Bathymetry Retrieval Research Articles

A Refined Machine Learning Method for Coastal Bathymetry Retrieval Using Minimum Distance from Coastline and Geographical Features

A Refined Machine Learning Method for Coastal Bathymetry Retrieval Using Minimum Distance from Coastline and Geographical Features

Potential to use free satellite imagery to retrieve the past bathymetry of large rivers

Information on bathymetric changes in large rivers is useful for flood planning, navigation, and predicting channel change. Recent studies have demonstrated the potential for measuring bathymetry of rivers from satellite images, but such studies have used high-resolution, expensive data applied to ideal river conditions, such as low turbidity. Many of the large rivers of the world are located in countries that cannot afford the carefully planned field surveys and commercial satellite images used in these studies. In this study we investigate whether past bathymetry of large rivers could be retrieved by using readily collated free satellite images, combined with the limited cross-sectional surveys that are typically available in less wealthy countries. We tested the method in the heavily regulated Han River of central China. The Landsat-8 and Sentinel-2 image-derived depth could quantitatively match in situ depths along cross-sections up to depths of 13.9 m, where the suspended sediment concentration was low due to dam regulation. However, in most of the river where turbidity was high, the method was less successful. The image-to-depth quantile transformation (IDQT) approach could improve passive bathymetry retrieval in cases like the Han River, where there was some spatial and temporal misalignment between in situ data and remote-sensing data. The lateral depth pattern up to the 2.5 m depth contour could also be qualitatively and visually interpreted over an extended length (15 km). These results suggest that there is potential to use free satellite imagery to derive bathymetry in many large rivers, but this potential can only be achieved in very limited (low turbidity) conditions, and with image to depth correlation analysis and band selection.

Georeferencing of UAV imagery for nearshore bathymetry retrieval

In dynamic coastal areas where rapid changes in the seabed can have a significant impact on day–to–day operations, as is the case of Figueira da Foz harbor inlet, it is important to have updated knowledge of the bathymetry. As in energetic wave conditions it is not possible to carry out traditional bathymetric surveys, this work proposes a methodology that allows, using UAVs, to estimate the bathymetry through bathymetric inversion methods. On two different occasions, flights were carried out with a commercial UAV to obtain local bathymetry at Figueira da Foz harbor inlet. Since this location is far from the coast, there is not a minimum amount of land to use ground control points that allow georeferencing the acquired images in the traditional way with the necessary spatial resolution. Therefore, an automatic image georeferencing methodology was developed and validated in this work. A spectral analysis is used to retrieve both wave periods and wavelengths, respectively, by evaluating the positions of the wave crests in both time and spatial domains. From this information, the water depths are estimated using the linear dispersion relation and the results are validated with field survey data. The automatic georeferencing methodology is shown to be capable of placing the image in the correct location by overlaying on a generic basemap. The spectral analysis showed positive results in estimating wavelengths and wave periods. However, the quality of bathymetric inversion in areas where morphological structures exist (sandbars), presents results with different accuracies depending on the wave and tide characteristics observed each day.

Cross-Comparison of the “BathySent” Coastal Bathymetry to Sonar Measurements and Ratio Model Technique: Pilot Sites in the Aegean Sea (Greece)

The proposed novel “BathySent” approach for coastal bathymetric mapping, using the Copernicus Sentinel-2 mission, as well as the assessment and specification of the uncertainties of the derived depth results, are the objectives of this research effort. For this reason, Sentinel-2 bathymetry retrieval results for three different pilot sites in Greece (islands of Kos, Kasos, and Crete) were compared with ground-truth data. These data comprised high-resolution swath bathymetry measurements, single-beam echosounder measurements at very shallow waters (1–10 m), and the EMODnet DTM 2018 release. The synthetic tests showed that the “BathySent” approach could restitute bathymetry in the range of 5–14 m depth, showing a standard deviation of 2 m with respect to the sonar-based bathymetry. In addition, a comparison with the “ratio model” multispectral technique was performed. The absolute differences between conventional Earth Observation-based bathymetry retrieval approaches (i.e., linear ratio model) and the suggested innovative solution, using the Sentinel-2 data, were mainly lower than 2 m. According to the outcome evaluation, both models were considered to provide results that are more reliable within the depth zone of 5–25 m. The “ratio model” technique exhibits a saturation at ~25 m depth and demands ground calibration. Though, the “BathySent” method provides bathymetric data at a lower spatial resolution compared to the “ratio model” technique; however, it does not require in situ calibration and can also perform reliably deeper than 25 m.

