Shallow Water Bathymetry Research Articles

SAR Imaging Features of Shallow Water Bathymetry

Purpose. The aim of the article is to study manifestations of the underwater topography features in the northern part of the White Sea in the images made by the spaceborn synthetic aperture radars (SAR) Sentinel-1A, Sentinel-1B. Methods and Results. In the SAR images, the bottom features are revealed as bright and dark brightness anomalies. The anomalies were observed at the wind speed ranging from 2.6 to 10.8 m/s, and became reverse (bright anomalies turned dark and vice versa), when a tidal current changed its direction. It is shown that the observed SAR imagery contrasts correlate to divergence of a current formed by interaction of a tidal flow with the bottom topography inhomogeneities. The simulated SAR contrasts agree with the observations, and confirm the relation between the observed SAR contrasts and the current divergence. Together with modeling the SAR contrasts, the contribution of different mechanisms to formation of the observed modulations of the normalized radar cross section was qualitatively estimated. The wave breaking effect was especially accentuated. The method for retrieving the underwater bottom topography based on the relationship between the bottom gradient and the SAR imagery contrasts is proposed. Conclusions. Location of the bottom inhomogeneities in the shallow water region on the whole coincides with that of the tidal current divergence and convergence zones, which are observed as brightness anomalies in the SAR images. Breaking of surface waves is the main contributor to the observed SAR contrasts. The bottom topography reconstructed from the SAR contrasts, and the actual one resulted from the bathymetry maps are in good agreement. Some discrepancies are interpreted as possible changes in depth and shape of the bottom topography features induced by action of strong currents and waves.

Shallow Water Bathymetry Derived from Green Wavelength Terrestrial Laser Scanner

Shallow water bathymetry has proved to be a challenging task for remote sensing applications. In this work, Green-Wavelength Terrestrial Laser Scanning (GWTLS) is employed to survey nearshore bathymetry under clear atmospheric and water conditions. First, the obtained seabed points were corrected for refraction and then geo-registration, and filtering processes were exerted to obtain an accurate bathymetric surface. Terrain analysis was performed with respect to a reference surface derived from classical surveying techniques. The overall analysis has shown that the best results stem from 35° to 50° incident angles, whereas for angles higher than 65° measurements are not acceptable, although for the same angle in front and close to the instrument accuracy is considered acceptable due to the high laser power. Also, high resolution micro-topography, shallower than 1 m water depth, was managed to be captured. Systematic experimental approaches are expected to improve the GWTLS technique to detect bathymetry, which is anticipated to assist in mapping very shallow foreshore, tidal, and deltaic environments, to contribute conceptual into developing hybrid observation systems for coastal monitoring, and also to be applied in various maritime applications.

The Contribution of Multispectral Satellite Image to Shallow Water Bathymetry Mapping on the Coast of Misano Adriatico, Italy

The results of absolute satellite-derived bathymetry (SDB) are presented in the current study. A comparative analysis was conducted on empirical methods in order to explore the potential of SDB in shallow water on the coast of Misano, Italy. Operations were carried out by relying on limited in situ water depth data to extract and calibrate bathymetry from a QuickBird satellite image acquired on a highly dynamic coastal environment. The image was processed using the log-band ratio and optimal band ratio analysis (OBRA) methods. Preprocessing steps included the conversion of the raw satellite image into top of atmosphere reflectance, spatial filtering, land and water classification, the determination of the optimal OBRA spectral band pairs, and the estimation of relative SDB. Furthermore, calibration and vertical referencing were performed via in situ bathymetry acquired in November 2007. The relative bathymetry obtained from different band ratios were vertically referenced to the local datum using in situ water depth in order to obtain absolute SDB. The coefficient of determination (R2) and vertical root mean square error (RMSE) were computed for each method. A strong correlation with in situ field bathymetry was observed for both methods, with R2 = 0.8682 and RMSE = 0.518 m for the log-band ratio method and R2 = 0.8927–0.9108 and RMSE = 0.35 m for the OBRA method. This indicated a high degree of confidence of the SDB results obtained for the study area, with a high performance of the OBRA method for SDB mapping in turbid water.

