135350702
Machine learning-based retrieval of benthic reflectance and Posidonia oceanica seagrass extent using a semi-analytical inversion of Sentinel-2 satellite data
ABSTRACT In the epoch of the human-induced climate change, seagrasses can mitigate the resulting negative impacts due to their carbon sequestration ability. The endemic and dominant in the Mediterranean Posidonia oceanica seagrass contains the largest stocks of organic carbon among all seagrass species, yet it undergoes a significant regression in its extent. Therefore, suitable quantitative assessment of its extent and optically shallow environment are required to allow good conservation and management practices. Here, we parameterise a semi-analytical inversion model which employs above-surface remote sensing reflectance of Sentinel-2A to derive water column and bottom properties in the Thermaikos Gulf, NW Aegean Sea, Greece (eastern Mediterranean). In the model, the diffuse attenuation coefficients are expressed as functions of absorption and backscattering coefficients. We apply a comprehensive pre-processing workflow which includes atmospheric correction using C2RCC (Case 2 Regional CoastColour) neural network, resampling of the lower spatial resolution Sentinel-2A bands to 10m/pixel, as well as empirical derivation of water bathymetry and machine learning-based classification of the resulting bottom properties using the Support Vector Machines. SVM-based classification of benthic reflectance reveals ~300 ha of P. oceanica seagrass between 2 and 16 m of depth, and yields very high producer and user accuracies of 95.3% and 99.5%, respectively. Sources of errors and uncertainties are discussed. All in all, recent advances in Earth Observation in terms of optical satellite technology, cloud computing and machine learning algorithms have created the perfect storm which could aid high spatio-temporal, large-scale seagrass habitat mapping and monitoring, allowing for its integration to the Analysis Ready Data era and ultimately enabling more efficient management and conservation in the epoch of climate change.
55655598
Detritus dynamics in the seagrass Posidonia oceanica: elements for an ecosystem carbon and nutrient budget
Leaf decay, leaf l~ t ter export, burial in belowground sinks, and resp~ratory consumption of detritus were exam~ned at 2 different depths in a Posidonia oceanica (L ) Delile meadow off the Medes Islands, NW Mediterranean. At 5 m , the amount of exported leaf litter represented carbon, nitrogen and phosphorus losses of 7 . 9 and 6%) of the plant primary productlon, respectively. About 26% of the carbon produced by the plant in 1 yr was immobilized by burial in the belowground compartment, i.e. as roots and rhizomes. Annual nitrogen and phosphorus burial in the sediment was 8 and 5",, of total N and P needs, respectively. Respiratory consumption (aerobic) of carbon leaf detritus represented 17"" of the annual production. An additional, but very substantial, loss of carbon as very fine particulate organic matter has been estimated at ca 48%. At 13 m the pattern of carbon losses was similar, but the lesser effect of wave action (reldtive to that a t 5 m) reduced exportation, hence increasing the role of respiratory consumption. Data on carbon losses indicated that only a small part of the plant productlon was actually available to fuel the food web of this ecosystem. Total nutrient losses were in the range of 21 to 47 " , I of annual needs. From differences found in N and P concentrations between l iv~ng and dead tissues, it is suggested that important nutrient recycling (50 to 70%)) may be due either to reclamation or to leaching immediately after plant death.
4932709
Seagrass ecosystems as a globally significant carbon stock
Seagrass meadows are some of the most productive ecosystems on Earth. An analysis of organic carbon data from just under one thousand seagrass meadows indicates that, globally, these systems could store between 4.2 and 8.4 Pg carbon.
56257891
Global significance of seagrass fishery activity
Seagrass meadows support fisheries through provision of nursery areas and trophic subsidies to adjacent habitats. As shallow coastal habitats, they also provide key fishing grounds; however, the nature and extent of such exploitation are poorly understood. These productive meadows are being degraded globally at rapid rates. For degradation to cease, there needs to be better appreciation for the value of these habitats in supporting global fisheries. Here, we provide the first global scale study demonstrating the extent, importance and nature of fisheries exploitation of seagrass meadows. Due to a paucity of available data, the study used a global expert survey to demonstrate the widespread significance of seagrass-based fishing activity. Our study finds that seagrass-based fisheries are globally important and present virtually wherever seagrass exists, supporting subsistence, commercial and recreational activity. A wide range of fishing methods and gear is used reflecting the spatial distribution patterns of seagrass meadows, and their depth ranges from intertidal (accessible by foot) to relatively deep water (where commercial trawls can operate). Seagrass meadows are multispecies fishing grounds targeted by fishers for any fish or invertebrate species that can be eaten, sold or used as bait. In the coastal communities of developing countries, the importance of the nearshore seagrass fishery for livelihoods and well-being is irrefutable. In developed countries, the seagrass fishery is often recreational and/or more target species specific. Regardless of location, this study is the first to highlight collectively the indiscriminate nature and global scale of seagrass fisheries and the diversity of exploitative methods employed to extract seagrass-associated resources. Evidence presented emphasizes the need for targeted management to support continued viability of seagrass meadows as a global ecosystem service provider.
14139947
Seagrass Ecosystem Services and Their Variability across Genera and Geographical Regions
Threats to and loss of seagrass ecosystems globally, impact not only natural resources but also the lives of people who directly or indirectly depend on these systems. Seagrass ecosystems play a multi-functional role in human well-being, e.g. food through fisheries, control of erosion and protection against floods. Quantifying these services reveals their contributions to human well-being and helps justify seagrass conservation. There has been no comprehensive assessment as to whether seagrass ecosystem services are perceived to vary over the globe or amongst genera. Our study compiles the most complete list of ecosystem services provided by seagrasses so far, including bioregional- and genus-specific information from expert opinion and published studies. Several seagrass ecosystem services vary considerably in their (known) provision across genera and over the globe. Seagrasses genera are clearly not all equal with regard to the ecosystem services they provide. As seagrass genera are not evenly distributed over all bioregions, the presence of an ecosystem service sometimes depends on the genera present. Larger sized seagrass genera (e.g. Posidonia, Enhalus) are perceived to provide more substantial and a wider variety of ecosystem services than smaller species (e.g. Halophila, Lepilaena). Nevertheless, smaller species provide important services. Our findings point out data gaps, provide new insight for more efficient management and recommend caution in economic valuation of seagrass services worldwide.
