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.
Highlights
Digital terrain models (DTMs) are effective and important tools of environmental investigations, engineering, and planning [1,2,3]
DTMs are often used for the management of natural risks, e.g., assessments of inundation exposure or volcanic active areas, especially if these areas are populated and involve infrastructure [4,5,6] There are several ways to produce these models, such as interpolating surfaces from surveyed field data or vectorized contours of maps and using the principles of stereo photogrammetry, SfM technique; the most dynamically developing technique is the application of airborne LiDAR/ALS (LiDAR—light detection and ranging; ALS—airborne laser scanning), which provides a three-dimensional point cloud stored in binary LAS
This study provides a brief description of point cloud processing from noise reduction to digital terrain generation
Summary
Digital terrain models (DTMs) are effective and important tools of environmental investigations, engineering, and planning [1,2,3]. DTMs are often used for the management of natural risks, e.g., assessments of inundation exposure or volcanic active areas, especially if these areas are populated and involve infrastructure [4,5,6] There are several ways to produce these models, such as interpolating surfaces from surveyed field data or vectorized contours of maps and using the principles of stereo photogrammetry (airborne and satellite), SfM (structure from motion) technique; the most dynamically developing technique is the application of airborne LiDAR/ALS (LiDAR—light detection and ranging; ALS—airborne laser scanning), which provides a three-dimensional point cloud stored in binary LAS (LiDAR archive standard) format [7,8,9,10,11,12,13,14,15]. Sensors 2020, 20, 2063 of the difficulties, there could be several ways to filter out the noise and the ground points of a three-dimensional point cloud before DTM generation; the final results can be improved and can be used for most purposes. Noise filtering can be based on principal component analysis [19], neighborhood distance [20,21], or distance from surface [22]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