Combining Low-Cost Sonar and High-Precision Global Navigation Satellite System for Shallow Water Bathymetry

Periodic bathymetry surveys are essential to provide data to keep navigation charts updated, obtain insights into water body bottom dynamics and processes, and for hydrodynamic modelling. Frequent bathymetry monitoring has become particularly important in a time of climate variability, which may affect hydrodynamics in yet unknown ways. Bathymetric data are, however, often scarce, because surveys are generally time consuming, expensive and complicated. A methodology combining a low-cost single beam sonar with a dual-frequency differential high-precision GNSS (Global Navigation Satellite System) is presented. Sonar depth measurements and GNSS positions were integrated optimizing sonar and GNSS track overlay. As a result, no physical, electronic link between both devices is needed, and precise positions and depths can be obtained without the need to apply the approach based on tide correction, which always introduces some uncertainty. The methodology was successfully tested and validated, with data collected inside an estuary and offshore the estuarine inlet. Vertical accuracies, assessed at track crossings and on locations of known depths, showed mean squared errors of about 20 cm, suggesting that the method is reliable in providing bathymetric data that satisfy the highest standards of the IHO for hydrographic surveys. Validation results suggest that the effects of boat pitch, roll and yaw on depth measurements were negligible in our survey, which covered depths between 0.4 and 24.5 m below MSL and were carried out in quite calm waters, though larger errors occurred in the off-shore zone. The use of an inertial measurement unit (IMU), which can easily be coupled with the GNSS to extract ship motion data and correct depths accordingly, is advised for less optimal survey conditions and deeper waters. The proposed method is accurate, simple and affordable, allowing for more frequent surveys and a better coverage of dynamic shallow water systems such as rivers and estuaries.

激光雷达测绘卫星发展及应用

The rapid development of laser altimetry satellite has a great impact on the traditional satellite earth observation field. The accurate elevation data can effectively compensate the lack of optical satellites, while laser altimetry satellite data processing and mapping applications are worthy of attention. Firstly, the principle and characteristics of laser altimetry satellite was introduced, and the its development history was presented. Then, the current on-orbit LiDAR satellite ICESat-2 was focued on, which had a high degree of attention. The satellite configuration characteristics and data processing methods were discussed, and its surveying and mapping capabilities in many fields, such as image joint adjustment, multi-source terrain fusion, global vegetation survey, and shallow water bathymetry et al. were analyzed and revealed. Finally, some thoughts about the development and construction of laser altimetry satellite in China were shared.

Bathymetry Model Based on Spectral and Spatial Multifeatures of Remote Sensing Image

Multispectral methods for remote sensing image have been widely applied to shallow water bathymetry by researchers. In nonideal conditions, even with the same spectral radiance, the points still have a very wide range of water depths. This means that spectral features alone are insufficient for water bathymetry. Hence, we need to extract other valuable features from a remote sensing image. This letter introduces a spatial feature for water bathymetry using remote sensing images. We propose a model that utilizes a multilayer perceptron (MLP) to integrate the spectral and spatial location features. Experimental results demonstrate that the proposed model yields a substantial performance improvement. The mean relative error is only 8.41%, and the root mean square error is reduced by 34%–68% when compared with three other models. Furthermore, the proposed model addresses well the problems caused by heterogeneous bottom types.

Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models

ABSTRACT Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R2 of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.