84587281
Mediterranean seagrass (Posidonia oceanica) loss between 1842 and 2009
Abstract We compiled published data in peer-review articles and grey literature reports, which we appended with our own data, to assess the changes in areal extent, cover and shoot density that P. oceanica meadows have experienced between years 1842 and 2009 in the Mediterranean basin. Our results demonstrate an overall tendency towards decline of the areal extent, cover and shoot density of P. oceanica meadows during the last 50 years, the period with the largest availability of records. Available estimates indicated that between 13% and 50% of seagrass areal extent of P. oceanica in the Mediterranean basin appear to be lost, and that the remaining meadows of the Mediterranean may have thinned shoot density by 50% for the last 20 years and have became more fragmented. Considering the changes quantified in P. oceanica areal extent, cover and density, about 6.9% of the potential P. oceanica vegetation would have been lost annually over the last 50 years. The loss of P. oceanica meadows in the Mediterranean may have lead to a substantial (between 11% and 52%) reduction of the capacity of this key coastal ecosystem to sequester carbon in the last 50 years, hence reducing the carbon sink capacity of the entire Mediterranean Sea. The major causes of P. oceanica loss were widespread local disturbances, but recently, global disturbances, such as climate change and the spread of invasive exotic species, were also seriously threatening Posidonia meadows in the Mediterranean. These findings urgently call for implementation of management measures aiming at mitigating coastal deterioration by combining local and global actions.
54014709
Seasonal and small-scale spatial variability of herbivory pressure on the temperate seagrass Posidonia oceanica
Despite increasing evidence that herbivory on seagrasses can be substantial in nearshore systems, there is only scarce experimental data on the direct quantification of seagrass removal by herbivores. To help fill this gap, and to evaluate the hypothesis that herbivory on the Mediterranean seagrass Posidonia oceanica (L.) Delile is generally low, we assessed the patterns of grazing pressure variability and its effects on seagrass in an undisturbed meadow in the NW Mediter- ranean. We quantified herbivore density and grazing pressure through both direct (tethering exper- iment) and indirect (through marks of herbivore attacks) measurements. Although grazing varied greatly both temporally and spatially, our results show that whereas consumption by the sea urchin Paracentrotus lividus is relatively minor, P. oceanica is intensely grazed by the fish Sarpa salpa in summer. During this period, fish are very abundant at a depth of 5 m, with consumption rates that temporarily exceed seagrass production, which is at its yearly minimum. This imbalance between consumption and production causes the appearance of mowed patches that can be seen from early summer to September; seagrass biomass is reduced by 50% in such patches. Through direct mea- surements of consumption, our study has revealed that P. oceanica consumption by herbivores can be substantial with respect to the total annual production and much higher than previously estimated through indirect measurements. Thus, it becomes apparent that estimation of consumption rates by indirect methods can grossly underestimate the importance of herbivory in seagrass ecosystems, which leads us to strongly advocate the use of direct methods whenever possible.
84326174
Is Posidonia oceanica regression a general feature in the Mediterranean Sea
Over the last few years, a widespread regression of Posidonia oceanica meadows has been noticed in the Mediterranean Sea. However, the magnitude of this decline is still debated. The objectives of this study are (i) to assess the spatio-temporal evolution of Posidonia oceanica around Cap Corse (Corsica) over time comparing available ancient maps (from 1960) with a new (2011) detailed map realized combining different techniques (aerial photographs, SSS, ROV, scuba diving); (ii) evaluate the reliability of ancient maps; (iii) discuss observed regression of the meadows in relation to human pressure along the 110 km of coast. Thus, the comparison with previous data shows that, apart from sites clearly identified with the actual evolution, there is a relative stability of the surfaces occupied by the seagrass Posidonia oceanica . The recorded differences seem more related to changes in mapping techniques. These results confirm that in areas characterized by a moderate anthropogenic impact, the Posidonia oceanica meadow has no significant regression and that the changes due to the evolution of mapping techniques are not negligible. However, others facts should be taken into account before extrapolating to the Mediterranean Sea (e.g. actually mapped surfaces) and assessing the amplitude of the actual regression.
84747576
Mediterranean seagrass vulnerable to regional climate warming
Lush meadows of the seagrass Posidonia oceanica represent an important coastal marine ecosystem in the Mediterranean Sea and a major carbon sink. However, an analysis predicts that, in the absence of mitigation, climate change will lead to the functional extinction of P. oceanica meadows by the middle of the twenty-first century.
55674312
Spatial, temporal and structural variations of a Posidonia oceanica seagrass meadow facing human activities
The Bay of Saint-Cyr (Provence, France, Mediterranean Sea) is the site of two harbours, coastal urban development, trawling, boat anchoring and a sewage outfall. The Posidonia oceanica seagrass distribution was mapped with the help of aerial photographs, side scan sonar and GIS. In addition, the temporal variations of its distribution were studied by aerial photographs and GIS from 1955 to 2000. Finally, coverage and shoot density were measured via scuba-diving. This work reveals (i) the regression of theP. oceanica meadow at sites where harbours have been built, (ii) the occurrence of spaces within the meadow free of live P. oceanica (‘‘intermattes’’), which account for 8% of its surface area, (iii) a deep area where P. oceanica coverage and shoot density are low and (iv) evidence of regression, although modest, of the meadow at its lower limit. Nevertheless, the study site also exhibits an extensive and on the whole relatively healthy meadow whose limits have changed little over time. # 2005 Elsevier B.V. All rights reserved.
23303149
Accelerating loss of seagrasses across the globe threatens coastal ecosystems
Coastal ecosystems and the services they provide are adversely affected by a wide variety of human activities. In particular, seagrass meadows are negatively affected by impacts accruing from the billion or more people who live within 50 km of them. Seagrass meadows provide important ecosystem services, including an estimated $1.9 trillion per year in the form of nutrient cycling; an order of magnitude enhancement of coral reef fish productivity; a habitat for thousands of fish, bird, and invertebrate species; and a major food source for endangered dugong, manatee, and green turtle. Although individual impacts from coastal development, degraded water quality, and climate change have been documented, there has been no quantitative global assessment of seagrass loss until now. Our comprehensive global assessment of 215 studies found that seagrasses have been disappearing at a rate of 110 km2 yr−1 since 1980 and that 29% of the known areal extent has disappeared since seagrass areas were initially recorded in 1879. Furthermore, rates of decline have accelerated from a median of 0.9% yr−1 before 1940 to 7% yr−1 since 1990. Seagrass loss rates are comparable to those reported for mangroves, coral reefs, and tropical rainforests and place seagrass meadows among the most threatened ecosystems on earth.
