Full-waveform airborne LiDAR bathymetry (ALB), which provides waveforms and point clouds, has become an essential technology for shallow water surveys. However, weak seafloor echoes are challenging to detect accurately because of waveform feature confusion caused by the complex measurement environments. To address this issue, waveform feature importances, feature histograms, and feature spaces of 14-dimensional waveform features are conducted to analyze the waveform feature confusion. Then, a random forest with optimized thresholds (RFOT) is proposed to detect normal seafloor echoes and weak seafloor echoes. Finally, waveform sharpening and condition screening are used to extract the seafloor echoes for overlapping waveforms in very shallow waters. The proposed method was verified with 14 swaths obtained by the Optech Aquarius system around Wuzhizhou Island. The results show that the energy features (area under curve, amplitude, etc.) can better discriminate the difference between weak seafloor echoes and noise than the shape features (RL area ratio, kurtosis, etc.). The number of seafloor echoes detected by the proposed method increased by 148.86% compared with the Aquarius system. The reference data prove that seafloor points detected by the proposed method are accurate and effective. Thus, this contribution effectively improves the bathymetric performance of the ALB system.

Space-borne photon-counting light detection and ranging (LiDAR) can obtain high-precision bathymetry information for nearshore waters. However, the signals obtained by the space-borne photon-counting LiDAR contain a large amount of noise due to atmospheric scattering, solar radiation and instrumental noise. Therefore, the denoising of the space-borne photon-counting LiDAR is a key component in the acquisition of bathymetric information. With respect to the difficulty of accurately denoise the density heterogeneity photons in different water depths, this paper proposes a hierarchical multiscale denoising method for space-borne photon-counting LiDAR based on minimum spanning tree. First, sea surface photons and water-column photons are obtained separately based on the kernel density estimation. Second, the minimum spanning tree is constructed and split based on the spatial distribution of water-column photons. In the process of splitting the minimum spanning tree, the length threshold of the interrupted branches is adaptively calculated. Then, water-column photons are denoised based on the hierarchical multiscale subtree node judgment strategy. Finally, water-column photon denoising results are refined using a box plot outlier detection method. The proposed method is compared with some state-of-the-art photon denoising algorithms using six trajectory data under various seafloor terrains. The experimental results demonstrate that the proposed method is better than other photon denoising algorithms, and can obtain accurately denoising results for different density distributions of photons under different seafloor terrains.

Based on Precipitable Water Vapor (PWV) obtained from five coastal Global Navigation Satellite System (GNSS) stations, this study investigates the characteristics and interrelationships of sea surface waves and atmospheric parameters during seawater encroachment in the Bohai and Yellow Seas of China in mid-October 2024. Results indicate that the correlation coefficient between GNSS PWV and ERA5 PWV data is 0.97 (p< 0.01), with a mean bias of -3.28 mm and a standard deviation of 2.70 mm. During the seawater encroachment, three notable increases in PWV were observed, with peaks ranging from 30 to 55 mm, all accompanied by precipitation (less than 10 mm). The PWV change rates varied spatially, with the Yellow Sea coast experiencing faster changes than the Bohai Sea coast. Under strong wind conditions, sea surface wave characteristics were manifested by increased wave heights and longer wave periods, alongside an increased whitecap coverage of breaking waves (3%–5%). The temporal variation curves reveal an inverse relationship between PWV and wave variables, with PWV lagging both breaking and non-breaking waves by approximately 3–12 h. Meanwhile, the process involves enhanced water vapor evaporation and intensified air-sea heat exchange. These factors affected the weighted mean temperature and caused a continuous decrease in PWV. Consequently, zonal differences in the spatial distribution of PWV were observed. By linking GNSS PWV with wave variables, this research introduces a novel insight for the study of air-sea interaction processes and coastal hazard monitoring under extreme weather events.

