Advances in remote sensing technologies in recent decades have allowed an ever-increasing capability to monitor the Earth’s surface - both onshore (Drăguţ and Eisank, 2011; Tarolli, 2014; Telling et al., 2017; Sofia, 2020) and offshore (Lecours et al., 2016; Hughes Clarke, 2018; Micallef et al., 2018; Wölfl et al., 2019). As a result, digital elevation models (hereafter DEMs) have become essential in geoscientific applications. Recent advances in computing and technology facilitate acquisition and processing of large quantities of elevation data and the creation of DEMs in increasingly higher resolutions (Sofia, 2020 and references therein). While the most recent advancements are focused towards quantitative analyses of elevation data and machine learning (Lecours et al., 2016; Maxwell and Shobe, 2022), qualitative analyses are still very important in geomorphological, environmental, archaeological, geographical, and geological studies. In these studies, very often one of the first steps involves the preliminary visual inspection of elevation data which then dictates the selection of the study site and directly impacts the study results. In later stages, a clear visual representation of elevation data is essential for efficiently communicating the results, analyses, and interpretations to the reader. For these reasons, representative and intuitive visualization of elevation data plays an essential role in research- and application-driven studies.

The importance of representative visualization of the Earth’s surface is even more pronounced in offshore (in this manuscript referring to marine, lacustrine and fluvial) environments where visual on-site inspection is rarely possible or very costly, and where the availability and resolution of bathymetric data is very limited compared to elevation data from onshore areas (Weatherall et al., 2015; Mayer et al., 2018; Wölfl et al., 2019). Due to relatively costly acquisition, a great majority of high-resolution bathymetric data is obtained by multibeam sonar in near-shore areas, areas containing important economic resources and at sites intended for larger infrastructural development (Mayer et al., 2018; Wölfl et al., 2019). Due to the relative scarcity of high-resolution offshore elevation data, it is of great importance to extract useful information from bathymetric data to the fullest.

Most commonly, elevation data and morphological features are visualized with the hillshading method in which the lightness or darkness of a surface is determined by the incidence angle between the illumination direction and the surface, resulting in an intuitive representation of the morphology of the Earth’s surface (Kokalj et al., 2011; Zakšek et al., 2011). Some recent publications displaying hillshaded bathymetric data include: Madricardo et al., 2019; Caporizzo et al., 2021; Fabbri et al., 2021; Wu et al., 2021; Aiello and Sacchi, 2022; Li et al., 2022; Piret et al., 2022; Post et al., 2022; Riddick et al., 2022; Sandwell et al., 2022; Streuff et al., 2022; Zheng et al., 2022. Despite the widespread use of the Hillshade, analytical hillshading has inherent limitations due to the directional bias induced by a single light source (Onorati et al., 1992; Smith and Clark, 2005; Zakšek et al., 2011; Kokalj and Somrak, 2019). The two most common problems with hillshading are 1) that morphological features which are parallel to the light source are barely visible (sometimes even invisible), and 2) that directly lit/shaded features are too light/dark to exhibit subtle relief (Zakšek et al., 2011). In order to partially mitigate these limitations, alternative visualizations of bathymetric data and morphological features are being used, among which Slope Gradient prevails by far (some more recent examples include: Walbridge et al., 2018; Watson et al., 2020; Georgiou et al., 2021; Verweirder et al., 2021; Berthod et al., 2022; Lebrec et al., 2022; Manstretta et al., 2022; Puga-Bernabéu et al., 2022). With this method the colour of a surface depends on its steepness. Even though the Slope Gradient visualization is less intuitive than Hillshade (Kokalj et al., 2019), it is still widely used since it is included as a standard function in commonly used GIS software packages. Other visualizations of bathymetric data and morphological features are only used occasionally (Marple and Hurd, 2019; Majcher et al., 2020; Zhou et al., 2022) as they are rarely included in the analytical toolbox of GIS software solutions and require the use of various programming languages for their calculation.