Application of machine learning algorithms and Sentinel-2 satellite for improved bathymetry retrieval in Lake Victoria, Tanzania

Estimating bathymetric information is vital for aquaculture and navigation applications. Free, high-resolution satellite imagery provides a cost-effective solution for routine bathymetric measurements. We tested six algorithms to retrieve water depth in the Mwanza Gulf of Lake Victoria using Sentinel-2 satellite imagery: the conventional Stumpf method, Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Neural Network (NNET), and Support Vector Machine (SVM). In-situ depth points collected via echo sounders were used to train and validate the algorithms. Performance evaluation metrics included coefficient of determination (R2), mean absolute error (MAE), root-mean-square error (RMSE), and spatial autocorrelation of residuals. Among the algorithms tested, the Stumpf model exhibited moderate performance with an R2 of 0.441, higher MAE (2.078 m), and RMSE (2.964 m) values. The RF algorithm improved performance with an R2 of 0.957, lower MAE (0.476 m), and RMSE (0.823 m). The GBM and XGB algorithms achieved R2 values of 0.960 and 0.956, respectively, with low MAE (0.484 m for GBM, 0.482 m for XGB) and RMSE (0.795 m for GBM, 0.830 m for XGB) values. The NNET algorithm outperformed the GBM and XGB models, obtaining an R2 of 0.963, the lowest MAE (0.438 m), and RMSE (0.761 m). The SVM algorithm demonstrated the best performance with an R2 of 0.965, the lowest MAE (0.403 m), and RMSE (0.745 m), implying the highest accuracy in depth estimation. SVM also showed stable generalization across different locations with insignificant spatial autocorrelation of residuals. Therefore, SVM is recommended for repetitive bathymetry calculations.

Bathymetry Retrieval From Spaceborne Multispectral Subsurface Reflectance

A few scholars have developed the models for retrieval of water depth from subsurface reflectance of multispectral images to avoid the influences of such as sun glitter. However, the models are only suitable for case I water. For this reason, this study proposes a bathymetry retrieval model using subsurface reflectance for both case I and case II water. The model first corrects the water surface reflectance image and then converts it into a subsurface reflectance image, and the subsurface reflectance image is used as the water depth retrieval image. Landsat 8 images were taken for experiments in case 1 water and case 2 water, and two water areas, Weizhou Island, Guangxi, China, and Molokai Island, Hawaii, USA, were used to verify the proposed model. The experimental results showed that the proposed model reduced the root mean squared error (RMSE) of the retrieved water depth in the Weizhou and Molokai areas from 3.113 m to 2.903 m and 4.239 m to 3.653 m, respectively, i.e., improve accuracy of water depth at 6.75% and 13.82% for Weizhou and Molokai areas, respectively. Therefore, the results demonstrate that the proposed model using subsurface reflectance can significantly improve the accuracy of bathymetry retrieval via spaceborne multispectral images.

