Complex tsunamigenic near-trench seafloor deformation during the 2011 Tohoku-Oki earthquake.

The near-trench coseismic rupture behaviour of the 2011 Tohoku-Oki earthquake remains poorly understood due to the scarcity of near-field observations. Differential bathymetry offers a unique approach to studying offshore coseismic seafloor deformation but has a limited horizontal resolution. Here we use differential bathymetry estimates with improved horizontal resolutions to investigate near-trench coseismic slip behaviours in the 2011 Tohoku-Oki earthquake. In the main rupture region, a velocity-strengthening behaviour in the shallow fault is observed. By contrast, the seafloor uplift decreases towards the trench, but the trend inverts near the backstop interface outcrop, revealing significant off-fault deformation features. Amongst various competing off-fault effects observed, we suggest that inelastic deformation plays a predominant role in near-trench tsunami excitation. Large trench-bleaching rupture is also observed immediately north of 39°, delimiting the northern extent of the main rupture region. Overall, striking spatial heterogeneity of the shallow rupture behaviour is revealed for the region.

Deriving vertical profiles of chlorophyll-a concentration in the upper layer of seawaters using ICESat-2 photon-counting lidar.

Chlorophyll-a concentration (chl-a) is a great indicator for estimating phytoplankton biomass and productivity levels and is also particularly useful for monitoring the water quality, biodiversity and species distribution, and harmful algal blooms. A great deal of studies investigated to estimate chl-a concentrations using ocean color remotely sensed data. With the development of photon-counting sensors, spaceborne photon-counting lidar can compensate for the shortcomings of passive optical remote sensing by enabling ocean vertical profiling in low-light conditions (e.g., at night). Using geolocated photons captured by the first spaceborne photon-counting lidar borne on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2), this research reported methods for deriving vertical profiles of chl-a concentration in the upper layer of ocean waters. This study first calculates the average numbers of backscattered subaqueous photons of ICESat-2 at different water depths, and then estimates the optical parameters in water column based on a discrete theoretical model of the expected number of received signal photons. With the estimated optical parameters, vertical profiles of chl-a concentration are calculated by two different empirical algorithms. In two study areas (mostly with Type I open ocean waters and small part of Type II coastal ocean waters), the derived chl-a concentrations are generally consistent when validated by BGC-Argo (Biogeochemical Argo) data in the vertical direction (MAPEs<15%) and compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data in the along-track direction (average R2>0.86). Using globally covered ICESat-2 data, this approach can be used to obtain vertical profiles of chl-a concentration and optical parameters at a larger scale, which will be helpful to analyze impact factors of climate change and human activities on subsurface phytoplankton species and their growth state.

Hierarchical registration of laser point clouds between airborne and vehicle-borne data considering building eave attributes.

Laser point cloud registration is a key step in multisource laser scanning data fusion and application. Aimed at the problems of fewer overlapping regional features and the influence of building eaves on registration accuracy, a hierarchical registration algorithm of laser point clouds that considers building eave attributes is proposed in this paper. After extracting the building feature points of airborne and vehicle-borne light detection and ranging data, the similarity measurement model is constructed to carry out coarse registration based on pseudo-conjugate points. To obtain the feature points of the potential eaves (FPPE), the building contour lines of the vehicle-borne data are extended using the direction prediction algorithm. The FPPE data are regarded as the search set, in which the iterative closest point (ICP) algorithm is employed to match the true conjugate points between the airborne laser scanning data and vehicle-borne laser scanning data. The ICP algorithm is used again to complete the fine registration. To evaluate the registration performance, the developed method was applied to the data processing near Shandong University of Science and Technology, Qingdao, China. The experimental results showed that the FPPE dataset can effectively address the coarse registration accuracy effects on the convergence of the iterative ICP. Before considering eave attributes, the mean registration errors (MREs) of the proposed method in the xoz plane, yoz plane, and xoy plane are 0.318, 0.96, and 0.786 m, respectively. After considering eave attributes, the MREs decrease to 0.129, 0.187, and 0.169 m, respectively. The developed method can effectively improve the registration accuracy of the laser point clouds, which not only solves the problem of matching true conjugate points under the effects of the eaves but also avoids converging to a local minimum due to ICP's poor coarse registration.

Sub-pixel registration of multi-resolution imagery by correlation matching of the bathymetry-related features.

