Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

A combined machine learning and residual analysis approach for improved retrieval of shallow bathymetry from hyperspectral imagery and sparse ground truth data

Mapping shallow bathymetry by means of optical remote sensing has been a challenging task of growing interest in recent years. Particularly, many studies exploit earlier empirical models together with the latest multispectral satellite imagery (e.g., Sentinel 2, Landsat 8). However, in these studies, the accuracy of resulting bathymetry is (a) limited for deeper waters (>15 m) and/or (b) is being influenced by seafloor type albedo. This study explores further the capabilities of hyperspectral satellite imagery (Hyperion), which provides several spectral bands in the visible spectrum, along with existing reference bathymetry. Bathymetry predictors are created by applying the semi-empirical approach of band ratios on hyperspectral imagery. Then, these predictors are fed to machine learning regression algorithms for predicting bathymetry. Algorithm performance is being further compared to bathymetry predictions from multiple linear regression analysis. Following the initial predictions, the residual bathymetry values are interpolated by applying the Ordinary Kriging method. Then, the predicted bathymetry from all three algorithms along with their associated residual grids is used as predictors at a second processing stage. Validation results show that by using a second stage of processing, the root-mean-square error values of predicted bathymetry is being improved by ≈1 m even for deeper water (up to 25 m). It is suggested that this approach is suitable for (a) contributing wide-scale, high-resolution shallow bathymetry toward the goals of the Seabed 2030 program and (b) as a coarse resolution alternative to effort-consuming single-beam sonar or costly airborne bathymetric laser surveying

Characterising the ocean frontier : a review of marine geomorphometry

Geomorphometry, the science that quantitatively describes terrains, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using Geographic Information Systems (GIS) has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade, a suite of geomorphometric techniques have been applied (e.g. terrain attributes, feature extraction, automated classification) to investigate the characterisation of seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is nevertheless much common ground between terrestrial and marine geomorphology applications and it is important that, in developing the science and application of marine geomorphometry, we build on the lessons learned from terrestrial studies. We note, however, that not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four- dimensional nature of the marine environment causes its own issues, boosting the need for a dedicated scientific effort in marine geomorphometry. This contribution offers the first comprehensive review of marine geomorphometry to date. It addresses all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry.peer-reviewe

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data

The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry and other oceanographic data using traditional survey methods. Much of the coastal shallow water zone worldwide remains unmapped using recent techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a useful tool in predicting water depth in coastal zones, particularly in conjunction with other standard datasets, though its quality and accuracy remains largely unconstrained. A common challenge in any prediction study is to choose a small but representative group of predictors, one of which can be determined as the best. In this respect, exploratory analyses are used to guide the make-up of this group, where we choose to compare a basic non-spatial model versus four spatial alternatives, each catering for a variety of spatial effects. Using one instance of RapidEye satellite imagery, we show that all four spatial models show better adjustments than the non-spatial model in the water depth predictions, with the best predictor yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also factor in the influence of bottom type in explaining water depth variation. However, the prediction ranges are too large to be used in high accuracy bathymetry products such as navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or coastal zone management

A review of marine geomorphometry, the quantitative study of the seafloor

Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four-dimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acousticbased mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry. This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.peer-reviewe

Mapping Nearshore Bathymetry with Spaceborne Data Fusion and State Space Modeling