Bathymetry Retrieval Algorithm Based on Hyperspectral Features of Pure Water Absorption From 570 to 600 nm

Current efforts for improving the hyperspectral optimization processing exemplar (HOPE) model include further testing of remote-sensing reflectance (Rrs) features containing useful information for bathymetry retrieval via the minimization of the interference stemming from the variability in inherent optical properties and benthic reflectance. In this paper, we found a novel feature originating from the pure water absorption within the narrow spectral region of 570–600 nm. In most coastal regions of clear water, for example, in a coral reefs environment, pure water accounts for the majority of the total absorption in this spectral range. In addition to the depth variation, the spectral behavior of Rrs(570–600) is primarily dominated by a steep increase in pure water absorption with wavelength, whereas the influence of other optical properties such as phytoplankton/CDOM absorption, particle backscattering, and benthic reflectance can be simplified using the spectrally constant shape model. A HOPE pure water (HOPE-PW) algorithm using this feature was developed based on Rrs measurements with a spectral resolution of near 3.5 nm, wherein only four unknown parameters must be resolved. The validation from LiDAR data and comparison with HOPE-BRUCE using PRISM data at 15 sites located in five distinct regions of Palau, Guam, Great Barrier Reef, Hawaiian Islands and Florida Key, confirmed that the HOPW-PW yielded a considerable performance and provided adequate transferability to other sites with varying bottom and water environments. The sensitivity analysis based on Hydrolight-simulated datasets showed that HOPE-PW was less effected by bottom type variations but still had limitations in retrieving water optical properties.

Shallow-Water Bathymetry Retrieval Based on an Improved Deep Learning Method Using GF-6 Multispectral Imagery in Nanshan Port Waters

Shallow-Water Bathymetry Retrieval Based on an Improved Deep Learning Method Using GF-6 Multispectral Imagery in Nanshan Port Waters

Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach

Shallow bathymetry mapping using proximal sensing techniques is an active field of research that offers a new perspective in studying the seafloor. Drone-based imagery with centimeter resolution allows for bathymetry retrieval in unprecedented detail in areas with adequate water transparency. The majority of studies apply either spectral or photogrammetric techniques for deriving bathymetry from remotely sensed imagery. However, spectral methods require a certain amount of ground-truth depth data for model calibration, while photogrammetric methods cannot perform on texture-less seafloor types. The presented approach takes advantage of the interrelation of the two methods, in order to predict bathymetry in a more efficient way. Thus, we combine structure-from-motion (SfM) outputs along with band-ratios of radiometrically corrected drone images within a specially designed deep convolutional neural network (CNN) that outputs a reliable and robust bathymetry estimation. To achieve effective training of our deep learning system, we utilize interpolated uncrewed surface vehicle (USV) sonar measurements. We perform several predictions at three locations in the southern Mediterranean Sea, with varying seafloor types. Our results show low root-mean-square errors over all study areas (average RMSE ≅ 0.3 m), when the method was trained and tested on the same area each time. In addition, we obtain promising cross-validation performance across different study areas (average RMSE ≅ 0.9 m), which demonstrates the potential of our proposed approach in terms of generalization capabilities on unseen data. Furthermore, areas with mixed seafloor types are suitable for building a model that can be applied in similar locations where only drone data is available.

Bathymetry retrieval from CubeSat image sequences with short time lags

The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.

Drone-Based Bathymetry Modeling for Mountainous Shallow Rivers in Taiwan Using Machine Learning

The river cross-section elevation data are an essential parameter for river engineering. However, due to the difficulty of mountainous river cross-section surveys, the existing bathymetry investigation techniques cannot be easily applied in a narrow and shallow field. Therefore, this study aimed to establish a model suitable for mountainous river areas utilizing an unmanned aerial vehicle (UAV) equipped with a multispectral camera and machine learning-based gene-expression programming (GEP) algorithm. The obtained images were combined with a total of 171 water depth measurements (0.01–1.53 m) for bathymetry modeling. The results show that the coefficient of determination (R2) of GEP is 0.801, the mean absolute error (MAE) is 0.154 m, and root mean square error (RMSE) is 0.195 m. The model performance of GEP model has increased by 16.3% in MAE, compared to conventional simple linear regression (REG) algorithm, and also has a lower bathymetry retrieval error both in shallow (&lt;0.4 m) and deep waters (&gt;0.8 m). The GEP bathymetry retrieval model has a considerable degree of accuracy and could be applied to shallow rivers or near-shore areas under similar conditions of this study.

