Search Results (59)

In airborne LiDAR measurements of shallow water bathymetry, conventional data processing often overlooks the radiative losses associated with multiple scattering events, affecting detection accuracy. This study presents a Monte Carlo-based approach to construct a mathematical model that accurately characterizes the multiple returns in airborne laser bathymetric systems. The model enables rapid simulation of laser propagation through water, accounting for multiple scattering events. Based on the Beer–Lambert law and incorporating the parameters of typical Jerlov 1 clear coastal water, the proposed model achieves a seamless integration of the H-G phase function with a Monte Carlo random process, enabling accurate simulation and validation of pulse temporal broadening in waters with varying optical transparency. Unlike most existing studies, which primarily focus on modeling the laser emission process, this work introduces a novel perspective by analyzing the probability of light reception in LiDAR return signals, offering a more comprehensive understanding of signal attenuation and detection performance in underwater environments. The results demonstrate that, for detecting underwater targets at depths of 10 m, considering three or more scattering events improves the accuracy by ~7%. For detecting underwater targets at depths of 50 m, considering three or more scattering events improves the accuracy by 15~33%. These findings can help enhance the detection accuracy and efficiency of experimental systems. Full article

The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a photon-counting detection mode with a 532 nm laser to obtain high-precision Earth surface elevation data and offers a new remote sensing method for nearshore bathymetry. The key issues in using ATLAS ATL03 data for bathymetry are achieving automatic and accurate extraction of signal photons in different water environments. Especially for areas with sharply fluctuating topography, the interaction of various impacts, such as topographic fluctuations, sea waves, and laser pulse direction, can result in a sharp change in photon density and distribution at the seafloor, which can cause the signal photon detection at the seafloor to be misinterpreted or omitted during analysis. Therefore, an improved size and direction adaptive filtering (ISDAF) method was proposed for nearshore bathymetry using ATLAS ATL03 data. This method can accurately distinguish between the original photons located above the sea surface, on the sea surface, and the seafloor. The size and direction of the elliptical density filter kernel automatically adapt to the sharp fluctuations in topography and changes in water depth, ensuring precise extraction of signal photons from both the sea surface and the seafloor. To evaluate the precision and reliability of the ISDAF, ATLAS ATL03 data from different water environments and seafloor terrains were used to perform bathymetric experiments. Airborne LiDAR bathymetry (ALB) data were also used to validate the bathymetric accuracy and reliability. The experimental findings show that the ISDAF consistently exhibits effectiveness in detecting and retrieving signal photons, regardless of whether the seafloor terrain is stable or dynamic. After applying refraction correction, the high accuracy of bathymetry was evidenced by a strong coefficient of determination (R²) and a low root mean square error (RMSE) between the ICESat-2 bathymetry data and ALB data. This research offers a promising approach to advancing remote sensing technologies for precise nearshore bathymetric mapping, with implications for coastal monitoring, marine ecology, and resource management. Full article

The traditional method for the establishment of the green laser underwater transmission model is purely based on the laser transmission mechanism in waterbodies, while neglecting a few exterior conditions. This paper proposes a novel method to establish the underwater transmission model from a maximum measurement depth perspective by refining the dynamic relationship between the effective received power P_A and the background noise power P_B. Different from the traditional empirical model of fixed P_A/P_B, this method combines the sensor, flight, and environmental parameters of airborne LiDAR (ALB) to achieve the dynamic calibration of P_A and P_B. In particular, the empirical relationship between the maximum underwater measurement depth and the laser attenuation coefficient, coupled parameters, etc., is considered. The established model is verified by different types of experiments. The experimental results discovered that the errors are approximately 0.86 m and 1.28, under the same water conditions, when compared to the existing models. The validation experiments demonstrated that the errors for the maximum depth prediction were 0.38 m (indoor tank), 1.58 m (indoor swimming pool), 0.44 m (Li River, Guangxi), and 1.20 m (Beibu Gulf, Pacific Ocean). The experimental results demonstrated that the established model enables us to widely predict the maximum water depth measurable using an airborne LiDAR under different environmental conditions. Full article