Multispectral imaging plays a significant role in coastal mapping and monitoring applications. For tasks involving the integration of multiple overlapped images, precise co-registration of the multisource satellite images is a crucial preliminary step. However, due to the limited terrestrial area and insufficient landscape features, the traditional methods become less efficient or even invalid in offshore island environments. This study addresses the problem by exploring the feasibility of using bathymetry information for geometric registration of satellite imagery. Instead of using the ground control points (GCPs) or extracting the tie points from the landscape features, the band ratio values are extracted from the multispectral images and are subsequently matched between different images through a correlation-based similarity measure. By searching the optimum correlation within the positioning uncertainty radius, the translation between two satellite images is estimated. Thus, the geometric inconsistency between the multispectral images of different sources and resolutions is effectively reduced. This result is obtained by using the ample bathymetry features without the aid of the GCPs and the in-situ bathymetry data. The experimental results using GeoEye-1, Sentinel-2, and Landsat-8 images at Ganquan Island show that for an island setting with a limited terrestrial area, the developed method achieves sub-pixel registration accuracy (less than 2 m) in planimetry. The effect of the nonlinearity and outliers are accounted for using the Spearman correlation measure. The improvement in image alignment enables the integration of multispectral images of different sources and resolutions for producing an accurate and consistent interpretation for coastal comparative and synergistic applications.

Effect Analysis of GNSS/INS Processing Strategy for Sufficient Utilization of Urban Environment Observations.

The occlusion of buildings in urban environments leads to the intermittent reception of satellite signals, which limits the utilization of observations. This subsequently results in a decline of the positioning and attitude accuracy of Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated system (GNSS/INS). This study implements a smooth post-processing strategy based on a tightly coupled differential GNSS/INS. Specifically, this strategy used the INS-estimated position to reinitialize integer ambiguity. The GNSS raw observations were input into the Kalman filter to update the measurement. The Rauch-Tung-Striebel smoothing (RTSS) algorithm was used to process the observations of the entire period. This study analyzed the performance of loosely coupled and tightly coupled systems in an urban environment and the improvement of the RTSS algorithm on the navigation solution from the perspective of fully mining the observations. The experimental results of the simulation data and real data show that, compared with the traditional tightly coupled processing strategy which does not use INS-aided integer ambiguity resolution and RTSS algorithm, the strategy in this study sufficiently utilized INS observations and GNSS observations to effectively improve the accuracy of positioning and attitude and ensure the continuity of navigation results in an obstructed environment.

Improved progressive triangular irregular network densification filtering algorithm for airborne LiDAR data based on a multiscale cylindrical neighborhood.

Laser point cloud filtering is a fundamental step in various applications of light detection and ranging (LiDAR) data. The progressive triangulated irregular network (TIN) densification (PTD) filtering algorithm is a classic method and is widely used due to its robustness and effectiveness. However, the performance of the PTD filtering algorithm depends on the quality of the initial TIN-based digital terrain model (DTM). The filtering effect is also limited by the tuning of a number of parameters to cope with various terrains. Therefore, an improved PTD filtering algorithm based on a multiscale cylindrical neighborhood (PTD-MSCN) is proposed and implemented to enhance the filtering effect in complex terrains. In the PTD-MSCN algorithm, the multiscale cylindrical neighborhood is used to obtain and densify ground seed points to create a high-quality DTM. By linearly decreasing the radius of the cylindrical neighborhood and the distance threshold, the PTD-MSCN algorithm iteratively finds ground seed points and removes object points. To evaluate the performance of the proposed PTD-MSCN algorithm, it was applied to 15 benchmark LiDAR datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission. The experimental results indicated that the average total error can be decreased from 5.31% when using the same parameter set to 3.32% when optimized. Compared with five other publicized PTD filtering algorithms, the proposed PTD-MSCN algorithm is not only superior in accuracy but also more robust.

A Positioning Error Compensation Method for a Mobile Measurement System Based on Plane Control.

Global navigation satellite system (GNSS)/inertial navigation system (INS) navigation technology is one of the core technologies in a mobile measurement system and can provide real-time geo-referenced information. However, in the environment measurements, buildings cover up the GNSS signal, causing satellite signals to experience loss-of-lock. At this time errors of INS independent navigation accumulate rapidly, so it cannot meet the needs of the mobile measurement system. In this paper, a positioning error compensation method based on plane control is proposed by analyzing the error characteristics of position and orientation caused by satellite signal loss-of-lock in the urban environment. This method control uses planar features and the laser point cloud positioning equation to establish an adjustment model that ignores the influence of the attitude error and finds the positioning error at the middle point of the GNSS signal loss-of-lock time period, and then compensates for the error at other points by using the characteristics of the positioning error. The experimental results show that the accuracy of the compensated laser point cloud has been significantly improved, and the feasibility of the method is verified. Meanwhile, the method can rely on the existing building plane so the method is adaptable and easy to implement.

Detecting the ocean surface from the raw data of the MABEL photon-counting lidar.

