Derivation Of Bathymetry Research Articles

Use of ICEsat-2 and Sentinel-2 Open Data for the Derivation of Bathymetry in Shallow Waters: Case Studies in Sardinia and in the Venice Lagoon

Despite the high accuracy of conventional acoustic hydrographic systems, measurement of the seabed along coastal belts is still a complex problem due to the limitations arising from shallow water. In addition to traditional echo sounders, airborne LiDAR also suffers from high application costs, low efficiency, and limited coverage. On the other hand, remote sensing offers a practical alternative for the extraction of depth information, providing fast, reproducible, low-cost mapping over large areas to optimize and minimize fieldwork. Satellite-derived bathymetry (SDB) techniques have proven to be a promising alternative to supply shallow-water bathymetry data. However, this methodology is still limited since it usually requires in situ observations as control points for multispectral imagery calibration and bathymetric validation. In this context, this paper illustrates the potential for bathymetric derivation conducted entirely from open satellite data, without relying on in situ data collected using traditional methods. The SDB was performed using multispectral images from Sentinel-2 and bathymetric data collected by NASA’s ICESat-2 on two areas of relevant interest. To assess outcomes’ reliability, bathymetries extracted from ICESat-2 and derived from Sentinel-2 were compared with the updated and reliable data from the BathyDataBase of the Italian Hydrographic Institute.

Satellite-derived bathymetry for maritime archaeology: Testing its effectiveness at two ancient harbours in the Eastern Mediterranean

High-resolution bathymetry is an essential tool in maritime archaeology, often used for underwater prospection, imaging, palaeo-landscape reconstruction and change detection. The commonest acquisition method relies on shipborne acoustic systems (e.g. multibeam echosounder). However, most of the world’s seabed has not been surveyed using these methods. Derivation of bathymetry from high-resolution multispectral satellite imagery (Satellite-Derived Bathymetry: SDB) can redress this in shallow (<20–30 m depth) clear water. It can be derived empirically using depth control points, or independent of control data through physics-based approaches. Given global satellite coverage, it is applicable almost anywhere with suitable water conditions and is becoming increasingly widespread in the hydrographic survey community. However, archaeological application of SDB is almost totally un-investigated. This paper examines the utility of SDB for maritime archaeology through 2 case studies: the ancient harbours of Tyre (Lebanon) and Ras Ibn Hani (Syria). Tested data comprise 2 m-resolution physics-based commercial SDB and medium-resolution (10–30 m) SDB empirically derived from open access multispectral imagery (Landsat 8, Sentinel-2). SDB was assessed for its ability to detect submerged cultural features and landforms and as input into palaeo-geographic reconstructions. Tested SDB performed poorly for feature detection, but was better-suited to landform identification and palaeo-geographic reconstruction. SDB also has clear advantages: low cost, wide area coverage, speed and data availability. Disadvantages include variable accuracy, technical limitations (e.g. need for clear water) and approach-specific requirements (e.g. depth control points for empirical and complex modelling for physics-based approaches). For maritime archaeology, its current limitations means that it will not replace acoustics for prospection and imaging but it does have utility for submerged landscape investigation and reconstruction, particularly in areas which now only have no/poor bathymetric data.

Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data

Polar-orbiting ocean color satellites such as Landsat-8, Suomi National Polar-orbiting Partnership (SNPP), and Sentinel-3 offer valuable image data for the derivation of water bathymetry in optically shallow environments. Because of the multi-spectral limitation, however, it is challenging to derive bathymetry over global shallow waters without reliable mechanistic algorithms. In this contribution, we present and test a physics-based algorithm for improved retrieval of bathymetry with multi-spectral sensors. The algorithm leverages the temporal variation of water-column optical properties in two satellite measurements. By incorporating two remote sensing reflectance spectra in an optimization procedure, it enhances the spectral constraining condition for the optimization, thus leading to improved retrieval accuracy. This scheme is evaluated using synthetic multi-spectral data. It is shown that the new approach can provide accurate estimation of water depths over 0–30 m range with three types of benthic substrates (corals, seagrass, and sand) and for a wide range of water column optical properties. Based on the degree of improvement, Landsat-8 appears to be benefited the most, followed by SNPP, and then Sentinel-3. The application of the new approach is demonstrated with satellite images over shallow waters (0–30 m) dominated with coral reefs, seagrass, and sand, respectively. This proof-of-concept study confirms the promise of multi-spectral satellite sensors for accurate water depth retrieval by accounting for the temporal characteristics in multiple measurements, suggesting a path forward for the derivation of bathymetry from the existing satellites over global shallow waters.

