Seabed mapping in coastal shallow waters using high resolution multispectral and hyperspectral imagery

Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.Peer ReviewedPostprint (published version

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

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process

BATHYMETRY EXTRACTION FROM SPOT 7 SATELLITE IMAGERY USING RANDOM FOREST METHODS

The scope of this research is the application of the random forest method to SPOT 7 data to produce bathymetry information for shallow waters in Indonesia. The study aimed to analyze the effect of base objects in shallow marine habitats on estimating bathymetry from SPOT 7 satellite imagery. SPOT 7 satellite imagery of the shallow sea waters of Gili Matra, West Nusa Tenggara Province was used in this research. The estimation of bathymetry was carried out using two in-situ depth-data modifications, in the form of a random forest algorithm used both without and with benthic habitats (coral reefs, seagrass, macroalgae, and substrates). For bathymetry estimation from SPOT 7 data, the first modification (without benthic habitats) resulted in a 90.2% coefficient of determination (R2) and 1.57 RMSE, while the second modification (with benthic habitats) resulted in an 85.3% coefficient of determination (R2) and 2.48 RMSE. This research showed that the first modification achieved slightly better results than the second modification; thus, the benthic habitat did not significantly influence bathymetry estimation from SPOT 7 imagery

Supplementary report to the final report of the coral reef expert group: S6. Novel technologies in coral reef monitoring

[Extract] This report summarises a review of current technological advances applicable to coral reef monitoring, with a focus on the Great Barrier Reef Marine Park (the Marine Park). The potential of novel technologies to support coral reef monitoring within the Reef 2050 Integrated Monitoring and Reporting Program (RIMReP) framework was evaluated based on their performance, operational maturity and compatibility with traditional methods. Given the complexity of this evaluation, this exercise was systematically structured to address the capabilities of technologies in terms of spatial scales and ecological indicators, using a ranking system to classify expert recommendations.An accessible copy of this report is not yet available from this repository, please contact [email protected] for more information

Analyzing Spatial Patterns in Reefscape Ecology Via Remote Sensing, Benthic Habitat Mapping, and Morphometrics

A growing number of scientists are investigating applications of landscape ecology principles to marine studies, yet few coral reef scientists have examined spatial patterns across entire reefscapes with a holistic ecosystem-based view. This study was an effort to better understand reefscape ecology by quantitatively assessing spatial structures and habitat arrangements using remote sensing and geographic information systems (GIS). Quantifying recurring patterns in reef systems has implications for improving the efficiency of mapping efforts and lowering costs associated with collecting field data and acquiring satellite imagery. If a representative example of a reef is mapped with high accuracy, the data derived from habitat configurations could be extrapolated over a larger region to aid management decisions and focus conservation efforts. The aim of this project was to measure repeating spatial patterns at multiple scales (10s m2 to 10s km2) and to explain the environmental mechanisms which have formed the observed patterns. Because power laws have been recognized in size-frequency distributions of reef habitat patches, this study further investigated whether the property exists for expansive reefs with diverse geologic histories. Intra- and inter-reef patch relationships were studied at three sites: Andavadoaka (Madagascar), Vieques (Puerto Rico), and Saipan (Commonwealth of the Northern Mariana Islands). In situ ecological information, including benthic species composition and abundance, as well as substrate type, was collected with georeferenced video transects. LiDAR (Light Detection and Ranging) surveys were assembled into digital elevation models (DEMs), while vessel-based acoustic surveys were utilized to empirically tune bathymetry models where LiDAR data were unavailable. A GIS for each site was compiled by overlying groundtruth data, classifications, DEMs, and satellite images. Benthic cover classes were then digitized and analyzed based on a suite of metrics (e.g. patch complexity, principle axes ratio, and neighborhood transitions). Results from metric analyses were extremely comparable between sites suggesting that spatial prediction of habitat arrangements is very plausible. Further implications discussed include developing an automated habitat mapping technique and improving conservation planning and delimitation of marine protected areas

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

Benthic habitat mapping in coastal waters of south–east Australia

The Victorian Marine Mapping Project will improve knowledge on the location, spatial distribution, condition and extent of marine habitats and associated biodiversity in Victorian State waters. This information will guide informed decision making, enable priority setting, and assist in targeted natural resource management planning. This project entails benthic habitat mapping over 500 square kilometers of Victorian State waters using multibeam sonar, towed video and image classification techniques. Information collected includes seafloor topography, seafloor softness and hardness (reflectivity), and information on geology and benthic flora and fauna assemblages collectively comprising habitat. Computerized semi-automated classification techniques are also being developed to provide a cost effective approach to rapid mapping and assessment of coastal habitats.Habitat mapping is important for understanding and communicating the distribution of natural values within the marine environment. The coastal fringe of Victoria encompasses a rich and diverse ecosystem representative of coastal waters of South-east Australia. To date, extensive knowledge of these systems is limited due to the lack of available data. Knowledge of the distribution and extent of habitat is required to target management activities most effectively, and provide the basis to monitor and report on their status in the future.<br /

Habitat Mapping in the Farasan Islands (Saudi Arabia) Using CASI and QuickBird Imagery

Map products derived from remote sensing technology increase our understanding and ability to manage tropical marine environments. The enhanced mapping capabilities of hyperspectral sensors are well understood; yet technology uptake, particularly for large scale tasks, has been slow. The study presented represents one of the largest hyperspectral projects to date, and paves the way towards increased use of this technology. Hyperspectral CASI-550 imagery and multispectral QuickBird imagery, was acquired over 3,168 km2 of the Farasan Islands. In addition to the typical image processing steps, inopportune water condensation in the CASI sensors lens necessitated further processing to remove an across-track artifact. We present a simple protocol for correcting this abnormality, utilizing an abundance of optically deep water to model and correct the error. Investment in optical, bathymetric, and other supporting field data, along with the acquisition of the QuickBird imagery was vital. Data pre-processing facilitated thematic mapping with accuracy comparable to other studies, while allowing the use of spectral unmixing to discriminate coral from within algae dominated patches in shallow water (0-5 m) environments. The unmixing model proved robust, was readily adaptable to the CASI sensor and provides additional habitat information beyond the level of thematic mapping alone