Shallow Water Bathymetry Retrieval by Optical Remote Sensing Based on Depth-Invariant Index and Location Features

At present, most machine learning bathymetry retrieval models use the band reflectance as the inversion feature only, without considering features related to the water substrate and pixel spatial correlation. In this study, in addition to band reflectance, two features, Depth-Invariant Index (DII) and pixel location, are taken into account. Two machine learning algorithms, Random Forest (RF) and Back Propagation (BP) neural network are used to retrieve bathymetry. The effects of the two features on the accuracy and performance of bathymetry retrieval are explored. The results show that: (i) Machine learning algorithms are generally superior to the widely used Stumpf model. Stumpf model performs better only in the depth range of 8–16 m, with a Root Mean Square Error (RMSE) of 0.85 m, but has poor performance in other depth ranges. (ii) Compared with models that use Band Reflectance (BR) only, DIIb,g (blue-green DII) + BR model, Location and Location + BR models are all superior to the BR model for RF and BP algorithms. It means that DII and location features are very effective in improving the bathymetry retrieval accuracy.

The Development of A Rigorous Model for Bathymetric Mapping from Multispectral Satellite-Images

Models for bathymetry retrieval from multispectral images have not considered the errors caused by tidal fluctuation. A rigorous bathymetric model that considers the variation in tide height time series, including the tide height calculation and instantaneous tide height correction at the epoch of satellite flight into the bathymetric retrieval model, is proposed in this paper. The model was applied on Weizhou Island, located in Guangxi Province, China, and its accuracy verificated with four check lines and seven checkpoints. A scene from the Landsat 8 satellite image was used as experimental data. The reference (“true”) water depth data collected by a RESON SeaBat 7125 multibeam instrument was used for comparison analysis. When satellite-derived bathymetry is compared, it is found that maximum absolute error, mean absolute error, and RMSE have decreased 54, 45, and 30% relative to that of the traditional model in the entire test field. The accuracy of the water depths retrieved by our model increased 30 and 56% when validated using four check lines and seven checkpoints, respectively. Therefore, it can be concluded that the model proposed in this paper can effectively improve the accuracy of bathymetry retrieved from Landsat 8 images.

Nearshore Bathymetry Retrieval from Wave-Based Inversion for Video Imagery

A wavelet-based method for bathymetry retrieval using a sequence of static images of the surface wave field, as obtained from video imagery, is proposed. Synthetic images of the water surface are generated from a numerical Boussinesq type model simulating the propagation of irregular waves. The spectral analysis is used to retrieve both wave periods and wavelengths by evaluating the spectral peaks in the time and spatial domains, respectively. The water depths are estimated using the linear dispersion relation and the results are validated with the model’s bathymetry. To verify the proposed methodology, 2D and 3D simulations considering effects of wave shoaling and refraction were performed for different sea conditions over different seafloors. The method’s ability to reproduce the original bathymetry is shown to be robust in intermediate and shallow waters, being also validated with a real case with images obtained with a shore-based video station. The main improvements of the new method compared to the consideration of a single image, as often used in Satellite Derived Bathymetry, is that the use of successive images enables the consideration of different wave periods, improving depth estimations and not requiring the use of subdomains or filters. This image processing methodology shows very positive results to provide bathymetry maps for shallow marine environments and can be useful to monitor the nearshore with high time- and space-resolution at low cost.

Satellite-derived bathymetry based on machine learning models and an updated quasi-analytical algorithm approach.