Coastal areas have been the target of interdisciplinary research aiming to support studies related to their socio-economic and ecological value and their role in protecting backshore ecosystems and assets from coastal erosion and flooding. Some of these studies focus on either onshore or inshore areas using sensors and collecting valuable information that remains unknown and untapped by other researchers. This research demonstrates how satellite, aerial, terrestrial and marine remote sensing techniques can be integrated and inter-validated to produce accurate information, bridging methodologies with different scope. High-resolution data from Unmanned Aerial Vehicle (UAV) data and multispectral satellite imagery, capturing the onshore environment, were utilized to extract underwater information in Coral Bay (Cyprus). These data were systematically integrated with hydroacoustic including bathymetric and side scan sonar measurements as well as ground-truthing methods such as drop camera surveys and sample collection. Onshore, digital elevation models derived from UAV observations revealed significant elevation and shoreline changes over a one-year period, demonstrating clear evidence of beach modifications and highlighting coastal zone dynamics. Temporal comparisons and cross-section analyses displayed elevation variations reaching up to 0.60 m. Terrestrial laser scanning along a restricted sea cliff at the edge of the beach captured fine-scale geomorphological changes that arise considerations for the stability of residential properties at the top of the cliff. Bathymetric estimations derived from PlanetScope and Sentinel 2 imagery returned accuracies ranging from 0.92 to 1.52 m, whilst UAV reached 1.02 m. Habitat classification revealed diverse substrates, providing detailed geoinformation on the existing sediment type distribution. UAV data achieved 89% accuracy in habitat mapping, outperforming the 83% accuracy of satellite imagery and underscoring the value of high-resolution remote sensing for fine-scale assessments. This study emphasizes the necessity of extracting and integrating information from all available sensors for a complete geomorphological and marine habitat mapping that would support sustainable coastal management strategies. Full article

In the active–passive fusion-based bathymetry inversion method using single-temporal images, image data often suffer from errors due to inadequate atmospheric correction and interference from neighboring land and water pixels. This results in the generation of noise, making high-quality data difficult to obtain. To address this problem, this paper introduces a multi-temporal image fusion method. First, a median filter is applied to separate land and water pixels, eliminating the influence of adjacent land and water pixels. Next, multiple images captured at different times are fused to remove noise caused by water surface fluctuations and surface vessels. Finally, ICESat-2 laser altimeter data are fused with multi-temporal Sentinel-2 satellite data to construct a machine learning framework for coastal bathymetry. The bathymetric control points are extracted from ICESat-2 ATL03 products rather than from field measurements. A backpropagation (BP) neural network model is then used to incorporate the initial multispectral information of Sentinel-2 data at each bathymetric point and its surrounding area during the training process. Bathymetric maps of the study areas are generated based on the trained model. In the three study areas selected in the South China Sea (SCS), the validation is performed by comparing with the measurement data obtained using shipborne single-beam or multi-beam and airborne laser bathymetry systems. The root mean square errors (RMSEs) of the model using the band information after image fusion and median filter processing are better than 1.82 m, and the mean absolute errors (MAEs) are better than 1.63 m. The results show that the proposed method achieves good performance and can be applied for shallow-water terrain inversion. Full article

“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable spatial density distribution of a photon to a linear relationship with water depth, causing a limited extraction effect. To address this limitation, we propose a two-stage filtering method that considers spatial relationships. Stage one constructs the adaptive photon density threshold by mapping a nonlinear relationship between the water depth and photon density to obtain initial signal photons. Stage two adopts a seed-point expanding method to fill gaps in initial signal photons to obtain continuous signal photons that more fully reflect seabed topography. The proposed method is applied to ICESat-2 data from Oahu Island and compared with three other density-based filtering methods: AVEBM (Adaptive Variable Ellipse filtering Bathymetric Method), Bimodal Gaussian fitting, and Quadtree Isolation. Our method (F-measure, F = 0.803) outperforms other methods (F = 0.745, 0.598, and 0.454, respectively). The accuracy of bathymetric data gained from seabed photons filtered using our method can achieve 0.615 m (Mean Absolute Error) and 0.716 m (Root Mean Squared Error). We demonstrate the effectiveness of incorporating photon spatial relationships to enhance the filtering of seabed signal photons. Full article