129302082
A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data
In order to retrieve bathymetry, substratum type and the concentrations of the optically active constituents of the water column, an integrated physics based mapping approach was applied to airborne hyperspectral data of Moreton Bay, Australia. The remotely sensed data were sub-optimal due to high and mid-level cloud covers. Critical to the correct interpretation of the resultant coastal bathymetry map was the development of a quality control procedure based on additional outputs of the integrated physics based mapping approach and the characteristics of the instrument. These two outputs were: an optical closure term which defines differences between the image and model based remote sensing signal; and an estimate of the relative contribution of the substratum signal to the remote sensing signal. This quality control procedure was able to identify those pixels with a reliable retrieval of depth and to detect thin and thick clouds and their shadows, which were subsequently masked out from further analysis. The derived coastal bathymetry in depths ranging 4–13 m for the mapped area was within ± 15% of boat-based multi-beam acoustic mapping survey of the same area. The agreement between the imaging spectrometry and the acoustic datasets varies as a function of the contribution of the bottom visibility to the remote sensing signal. As expected, there was greater agreement in shallower clear water (± 0.67 m) than quasi-optically deep water (± 1.35 m). The quantitative identification and screening of the optically deep waters and the quasi-optically deep waters led to improved precision in the depth retrieval. These results suggest that the physics based mapping approach adopted in this study performs well for retrieving water column depths in coastal waters in water depths ranging 4–13 m for the area and conditions studied, even with sub-optimal imagery. Crown copyright © 2008 Published by Elsevier Inc.
129442691
Efficient radiative transfer model inversion for remote sensing applications
A simple method for efficient inversion of arbitrary radiative transfer models for image analysis is presented. The method operates by representing the shape of the function that maps model parameters to spectral reflectance by an adaptive look-up tree (ALUT) that evenly distributes the discretization error of tabulated reflectances in spectral space. A post-processing step organizes the data into a binary space partitioning tree that facilitates an efficient inversion search algorithm. In an example shallow water remote sensing application, the method performs faster than an implementation of previously published methodology and has the same accuracy in bathymetric retrievals. The method has no user configuration parameters requiring expert knowledge and minimizes the number of forward model runs required, making it highly suitable for routine operational implementation of image analysis methods. For the research community, straightforward and robust inversion allows research to focus on improving the radiative transfer models themselves without the added complication of devising an inversion strategy.
123344369
Retrieving key benthic cover types and bathymetry from hyperspectral imagery
Bathymetry and the spatial distribution of benthic cover in coastal waters are of key importance in managing and monitoring our coastal water environments. Currently very little of the Western Australian shallow coastal water habitats are mapped, and for those maps that do exist, the spatial resolution generally is poor and the information is dated. Aircraft and space-borne hyperspectral sensors have been shown to be useful in imaging substrate features in shallow coastal waters. This paper describes a method for quantitatively estimating both bathymetry and benthic cover in shallow waters from hyperspectral imagery. The method incorporates a shallow water reflectance model, which accounts for the water column absorption and backscattering, water depth and substrate reflectance. The model was tested against simulated reflectance data, demonstrating the models' ability to retrieve appropriate fractional coverage of sediment, sea grass and brown algae for depths ranging from 1 - 12 m. The model was applied to a HyMap image encompassing a portion of the Jurien Bay Marine Park off the coast of Western Australia. The retrieved benthic cover products were compared to underwater video observations sampled within the image. The comparison shows the method's great potential for characterizing key aspects of marine ecosystems from remotely sensed hyperspectral data.
25014287
Hyperspectral remote sensing for shallow waters. I. A semianalytical model.
For analytical or semianalytical retrieval of shallow-water bathymetry and/or optical properties of the water column from remote sensing, the contribution to the remotely sensed signal from the water column has to be separated from that of the bottom. The mathematical separation involves three diffuse attenuation coefficients: one for the downwelling irradiance (K(d)), one for the upwelling radiance of the water column (K(u)(C)), and one for the upwelling radiance from bottom reflection (K(u)(B)). Because of the differences in photon origination and path lengths, these three coefficients in general are not equal, although their equality has been assumed in many previous studies. By use of the Hydrolight radiative-transfer numerical model with a particle phase function typical of coastal waters, the remote-sensing reflectance above (R(rs)) and below (r(rs)) the surface is calculated for various combinations of optical properties, bottom albedos, bottom depths, and solar zenith angles. A semianalytical (SA) model for r(rs) of shallow waters is then developed, in which the diffuse attenuation coefficients are explicitly expressed as functions of in-water absorption (a) and backscattering (b(b)). For remote-sensing inversion, parameters connecting R(rs) and r(rs) are also derived. It is found that r(rs) values determined by the SA model agree well with the exact values computed by Hydrolight (~3% error), even for Hydrolight r(rs) values calculated with different particle phase functions. The Hydrolight calculations included b(b)/a values as high as 1.5 to simulate high-turbidity situations that are occasionally found in coastal regions.
26089968
Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization.
In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R2 = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.
34488748
Passive remote sensing techniques for mapping water depth and bottom features.
Ratio processing methods are reviewed, and a new method is proposed for extracting water depth and bottom type information from passive multispectral scanner data. Limitations of each technique are discussed, and an error analysis is performed using an analytical model for the radiance over shallow water.
129811946
Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data
Abstract The reflectance of shallow water areas to solar illumination is a function of the water depth, the water optical properties and the bottom reflectance. Assuming the water optical properties to be uniform over a given scene area, the signals recorded by a multispectral scanner system may be combined to obtain information on the water attenuation and bottom reflectance parameters without knowledge of the water depth. These techniques are described and evaluated for a test site near North Cat Cay in the Bahamas.
22434742
Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables.
A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.
120202609
Mapping Posidonia oceanica from IKONOS
Posidonia oceanica is the dominant seagrass in the Mediterranean Sea that affects biological, biogeochemical and physical processes in Mediterranean coastal areas. The widespread loss of this species is attributed to excessive anthropic pressure and other large-scale environmental changes. Seagrass conservation requires mapping to estimate the extent of existing stocks and to measure changes over time. Optical remote sensing provides a cost-effective method to monitor vast areas of shallow waters that are potential P. oceanica habitat. As part of an interdisciplinary research effort, where the effects of these seagrasses on the hydrodynamics were investigated, new technologies of reliable, fast and effective monitoring of P. oceanica were essential. This paper presents a method for using IKONOS multispectral imagery for bottom classification in a shallow coastal area of Mallorca (Balearic Islands). After applying a supervised classification, pixels are automatically classified in four classes: sand, rock, P. oceanica bottoms and unclassifiable pixels. Results indicate that, in these clear waters, the spectral response of P. oceanica can be determined to a depth of about 15 m. In order to validate the method, the image classification is compared with a bottom classification derived from an acoustical survey. Agreement with the reference acoustic seabed classification is up to 84% for the sampled area. Spectral IKONOS image analysis is presented as an effective approach for monitoring P. oceanica meadows in most clear, shallow waters of the western Mediterranean.