Despite numerous techniques for measuring and estimating water depth, bathymetry in the nearshore zone is notoriously difficult to map. Dangerous sea states, noisy environmental conditions, and expensive survey operations, particularly in remote areas, contribute to the difficulties of obtaining data along the coast. Global datasets, derived mainly from satellite altimetry methods, do exist, but they have significant limitations nearshore. Numerous high-resolution datasets, conventionally acquired with acoustic and lidar techniques, also exist, but they cover only a small percentage of the world's coasts. Spaceborne data fusion employing multispectral satellite derived bathymetry (SDB) offers the potential to significantly reduce the global lack of nearshore bathymetry, coined the "white ribbon" by the hydrographic community, referring to the alongshore data gap on many nautical charts. A broad term, multispectral SDB spans a diverse spectrum of methods that have been used extensively in specific case studies, but the application of multispectral SDB on a global or regional scale is significantly limited by the availability of in situ reference depths needed to tune derived values. Additionally, many existing approaches only use a single multispectral image, which can result in significant errors or missing data if the image contains environmental or sensor noise, such as clouds, sediment plumes, or detector-edge artifacts. This dissertation presents two spaceborne empirical multispectral SDB methods to address shortcomings of existing SDB approaches and reduce the global shortage of nearshore bathymetry – (1) active/passive spaceborne data fusion combining MABEL/ICESat-2 and multispectral data and (2) state space modeling of Sentinel-2 and Landsat 8 multispectral data to generate gap-free models of relative SDB (rSDB) with corresponding uncertainty estimates. The recently launched ICESat-2 mission offers an opportunity for a completely spaceborne active-passive data fusion approach to nearshore bathymetry by potentially providing a global source of nearshore reference depths to tune empirical multispectral SDB algorithms. The main objectives of the ICESat-2 mission are to measure ice-sheet elevations, sea-ice thickness, and global biomass, but ICESat-2’s 532-nm wavelength photon-counting Advanced Topographic Laser Altimeter System (ATLAS) was first posited, then demonstrated capable of detecting bathymetry in certain nearshore environments. Presented in two studies conducted prior to ICESat-2’s launch, the active-passive approach is demonstrated with data from MABEL, NASA’s high-altitude ATLAS simulator system. The first study assessed the ability to derive bathymetry from MABEL and then evaluated the accuracy and reliability of MABEL bathymetry using data acquired in Keweenaw Bay, Lake Superior. The study also developed and verified a baseline model to predict numbers of bottom returns as a function of water depth. The second study completed the demonstration of the spaceborne active/passive data fusion method by synergistically fusing MABEL-derived bathymetry and Landsat 8 multispectral Operational Land Imager (OLI) imagery over the entire Keweenaw Bay study site using the Stumpf band-ratio algorithm. The study also assessed the spatiotemporal viability of the data fusion approach by characterizing the variability of global coastal water clarity as interpreted from Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data. The calculated SDB agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.7 m, and the spatiotemporal viability analysis indicated that the spaceborne active-passive data fusion approach may be viable over many regions of the globe throughout the course of a year. State space modeling of empirical multitemporal SDB overcomes limitations of single-image SDB by leveraging the bathymetric signal in multispectral time series to create gap-free models of relative SDB (rSDB) for an arbitrary date, enabling SDB for dates with noisy or no data. State space models (SSMs) are well established in many applications but are absent in empirical SDB literature. Consisting of a state equation, which relates consecutive state vectors, and an observation equation, which relates observations to the state vector, SSMs are typically solved using Kalman filtering techniques, which provide estimates of uncertainties along with state estimates. SSMs also provide a mechanism for data fusion by allowing an observation equation for multiple observed time series. The third study demonstrates a state space approach to empirical multispectral SDB by applying local level SSMs to Landsat 8 OLI and Sentinel-2 MSI rSDB time series, both separately and fused. A representative single-sensor SSM (Landsat 8) was transformed to SDB that agreed with a high-resolution topobathymetric lidar dataset to within an RMSE of 0.29 m, which indicates the promising performance of the state space framework. Internally consistent fused-sensor SSMs verified that state space modeling also offers a data-fusion method capable of incorporating time series from a diverse suite of multispectral sensors

A MACHINE LEARNING APPROACH TO MULTISPECTRAL SATELLITE DERIVED BATHYMETRY

Abstract. Bathymetry in coastal environment plays a key role in understanding erosion dynamics and evolution along coasts. In the presented investigation depth along the shore-line was estimated using different multispectral satellite data. Training and validation data derived from a traditional bathymetric survey developed along transects in Cesenatico; measured data were collected with a single-beam sonar returning centimetric precision. To limit spatial auto-correlation training and validation dataset were built choosing alternatively one transect as training and another as validation. Each set was composed by a total of ~6000 points. To estimate water depth two methods were tested, Support Vector Machine (SVM) and Random Forest (RF). The RF method provided the higher accuracy with a root mean square error value of 0.228 m and mean absolute error of 0.158 m, against values of 0.409 and 0.226 respectively for SVM. Results show that application of machine learning methods to predict depth near shore can provide interesting results that can have practical applications

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs