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

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 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

Assessment of different models for bathymetry calculation using SPOT multispectral images in a high-turbidity area: the mouth of the Guadiana Estuary

Periodic calculation of coastal bathymetries can show the evolution of geomorpholo- gical features in active areas such as mesotidal estuary mouths. Bathymetries in shallow coastal areas have been addressed mainly by two technologies, lidar and optical remote sensing. Lidar provides good accuracy, but is an expensive technique, requiring planned flights for each region and dates of interest. Optical remote sensing acquires images periodically but its results are limited by water turbidity. Here we use a lidar bathymetry to compare different bathymetry computation methods using a SPOT optical image from a nearby date. Three statistical models (green-band, PCA correlations, and GLM) were applied to obtain mathematical expressions to estimate bathymetry from that image: all gave errors lower than 1 m in an area with depths ranging from 0 to 6 m. These algorithms were then applied to images from three different dates, correcting the effects caused by different tidal and atmospheric condi- tions. We show how this allows the study of morphological changes. We discuss the accuracy obtained with respect to the reference bathymetry (0.9 m on average, but less than 0.5 m in low-turbidity areas), the effects of the turbidity on our estimations, and compare both with previously published results. The results show that this approach is effective and allows identification of known features of coastal dynamics, and thus it would be an important step towards short-term bathymetry monitoring based on optical satellite remote sensing.Ministerio de Ciencia e Innovación CSO2010-15807Consejería de Innovación, Ciencia y Empresa P10-RNM-620

Using RapidEye high spatial resolution imagery in mapping shallow coastal water benthic habitats

http://www.ester.ee/record=b448589

Satellite-derived bathymetry in optically complex waters using a model inversion approach and Sentinel-2 data

This study presents an assessment of a model inversion approach to derive shallow water bathymetry in optically complex waters, with the aim of both understanding localised capability and contributing to the global evaluation of Sentinel-2 for coastal monitoring. A dataset of 12 Sentinel-MSI images, in three different study areas along the Irish coast, has been analysed. Before the application of the bathymetric model two atmospheric correction procedures were tested: Deep Water Correction (DWC) and Case 2 Regional Coastal Color (C2RCC) processor. DWC outperformed C2RCC in the majority of the satellite images showing more consistent results. Using DWC for atmospheric correction before the application of the bathymetric model, the lowest average RMSE was found in Dublin Bay (RMSE ¼ 1.60, bias ¼ \u100000 0.51), followed by Mulroy Bay (RMSE ¼ 1.66, bias ¼ 1.30) while Brandon Bay showed the highest average error (RMSE ¼ 2.43, bias ¼ 1.86). However, when the optimal imagery selection was considered, depth estimations with a bias less than 0.1 m and a spread of 1.40 m were achieved up to 10 m. These results were comparable to those achieved by empirical tuning methods, despite not relying on any in situ depth data. This conclusion is of particular relevance as model inversion approaches might allow future modifications in crucial parts of the processing chain leading to improved results. Atmospheric correction, the selection of optimal images (e.g. low turbidity), the definition of suitably limited ranges for the per-pixel occurrence of optical constituents (phytoplankton, CDOM, backscatter) and seabed reflectances, in combination with the understanding of the specifics characteristics at each particular site, were critical steps in the derivation of satellite bathymetry

Posidonia Oceanica habitat mapping in shallow coastal waters along Losinj Island, Croatia using Geoeye-1 multispectral imagery

Seagrasses are important components of marine ecosystem. They are a primary food source for many organisms and provide shelter and nursery areas for many more. Also they stabilize sediments and act as a natural barrier against coastal erosion. But despite their valuable rule, Seagrasses are facing large threats because of human development. The stresses caused by human activities like trawling and anchoring, seabed mining, gas or mineral exploration and production, industrial chemical waste, agricultural run-off and coastal development results in a worldwide decline of seagrass meadows coverage. In this thesis, a study was carried out using Geoeye-1 satellite data acquired on July and August 2011 to extract bottom type features, i.e. seagrass (Posidonia Oceanica), sand and rock in shallow coastal waters of Losinj Island, Croatia. To conduct the study, atmospheric correction, glint removal and water column correct were done to remove the noise from the seabed reflectance but due to some quality problems (sensor calibration) with the imagery dataset prevented us to get satisfactory results from glint removal and water column correction. These techniques are based on empirical models among different band pairs and in the case of a problem in making an accurate reflectance values, their result would be unreliable. So it was decided to perform a principle component analysis to improve the spectral separability of desired classes. Then a hard supervised classification was performed to identify the spectral clusters and label them based on the training phase of the classification algorithm. But before running the classifier to compensate the attenuation effect of water body, it was decided to consider each training sample as a separate class and afterwards reclassify the results into our primary classes. At the end of the classification result were edited using a majority filter to reduce the salt and pepper effect of the classification results and the accuracy of the classification was calculated for each scene. Afterwards a mosaic was produced from the classification results. The overall accuracy of the mosaic and its kappa coefficient was calculated as 80% and 0.7 respectively which proved that the classification was successful and Geoeye-1 imagery can be used reliably to identify the extent of seagrass community in a fast and cost-effective way.Seagrasses are important components of marine ecosystem. They are a primary food source for many organisms and provide shelter and nursery areas for many more. Also they stabilize sediments and act as a natural barrier against coastal erosion. But despite their valuable rule, Seagrasses are facing large threats because of human development. The stresses caused by human activities like trawling and anchoring, seabed mining, gas or mineral exploration and production, industrial chemical waste, agricultural run-off and coastal development results in a worldwide decline of seagrass meadows coverage. In this thesis, a study was carried out using Geoeye-1 satellite data acquired on July and August 2011 to extract bottom type features, i.e. seagrass (Posidonia Oceanica), sand and rock in shallow coastal waters of Losinj Island, Croatia. To conduct the study, atmospheric correction, glint removal and water column correction were done to remove the noise from the seabed reflectance but due to some quality problems with the imagery dataset prevented us to get satisfactory results from the statistical analysis; it was decided to perform a principle component analysis (PCA) to improve the spectral separability of desired classes. Then a hard supervised classification was performed to identify the spectral clusters and label them based on the training phase of the classification algorithm. But before running the classifier to compensate the attenuation effect of water body, it was decided to consider each training sample as a separate class and afterwards reclassify the results into our primary classes. At end the classification result were edited using a majority and the accuracy of the classification was calculated for each scene. Afterwards a mosaic was produced from the classification results. The overall accuracy of the mosaic and its kappa coefficient was calculated as 80% and 0.7 respectively which proved that the classification was successful and Geoeye-1 imagery can be used reliably to identify the extent of seagrass community in a fast and cost-effective way