With much smaller footprints (approximately a few tens of meters), the data of a laser altimeter are promising for obtaining the sea level near offshore areas, where radar altimeters with larger footprints cannot operate. However, the current ocean surface detection methods for a photon-counting lidar cannot effectively eliminate the noise photons when measuring the sea surface, thereby introducing a ranging bias. In this paper, a new ocean surface detection method is derived based on the JONSWAP (Joint North Sea Wave Project) wave spectrum and LM (Levenberg-Marquardt) nonlinear least-squares fitting. Using the data photons that are captured by the NASA MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar, the new method is tested and compared to the MABEL standard result. The new method achieved better profile detection of sea surfaces and successfully discarded the noise photons in a sub-layer below the sea surface from the MABEL standard result. By reconstructing the "accumulated waveform", we found that the noise photons in the sub-layer produce small tails after the main waveform, which introduces an overestimated ranging bias of 9 cm. This difference of 9 cm is similar to the sea level bias of 10 cm that was obtained from the ICESat/GLAS laser altimeter data and the TOPEX/Poseidon radar altimeter data in an earlier study, which limited the use of laser altimeter data. According to the analysis in this paper, we can partially interpret what occurred for the ICESat/GLAS waveform tails when ICESat was measuring sea surfaces. The newly derived method can protect the MABEL and incoming ICESat-2 data photons from noise photon interference and ranging bias when measuring the sea surface.

Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea.

Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about seafloor features and are important data sources representing seafloor topography and geomorphology. Currently, to classify seafloor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify seafloor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new seafloor classification method that merges multiple features is proposed for a beneficial exploration of acousto-optic fusion. The experimental results of the seafloor classification around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classification accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.

Theoretical surface type classifier based on a waveform model of a satellite laser altimeter and its performance in the north of Greenland.

Current land-cover classification methods using ICESat/GLAS's (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) datasets are based on empirical thresholds or machine learning by training multiple GLAS parameters, e.g., the reflectivity and elevation of the target and width, amplitude, kurtosis, and skewness of the return waveform. A theoretical classifier is derived based on a waveform model of an actual laser altimeter illuminating the sea surface. With given system parameters and the sea surface wind corresponding to the location of a laser footprint (the wind can be calculated by using the National Centers for Environmental Prediction dataset), a precise theoretical waveform can be generated as a reference. Compared with the measured waveform, a weighted total difference, which is very sensitive to small-scale sea ice within the laser footprint, can be calculated to classify the GLAS measured data as open water. In the north of Greenland, after discarding the saturated GLAS data, the new theoretical classifier performed better [overall accuracy (OA)=95.62%, Kappa coefficient=0.8959] compared to the classical support vector machine (SVM) classifier (OA=90.44%, Kappa=0.7901), but the SVM classifier showed a better result for the user's accuracy of sea ice. Benefiting from the synergies of the theoretical and SVM classifiers, the integrated theoretical and SVM classifier achieved excellent accuracy (OA=98.21%, Kappa=0.9588). In the future, the new ICESat-2 photon counting laser altimeter will also construct a "waveform" (elevation distribution) by selecting the photon cloud, and thus, this new analytical method will be potentially useful for detecting open water in the Arctic.

Variability of particle size distribution with respect to inherent optical properties in Poyang Lake, China.

Suspended particulate matter plays a significant role in the studies of sediment fluxes, phytoplankton dynamics, and water optical properties. This study focuses on the relationships between particle size distribution (PSD), water's inherent optical properties (IOPs), and water constituents. We investigated the complex waters of Poyang Lake, the largest freshwater lake in China, in wet and dry seasons during 2008-2011. Because of the distinct temporal-spatial variation of Poyang Lake, these parameters and relationships also demonstrate seasonal and regional variability. The variation range of the concentration of suspended particulate matter is 0.32-69.08 mg/l, with a mean value of 22.21 mg/l. The median particle size in the dry season is much larger than that of the wet season. The Junge distribution fits the PSD of Poyang Lake very well in the scope of 6.21-331 µm. Furthermore, the slopes of the PSD range from 3.54 to 4.69, with a mean value of 4.11, with the steepest slopes (>4.5) occurring in the waters around Songmen Mountain Island and the northern waterway. A negative correlation was found between median particle size (Dv50) and the mass-specific absorption coefficient at 443 nm [apm(443)] for both wet and dry seasons. Identical to analogous waters, the spectral slopes of the PSD correlate well with the spectral slopes of the attenuation coefficient, but with different fitted formulas. In the dry season, the particle size can better explain the variability of the scattering coefficient, while the mass-specific scattering coefficient is better explained by the apparent density. However, no similar results were found for the wet season. In addition, the spectral slopes of the backscattering coefficient correlated well with the PSD slope, and the bulk refractive index calculated from the backscattering ratio and PSD slope can indicate the particle composition of Poyang Lake. Overall, the knowledge on the PSD and IOPs gained in this study broadens our understanding of water optics in highly turbid water columns.