Technical Framework for Shallow-Water Bathymetry With High Reliability and No Missing Data Based on Time-Series Sentinel-2 Images

Shallow-water bathymetry based on multispectral satellite imagery (MSI) is an important technology for depth measurement, but it is difficult to obtain a bathymetric map with high reliability and no missing data because of the ubiquitous image noise. Here, we propose a time-series-based bathymetry framework (TSBF). First, a pixel-level time series is constructed using remote sensing images collected at multiple points in time. Then, a new time-domain denoising method, the maximum outlier removal method, is used to create an optimal image from this time series. Finally, bathymetric inversion is performed using this optimal image to obtain a bathymetric map. Anda Reef and northeastern Jiuzhang Atoll, which have complex noise features, were selected as test cases to validate the proposed framework. Results show that the proposed TSBF can obtain bathymetric maps with high accuracy, reliability, and no missing data, outperforming the conventional bathymetry framework based on a single image.

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

The determination of accurate bathymetric information is a key element for near offshore activities; hydrological studies, such as coastal engineering applications, sedimentary processes, hydrographic surveying, archaeological mapping and biological research. Through structure from motion (SfM) and multi-view-stereo (MVS) techniques, aerial imagery can provide a low-cost alternative compared to bathymetric LiDAR (Light Detection and Ranging) surveys, as it offers additional important visual information and higher spatial resolution. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image-based refraction correction algorithms or by modifying the collinearity equation. In this article, in order to overcome the water refraction errors in a massive and accurate way, we employ machine learning tools, which are able to learn the systematic underestimation of the estimated depths. In particular, an SVR (support vector regression) model was developed, based on known depth observations from bathymetric LiDAR surveys, which is able to accurately recover bathymetry from point clouds derived from SfM-MVS procedures. Experimental results and validation were based on datasets derived from different test-sites, and demonstrated the high potential of our approach. Moreover, we exploited the fusion of LiDAR and image-based point clouds towards addressing challenges of both modalities in problematic areas.

Using overlapping VIIRS scenes to observe short term variations in particulate matter in the coastal environment

In coastal areas, the concentrations and the optical properties of the water components have a large spatial and temporal variability, due to river discharges and meteo-marine conditions, such as wind, wave and current, and their interaction with shallow water bathymetry. This large temporal variability cannot be captured using the standard Ocean Colour Radiometry (OCR) polar orbiting satellites, the latter providing almost one image per day. On the contrary, the use of OCR geostationary sensors, like the Geostationary Ocean Colour Imager (GOCI), centred above the Korean Peninsula, enable to capture the short-term variability of the optical properties. To compensate the lack of a geostationary sensor similar to GOCI over other coastal environments, like the North Adriatic Sea (NAS), the multiple observations provided during the same day by the Visible Infrared Imaging Radiometer Suite (VIIRS) mounted on the SUOMI NPP satellite, can be exploited. Indeed, due to its large swath of 3060 km, the VIIRS orbits can overlap over the NAS during the same day within 1 h and 42 min, an important feature that can be useful in capturing the short term variability of the optical properties.A large number of VIIRS overlaps in the NAS are characterized by high sensor zenith angle (SZA) of the observation, resulting in a large portion of images masked by the high satellite zenith flag. In order to make available those observations and, in general, to reduce the dependence of the VIIRS observations from the SZA, an adjustment based on a multi linear regression scheme, which exploits radiometric in situ observations, was here applied.This study aims to prove the suitability of the adjusted overlapping VIIRS in capturing the short time scale dynamics of particulate backscattering, and this was demonstrated by the analysis of a case study for the 21st and 22nd of March 2013. In order to evaluate the advantages in using multiple observations during the same day, also the ~24 h dynamics was analysed, comparing the overlapping VIIRS results with the ones obtained from the daily product.