12495264
Mapping Posidonia meadow from high spatial resolution images in the Gulf of Oristano (Italy)
This work deals with the retrieval of distribution of seabed vegetation cover (essentially Posidonia Oceanica) in the coastal waters (1-10m) of the Gulf of Oristano. A physical approach was used to derive bottom cover map from airborne and satellite imagery (MIVIS, KOMPSAT-2 and RapidEye) acquired in summer 2001, 2011 and 2013. A bathymetric map was also derived from the RapidEye image of 2003. A field campaign was held on 1-8 August 2013 to parameterize the physical model and to validate the 2013 results. Accuracy of bottom cover map and regression between measured and modelled water depths was good providing an overall accuracy of 0.88% and a correlation coefficient of 0.84, respectively. The retrospective analysis revealed that P. oceanica meadow seems to be stable over the 12 years showing only very small variations (-0.2km2). Sea bottom morphology and vegetation cover were also related.
129782665
Use of SPOT 5 for mapping seagrasses: An application to Posidonia oceanica
The SPOT 5 satellite was launched in May 2002; it provides multispectral imagery with a spatial resolution of 10 m and fused imagery with a spatial resolution of 2.5 m. These types of satellite imagery were used for mapping beds of Posidonia oceanica in the Mediterranean Sea, where it is a dominant species forming monospecific beds in a structurally simple environment (four classes: sand, photophilous algae on rock, patchy seagrass beds and continuous seagrass beds). Supervised classifications by depth range were made of both types of image. A direct comparison of overall accuracy between SPOT 2.5 m and SPOT 10 m revealed that this tool provided accurate mapping in both cases (between 73% and 96% accuracy). Although SPOT 2.5 m provides lower overall accuracy than SPOT 10 m, it is a very useful tool for the mapping of P. oceanica, as it allows the patchiness of the formations to be better taken into account. The opportunity to use a reliability scale, which takes into account the effects of extrinsic factors on the processing of the images, confirmed the usefulness of the option of using a reduced pixel size in order to obtain an improved match between the results from mapping and field observations.
37653156
CUBESAT-DERIVED DETECTION OF SEAGRASSES USING PLANET IMAGERY FOLLOWING UNMIXING-BASED DENOISING: IS SMALL THE NEXT BIG?
Abstract. Seagrasses are one of the most productive and widespread yet threatened coastal ecosystems on Earth. Despite their importance, they are declining due to various threats, which are mainly anthropogenic. Lack of data on their distribution hinders any effort to rectify this decline through effective detection, mapping and monitoring. Remote sensing can mitigate this data gap by allowing retrospective quantitative assessment of seagrass beds over large and remote areas. In this paper, we evaluate the quantitative application of Planet high resolution imagery for the detection of seagrasses in the Thermaikos Gulf, NW Aegean Sea, Greece. The low Signal-to-noise Ratio (SNR), which characterizes spectral bands at shorter wavelengths, prompts the application of the Unmixing-based denoising (UBD) as a pre-processing step for seagrass detection. A total of 15 spectral-temporal patterns is extracted from a Planet image time series to restore the corrupted blue and green band in the processed Planet image. Subsequently, we implement Lyzenga’s empirical water column correction and Support Vector Machines (SVM) to evaluate quantitative benefits of denoising. Denoising aids detection of Posidonia oceanica seagrass species by increasing its producer and user accuracy by 31.7 % and 10.4 %, correspondingly, with a respective increase in its Kappa value from 0.3 to 0.48. In the near future, our objective is to improve accuracies in seagrass detection by applying more sophisticated, analytical water column correction algorithms to Planet imagery, developing time- and cost-effective monitoring of seagrass distribution that will enable in turn the effective management and conservation of these highly valuable and productive ecosystems.
3574384
Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series
Recent research studies have highlighted the decrease in the coverage of Mediterranean seagrasses due to mainly anthropogenic activities. The lack of data on the distribution of these significant aquatic plants complicates the quantification of their decreasing tendency. While Mediterranean seagrasses are declining, satellite remote sensing technology is growing at an unprecedented pace, resulting in a wealth of spaceborne image time series. Here, we exploit recent advances in high spatial resolution sensors and machine learning to study Mediterranean seagrasses. We process a multispectral RapidEye time series between 2011 and 2016 to detect interannual seagrass dynamics in 888 submerged hectares of the Thermaikos Gulf, NW Aegean Sea, Greece (eastern Mediterranean Sea). We assess the extent change of two Mediterranean seagrass species, the dominant Posidonia oceanica and Cymodocea nodosa, following atmospheric and analytical water column correction, as well as machine learning classification, using Random Forests, of the RapidEye time series. Prior corrections are necessary to untangle the initially weak signal of the submerged seagrass habitats from satellite imagery. The central results of this study show that P. oceanica seagrass area has declined by 4.1%, with a trend of −11.2 ha/yr, while C. nodosa seagrass area has increased by 17.7% with a trend of +18 ha/yr throughout the 5-year study period. Trends of change in spatial distribution of seagrasses in the Thermaikos Gulf site are in line with reported trends in the Mediterranean. Our presented methodology could be a time- and cost-effective method toward the quantitative ecological assessment of seagrass dynamics elsewhere in the future. From small meadows to whole coastlines, knowledge of aquatic plant dynamics could resolve decline or growth trends and accurately highlight key units for future restoration, management, and conservation.
7138354
The Nature of Statistical Learning Theory
Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
2171287
Water Constituents and Water Depth Retrieval from Sentinel-2A - A First Evaluation in an Oligotrophic Lake
Satellite remote sensing may assist in meeting the needs of lake monitoring. In this study, we aim to evaluate the potential of Sentinel-2 to assess and monitor water constituents and bottom characteristics of lakes at spatio-temporal synoptic scales. In a field campaign at Lake Starnberg, Germany, we collected validation data concurrently to a Sentinel-2A (S2-A) overpass. We compared the results of three different atmospheric corrections, i.e., Sen2Cor, ACOLITE and MIP, with in situ reflectance measurements, whereof MIP performed best (r = 0.987, RMSE = 0.002 sr−1). Using the bio-optical modelling tool WASI-2D, we retrieved absorption by coloured dissolved organic matter (aCDOM(440)), backscattering and concentration of suspended particulate matter (SPM) in optically deep water; water depths, bottom substrates and aCDOM(440) were modelled in optically shallow water. In deep water, SPM and aCDOM(440) showed reasonable spatial patterns. Comparisons with in situ data (mean: 0.43 m−1) showed an underestimation of S2-A derived aCDOM(440) (mean: 0.14 m−1); S2-A backscattering of SPM was slightly higher than backscattering from in situ data (mean: 0.027 m−1 vs. 0.019 m−1). Chlorophyll-a concentrations (~1 mg·m−3) of the lake were too low for a retrieval. In shallow water, retrieved water depths exhibited a high correlation with echo sounding data (r = 0.95, residual standard deviation = 0.12 m) up to 2.5 m (Secchi disk depth: 4.2 m), though water depths were slightly underestimated (RMSE = 0.56 m). In deeper water, Sentinel-2A bands were incapable of allowing a WASI-2D based separation of macrophytes and sediment which led to erroneous water depths. Overall, the results encourage further research on lakes with varying optical properties and trophic states with Sentinel-2A.
55909019
Sunglint correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands
Abstract Remote sensing of inland and sea waters depends on the quality of the retrieval of the water-leaving radiance from the top-of-atmosphere measurements. The water-leaving radiance can be difficult to observe due to the reflection of direct sunlight on the air-water interface (sunglint) in the direction of the satellite field of view. The viewing geometry of Sentinel-2 satellite ( European Space Agency ) makes it vulnerable to sunglint contamination. In this paper, an original method is proposed to correct Sentinel-2-like imagery for sunglint contamination. The sunglint contribution is first estimated from the shortwave-infrared (SWIR) part of the spectrum and then extrapolated toward the near-infrared and visible bands. The spectral variation of the sunglint signal is thus revisited for a wide spectral range (from 350 to 2500 nm). The bidirectional reflectance distribution function related to the sunglint is shown to vary by > 28% from the SWIR to the blue bands of Sentinel-2. The application of the proposed algorithm on actual Sentinel-2 data demonstrates that sunglint patterns are satisfactorily removed over the entire images whatever the altitude of the observed target. Comparison with in situ data of water-leaving radiances (AERONET-OC) showed that our proposed algorithm significantly improves the correlation between satellite and in situ data by 55% (i.e., from R2 = 0.56 to R2 = 0.87). In addition, the discrepancies between satellite and in situ measurements are reduced by 60%. It is also shown that the aerosol data provided by the Copernicus Atmosphere Monitoring Service (CAMS) can be safely used within the proposed algorithm to correct the Sentinel-2-like satellite data for both sunglint and atmospheric radiances. Improvements of the proposed method potentially rely on simultaneous retrievals of the aerosol optical properties. The proposed method is applicable to any satellite sensor which is able to measure in SWIR spectral bands over aquatic environments.
134676343
Sentinel-2 MultiSpectral Instrument (MSI) data processing for aquatic science applications: Demonstrations and validations
Abstract The European Space Agency's Sentinel-2A mission with the MultiSpectral Instrument (MSI) onboard was launched in 2015, initiating a new era in high-to-moderate-resolution (i.e., 10 to 60 m) imaging of Earth's resources. This manuscript describes the implementations of MSI processing into the SeaWiFS Data Analysis System (SeaDAS) and provides qualitative and quantitative analyses of remote sensing reflectance products (R rs ), which are essential in the retrievals of near-surface concentrations of water constituents in aquatic systems. In situ validations and intercomparisons of MSI-derived R rs products with those derived from Landsat-8's Operational Land Imager (OLI) both indicated reasonable products in coastal/inland waters. Following vicarious calibrations using reference in situ water-leaving radiances, the overall absolute relative differences and the root mean squared differences (RMSD) found for the matchup analyses were, rs products from the combined Sentinel-2A (and B) and Landsat-8 missions are now available to the science/user community for developing algorithms suited for coastal/inland waters. Nonetheless, further research needs to be dedicated to a) improving atmospheric corrections over bodies of waters rich in dissolved organic matter or suspended particles, b) mitigating the impact of haze- or sea surface-reflected solar radiations at low solar zenith angles, and c) minimizing image artifacts to maximize the use of multi-mission products.
51965337
Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2
Bathymetry mapping forms the basis of understanding physical, economic, and ecological processes in the vastly biodiverse coastal fringes of our planet which are subjected to constant anthropogenic pressure. Here, we pair recent advances in cloud computing using the geospatial platform of the Google Earth Engine (GEE) with optical remote sensing technology using the open Sentinel-2 archive, obtaining low-cost in situ collected data to develop an empirical preprocessing workflow for estimating satellite-derived bathymetry (SDB). The workflow implements widely used and well-established algorithms, including cloud, atmospheric, and sun glint corrections, image composition and radiometric normalisation to address intra- and inter-image interferences before training, and validation of four SDB algorithms in three sites of the Aegean Sea in the Eastern Mediterranean. Best accuracy values for training and validation were R2 = 0.79, RMSE = 1.39 m, and R2 = 0.9, RMSE = 1.67 m, respectively. The increased accuracy highlights the importance of the radiometric normalisation given spatially independent calibration and validation datasets. Spatial error maps reveal over-prediction over low-reflectance and very shallow seabeds, and under-prediction over high-reflectance ( 17 m). We provide access to the developed code, allowing users to map bathymetry by customising the time range based on the field data acquisition dates and the optical conditions of their study area.
52277133
Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas
Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, timeand cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.
94563154
Light and Water: Radiative Transfer in Natural Waters
Fundamentals: Radiometry. Photometry. Optical Properties of Water. Radiative Transfer: Across the Surface. Within the Water. Solutions Methods: Monte Carlo Methods. Invariant Imbedding Methods: Introduction. Invariant Imbedding Methods: Solving the RTE. Eigenmatrix Methods. Inverse Methods. Solutions: Underwater Light Fields. Epilogue. Chapter Problems. References.
30964646
Copernicus Sentinel-2A Calibration and Products Validation Status
As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and the status of the mission products validation activities after one year in orbit. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. The presented results show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.
36400967
Computed relationships between the inherent and apparent optical properties of a flat homogeneous ocean.
Monte Carlo simulations of the transfer of radiation in the ocean are used to compute the apparent optical properties of a flat homogeneous ocean as a function of the inherent optical properties. The data are used to find general relationships between the inherent and apparent optical properties for optical depths tau </= 4. The results indicate that the apparent optical properties depend on the phase function only through the back scattering probability. It is shown that these relations can be used with measurements of the upwelling and downwelling irradiance, the beam attenuation coefficient, and the incident radiance distribution to determine the absorption coefficient, the scattering coefficient, and the backward and forward scattering probabilities.
55700035
Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery
The normalized difference water index (NDWI) of McFeeters (1996) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI. The modified NDWI (MNDWI) can enhance open water features while efficiently suppressing and even removing built‐up land noise as well as vegetation and soil noise. The enhanced water information using the NDWI is often mixed with built‐up land noise and the area of extracted water is thus overestimated. Accordingly, the MNDWI is more suitable for enhancing and extracting water information for a water region with a background dominated by built‐up land areas because of its advantage in reducing and even removing built‐up land noise over the NDWI.
55665360
Properties of the Water Column and Bottom Derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Data
Using Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data as an example, we show in this study that the properties of the water column and bottom of a large, shallow area can be adequately retrieved using a model-driven optimization technique. The simultaneously derived properties include bottom depth, bottom albedo, and water absorption and backscattering coefficients, which in turn could be used to derive concentrations of chlorophyll, dissolved organic matter, and suspended sediments in the water column. The derived bottom depths were compared with a bathymetry chart and a boat survey and were found to agree very well. Also, the derived bottom albedo image shows clear spatial patterns, with end-members consistent with sand and seagrass. The image of absorption and backscattering coefficients indicates that the water is quite horizontally mixed. Without bottom corrections, chlorophyll a retrievals were ∼50 mg m−3, while the retrievals after bottom corrections were tenfold less, approximating real values. These results suggest that the model and approach used work very well for the retrieval of subsurface properties of shallow-water environments even for rather turbid environments like Tampa Bay, Florida.
38941291
Diffuse reflectance of oceanic waters. II Bidirectional aspects.
For visible wavelengths and for most of the oceanic waters, the albedo for single scattering (?) is not high enough to generate within the upper layers of the ocean a completely diffuse regime, so that the upwelling radiances below the surface, as well as the water-leaving radiances, generally do not form an isotropic radiant field. The nonisotropic character and the resulting bidirectional reflectance are conveniently expressed by the Q factor, which relates a given upwelling radiance L(u) (θ',φ) to the upwelling irradiance E(u) (θ' is the nadir angle, φ is the azimuth angle, and Q = E(u)/L(u)); in addition the Q function is also dependent on the Sun's position. Another factor, denoted f, controls the magnitude of the global reflectance, R (= E(u) /E(d), where E(d) is the downwelling irradiance below the surface); f relates R to the backscattering and absorption coefficients of the water body (b(b) and a, respectively), according to R = f(b(b)/a). This f factor is also Sun angle dependent. By operating an azimuth-dependent Monte Carlo code, both these quantities, as well as their ratio (f/Q) have been studied as a function of the water optical characteristics, namely ? and η; η is the ratio of the molecular scattering to the total (molecular + particles) scattering. Realistic cases (including oceanic waters, with varying chlorophyll concentrations; several wavelengths involved in the remote sensing of ocean color and variable atmospheric turbidity) have been considered. Emphasis has been put on the geometrical conditions that would be typical of a satellite-based ocean color sensor, to derive and interpret the possible variations of the signal emerging from various oceanic waters, when seen from space under various angles and solar illumination conditions.
131083254
Semianalytic Moderate‐Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio‐optical domains based on nitrate‐depletion temperatures
This paper describes algorithms for retrieval of chlorophyll a concentration and phytoplankton and gelbstoff absorption coefficients for the Moderate-Resolution Imaging Spectrometer (MODIS) or sensors with similar spectral channels. The algorithms are based on a semianalytical, bio-optical model of remote sensing reflectance, Rrs(λ). The Rrs(λ) model has two free variables, the absorption coefficient due to phytoplankton at 675 nm, aϕ(675), and the absorption coefficient due to gelbstoff at 400 nm, ag(400). The Rrs model has several parameters that are fixed or can be specified based on the region and season of the MODIS scene. These control the spectral shapes of the optical constituents of the model. Rrs(λi) values from the MODIS data processing system are placed into the model, the model is inverted, and aϕ(675), ag(400), and chlorophyll a are computed. The algorithm also derives the total absorption coefficients a(λi) and the phytoplankton absorption coefficients aϕ(λi) at the visible MODIS wavelengths. MODIS algorithms are parameterized for three different bio-optical domains: (1) high photoprotective pigment to chlorophyll ratio and low self-shading, which for brevity, we designate as “unpackaged”; (2) low photoprotective pigment to chlorophyll ratio and high self-shading, which we designate as “packaged”; and (3) a transitional or global-average type. These domains can be identified from space by comparing sea-surface temperature to nitrogen-depletion temperatures for each domain. Algorithm errors of more than 45% are reduced to errors of less than 30% with this approach, with the greatest effect occurring at the eastern and polar boundaries of the basins.