In this paper we present a fresh approach to offshore mapping by exploring the different visualization algorithms provided by the freely available “Relief Visualization Toolbox” software package (hereafter RVT). We assess the suitability of RVT algorithms for visualizing bathymetric data in different settings, resolutions, and regions and try to identify the most suitable algorithms to highlight different natural (i.e. geological) and anthropogenic geomorphic sea-, lake- and riverbed features. To our knowledge, we provide the first summary of the suitability of RVT algorithms for highlighting submerged features. Finally, we try to convince the reader that there are user-friendly simple-to-use alternatives to the Analytical Hillshade that have great potential to more effectively display bathymetric data and allow users to more efficiently communicate their findings and ideas.

This section contains an overview of the visualizations which were created from the four different datasets which were used in this study ( Table 2 ). We show the most striking examples and assess the effectiveness of the different algorithms for highlighting different geomorphic features. We first assess which visualizations are suitable to give a general overview of the bathymetric relief of a research area and then determine which visualizations most effectively highlight the general shapes listed in Table 1 . The results of our qualitative assessment are summarized in Table 3 which shows the suitability of the different algorithms for highlighting specific morphological sea-, lake-, and riverbed features.

The abbreviations which are used in the figure captions refer to the: geographic location of the dataset (abbr. explained in Sect. 2.2 and Table 2 ), used visualization algorithm (abbr. explained in Sect. 2.1), and the used parameters (where applicable). The abbreviations for the latter are: A - azimuth of illumination (in degrees), An - main direction of anisotropy (in degrees); H - height of illumination source (in degrees), Ve - vertical exaggeration (in multiples), D - number of directions of illumination, R - radius for trend assessment (in pixels), and M[min]-[max] - minimum to maximum search radius (in meters). The different parameters are separated by an underscore symbol.

Visual communication of data, results and interpretation has always been a vital part of geosciences, albeit more often than not subconsciously (Libarkin and Brick, 2002; Alcalde et al., 2017; Morse et al., 2019; Crameri et al., 2020 among others). Especially in recent years with the ever-increasing amount and coverage of the Earth’s surface with high-resolution DEMs it has become even more important to strive towards effective representation and communication of elevation data. This is even more significant in the case of bathymetric data where possibilities for direct observations are severely limited compared to the onshore realm (Weatherall et al., 2015; Mayer et al., 2018; Wölfl et al., 2019). In this sense, the good and proven practices for visualization of land elevation data should be considered, transferred, and implemented also for offshore data.

The algorithms contained in RVT have been used in a variety of studies and applications demonstrated by the extensive list of more than 400 references available on their website (RVT, 2023). More than half of these are represented by archaeological studies, where RVT (and especially SVF) is commonly used for representation of elevation data (some examples include Burigana and Magnini, 2017; Masini et al., 2018; Costa-García et al., 2019; Kokalj and Somrak, 2019; Bernardini and Vinci, 2020; Lim et al., 2020; Lozić and Štular, 2021; Šprajc et al., 2022; Affek et al., 2022; Danese et al., 2022). Use of RVT is much less common in geoscientific studies which represent less than ten percent of the references (RVT, 2023). Among these, RVT is most commonly used to create visualizations in landslide research (Van Den Eeckhaut et al., 2012; Lo et al., 2017; Tsou et al., 2017; Chudý et al., 2019; Knevels et al., 2019; Verbovšek et al., 2019; Guo et al., 2021) and geomorphology (Atkinson et al., 2014; Carrasco et al., 2020; Tóth et al., 2020; Novak and Oštir, 2021; Rolland et al., 2022), while other geoscientific topics are represented by just a few papers (Mateo Lázaro et al., 2014; Djuricic et al., 2016; Favalli and Fornaciai, 2017; Delaney et al., 2018; Favalli et al., 2018; Lkebir et al., 2020; Craven et al., 2021; Delaney, 2022; Jamšek Rupnik et al., 2022). The large discrepancy between the use of RVT in archeological vs geoscientific studies indicates the unrecognised potential for alternative visualization of elevation data in geosciences. An even more striking disparity becomes evident when we consider the type of the input elevation data – to our knowledge only a few instances of the use of RVT for bathymetric data exist in published literature (Craven et al., 2021) and they are mostly limited to archaeological studies (Doneus et al., 2013; Doneus et al., 2020). This highlights the need and potential for (better) alternative visualizations of bathymetric data in geosciences, especially when we consider that the bathymetry of the research area often controls later decision regarding the locations of sampling points/coring or geophysical profiles.

