Abstract
Light detection and ranging (LiDAR) technique is currently one of the most important tools for collecting elevation points with a high density in the context of digital elevation model (DEM) construction. However, the high density data always leads to serious time and memory consumption problems in data processing. In this paper, we have developed a thin plate spline (TPS)-based feature-preserving (TPS-F) method for LiDAR-derived ground data reduction by selecting a certain amount of significant terrain points and by extracting geomorphological features from the raw dataset to maintain the accuracy of constructed DEMs as high as possible, while maximally keeping terrain features. We employed four study sites with different topographies (i.e., flat, undulating, hilly and mountainous terrains) to analyze the performance of TPS-F for LiDAR data reduction in the context of DEM construction. These results were compared with those of the TPS-based algorithm without features (TPS-W) and two classical data selection methods including maximum z-tolerance (Max-Z) and the random method. Results show that irrespective of terrain characteristic, the two versions of TPS-based approaches (i.e., TPS-F and TPS-W) are always more accurate than the classical methods in terms of error range and root means square error. Moreover, in terms of streamline matching rate (SMR), TPS-F has a better ability of preserving geomorphological features, especially for the mountainous terrain. For example, the average SMR of TPS-F is 89.2% in the mountainous area, while those of TPS-W, max-Z and the random method are 56.6%, 34.7% and 35.3%, respectively.
Highlights
The light detection and ranging (LiDAR) technique is currently one of the most prominent and effective tools for capturing high-density elevation data sets [1,2,3,4]
In order to overcome the shortcoming of linear interpolation in critical point selection and feature line integration, the aim of this paper is to propose a thin plate spline (TPS)-based feature-preserving method (TPS-F)
An area located in the Heihe River basin (HRB) in the arid region of northwest China was selected as the study site
Summary
The light detection and ranging (LiDAR) technique is currently one of the most prominent and effective tools for capturing high-density elevation data sets [1,2,3,4]. LiDAR data in the Netherlands has approximately 640 billion points with the density of 6–10 points/m2, and an approximately 1 km of a Dublin city center point cloud has 225 million points with the density of 225 points/m2 [5]. Such a huge amount of data always leads to serious time and memory problems in data processing, storage, visualization and transmission [6,7]. It should be noted that the points located on non-ground objects such as trees, buildings, cars and bridges in the raw LiDAR data set must be filtered before DEM generation [8]. Our data density optimization method only deals with the randomly distributed ground points derived from LiDAR
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