the problem. The proposed method can alleviate difficulties in numerical computation and can suppress the propagation of noise. Numerical evidence is given of the success of the approach presented for recovering the backscatter cross section in lidar data.

Radiometrically Calibrated Features of Full-Waveform Lidar Point Clouds Based on Statistical Moments

Full-waveform lidar has gained increasing attention in 3-D remote sensing and related disciplines during the last decade due to its capability of delivering both geometric and radiometric information in the same spatial resolution. Radiometric information may either be related to the echo, e.g., echo amplitude and width, or to the target itself, e.g., the backscatter cross section (BCS). Echo parameters, often obtained by Gaussian decomposition, as well as target properties, which are (geo)physical properties and therefore independent of data acquisition mission parameters, are considered as additional features of the point cloud generated by laser scanning. The BCS commonly is derived by performing a deconvolution which results in its temporal derivative, the differential backscatter cross-section (dBCS), and subsequent integration. The temporal shape of the dBCS has gained little attention in the literature so far. In this letter, we discuss the derivation of additional target parameters, namely the statistical moments of the respective target dBCS. Besides discussing the applicability of established deconvolution approaches for the extraction of statistical moments in the dBCS, special emphasis is laid on their derivation in B-spline-based deconvolution. Uniform B-splines allow for linear deconvolution and subsequent radiometric calibration. We illustrate the potential of the proposed method by a sample data set stemming from an airborne lidar campaign in complex mountainous terrain.

Full-Waveform Airborne Laser Scanning Systems and Their Possibilities in Forest Applications

Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data acquisition around the year 2004. These systems typically digitize the analogue backscattered echo of the emitted laser pulse with a high frequency. FWF digitization has the advantage of not limiting the number of echoes that are recorded for each individual emitted laser pulse. Studies utilizing FWF data have shown that more echoes are provided from reflections in the vegetation in comparison to discrete echo systems. To obtain geophysical metrics based on ALS data that are independent of a mission’s flying height, acquisition time or sensor characteristics, the FWF amplitude values can be calibrated, which is an important requirement before using them in further classification tasks. Beyond that, waveform digitization provides an additional observable which can be exploited in forestry, namely the width of the backscattered pulse (i.e. echo width). An early application of FWF ALS was to improve ground and shrub echo identification below the forest canopy for the improvement of terrain modelling, which can be achieved using the discriminative capability of the amplitude and echo width in classification algorithms. Further studies indicate that accuracies can be increased for classification (e.g. species) and biophysical parameter extraction (e.g. diameter at breast height) for single-tree- and area-based methods by exploiting the FWF observables amplitude and echo width.

Laser Pulse Interaction with Forest Canopy: Geometric and Radiometric Issues

This chapter focuses upon retrieving forest biophysical parameters by extracting three-dimensional point cloud information from small-footprint full-waveform airborne laser scanner data. This full waveform gives the end user the possibility to gain control over range determination and the subsequent derivation of the point clouds. Furthermore, the attribution of physical parameters to the single points using these waveforms becomes additionally possible. The underlying physical principles form the begin of this chapter, followed by forward modeling of waveforms over simulated forested areas, the treatment of real waveforms and an example for validating the results of full-waveform analysis.

A linear approach for radiometric calibration of full-waveform Lidar data

During the past decade, small-footprint full-waveform lidar systems have become increasingly available, especially airborne. The primary output of these systems is high-resolution topographic information in the form of three-dimensional point clouds over large areas. Recording the temporal profile of the transmitted laser pulse and of its echoes enables to detect more echoes per pulse than in the case of discrete-return lidar systems, resulting in a higher point density over complex terrain. Furthermore, full-waveform instruments also allow for retrieving radiometric information of the scanned surfaces, commonly as an amplitude value and an echo width stored together with the 3D coordinates of the single points. However, the radiometric information needs to be calibrated in order to merge datasets acquired at different altitudes and/or with different instruments, so that the radiometric information becomes an object property independent of the flight mission and instrument parameters. State-of-the-art radiometric calibration techniques for full-waveform lidar data are based on Gaussian Decomposition to overcome the ill-posedness of the inherent inversion problem, i.e. deconvolution. However, these approaches make strong assumptions on the temporal profile of the transmitted laser pulse and the physical properties of the scanned surfaces, represented by the differential backscatter cross-section. In this paper, we present a novel approach for radiometric calibration using uniform B-splines. This kind of functions allows for linear inversion without constraining the temporal shape of the modeled signals. The theoretical derivation is illustrated by examples recorded with a Riegl LMS-Q560 and an Optech ALTM 3100 system, respectively.