97938448
Intercomparison of shallow water bathymetry, hydro‐optics, and benthos mapping techniques in Australian and Caribbean coastal environments
Science, resource management, and defense need algorithms capable of using airborne or satellite imagery to accu rately map bathymetry, water quality, and substrate composition in optically shallow waters. Although a variety of inver sion algorithms are available, there has been limited assessment of performance and no work has been published com paring their accuracy and efficiency. This paper compares the absolute and relative accuracies and computational effi ciencies of one empirical and five radiative-transfer-based published approaches applied to coastal sites at Lee Stocking Island in the Bahamas and Moreton Bay in eastern Australia. These sites have published airborne hyperspectral data and field data. The assessment showed that (1) radiative-transfer‐based methods were more accurate than the empirical approach for bathymetric retrieval, and the accuracies and processing times were inversely related to the complexity of the models used; (2) all inversion methods provided moder ately accurate retrievals of bathymetry, water column inher ent optical properties, and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benth ic/substrate covers; (3) slightly higher accuracy retrievals were obtained from locally parameterized methods; and (4) no method compared here can be considered optimal for all situ ations. The results provide a guide to the conditions where each approach may be used (available image and field data and processing capability). A re-analysis of these same or addi tional sites with satellite hyperspectral data with lower spatial and radiometric resolution, but higher temporal resolution would be instructive to establish guidelines for repeatable regional to global scale shallow water mapping approaches. Optically shallow waters, those where the bottom is visible from the water surface and measurably influences the waterleaving radiance, include inland waters through to estuarine and tropical coral reef and temperate coastal ecosystems. Over
129194326
Remote Sensing of Seagrass Ecosystems: Use of Spaceborne and Airborne Sensors
The focus of this chapter lies in describing digi-tal multispectral and hyperspectral remote sensingdevelopments and applications in the mapping andmonitoring of seagrass ecosystems. Multispectralrefers to a sensor that registers light in a limitednumber of relatively broad spectral bands (band-widths of 20–60 nm); hyperspectral (also referred toas imaging spectrometry) is deﬁned for sensors thatmeasure the entire spectrum under consideration incontiguous narrow spectral bands (bandwidths be-tween 2 and 20 nm).Currently, seagrass maps are still predominantlybeingproducedfromtheinterpretationofaerialpho-tographyalthoughitislikelythatairborneandspace-borne remote sensing methods will rapidly take overthisrolegiventheadvantagestheypresentintermsofaccuracy, repeatability, versatility, and informationcontent. Nevertheless, retrospective studies of sea-grass change using the more modern methodologieswillstillneedtomakeuseofresultsgeneratedbythemore traditional methods since aerial photographsare the dominant archival source of historical spatial
29862646
High-resolution ocean color remote sensing of benthic habitats: a case study at the Roatan island, Honduras
Natural resource managers clamor for detailed reef habitat maps for monitoring smaller scale disturbances in reef communities. Coastal ocean color remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before. The objective of this research was to develop an accurate benthic habitat map for an area off the northwest coast of Roatan Island, Honduras, using high-resolution multispectral IKONOS data. Atmospheric (Rayleigh and aerosol path radiance) and water column corrections (water depth and water column attenuation) were applied to the imagery, making it a robust method for mapping benthic habitats. Water depth for each pixel was calculated based on a site-specific polynomial model. A mechanistic radiative transfer approach was developed that removed the confound effect of the water column (absorption and scattering) from the imagery to retrieve an estimate of the bottom reflectance (albedo). Albedos were /spl les/ 12% for seagrass benthos, 12% to 24% for coral areas, and /spl ges/ 24% for sand-dominated areas. The retrieved bottom albedos were then used to classify the benthos, generating a detailed map of benthic habitats, followed by accuracy assessment.
10123879
Determination of water depth with high-resolution satellite imagery over variable bottom types
A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms—the standard linear transform and the new ratio transform— were compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10‐15 m. However, the linear transform does not distinguish depths .15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in .25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4‐5 pixels) in water depths .15‐20 m. In general, the ratio transform is more robust than the linear transform.
961425
LIBSVM: A library for support vector machines
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
2357415
Support vector machines in remote sensing: A review
A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.
43993321
Exploiting noisy hyperspectral bands for water analysis
This paper proposes a novel algorithm for the recovery of noisy bands from hyperspec-tral images. The method, based on spectral unmixing, relies on the spectral behavior of the materi-als on ground composing each image element. Firstly, reference spectra related to the classes of in-terest are used to perform spectral unmixing: these exhibit negligible noise influences as they are averaged over areas for which ground truth is available. After the unmixing process, the residual vector is mostly composed by the contributions of uninteresting materials, unwanted atmospheric influences and sensor-induced noise, and is thus ignored in the reconstruction of each spectrum. Finally, the value of a pixel in a given band is predicted as a combination of the noise-free end-members, resulting in a signal with high signal-to-noise ratio in any spectral band. Experiments show that this method could be used to retrieve spectral information from corrupted bands, such as the ones placed at the edge between Ultraviolet and visible light frequencies, which are usually dominated by atmospheric effects and are thus discarded in practical applications. The proposed algorithm could then be exploited in the study of Coloured dissolved organic matter (CDOM) in natural waters.
32397018
High-Resolution Maps of Bathymetry and Benthic Habitats in Shallow-Water Environments Using Multispectral Remote Sensing Imagery
Coastlines, shoals, and reefs are some of the most dynamic and constantly changing regions of the globe. The emergence of high-resolution satellites with new spectral channels, such as the WorldView-2, increases the amount of data available, thereby improving the determination of coastal management parameters. Water-leaving radiance is very difficult to determine accurately, since it is often small compared to the reflected radiance from other sources such as atmospheric and water surface scattering. Hence, the atmospheric correction has proven to be a very important step in the processing of high-resolution images for coastal applications. On the other hand, specular reflection of solar radiation on nonflat water surfaces is a serious confounding factor for bathymetry and for obtaining the seafloor albedo with high precision in shallow-water environments. This paper describes, at first, an optimal atmospheric correction model, as well as an improved algorithm for sunglint removal based on combined physical and image processing techniques. Then, using the corrected multispectral data, an efficient multichannel physics-based algorithm has been implemented, which is capable of solving through optimization the radiative transfer model of seawater for bathymetry retrieval, unmixing the water intrinsic optical properties, depth, and seafloor albedo contributions. Finally, for the mapping of benthic features, a supervised classification methodology has been implemented, combining seafloor-type normalized indexes and support vector machine techniques. Results of atmospheric correction, remote bathymetry, and benthic habitat mapping of shallow-water environments have been validated with in situ data and available bionomic profiles providing excellent accuracy.
11170031
Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea
The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. ...
27798276
Towards Deeper Measurements of Tropical Reefscape Structure Using the WorldView-2 Spaceborne Sensor
Owing to the shallowness of waters, vast areas, and spatial complexity, reefscape mapping requires Digital Depth Models (DDM) at a fine scale but over large areas. Outperforming waterborne surveys limited by shallow water depths and costly airborne campaigns, recently launched satellite sensors, endowed with high spectral and very high spatial capabilities, can adequately address the raised issues. Doubling the number of spectral bands, the innovative eight band WorldView-2 (WV2) imagery is very susceptible to enhance the DDM retrieved from the traditional four band QuickBird-2 (QB2). Based on an efficiently recognized algorithm (ratio transform), resolving for the clear water bathymetry, we compared DDM derived from simulated QB2 with WV2 spectral combinations using acoustic ground-truthing in Moorea (French Polynesia). Three outcomes emerged from this study. Increasing spatial resolution from 2 to 0.5 m led to reduced agreement between modeled and in situ water depths. The analytical atmospheric correction (FLAASH) provided poorer results than those derived without atmospheric correction and empirical dark object correction. The purple, green, yellow and NIR3 (WV2 1st-3rd-4th-8th bands) spectral combination, processed with the atmospheric correction at the 2 m resolution, furnished the most robust consistency with ground-truthing (30 m (r = 0.65)), gaining 10 m of penetration relative to other spaceborne-derived bathymetric retrievals. The integration of the WV2-boosted bathymetry estimation into radiative transfer model holds great promise to frequently monitor the reefscape features at the colony-scale level.