Nearshore sandbars are dynamic features that characterize shallow morphobathymetry and vary over a wide range of geometries and temporal lifespans. Nearshore sandbars influence beach geometry by altering the energy of incoming waves; thus, monitoring the evolution of sandbars is a fundamental approach in effective coastal planning. Due to several natural and technical limitations related to shallow seafloor mapping, there is a significant gap in the availability of high-resolution, shallow bathymetric data for monitoring the dynamic behaviour of nearshore sandbars effectively. This study introduces a novel image-processing technique that produces time series of pseudo-bathymetric data by utilizing multi-temporal (monthly) drone imagery, and it provides an assessment of local morphodynamics at a sandy beach in the southeast Mediterranean. The technique is called standardized-ratio bathymetric index (SRBI), and it transforms natural-colour drone imagery to pseudo-bathymetric data by applying an empirical formula used for satellite-derived bathymetry. This technique correlates well with laser altimetry depth measurements; however, it does not require in situ depth data for implementation. The resulting pseudo-bathymetric data allows for extracting cross-shore profiles and delineating the sandbar crest with 4 m horizontal accuracy. Stacking of temporal profiles allowed for the quantification of the sandbar’s crest and trough changes at different alongshore sections. The main findings suggest that the nearshore crescentic sandbar at Episkopi Beach (north Crete) shows strong seasonality regarding net offshore migration that is promoted by enhanced wave action during winter months. In addition, the crescentic sandbar is susceptible to morphology arrestment during prolonged weeks of low wave action. The average migration rate during winter is 10 m.month⁻¹, with some sections exhibiting a maximum of 60 m.month⁻¹. This study (a) offers a novel remote-sensing approach, suitable for nearshore seafloor monitoring with low computational complexity, (b) reveals sandbar geometry and temporal change in superior detail compared to other observational methods, and (c) advances knowledge about nearshore sandbar monitoring in the Mediterranean region. Full article

This paper presents a Compact Integrated Water–Land Survey System (CIWS), which combines a remote sensing camera and a LiDAR module, and proposes an innovative underwater topography retrieval technique based on this system. This technique utilizes high-precision water depth points obtained from LiDAR measurements as control points, and integrating them with the grayscale values from aerial photogrammetry images to construct a bathymetry retrieval model. This model can achieve large-scale bathymetric retrieval in shallow waters. Calibration of the UAV-mounted LiDAR system was conducted using laboratory and Dongjiang Bay marine calibration fields, with the results showing a laser depth measurement accuracy of up to 10 cm. Experimental tests near Miaowan Island demonstrated the generation of high-precision 3D seabed topographic maps for the South China Sea area using LiDAR depth data and remote sensing images. The study validates the feasibility and accuracy of this integrated scanning method for producing detailed 3D seabed topography models. Full article

Submerged aquatic vegetation, also referred to as submerged macrophytes, provides important habitats and serves as a significant ecological indicator for assessing the condition of water bodies and for gaining insights into the impacts of climate change. In this study, we introduce a novel approach for the classification of submerged vegetation captured with bathymetric LiDAR (Light Detection And Ranging) as a basis for monitoring their state and change, and we validated the results against established monitoring techniques. Employing full-waveform airborne laser scanning, which is routinely used for topographic mapping and forestry applications on dry land, we extended its application to the detection of underwater vegetation in Lake Constance. The primary focus of this research lies in the automatic classification of bathymetric 3D LiDAR point clouds using a decision-based approach, distinguishing the three vegetation classes, (i) Low Vegetation, (ii) High Vegetation, and (iii) Vegetation Canopy, based on their height and other properties like local point density. The results reveal detailed 3D representations of submerged vegetation, enabling the identification of vegetation structures and the inference of vegetation types with reference to pre-existing knowledge. While the results within the training areas demonstrate high precision and alignment with the comparison data, the findings in independent test areas exhibit certain deficiencies that are likely addressable through corrective measures in the future. Full article