Deriving High-Resolution Reservoir Bathymetry From ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery

Knowledge of reservoir bathymetry is essential for many studies on terrestrial hydrological and biogeochemical processes. However, there are currently no cost-effective approaches to derive reservoir bathymetry at the global scale. This study explores the potential of generating high-resolution global bathymetry using elevation data collected by the 532-nm Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2). The novel algorithm was developed and tested using the ICESat-2 airborne prototype, the Multiple Altimeter Beam Experimental Lidar (MABEL), with Landsat-based water classifications (from 1982 to 2017). MABEL photon elevations were paired with Landsat water occurrence percentiles to establish the elevation–area (E–A) relationship, which in turn was applied to the percentile image to obtain partial bathymetry over the historic dynamic range of reservoir area. The bathymetry for the central area was projected to achieve the full bathymetry. The bathymetry image was then embedded onto the digital elevation model (DEM). Results were validated over Lake Mead against survey data. Results over four transects show coefficient of determination ( ${R} ^{2}$ ) values from 0.82 to 0.99 and root-mean-square error (RMSE) values from 1.18 to 2.36 m. In addition, the E–A and elevation–storage (E–S) curves have RMSEs of 1.56 m and 0.08 km3, respectively. Over the entire dynamic reservoir area, the derived bathymetry agrees very well with independent survey data, except for within the highest and lowest percentile bands. With abundant overpassing tracks and high spatial resolution, the newly launched ICESat-2 should enable the derivation of bathymetry over an unprecedented number of reservoirs.

Derivation of Bathymetry from High-resolution Optical Satellite Imagery and USV Sounding Data

ABSTRACTRemote sensing bathymetry inversion can quickly obtain water depth data of large areas, but this process relies on a large number of in-situ depth data points. USV-based (Unmanned Surface Vehicle) technique can obtain the bathymetry data of shallow water where ordinary ships are inaccessible, but this technique is inefficient and generally only data along survey line can be collected. The combination of USV and high-resolution remote sensing provides a new solution for water depth surveying and mapping around an island. This paper focuses on the key techniques, using USV sounding data and GeoEye-1 multispectral remote sensing images covering the region of Wuzhizhou island in the experiment. The results show that the MAE (Mean Absolute Error) of USV sounding is 0.25 m, while the MRE (Mean Relative Error) is 1.41%, and the MRE of remote sensing bathymetry aided by USV sounding can be controlled within 20%. Errors are mainly from areas shallower than 5 m, and are also affected by the USV sounding position accuracy. It shows that it is feasible to combine the USV sounding and high-resolution remote sensing bathymetry, and this technique has broad application prospects in the field of bathymetry in large shallow areas.

Submerged and Emergent Land Cover and Bathymetric Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery

Tidal creeks are small estuarine watersheds characterized by low freshwater input, marine to brackish salinity, and subtidal, intertidal, and supratidal habitats. Most people are familiar with large rivers and estuaries, but the smaller tidal watersheds comprise a greater percentage of the coastline. As the population along coasts rises there is growing concern about water quality and increased sedimentation rates. Therefore, these smaller tidal creek watersheds are at risk to pollution, decreased environmental health, and deterioration of protective salt marshes. The purpose of this study was to test methods for high spatial resolution mapping of benthic (submerged) and emergent habitats as well as the derivation of bathymetry using DigitalGlobe’s WorldView-2 imagery. An intensive field effort was conducted to test and assess several image processing techniques. Results concluded that: (1) supervised habitat classification produced the highest map accuracy (95%); (2) sand, water, scrub/shrub, and docks/rubble were mapped the most accurately at greater than 95%; (3) saltmarsh habitats (high and low density cordgrass, Spartina alterniflora, and black needlerush, Juncus roemerianus), mud, and oyster beds were between 80 and 85% accurate; (4) pan-sharpening and atmospheric correction did not improve map accuracy; (5) LiDAR (light detection and ranging) data increased habitat map accuracy; and (6) WorldView-2 imagery was capable of deriving water depth and these data increased the map accuracy of benthic habitats. The project produced habitat maps for benthic and emergent species at high spatial resolution (4 m2) which will be useful for studying the dynamic processes in this tidal environment. The data and methods developed here could be used by state and local government planning agencies to assess potential long-term changes and develop appropriate management strategies.

