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

Examination of the spatial resolution and discrimination capability of various acoustic seafloor classification techniques based on MBES backscatter data

This thesis focuses on two major topics regarding acoustic seafloor classification techniques. The first topic is about acoustic class separation which affects the discriminative power of classification techniques and the quality of final results. The second topic is the spatial resolution of seafloor acoustic maps that is fundamentally coupled with acoustic class separation. The approach followed here, a) employs an advanced unsupervised classification technique and b) analyzes its implications on the angular response analysis (ARA) of acoustic backscatter. Moreover, a novel approach for improving the ARA technique is described. Applying an unsupervised Bayesian technique that performs an internal cluster validation test, we obtain objective classification of the entire backscatter dataset. This technique utilizes single-angle backscatter measurements from the middle range of the sonar swath offering better discrimination of acoustic classes. The main advantages of the Bayesian technique are that it does not require sonar calibration, it resolves along-swath seafloor variations and that it outputs ordinal categorical values for acoustic classes. Furthermore, the concept of the Hyper-Angular Cube (HAC) is applied and its results are compared with the Bayesian classification results. The HAC is built by several angular backscatter layers which can result either by interpolation of dense soundings or by normalization of backscatter mosaics at different incidence angles. The high dimensional data of the HAC is suitable for supervised classification using machine learning techniques and restricted amount of ground truth information. This approach takes angular dependence of backscatter into consideration and utilizes hydro-acoustic and ground truth data in a more efficient way than it was possible until now

The Hyper-Angular Cube Concept for Improving the Spatial and Acoustic Resolution of MBES Backscatter Angular Response Analysis

This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data structure which offers the possibility to extract one angular response from each cell of the cube. The HAC consists of a finite number of backscatter layers, each representing backscatter values corresponding to single-incidence angle ensonifications. The construction of the HAC layers can be achieved either by interpolating dense soundings from highly overlapping multibeam echo-sounder (MBES) surveys (interpolated HAC, iHAC) or by producing several backscatter mosaics, each being normalized at a different incidence angle (synthetic HAC, sHAC). The latter approach can be applied to multibeam data with standard overlap, thus minimizing the cost for data acquisition. The sHAC is as efficient as the iHAC produced by actual soundings, providing distinct angular responses for each seafloor type. The HAC data structure increases acoustic class separability between different acoustic features. Moreover, the results of angular response analysis are applied on a fine spatial scale (cell dimensions) offering more detailed acoustic maps of the seafloor. Considering that angular information is expressed through high-dimensional backscatter layers, we further applied three machine learning algorithms (random forest, support vector machine, and artificial neural network) and one pattern recognition method (sum of absolute differences) for supervised classification of the HAC, using a limited amount of ground truth data (one sample per seafloor type). Results from supervised classification were compared with results from an unsupervised method for inter-comparison of the supervised algorithms. It was found that all algorithms (regarding both the iHAC and the sHAC) produced very similar results with good agreement (>0.5 kappa) with the unsupervised classification. Only the artificial neural network required the total amount of ground truth data for producing comparable results with the remaining algorithms

Global ocean spatial suitability for macroalgae offshore cultivation and sinking

Macroalgae offshore cultivation and sinking is considered a potentially practical approach for ocean-based carbon dioxide removal. However, several considerations need to be resolved to assess the effectiveness and sustainability of this approach. Currently, several studies focus on the area required for climate-relevant carbon sequestration through macroalgae cultivation and sinking without considering realistic, global spatial limitations. This study uses a spatially-explicit suitability assessment model for optimised open-ocean afforestation and sinking site designation. By applying specific maritime, ecological and industrial constraints, two maps are produced: a) suitable areas for macroalgae offshore cultivation and sinking, and b) suitable areas for macroalgae sinking only (for sequestration purposes). These data provide a more realistic approach to quantifying the ocean surface (including the corresponding depths) required for macroalgae offshore cultivation and sinking within a spatially sustainable framework. The resulting maps estimate the respective suitability areas within the EEZs of the world countries. A total area suitable for macroalgae offshore cultivation and sinking is calculated at 10.8M km2, whereas sinking-only areas account for 32.8M km2 of the offshore ocean. The implications of spatial suitability patterns at global and national levels are discussed. We suggest that the concept of ‘grow nearshore, sink offshore’ should be explored as an alternative to offshore cultivation

Quantification of the fine-scale distribution of Mn-nodules: insights from AUV multi-beam and optical imagery data fusion

Autonomous underwater vehicles (AUVs) offer unique possibilities for exploring the deep seafloor in high resolution over large areas. We highlight the results from AUV-based multibeam echosounder (MBES) bathymetry / backscatter and digital optical imagery from the DISCOL area acquired during research cruise SO242 in 2015. AUV bathymetry reveals a morphologically complex seafloor with rough terrain in seamount areas and low-relief variations in sedimentary abyssal plains which are covered in Mn-nodules. Backscatter provides valuable information about the seafloor type and particularly about the influence of Mn-nodules on the response of the transmitted acoustic signal. Primarily, Mn-nodule abundances were determined by means of automated nodule detection on AUV seafloor imagery and nodule metrics such as nodules m−2 were calculated automatically for each image allowing further spatial analysis within GIS in conjunction with the acoustic data. AUV-based backscatter was clustered using both raw data and corrected backscatter mosaics. In total, two unsupervised methods and one machine learning approach were utilized for backscatter classification and Mn-nodule predictive mapping. Bayesian statistical analysis was applied to the raw backscatter values resulting in six acoustic classes. In addition, Iterative Self-Organizing Data Analysis (ISODATA) clustering was applied to the backscatter mosaic and its statistics (mean, mode, 10th, and 90th quantiles) suggesting an optimum of six clusters as well. Part of the nodule metrics data was combined with bathymetry, bathymetric derivatives and backscatter statistics for predictive mapping of the Mn-nodule density using a Random Forest classifier. Results indicate that acoustic classes, predictions from Random Forest model and image-based nodule metrics show very similar spatial distribution patterns with acoustic classes hence capturing most of the fine-scale Mn-nodule variability. Backscatter classes reflect areas with homogeneous nodule density. A strong influence of mean backscatter, fine scale BPI and concavity of the bathymetry on nodule prediction is seen. These observations imply that nodule densities are generally affected by local micro-bathymetry in a way that is not yet fully understood. However, it can be concluded that the spatial occurrence of Mn-covered areas can be sufficiently analysed by means of acoustic classification and multivariate predictive mapping allowing to determine the spatial nodule density in a much more robust way than previously possible

