Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Abstract

Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain.

A New Adaptive Method for the Extraction of Steel Design Structures from an Integrated Point Cloud

“…As demonstrated in [ 68 ], cloud pre-filtration is very important and enables the isolation of vital infrastructure elements. Pre-filtration was also applied in [ 69 ]. Pre-filtration consists of four stages: noise filtering, cloth simulation filtering [ 70 ] (CSF), data reduction and statistical outlier removal (SOR) filtering.…”

A Method for Tree Detection Based on Similarity with Geometric Shapes of 3D Geospatial Data

“…For generating the grids, we selected the natural neighbor interpolation method with ground belonging to the DSM [59]. This choice was in line with recent studies [60], proving that this interpolation method yields the most accurate models. Finally, we clipped regular grids corresponding to Sites 1 and 2 from the CHM and the DSM.…”

Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

“…The wetland areas #b1 and #b2 in Figure 9 were also oxbow lakes, but in the next phase of the succession, the proportion of open water was lower, and according to the CLC classification, these lakes were interpreted as wetlands (as these smaller lakes can run dry during dry summers [67], the classification can be accepted). The wetland area #b3 ( Figure 9) was a set of swales and point bars (i.e., negative and positive fluvial forms) with different water cover and height of vegetation, where the reflectance pattern can be similar both to grasslands and eutrophic lakes [68,69]. The wetland area #b4 (Figure 9) was also a set of swales and point bars (consisting of two different parts, of which the northern part was the older with transformed forms), while the fifth wetland area (Figure 9/b5) was a floodplain lake with its surroundings (open water with aquatic vegetation and marshlands).…”