Spectral Information Analysis for the Semiautomatic Derivation of Shallow Lake Bathymetry Using High-resolution Multispectral Imagery: A Case Study of Antarctic Coastal Oasis

Bathymetry is a technique of measuring depths to determine the topography of water bodies. The derivation of bathymetry from satellite imagery is one of the basic researches in remote sensing of the aquatic environment, which has several practical implications to the coastal environment and it's monitoring. The spectral band information provided by the multispectral (eight bands) Worldview-2 (WV-2) satellite improves the quality of coastal environmental products including bathymetry. This study provides a method for mapping of shallow lake bathymetry (depth) in Larsemann hills, eastern Antarctica, using digital WV-2 imagery. Out of various bathymetry determination models available for multispectral images, two most popular models- linear band and linear ratio model were used to estimate water depth of the shallow water bodies. These models were executed using various band combinations to study their specific role in bathymetry derivation. The derived depths were validated against the in-situ measurements and root mean square errors (RMSE) were computed. We quantify the error between in-situ measured and satellite-estimated lake depth values for lakes selected in this study. Our results indicate the high correlation (0.7∼0.9) between estimated and in situ depth measurements, with RMSE ranging from 0.2 to 1 m. This study suggests that the coastal band in the WV-2 imagery could retrieve accurate bathymetry information compared to other bands.

Multispectral derivation of bathymetry in Singapore's shallow, turbid waters

In very shallow waters, active sensing determinations of bathymetry are often expensive and unwieldy. Sea depth estimation using passive remote-sensing methods is an attractive alternative, especially using cheap multispectral imagery with high spatial resolution. Three models for the determination of bathymetry from multispectral imagery were utilized with new eight-band images from DigitalGlobe's Worldview-2 satellite platform. All three were trained with electronic navigational chart data and evaluated for accuracy in Singapore's turbid shallow coastal waters. These waters are characterized by high turbidity, suspended sediment, and vehicle traffic. Of the three models, a linear band algorithm performed best, with a root-mean-square error (RMSE) of 0.48 m. A look-up table classification provided a precision of 0.64 m, but was limited by a training set that did not fully represent variance in water column and benthic properties. Possibly owing to the domination of particle backscatter over pigment absorption in these turbid waters, a linear ratio algorithm did not perform as well as the linear band algorithm, achieving an RMSE of only 0.56 m. Analysis found that the usual relationship between ratios of low-absorption to high-absorption bands and depth does not hold as well for these waters, likely due to backscatter dominating leaving-water signals, masking relative absorption effects. High turbidity, with a Secchi disk depth of 1.9 m, limited analysis to shallow reefs and coastline and likely impacted the sensitivity of the bathymetric algorithms. A larger validation data set containing water quality and benthic data is required for further investigation to determine specific sources of error.

The Influence of the Inverted Sea Wave Theories on the Derivation of Coastal Bathymetry

The estimation of nearshore bathymetry by inverting the wave dispersion function is an accepted method. The performance of four different wave theories inverted with dispersive surface classificator for the derivation of the bathymetry is examined during gale oceanographic conditions. The physical and technical limitations for the inversion are taken into consideration. The analyzed data are radar image sequences, and ground truth is an in situ echosounder's bathymetry data set. All geomorphological features detectable with a 41-m spatial resolution have been identified, but there is an underestimation of the absolute depth. The applicable theories have mean error less than 7%. The nonlinear theories are compared with the linear theory, and the experimental difference of their performance is on the order of the theoretical expectation <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (6%-8%).