Through-Water Dense Image Matching for Shallow Water Bathymetry

The introduction of Dense Image Matching (DIM) has reactivated the interest in photogrammetric surface mapping, as it allows the derivation of Digital Elevation Models with a spatial resolution in the range of the ground sampling distance of the aerial images. While the primary field of application is wide-area mapping of topography and urban scenes, charting bathymetry of clear and shallow water areas is equally feasible via application of multimedia photogrammetry. The article specifically investigates the potential of through-water DIM for high resolution mapping of generally low textured shallow water areas using modern techniques like semiglobal matching and off-the-shelf software. In a case study, the DIM-derived underwater surfaces of coastal and inland water bodies are compared to concurrently acquired laser bathymetry data. With an achieved penetration depth of more than 5 m and deviations in the dm-range compared to the laser data as reference, the results confirm the general feasibility of through-water DIM. However, sufficient bottom texture and favorable environmental conditions are a precondition for achieving accurate results.

Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images

Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. Although many studies have been conducted on satellite derived bathymetry (SDB), previously used methods basically require supervised data for analysis, and cannot be used to analyze areas that are unreachable by boat or airplane. In this study, a mapping method for shallow water bathymetry was developed, using random forest machine learning and multi-temporal satellite images to create a generalized depth estimation model. A total of 135 Landsat-8 images, and a large amount of training bathymetry data for five areas were analyzed with the Google Earth Engine. The accuracy of SDB was evaluated by comparison with reference bathymetry data. The root mean square error in the final estimated water depth in the five test areas was 1.41 m for depths of 0 to 20 m. The SDB creation system developed in this study is expected to be applicable in various shallow water regions under highly transparent conditions.

Comparison of remote sensing based approaches for mapping bathymetry of shallow, clear water rivers

Shallow rivers provide important habitat for various aquatic and terrestrial species. The bathymetry of such environments is, however, difficult to measure as devices and approaches have been traditionally developed mainly for deeper waters. This study addresses the mapping of shallow water bathymetry with high spatial resolution and accuracy by comparing three remote sensing (RS) approaches: one based on echo sounding (active RS) and two on photogrammetry (passive RS): bathymetric Structure from Motion (SfM) and optical modelling. The tests were conducted on a 500 m long and ~30 m wide reach of sand-bedded meandering river: (1) during a rising spring flood (Q = 10–15 m3/s) with medium turbidity and high water color and; (2) during autumn low discharge (Q = 4 m3/s) with low turbidity and color. Each method was used to create bathymetric models. The models were compared with high precision field measurements with a mean point spacing of 0.86 m. Echo sounding provided the most accurate (ME~−0.02 m) and precise (SDE = ± 0.08 m) bathymetric models despite the high degree of interpolation needed. However, the echo sounding-based models were spatially restricted to areas deeper than 0.2 m and no small scale bathymetric variability was captured. The quality of the bathymetric SfM was highly sensitive to flow turbidity and color and therefore depth. However, bathymetric SfM suffers less from substrate variability, turbulent flow or large stones and cobbles on the river bed than optical modelling. Color and depth did affect optical model performance, but clearly less than the bathymetric SfM. The optical model accuracy improved in autumn with lower water color and turbidity (ME = −0.05) compared to spring (ME = −0.12). Correlations between the measured and modelled depth values (r = 0.96) and the models precision (SDE = 0.09–0.11) were close to those achieved with echo sounding. Shadows caused by riparian vegetation restricted the spatial extent of the optical models.

Shallow water bathymetry from WorldView-2 stereo imagery using two-media photogrammetry

ABSTRACT Two-media photogrammetry appears to be a useful technology for mapping shallow water depth. The objective of this paper is to explore the feasibility of shallow water bathymetry using satellite two-media photogrammetry. This paper presents a two-media photogrammetry technique for WorldView-2 stereo multispectral imagery. In this method, near-infrared band is used for sun glint elimination. A combination of Scale Invariant Feature Transform and Semi-Global Matching algorithm is adopted for dense stereo matching. Rational Function Model is applied for raw Digital Elevation Model generation and the water surface elevation is determined by interpolating water edge elevation. Finally, the approximate refraction correction model is adopted in the water region to correct the vertical coordinate offsets. Our experimental results of Ganquan Island and Zhaoshu Island show that a reasonable depth estimate with relative error less than 17% and 14% can be obtained for the water depth range between 5 and 20 m, and the root-mean-square error are 2.09 m and 1.76 m, respectively. However, less accurate estimate is found for the very shallow water depth range between 0 and 5 m. The overall accuracy of satellite two-media photogrammetry is at the same level with the traditional multispectral-based model for bottom depth retrieval.