Retrieving the water depth by satellite is a rapid and effective method for obtaining underwater terrain. In the optical shallow waters, the bottom signal has a great impact on the radiation from the water which related to water depth. In the optical shallow waters, the spatial distribution characteristic of water quality parameters derived by the updated quasi analysis algorithm (UQAA) is highly correlated with the bottom brightness. Because the bottom reflection signal is strongly correlated with the spatial distribution of water depth, the derived water quality parameters may helpful and applicable for optical remote sensing based satellite derived bathymetry. Therefore, the influence on bathymetry retrieval of the UQAA IOPs is worth discussing. In this article, different machine learning algorithms using a UQAA were tested and remote sensing reflectance at water depth in situ points and their detection accuracy were evaluated by using Worldwiew-2 multispectral remote sensing images and laser measurement data. A backpropagation (BP) neural network, extreme value learning machine (ELM), random forest (RF), Adaboost, and support vector regression (SVR) machine models were utilized to compute the water depth retrieval of Ganquan Island in the South China Sea. According to the obtained results, bathymetry using the UQAA and remote sensing reflectance is better than that computed using only remote sensing reflectance, in which the overall improvements in the root mean square error (RMSE) were 1 cm to 5 cm and the overall improvement in the mean relative error (MRE) was 1% to 5%. The results showed that the results of the UQAA could be used as a main water depth estimation eigenvalue to increase water depth estimation accuracy.

A Combined Approach for Retrieving Bathymetry from Aerial Stereo RGB Imagery

Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combination of a new stereo triangulation method (an improved projection image based two-medium stereo triangulation method) and spectral inversion models. In general, the inversion models require some bathymetry reference points, which are not always feasible in many scenarios, and the proposed approach employs a new stereo triangulation method to obtain reliable bathymetric points, which act as the reference points of the inversion models. Using various numbers of triangulation points as the reference points together with a Geographical Weighted Regression (GWR) model, a series of experiments were conducted using UAV RGB images of a small island, and the results were validated against LiDAR points. The promising results indicate that the proposed approach is an efficient technique for shallow water bathymetry retrieval, and together with UAV platforms, it could be deployed easily to conduct a broad range of applications within marine environments.

Evaluation of radiometric calibration of drone-based imagery for improving shallow bathymetry retrieval

ABSTRACT Extracting high resolution, shallow bathymetry from drone-based imagery using empirical methods is a novel approach that gains increasing interest. However, due to the rapidly expanding character of this field, there are not sufficient guidelines for optimal pre-processing of drone-based imagery regarding bathymetry retrieval. This study explores the suitability of imagery resulted from commonly used drone cameras in terms of producing good correlation with ground-truth depth measurements obtained from an unmanned surface vehicle (USV). Thus, imagery with similar spectral responses, from two commercial sensors (DJI Phantom 4 Pro and MicaSense-RedEdge multispectral camera), is examined along with and without radiometric corrections applied, using proprietary software. The results show that radiometric image corrections using a reference reflectance panel, greatly enhance the correlation coefficient between bathymetry data and single-band or band-ratio reflectance mosaics at two study areas with mixed seafloor types. However, it was observed that the Red band and its associated logarithmic band ratios (Blue/Red, Green/Red) from both sensors show good correlation with depth as well, regardless of radiometric corrections. This result applies mainly to areas with favourable optical properties of water. This study suggests that radiometric calibration is crucial in empirical bathymetry retrieval from drone-based imagery. Furthermore, radiometric corrections applied on common Red, Green, Blue (RGB) cameras provide useful reflectance mosaics at lower costs compared to multispectral cameras.

Assessment of PRISMA Level-2 Hyperspectral Imagery for Large Scale Satellite-Derived Bathymetry Retrieval

Currently, several satellite-derived bathymetry (SDB) workflows are based on a variety of satellite imagery which are analyzed by empirical or analytical methods. The latest availability of PRISMA hyperspectral data provides a new opportunity for testing their application in shallow water bathymetry mapping. Here we utilize two Level-2 PRISMA scenes from the Caribbean Sea capturing seafloor areas with diverse benthic features and we analyze them using the shallow water analytical models provided by the water-color simulator (WASI) software. The presented study examines the influence of spatial resolution and end-member spectra on the SDB output. Consequently, in one study area we apply inversion using additional reference spectra and in the other study area we exploit the PRISMA panchromatic band for producing a pan-sharpened, hyperspectral cube for bathymetry inversion. The results show good correlation with reference bathymetry data (sonar and admiralty chart) suggesting that PRISMA imagery has a clear potential in optical bathymetry studies. The use of appropriate end-member spectra assists in enhancing the accuracy of SDB, and pan-sharpened PRISMA imagery assists in improving the results when detailed bathymetry is required. PRISMA imagery can be effectively analyzed with open-source software WASI-2D and thus contribute new bathymetric data to regional-scale seafloor mapping projects.