Our work demonstrates that excellent results can be obtained with RVT also with bathymetric data of various resolution and from different geological and geographical settings (Section 3). All the examples shown in Figures 2 – 11 demonstrate that the algorithms outperform HS in every tested scenario – either when visualizing the general morphology of a study area, or when identifying specific feature shapes. One of the greatest strengths of the algorithms provided by the RVT is the possibility to highlight the feature shape of interest ( Table 3 ). This not only facilitates the communication of observations and interpretations of digital bathymetric models, but also allows more accurate sea-, lake-, or riverbed mapping. Additionally, it has great potential to serve as an aid for locating points of interest for further surveys, not only in geoscientific, environmental, or archaeological applications, but also in search and salvage efforts (e.g. for shipwreck sites, locations of lost cargo, …). The results summarized in Table 3 indicate the most suitable algorithms for highlighting features of interest and provide a reference for future users of RVT in submerged marine, lacustrine and fluvial settings.

The quality of the visualizations produced by the algorithms of the RVT is obviously dependent on the set parameters. In this work we only demonstrate the effects of an improper setting for the Ve parameter, which results in sub-optimal visualizations ( Figure 2 ). However, low-quality visualizations can also result from improper settings of all the other parameters which are listed at the beginning of Section 3. When deciding on the values of the parameters, RVT users should consult the RVT manual (Kokalj et al., 2019) which contains the basic guidelines for parameter determination.

While we focus on the use of the RVT software in this paper, it should be noted that several of the described algorithms are also integrated into other open and licensed software solutions, such as Global Mapper, ArcGIS, Whitebox software, QGIS, etc. with HSM being one of the most wide spread algorithms as it is presently optional in all the mentioned solutions. However, several other algorithms (e.g. ONEG, OPOS, SVF, etc.) are also available in some of the mentioned programs.

Finally, it should be pointed out that the examples demonstrated in his paper are used only for qualitatively improving the visual representation of sea-, lake-, and riverbed features and are prone to the subjective bias. For unbiased results one should revert to quantitative geomorphometric analyses, such as feature extraction and automated classification (see Lecours et al., 2016).

This work deals with the visual representation of bathymetric data by using non-standard algorithms with the intention to present a user-friendly alternative to conventional visualization techniques for displaying sea-, lake-, and riverbed morphology. We test out the algorithms on digital bathymetric models which were created from different types of bathymetric data, have different resolutions, cover the shallow-to-abbysal depth range and span through different regions and settings. We identify the best algorithms to display the general morphology of a study area and the optimal algorithms for highlighting geomorphic features of interest. The results of our tests show that the algorithms contained within RVT are far superior to the conventional Hillshade and Slope gradient visualization techniques for bathymetric data. We provide a summary of our observations which can be considered a promotion of the RVT within the offshore and submerged terrestrial scientific community and as a set of guidelines for future users of RVT working in the offshore and submerged terrestrial domains. Our study demonstrates the importance, versatility, and efficacy of RVT for the creation of better and more informative bathymetric data visualizations.

Publicly available datasets were analyzed in this study. This data can be found here: https://www.swisstopo.admin.ch/en/geodata/height/bathy3d.html, https://doi.org/10/gn6h.

AN: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. SP: Data curation, Investigation, Writing – review & editing. MV: Funding acquisition, Writing – review & editing.