Satellite-derived bathymetry: A case study of Mombasa Port Channel and its approaches, Kenya

Bathymetry refers to the depth of the water column in relation to sea level. It is fundamental in marine spatial plan - ning, resource exploration and sustainable management of marine resources. It is also vital for safety of navigation, and planning of coast-based infrastructural developments. However, acquisition of bathymetry data is very expen - sive due to the cost of equipment, expertise and technology needed to collect data and produce maps. Satellite-de- rived bathymetry (SDB) therefore offers an opportunity to generate shallow water bathymetry at extremely reduced costs, mainly due to freely-available multispectral satellite imagery and open-source processing software. This paper presents the application of an already developed and published shallow-water bathymetry derivation model and protocols. The results indicate that the technique could be effective for mapping shallow water bathymetry, with higher accuracy in low to non-turbid waters. The SDB of Mombasa Port was identical to the official charted depths upon comparison, with a R 2 value of ~90% and a RMSE of 1.61 m. SDB maps can be categorized as medium resolu - tion due to their relatively low spatial resolution. SDB cannot be used as a stand-alone hydrographic tool but it rather offers a viable reconnaissance solution for mapping shallow coastal waters where hydrographic data gaps exist.

Bathymetry retrieval from optical images with spatially distributed support vector machines

This paper presents a spatially distributed support vector machine (SVM) system for estimating shallow water bathymetry from optical satellite images. Unlike the traditional global models that make predictions from a unified global model for the entire study area, our system uses locally trained SVMs and spatially weighted votes to make predictions. By using IKONOS-2 multi-spectral image and airborne bathymetric LiDAR water depth samples, we developed a spatially distributed SVM system for bathymetry estimates. The distributed model outperformed the global SVM model in predicting bathymetry from optical satellite images, and it worked well at the scenarios with a low number of training data samples. The experiments showed the localized model reduced the bathymetry estimation error by 60% from RMSE of 1.23 m to 0.48 m. Different from the traditional global model that underestimates water depth near shore and overestimates water depth offshore, the spatially distributed SVM system did not produce regional prediction bias and its prediction residual exhibited a random pattern. Our model worked well even if the sample density was much lower: The model trained with 10% of the samples was still able to obtain similar prediction accuracy as the global SVM model with the full training set.

FEASIBILITY INVESTIGATION ON SINGLE PHOTON LIDAR BASED WATER SURFACE MAPPING

Abstract. The recent advent of single photon sensitive airborne LiDAR (Light Detection And Ranging) sensors has enabled higher areal coverage performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light capable of penetrating clear shallow water. Although primarily designed for large area topographic mapping, the technique can also be used for mapping the water surface and shallow water bathymetry. In this contribution we investigate the capability of Single Photon LiDAR for large area mapping of water surface heights. While interface returns from conventional green-only bathymetric sensors generally suffer from water level underestimation due to the water penetration capabilities of green laser radiation, the specific questions are, if Single Photon LiDAR (i) is less affected by this well known effect due to the high receiver sensitivity and (ii) consequently delivers a higher number of water surface echoes. The topic is addressed empirically in a case study by comparing the water surface responses of Single Photon LiDAR (Navarra, Spain) and Multi-Photon Topo-Bathymetric LiDAR (Neubacher Au, Austria) for selected water bodies with a horizontal water surface (reservoirs, ponds). Although flown at different altitudes, both datasets are well comparable as they exhibit the same strip point density of ca. 14 points/m2. The expected superiority of Single Photon LiDAR over conventional green-only bathymetric LiDAR for mapping water surfaces could not be verified in this investigation. While both datasets show good agreement compared to a reference water level when aggregating points into cells of 10 × 10 m2 (mean deviations < 5 cm), higher resolution Single Photon LiDAR based water surface models (grid size 1–5 m) show a systematic water level underestimation of 5–20 cm. However, independently measured ground truth observations and simultaneous data acquisition of the same area with both techniques are necessary to verify the results.

