A Purely Spaceborne Open Source Approach for Regional Bathymetry Mapping

Thomas, Nathan; Lee, Brian; Coutts, Oliver; Bunting, Peter; Lagomasino, David; Fatoyinbo, Lola

doi:10.1109/tgrs.2022.3192825

Cited by 15 publications

(4 citation statements)

References 43 publications

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“…Covering >100 000 km 2 , the GBB possesses a multitude of microenvironments that span high‐energy margins to the quiescent expanses of the platform interior (Purdy, 1961, 1963; Traverse & Ginsburg, 1966; Enos, 1974; Reijmer et al ., 2009; Swart et al ., 2009; Harris et al ., 2015; Purkis & Harris, 2016). Furthermore, water depth on the platform varies, with the south being generally deeper than the north (Reijmer et al ., 2009; Harris et al ., 2015; Purkis et al ., 2019; Thomas et al ., 2022). Such variation means that the drivers of suspended sediment vary across the platform top.…”

Section: Methodsmentioning

confidence: 99%

Spatial, seasonal and climatic drivers of suspended sediment atop Great Bahama Bank

Lopez‐Gamundi,

Barnes,

Bakker

et al. 2023

Sedimentology

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Suspension is the key mechanism by which fine‐grained sediment (≤125 μm) is winnowed and transported across shallow‐water carbonate platforms into adjacent deep waters. Unlike sliding and saltation, which deliver sedimentary structures via bedload, the sedimentological signature of suspended sediment is more cryptic. This study focuses on suspended sediment, and its drivers – wind, waves and tides – to better constrain the processes responsible for the transport of fine‐grained sediments. By building forward from remote sensing algorithms developed for deep‐waters, sediment suspension in the shallow water column can be mapped from satellite. By applying machine learning to Moderate Resolution Imaging Spectroradiometer data for Great Bahama Bank, this study demonstrates how the drivers of sediment suspension change over 18 years across this 100,000 km2 carbonate platform. Through time, both seasonal patterns of suspension, as well as those induced by El Niño‐Southern Oscillation and, more subtly, the Atlantic Meridional Overturning Circulation were tracked. El Niño‐Southern Oscillation modulates wind‐induced currents, while Atlantic Meridional Overturning Circulation affects local sea level. Across space, this study shows how the eastern margin of GBB, which is traditionally considered to be wind‐dominated, primarily owes its suspended sediment to tidal currents focused between islands. Sediment suspension across the leeward margin of Great Bahama Bank, meanwhile, can be attributed to wind‐induced currents and waves. The authors consider this work a step towards delivering a quantitative, reproducible, process‐based understanding of sediment suspension atop carbonate platforms using Earth observation data.

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Section: Methodsmentioning

confidence: 99%

Spatial, seasonal and climatic drivers of suspended sediment atop Great Bahama Bank

Lopez‐Gamundi,

Barnes,

Bakker

et al. 2023

Sedimentology

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“…In this study, we built two classical methods (Lyzenga and Stumpf) and six machine learning models (LGBM, XGBoost, RF, SVM, KNN, and MLP). Many previous studies have shown that these algorithms effectively estimate water depth [3,6,11,16,30,54]. The red, green, and blue bands of Sentinel-2 images and ICESat-2 water depth were split into training and test datasets with a segmentation ratio of 8:2.…”

Section: Methods Of Sdbmentioning

confidence: 99%

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Cheng,

Chu

et al. 2023

IJGI

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Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R2) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies.

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“…Most of the above mentioned models for SDB are supervised learning based models, where the models are trained to estimate depth of water columns from satellite data/images using certain ground truth data. The works use depth information collected from acoustic systems [11]- [13], [15], [16], [20]- [23], [31]- [35], airborne LiDAR systems [10], [11], [14], [17], [19], [26], [28], [36]- [38] or LiDAR data obtained from Ice, Cloud, and Elevation Satellite-2 (ICESat-2) satellites [18], [29], [30], as ground truth data. The study areas of interest in most of the above works belong to coastal regions [10]- [12], [14], [15], [17]- [19], [21], [28], [32], [34], [37], [39] and only a small number of works study SDB for inland water bodies [13], [16], [38].…”

Section: A Literature Surveymentioning

confidence: 99%

Satellite-Derived Bathymetry of an Inland Reservoir in India

Gupta¹,

Bhat²,

Balan³

2023

Preprint

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<p>Estimating depth of water columns in reservoirs, referred to as bathymetry, is an important task in water resource management. Satellite-derived bathymetry (SDB), where depths of water columns are estimated using satellite data, is an emerging and promising technique, especially for shallow-water scenarios. In this work, our objective is to obtain deep-learning models for SDB of a shallow water reservoir in Western India using supervised learning. For accomplishing this, after a series of pre-processing steps, we generate training data consisting of band reflectance values provided by Sentinel-2 satellite as input features and corresponding depth values collected by acoustic bathymetry as the ground truth data. The main contributions of the work include obtaining deep learning models that perform better than other classical techniques in terms of estimation accuracy, towards which we first obtain one dimensional (1D) convolutional neural network (CNN) and 2D-CNN models, which estimate depth using satellite data in tabular and image formats respectively. We then propose a hybrid model where we first classify each pixel of the image to correspond to high or low depth values and then perform regression on each of them separately. From the study, we show that (i) considering all the available bands of the Sentinel-2 satellites or those having <em>high </em>correlation with the ground truth depths gives significant performance improvement over considering only a subset of the bands having resolution of 10 meters, (ii) the 2D-CNN model trained using the image dataset yields significant performance improvement compared to other models trained on tabular data, (iii) and that the hybrid model which uses 2D-CNN for both the classification and regression tasks, trained on image data, performs best among all the models. </p>

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A Purely Spaceborne Open Source Approach for Regional Bathymetry Mapping

Cited by 15 publications

References 43 publications

Spatial, seasonal and climatic drivers of suspended sediment atop Great Bahama Bank

Spatial, seasonal and climatic drivers of suspended sediment atop Great Bahama Bank

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Satellite-Derived Bathymetry of an Inland Reservoir in India

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