Due to the limitations of measurement equipment and the influence of factors such as the environment and target, measurement errors may occur during the data acquisition process of airborne LiDAR bathymetry (ALB). The refractive index of water is defined as the propagation ratio of the speed of light waves in a vacuum to that in water; this ratio influences not only the propagation speed of the laser pulse in water but also the propagation direction of the laser pulse entering water. Therefore, the influence of refractive index changes in water on the ALB errors needs to be analyzed. To this end, the principle of ALB is first briefly introduced. Then, the calculation method for the refractive index of water is described with Snell’s law and an empirical formula. Finally, the influence of refractive index changes on ALB errors is analyzed using the derived formula at the water–air interface and in the water column. The experimental results showed that in a constant elevation of 50 m for a bathymetric floor, the refractive index changes in water caused by temperature, salinity, and depth are less than 0.001. The maximum bathymetric error and maximum planimetric error caused by the refractive index changes at the water–air interface are 0.036 m and 0.015 m, respectively. The ALB errors caused by refractive index changes in the water column are relatively low, and the water column does not need to be layered to calculate the ALB errors. The influence of refractive index changes in water on the ALB error is minimal, accounting for only a small proportion of all bathymetric errors. Thus, it is necessary to determine whether the effect of the ALB error due to refractive index changes in water needs to be corrected based on the accuracy requirements of the data acquisition. This study and analysis can provide a reference basis for correcting ALB errors. Full article

The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through the water with high precision and accuracy if the bottom peak intensity of the waveform is greater than the noise level. However, passive optical imaging of optically shallow water involves measuring the radiance after the sunlight undergoes downward attenuation on the way to the sea floor, and the reflected light is then attenuated while moving back upward to the water surface. The difficulty of satellite-derived bathymetry (SDB) arises from the fact that the measured radiance is a result of a complex association of physical elements, mainly the optical properties of the water, bottom reflectance, and depth. In this research, we attempt to apply physics-based algorithms to solve this complex problem as accurately as possible to overcome the limitation of having only a few known values from a multispectral sensor. Major analysis components are atmospheric correction, the estimation of water optical properties from optically deep water, and the optimization of bottom reflectance as well as the water depth. Specular reflection of the sky radiance from the water surface is modeled in addition to the typical atmospheric correction. The physical modeling of optically dominant components such as dissolved organic matter, phytoplankton, and suspended particulates allows the inversion of water attenuation coefficients from optically deep pixels. The atmospheric correction and water attenuation results are used in the ocean optical reflectance equation to solve for the bottom reflectance and water depth. At each stage of the solution, physics-based models and a physically valid, constrained Levenberg–Marquardt numerical optimization technique are used. The physics-based algorithm is applied to Landsat Operational Land Imager (OLI) imagery over the shallow coastal zone of Guam, Key West, and Puerto Rico. The SDB depths are compared to airborne lidar depths, and the root mean squared error (RMSE) is mostly less than 2 m over water as deep as 30 m. As the initial choice of bottom reflectance is critical, along with the bottom reflectance library, we describe a pure bottom unmixing method based on eigenvector analysis to estimate unknown site-specific bottom reflectance. Full article

The recently developed Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2), furnished with the Advanced Terrain Laser Altimeter System (ATLAS), delivers considerable benefits in providing accurate bathymetric data across extensive geographical regions. By integrating active lidar-derived reference seawater depth data with passive optical remote sensing imagery, efficient bathymetry mapping is facilitated. In recent times, machine learning models are frequently used to define the nonlinear connection between remote sensing spectral data and water depths, which consequently results in the creation of bathymetric maps. A salient model among these is the convolutional neural network (CNN), which effectively integrates contextual information concerning bathymetric points. However, current CNN models and other machine learning approaches mainly concentrate on recognizing mathematical relationships within the data to determine a water depth function and remote sensing spectral data, while oftentimes disregarding the physical light propagation process in seawater before reaching the seafloor. This study presents a physics-informed CNN (PI-CNN) model which incorporates radiative transfer-based data into the CNN structure. By including the shallow water double-band radiative transfer physical term (swdrtt), this model enhances seawater spectral features and also considers the context surroundings of bathymetric pixels. The effectiveness and reliability of our proposed PI-CNN model are verified using in situ data from St. Croix and St. Thomas, validating its correctness in generating bathymetric maps with a broad experimental R² accuracy exceeding 95% and remaining errors below 1.6 m. Preliminary results suggest that our PI-CNN model surpasses conventional methodologies. Full article

