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Over the past decades, plenty of filtering algorithms have been presented to distinguish ground and non-ground points from airborne LiDAR point clouds. However, with the existing methods, it is difficult to derive satisfactory filtering results on rugged terrains with dense vegetation due to the low-level penetration ability of laser pulses. Therefore, a multi-level interpolation-based filter is developed in this paper. The novelty of the algorithm lies within its usage of multi-scale morphological operations and robust z-score to correctly select ground seeds as more as possible, and a terrain-adaptive residual threshold to adapt to various terrain characteristics. Rural samples provided by International Society for Photogrammetry and Remote Sensing (ISPRS) were employed to assess the performance of the proposed method and its results were compared with 15 filtering algorithms developed in recent 5 years (2015-2019). Results show that the proposed method with the optimized parameters produces the best accuracy with the average total error and kappa coefficient of 1.89% and 87.88%, respectively. We further filtered high-density point clouds in six forested areas with different vegetation covers and terrain slopes. Results demonstrate that the proposed algorithm is more accurate than the well-known filtering methods including morphological-based filter, progressive TIN densification filter (PTD), improved PTD and cloth simulation filter, with the average total error decreased by 26.2%, 19.9%, 3.8% and 40.4%, respectively. Moreover, the DEMs of the proposed method have lower average root mean square errors than the four classical filters. Therefore, the proposed method can be considered as an effective ground filtering algorithm for airborne LiDAR point clouds in forested areas.

The high noise levels of high-rate Global Navigation Satellite System (GNSS) solutions limit their seismological applications, including capturing earthquake-induced coseismic displacements. In this study, we developed a new adaptive denoising approach for high-rate GNSS observations to improve the precision of seismic displacements and preserve seismic waveforms in GNSS coseismic signals. The performance of the proposed method was evaluated using high-rate (5 Hz) GNSS data acquired from the moderate EI Mayor-Cucapah earthquake (Mw 7.2) on 4 April 2010 and the small Brawley seismic swarm (Mw 4.6-5.5) on 26 August 2012. The performance of the proposed method was compared with those of modified sidereal filtering, Stacking filtering, and MSF plus Stacking. The comparison showed that the proposed method is more precise than other widely used method. It can significantly remove high-frequency white noise and low-frequency colored noise caused by CME, multipath errors, and/or other unmodeled systematic errors in high-rate GNSS displacements. The results were also compared with collocated strong motion data (50 and 200 Hz). The high precision of the proposed method was mainly afforded by the high performance of complete ensemble empirical mode decomposition, which was used to decompose the GNSS signal into different frequency modes. However, the normalized autocorrelation function and correlation coefficients used to determine the noise-dominated high-frequency modes and the “wavelet-like” soft-threshold used for direct denoising of the noise-dominated high-frequency modes also contributed. Despite the high noise levels of GNSS solutions, especially regarding the vertical displacement components, some small-amplitude details, which are usually only detectable by seismic instruments, could be observed in the denoised displacements in this study. The results reported herein indicate that the proposed method significantly improves the precision and reliability of GNSS displacements and the effectiveness of seismic signal detection, which is particularly critical for the measurement of earthquake-induced coseismic displacements.