River Bathymetry Retrieval From Landsat-9 Images Based on Neural Networks and Comparison to SuperDove and Sentinel-2

The Landsat mission has kept an eye on our planet, including water bodies, for 50 years. With the launch of Landsat-9 and its onboard Operational Land Imager 2 (OLI-2) in September 2021, more subtle variations in brightness (14-bit dynamic range) can be captured than previous sensors in the Landsat series (e.g., 12-bit Landsat-8). The enhanced radiometric resolution of OLI-2 appeals to the aquatic remote sensing community because the instrument might be capable of resolving smaller differences in water-leaving radiance. This study evaluates the potential to map river bathymetry from Landsat-9 imagery. We employ a neural network (NN)-based regression model for bathymetry retrieval and compare the results with optimal band ratio analysis (OBRA). The effect of Landsat-9 pan-sharpening on depth retrieval is also examined. In addition, we perform an intersensor comparison with Sentinel-2 and newly available 8-band SuperDoves from the PlanetScope constellation. Depth retrieval results from the Colorado and Potomac Rivers imply that Landsat-9 provided more accurate bathymetry across a range of depths up to 20 m, particularly when pan-sharpened. Downsampling the SuperDove data improved bathymetry retrieval due to enhanced signal-to-noise ratio, most notably in deep waters (maximum detectable depth increased from &#x223C;15 to &#x223C;20 m). Similarly, the enhanced spectral resolution of 8-band SuperDoves improved depth retrieval relative to 4-band Doves. The NN-based model outperformed OBRA by incorporating more spectral information.

Retrieval of nearshore bathymetry from Sentinel-1 SAR data in high energetic wave coasts: The Portuguese case study

The ability to derive bathymetry using remote sensing techniques enables rapid and cost-efficient mapping of large coastal areas. This contribution focuses on the application of both fast Fourier transform (FFT) and wavelet spectral analysis to obtain satellite-derived bathymetry maps of the nearshore, from freely available and easily accessible Sentinel-1 synthetic aperture radar (SAR) data with 10 m pixel resolution. For this purpose, an extension of 220 km of the Portuguese west coast is analyzed using six satellite images obtained during the years 2018, 2019 and 2020. This extension allows to assess the applicability to coastal sectors with distinct geomorphological constraints. The peak wave periods corresponding to the acquisition of these images approximately range between 11 and 16 s. The spectral analysis is carried to estimate the water depths in near-shore water regions from the observed wavelength patterns. This signature of the sea surface, reflected in the variations of the wavelengths, is captured by the satellite images, making it possible to infer the underlying bathymetry. The bathymetric estimates obtained from both methodologies are compared with data extracted from the Coastal Nautical Charts provided by the Portuguese Hydrographic Institute. Wavelet image processing methodology shows very positive results, particularly extending the depth inversion limits of the FFT methodology, allowing to obtain bathymetric data for the entire shoaling zone where the wavelength patterns are visible. The achieved results also highlight that both FFT and wavelet methodologies are dependent from the seabed slope. For gentle slopes, the inferred depths from 2018 SAR images lead to relative errors between 2.5 and 20% when compared with the observed isobaths (10, 20 and 30 m). For steeper slopes, the errors are generally greater than 20% and increase with depth. The capabilities of the wavelet methodology to map shallow marine environments for high energetic coasts seems promising, regarding research purposes and management interventions.