INVESTIGATING THE USE OF COASTAL BLUE IMAGERY FOR BATHYMETRIC MAPPING OF INLAND WATER BODIES

Abstract. In this contribution, we report on an experimental airborne data acquisition with two medium format cameras (Coastal Blue, RGB) and a topo-bathymetric laser scanner for capturing the bathymetry of a dozen of groundwater supplied lakes located near Augsburg, Germany. The specific research question was to investigate whether the use of high-resolution Coastal Blue imagery (λ = 400–460 nm) provides added value for mapping bathymetry and characterization of water bottom features. While data processing is still in progress, preliminary results indicate that the blue (λ = 420–500 nm) and green (λ = 490–570 nm) color channels of the RGB camera are better suited for estimating bathymetry, but the Coastal Blue channel adds an additional water penetrating band increasing the number of useful band combinations with a positive effect on the water bottom classification capabilities. Whereas Coastal Blue channels are rather used from satellite platforms (Landsat 8, WorldView-2) with spatial resolutions in the meter range, our experiment aims at using higher resolution Coastal Blue imagery with a ground sampling distance of around 5 cm enabling not only spectrally based shallow water depth mapping but also the application of multi-media photogrammetry in high spatial resolution. To the best of our knowledge the use of high-resolution Coastal Blue captured from airborne platforms is novel in the context of mapping shallow water bathymetry.

Application of Web-GIS and Geovisual Analytics to Monitoring of Seabed Evolution in South Baltic Sea Coastal Areas

Regular surveys of coastal zone seabed deliver important information about geomorphologic processes such as silting of waterways. The recent introduction of the Sentinel series of sensors has allowed for the use of satellite sensing for shallow bathymetry morphology monitoring. In this context, this article presents a dedicated Geographic Information System for Baltic Sea shallow water depth monitoring on the basis of Sentinel-2 imagery. The system employs Geovisual Analytics for differential analysis of bathymetry changes as well as monitoring the visibility of known wrecks in the coastal waters of Southern Baltic Sea. Results are verified with regard to known changes in shallow water bathymetry between 28 June 2015 and 3 March 2017.

A CASE STUDY ON THROUGH-WATER DENSE IMAGE MATCHING

Abstract. In the last years, the tremendous progress in image processing and camera technology has reactivated the interest in photogrammetrybased surface mapping. With the advent of Dense Image Matching (DIM), the derivation of height values on a per-pixel basis became feasible, allowing the derivation of Digital Elevation Models (DEM) with a spatial resolution in the range of the ground sampling distance of the aerial images, which is often below 10 cm today. While mapping topography and vegetation constitutes the primary field of application for image based surface reconstruction, multi-spectral images also allow to see through the water surface to the bottom underneath provided sufficient water clarity. In this contribution, the feasibility of through-water dense image matching for mapping shallow water bathymetry using off-the-shelf software is evaluated. In a case study, the SURE software is applied to three different coastal and inland water bodies. After refraction correction, the DIM point clouds and the DEMs derived thereof are compared to concurrently acquired laser bathymetry data. The results confirm the general suitability of through-water dense image matching, but sufficient bottom texture and favorable environmental conditions (clear water, calm water surface) are a preconditions for achieving accurate results. Water depths of up to 5 m could be mapped with a mean deviation between laser and trough-water DIM in the dm-range. Image based water depth estimates, however, become unreliable in case of turbid or wavy water and poor bottom texture.