140646861
Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters
Abstract Nearshore bathymetry is likely to be the coastal variable that most limits the investigation of coastal processes and the accuracy of numerical models in coastal areas, as acquiring medium spatial resolution data in the nearshore is highly demanding and costly. As such, the ability to derive bathymetry using remote sensing techniques is a topic of increasing interest in coastal monitoring and research. This contribution focuses on the application of the linear transform algorithm to obtain satellite-derived bathymetry (SDB) maps of the nearshore, at medium resolution (30 m), from freely available and easily accessible Landsat 8 imagery. The algorithm was tuned with available bathymetric Light Detection and Ranging (LiDAR) data for a 60-km-long nearshore stretch of a highly complex coastal system that includes barrier islands, exposed sandy beaches, and tidal inlets (Ria Formosa, Portugal). A comparison of the retrieved depths is presented, enabling the configuration of nearshore profiles and extracted isobaths to be explored and compared with traditional topographic/bathymetric techniques (e.g., high- and medium-resolution LiDAR data and survey-grade echo-sounding combined with high-precision positioning systems). The results demonstrate that the linear algorithm is efficient for retrieving bathymetry from multi-spectral satellite data for shallow water depths (0 to 12 m), showing a mean bias of − 0.2 m, a median difference of − 0.1 m, and a root mean square error of 0.89 m. Accuracy is shown to be depth dependent, an inherent limitation of passive optical detection systems. Accuracy further decreases in areas where turbidity is likely to be higher, such as locations adjacent to tidal inlets. The SDB maps provide reliable estimations of the shoreline position and of nearshore isobaths for different cases along the complex coastline analysed. The use of freely available satellite imagery proved to be a quick and reliable method for acquiring updated medium-resolution, high-frequency (days and weeks), low-cost bathymetric information for large areas and depths of up to 12 m in clear waters without wave breaking, allowing almost constant monitoring of the submerged beach and the shoreface.
29874144
Spectral reflectance of the seagrasses: Thalassia testudinum, Halodule wrightii, Syringodium filiforme and five marine algae
Spectral reflectance of three Western Atlantic subtropical/tropical dominant benthic seagrasses (Thalassia testudinum, Halodule wrightii and Syringodium filiforme) measured in the laboratory by UNIspec Spectral Analysis system over 400–1000 nm are compared to five Western Atlantic dominant benthic and intertidal algae (Fucus vesiculosus, Ulva lactuca, Halimeda incrassata, Udotea flabellum and Penicillus capitatus). Thalassia spectra are also compared to previously studied forest species, showing that the spectral reflectance signature of Thalassia was of the same general shape as forest species, but of lesser peak height (18% vs 28–50%) in the 750–759 nm range. For marine plants, near‐infrared reflectance of Thalassia, Syringodium and Halodule was intermediate (18%) between the ubiquitous green alga Ulva (15%) and the temperate brown alga, Fucus (27%). The brown alga had a unique spectral signature in the first difference spectrum (three secondary peaks at differing spectral values than the other species' secondary peaks). Comparisons between Halodule, Syringodium and Thalassia near‐infrared peaks and Halimeda (58%), Penicillus (29%) and Udotea (42%), subtropical/tropical algae, were also carried out. Halimeda differed most from the other green algae in terms of numerous oscillations from 450 to 700 nm seen in the first difference spectrum. The three seagrasses were similar in spectral reflectance curve shape to one another but differed somewhat in peak height. The first difference spectrum was helpful in pointing out subtle differences among the species and between algae and seagrass. Spectral reflectance data such as those presented here appear potentially useful for remote sensing of shallow habitats, and may additionally be used to monitor their health.
12268240
Water and bottom properties of a coastal environment derived from Hyperion data measured from the EO-1 spacecraft platform
Hyperion is a hyperspectral sensor on board NASA's EO-1 satellite with a spatial resolution of approximately 30 m and a swath width of about 7 km. It was originally designed for land applications, but its unique spectral configuration (430 nm - 2400 nm with a ~10 nm spectral resolution) and high spatial resolution make it attractive for studying complex coastal ecosystems, which require such a sensor for accurate retrieval of environmental properties. In this paper, Hyperion data over an area of the Florida Keys is used to develop and test algorithms for atmospheric correction and for retrieval of subsurface properties. Remote-sensing reflectance derived from Hyperion data is compared with those from in situ measurements. Furthermore, water's absorption coefficients and bathymetry derived from Hyperion imagery are compared with sample measurements and LIDAR survey, respectively. For a depth range of ~ 1 - 25 m, the Hyperion bathymetry match LIDAR data very well (~11% average error); while the absorption coefficients differ by ~16.5% (in a range of 0.04 - 0.7 m -1 for wavelengths of 410, 440, 490, 510, and 530 nm) on average. More importantly, in this top-to-bottom processing of Hyperion imagery, there is no use of any a priori or ground truth information. The results demonstrate the usefulness of such space-borne hyperspectral data and the techniques developed for effective and repetitive observation of complex coastal regions.
123140751
Classification of benthic composition in a coral reef environment using spectral unmixing
Remote sensing is being applied with increasing success in the evaluation and management of coral ecosystems. We demonstrate a successful application of hyperspectral image analysis of the benthic composition in Kaneohe Bay, Hawaii using data acquired from NASA's Airborne Visible Infrared Imaging Spectrometer. We employ a multi-level approach, combining a semi-analytical inversion model with linear spectral unmixing, to extract information on the coral, algae and sand composition of each pixel. The unmixing model is based on the spectral characteristics of the dominant species and substrate types in Kaneohe Bay, and uses an optimization routine to mathematically invert the relationship of how each component spectrally interacts and mixes. The functional result is the ability to quantitatively classify individual pixel composition according to the percent contribution from each of three main reef components. Output compares favorably with available field measurements and habitat information for Kaneohe Bay, and the overall analysis illustrates the capacity to simultaneously derive information on water properties, bathymetry and habitat composition from hyperspectral remote sensing data. Further, the resulting spatial analysis capacity contributes an improved capability for monitoring coral ecosystems and an important basis for resource management decisions.