The coastal zone faces an ever-growing risk associated with climate-driven change, including sea level rise and increased frequency of extreme natural hazards. Often the location and dynamism of coastal regions makes them a formidable environment to adequately study with in-situ methods. In this study we use Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) to make measurements of basic wave parameters and wave directionality in the coastal zones of the Hawaiian Islands and North Carolina, USA. Our goal was to leverage as much of the full resolution data available in the ATL03 data product to generate wave metrics out from shore up to ~25 km. Using a combination of statistical and signal processing methods, including cross-correlation and wavelets, we can use ICESat-2 to generate basic wave metrics, including significant wave heights with an accuracy of ±0.5 m. In some profiles we can identify wave shoaling, which could be useful to infer bathymetry and coastal dynamics. In areas with complex wave dynamics, the nature of how ICESat-2 measures elevations (parallel laser altimetry beams) can make extracting some wave parameters, especially wavelength and directionality, more challenging. These wave metrics can provide important data in support of validating wave and tidal models and may also prove useful in extended ICESat-2 applications like bathymetric corrections and satellite-derived bathymetry. Full article

Accurate bathymetric data in shallow water is of increasing importance for navigation safety, coastal management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustic measurements in coastal areas, providing high spatial and temporal resolution combined with extensive repetitive coverage. Many previous empirical SDB approaches are unsuitable for precision bathymetry mapping in various scenarios, due to the assumption of homogeneous bottom over the whole region, as well as the neglect of various interfering factors (e.g., turbidity) causing radiation attenuation. Therefore, this study proposes a bottom-type adaption-based SDB approach (BA-SDB). Under the consideration of multiple factors including suspended particulates and phytoplankton, it uses a particle swarm optimization improved LightGBM algorithm (PSO-LightGBM) to derive depth of each pre-segmented bottom type. Based on multispectral images of high spatial resolution and in situ observations of airborne laser bathymetry and multi-beam echo sounder, the proposed approach is applied in shallow water around Yuanzhi Island, and achieves the highest accuracy with an RMSE value of 0.85 m compared to log-ratio, multi-band, and classical machine learning methods. The results of this study show that the introduction of water-environment parameters improves the performance of the machine learning model for bathymetric mapping. Full article

The second-generation spaceborne LiDAR-Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) carries the Advanced Topographic Laser Altimeter System (ATLAS), which can penetrate a certain depth of water, and is one of the important means to obtain the water depth information of nearshore water. However, due to the influence of the atmospheric environment, water quality and color, the system itself and other factors, the photon point cloud introduces survey noise, which restricts the survey accuracy and reliability of nearshore water depth. Therefore, in this study, we presented a photon denoising algorithm for layered processing of submarine surface. Firstly, rough denoising of the original photon data was completed by smoothing filtering. Then, elevation histogram statistics were carried out on the photon data, two peaks of the histogram were fitted by a double Gaussian function, and the intersection of two curves was then taken to separate the water surface and underwater photons. The surface photons were denoised by the DBSCAN clustering algorithm. Then according to the distribution characteristics of underwater signal photons, a single-photon point cloud filtering bathymetric method was proposed based on improved local distance statistics (LDSBM), which was used for fine denoising of underwater point cloud data. Finally, the Gaussian function was used to fit the frequency histogram, and the signal photons were screened to extract the water depth information. In this study, 13 groups of the ATL03 dataset from the Xisha Islands, the St. Thomas and the Acklins Island were used for denoising. The denoising results were compared with the signal photons manually marked and the signal photons extracted by the official built-in method (OM). The experimental results showed that, compared with the official method results of ATL03, the LDSBM had a higher F value (comprehensive evaluation index), with an average of more than 96.70%. In conclusion, the proposed underwater single-photon point cloud filtering bathymetric method was superior to the traditional algorithm and could recover terrain information accurately. Full article