Parallel model exploration for tumor treatment simulations

Abstract Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a genetic algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.This work has received funding from the EU Horizon 2020 RIA program INFORE under grant agreement No 825070Peer ReviewedPostprint (author's final draft

Linkages between sediment thickness, geomorphology and Mn nodule occurrence: New evidence from AUV geophysical mapping in the Clarion-Clipperton Zone

The relationship between polymetallic nodules (Mn nodules) and deep-sea stratigraphy is relatively poorly studied and the role of sediment thickness in determining nodule occurrence is an active field of research. This study utilizes geophysical observations from three types of autonomous underwater vehicle (AUV) data (multi-beam bathymetry, sub-bottom profiles and underwater photography) in order to assess this relationship. Multi-beam bathymetry was processed with a pattern recognition approach for producing objective geomorphometric classes of the seafloor for examining their relation to sediment thickness and nodule occurrence. Sub-bottom profiles were used for extracting sediment thickness along a dense network of tracklines. Close-range AUV-photography data was used for automated counting of polymetallic nodules and their geometric features and it served as ground truth data. It was observed that higher nodule occurrence were related to layers with increased sediment thickness. This evidence reveals the role of local seafloor heterogeneity in nodule formation and suggests that unique patterns of local stratigraphy may affect geochemical processes that promote polymetallic nodule development at local scales

An Object-Based Seafloor Classification Tool Using Recognition of Empirical Angular Backscatter Signatures

This study presents a novel concept of seafloor acoustic mapping utilizing the angular dependence of high density soundings. A prerequisite is that data should result from a backscatter-dedicated survey (>100% swath overlap) in order to obtain small-scale seafloor areas ensonified from multiple incidence angles. Accordingly, backscatter data should be geometrically and radiometrically corrected in order to represent only variations due to seafloor type. This method is considered as a mixture of OBIA with empirical ARA and pattern recognition concepts and it provides supervised classification based on empirical backscatter angular signatures of a known set of seafloor types. Therefore it requires a library with all angular signatures corresponding to ground truth locations (seafloor type, dB and angle). The backscatter only needs to be stable and hence this approach is not only applicable on calibrated sonars but works for any MBES system that records backscatter in a stable way. The library should consist of sediment samples, underwater images and/or video which are used to drive the classification and validate its results. Ideally, the ground truth set should cover all different seafloor types from the study area. The concept is that angular backscatter signatures of known seafloor types that have been extracted from fine square areas of seafloor can be utilized for comparison with angular signatures of unknown seafloor. Initially, the study area is segmented into fine squares within which soundings from various beam-angles fall. The smaller the square size, the higher the seafloor homogeneity can be achieved; hence more representative angular backscatter signatures can be extracted for each seafloor type. In this study 5x5 m squares were used for representing naturally homogeneous seafloor. By extracting the angular signatures from the vicinity of sediment sample locations it was possible to use them as reference vectors for performing supervised classification. The classification works in the following way: vectors carrying the mean backscatter value per swath angle are being created from each group of soundings belonging to the same square. Following, each vector is compared to the reference vectors that represent ground-truthed seafloor types. The comparison tests whether the backscatter values of the vector under-comparison fall within a user-defined envelope (range of values) above and below the mean backscatter values of the reference vectors. If the backscatter values for the majority (>85%) of corresponding swath angles belong to the envelope of a reference vector, then these soundings are assigned with the class number of the reference vector. Empirical ARA is more flexible in describing seafloor heterogeneity, compared to physical backscatter models, therefore allowing for classification of a wider variety of seafloor types in a consistent way

Grid files from two AUV missions in the DISCOL area during the SONNE cruise SO242/1

The zip file contains grid files in UTM 16S resulted from AUV mutlibeam data processing and a table with descriptions of these grid files. AUV bathymetry data resulted from interpolation of multibeam depth measurements using the IDW algorithm in SAGA GIS. The AUV bathymetric derivatives (Bathymetric Position Index, Concavity, LS factor, and Terrain Ruggedness Index were calculated in SAGA GIS. The slope derivative was calculated in ArcMap. The AUV backscatter statistics (10th quantile, 90th quantile, mean and mode) were calculated in FMGT Geocoder. The Bayesian classification map was created in SAGA GIS using data from Bayesian classification in Matlab. The ISODATA classification map was created in SAGA GIS using the the AUV backscatter statistics and the Random Forest predictive map was created using the MGET toolbox in ArcMap and the AUV bathymetry, bathymetric derivatives and backscatter statistics data