Since the advent of decentralized financial applications based on blockchains, new attacks that take advantage of manipulating the order of transactions have emerged. To this end, order fairness protocols are devised to prevent such order manipulations. However, existing order fairness protocols adopt time-consuming mechanisms that bring huge computation overheads and defer the finalization of transactions to the following rounds, eventually compromising system performance. In this work, we present Auncel, a novel consensus protocol that achieves both order fairness and high performance. Auncel leverages a weight-based strategy to order transactions, enabling all transactions in a block to be committed within one consensus round, without cost computation and further delays. Furthermore, Auncel achieves censorship resistance by integrating the consensus protocol with the fair ordering strategy, ensuring all transactions can be ordered fairly. To reduce the overheads introduced by the fair ordering strategy, we also design optimization mechanisms, including dynamic transaction compression and adjustable replica proposal strategy. We implement a prototype of Auncel based on HotStuff and construct extensive experiments. Experimental results show that Auncel can increase the throughput by 6× and reduce the confirmation latency by 3× compared with state-of-the-art order fairness protocols.

Enteromorpha prolifera is as a frequent marine ecological environment disaster. How to quickly and accurately monitor Enteromorpha prolifera is of great significance to its management and protection of the marine ecological environment. The detection of Enteromorpha prolifera from high spatial resolution remote sensing images (HSRIs) is an important technical means for monitoring Enteromorpha prolifera disasters. With respect to the difficulty of accurate detection of Enteromorpha prolifera area boundary in HSRIs, this paper proposes an Enteromorpha prolifera detection method for HSRIs based on boundary-assisted dual-path convolutional neural networks (CNN). First, a large-scale HSRIs Enteromorpha prolifera detection dataset, FIO-EP, is created and made publication to facilitate the field of HSRIs Enteromorpha prolifera detection. Then, a boundary-assisted dual-path CNN framework is designed to detect Enteromorpha prolifera in HSRIs based on the shape distribution characteristics of Enteromorpha prolifera. In the CNN framework, accurate detection of Enteromorpha prolifera areas in HSRIs is achieved by fusing initial detection and boundary detection results of Enteromorpha prolifera. The proposed method is compared with some state-of-the-art Enteromorpha prolifera detection algorithms using the FIO-EP dataset. The experimental findings demonstrate that the proposed method can obtain 88.28% F1-score and 79.02% intersection-over-union (IOU), and is superior to other state-of-the-art Enteromorpha prolifera detection algorithms.

Global Navigation Satellite System-Acoustic (GNSS-A) technology has been widely used in ocean engineering and ocean environmental science. Accurate sound travel time modeling is essential for GNSS-A seafloor positioning. Currently, half of the two-way travel time (TWTT) has been used as an approximation for the one-way travel time (OWTT). In this work, the time error of the approximate OWTT is investigated under different survey configurations, and a sequential GNSS-A seafloor positioning method using the extended Kalman filter (EKF) is developed to investigate the impact of sound travel time modeling. Simulations show that the time error induced under the static survey configuration is less than 0.6 ms; the time error induced under the circle survey configuration with a stable inclination angle is stable, but the time error of the line survey configuration can reach 28 ms. As confirmed through field experiments, sequential GNSS-A seafloor positioning using TWTT modeling is more stable than OWTT modeling. The positioning residuals of TWTT modeling are similar to those of OWTT modeling under the circle configuration but at least 2 times less than those of OWTT modeling under the line survey configuration. Furthermore, the average positioning residuals of OWTT and TWTT modeling can be greatly reduced for a survey configuration combining circular and linear tracks. These findings provide a feasible method for improving the precision and efficiency of GNSS-A seafloor positioning.

Airborne laser bathymetry (ALB) systems with digital full-waveform signal collection can obtain corresponding temporal positions from several backscattering surfaces by laser beam irradiation. This information can help describe the multi-elevation structures of the target and explore the echo signal attenuation response in different nonuniform mediums during laser propagation. Therefore, a full-waveform echo signal is quite practical for integrated water-land detection. However, the wavelength used in the ALB system is generally in the visible band range of 470~580 nm, and the received signal is constantly interfered with by many nontarget factors, such as imperfections in the receiving channel or the strong scattering from the transmission medium. The conventional processing method transforms nontarget interference into noise point cloud filtering or classification extraction, enabling the detection of a single surface or regular geometry. The accuracy of the identification and extraction for multi-elevation target surfaces echo signal is always reduced due to the significant noise signal intensity. We proposed a signal component detection method by constructing the echo signal feature functions and the conditional random field (CRF) model based on the full-waveform decomposition. The processing result for actual measurement data verified that the CRF strategy can effectively reduce the uncertainty of target surface detection. Compared with the single-beam echo sounder, the root mean square errors of the elevation deviation underwater were reduced by 3.2 cm and 4.9 cm respectively in the two different experimental areas.