The global decline in estuarine and coastal ecosystems (ECEs) is affecting a number of critical benefits, or ecosystem services. We review the main ecological services across a variety of ECEs, including marshes, mangroves, nearshore coral reefs, seagrass beds, and sand beaches and dunes. Where possible, we indicate estimates of the key economic values arising from these services, and discuss how the natural variability of ECEs impacts their benefits, the synergistic relationships of ECEs across seascapes, and management implications. Although reliable valuation estimates are beginning to emerge for the key services of some ECEs, such as coral reefs, salt marshes, and mangroves, many of the important benefits of seagrass beds and sand dunes and beaches have not been assessed properly. Even for coral reefs, marshes, and mangroves, important ecological services have yet to be valued reliably, such as cross-ecosystem nutrient transfer (coral reefs), erosion control (marshes), and pollution control (mangroves). An important issue for valuing certain ECE services, such as coastal protection and habitat-fishery linkages, is that the ecological functions underlying these services vary spatially and temporally. Allowing for the connectivity between ECE habitats also may have important implications for assessing the ecological functions underlying key ecosystems services, such coastal protection, control of erosion, and habitat-fishery linkages. Finally, we conclude by suggesting an action plan for protecting and/or enhancing the immediate and longer-term values of ECE services. Because the connectivity of ECEs across land-sea gradients also influences the provision of certain ecosystem services, management of the entire seascape will be necessary to preserve such synergistic effects. Other key elements of an action plan include further ecological and economic collaborative research on valuing ECE services, improving institutional and legal frameworks for management, controlling and regulating destructive economic activities, and developing ecological restoration options.

https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/10-1510.1

The value of estuarine and coastal ecosystem services

https://www.cs.montana.edu/courses/csci566/Ref/WMSN-Survey.pdf

A survey on wireless multimedia sensor networks

https://oss.deltares.nl/documents/48999/59759/Roelvink_2009.pdf

Modelling storm impacts on beaches, dunes and barrier islands

BOAK, E.H. and TURNER, I.L., 2005. Shoreline Definition and Detection: A Review. Journal of Coastal Research, 21(4), 688‐703. West Palm Beach (Florida), ISSN 0749-0208. Analysis of shoreline variability and shoreline erosion-accretion trends is fundamental to a broad range of investigations undertaken by coastal scientists, coastal engineers, and coastal managers. Though strictly defined as the intersection of water and land surfaces, for practical purposes, the dynamic nature of this boundary and its dependence on the temporal and spatial scale at which it is being considered results in the use of a range of shoreline indicators. These proxies are generally one of two types: either a feature that is visibly discernible in coastal imagery (e.g., highwater line [HWL]) or the intersection of a tidal datum with the coastal profile (e.g., mean high water [MHW]). Recently, a third category of shoreline indicator has begun to be reported in the literature, based on the application of imageprocessing techniques to extract proxy shoreline features from digital coastal images that are not necessarily visible to the human eye. Potential data sources for shoreline investigation include historical photographs, coastal maps and charts, aerial photography, beach surveys, in situ geographic positioning system shorelines, and a range of digital elevation or image data derived from remote sensing platforms. The identification of a ‘‘shoreline’’ involves two stages: the first requires the selection and definition of a shoreline indicator feature, and the second is the detection of the chosen shoreline feature within the available data source. To date, the most common shoreline detection technique has been subjective visual interpretation. Recent photogrammetry, topographic data collection, and digital image-processing techniques now make it possible for the coastal investigator to use objective shoreline detection methods. The remaining challenge is to improve the quantitative and process-based understanding of these shoreline indicator features and their spatial relationship relative to the physical land‐water boundary.

/pdf/shoreline-definition-and-detection-a-review-41qp0ofrkp.pdf

Shoreline Definition and Detection: A Review

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV) The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy) The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys Such a comparison served to highlight an average difference in the vertical values of 005 m (RMS = 019 m) However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0011 m) The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0015 m, with RMS = 0220 m Larger values are encountered in areas where sudden changes in topography are present The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology

https://www.mdpi.com/2072-4292/5/12/6880/pdf

Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments

Empirical parameterization of setup, swash, and runup

A method has been developed for estimating shun-line position from airborne scanning laser data. This technique allows rapid estimation of objective, GPS-based shoreline positions over hundreds of kilometers of coast, essential for the assessment of large-scale coastal behavior. Shoreline position, defined as the cross-shore position of a vertical shoreline datum, is found by fitting a function to cross-shore profiles of laser altimetry data located in a vertical range around the datum and then evaluating the function at the specified datum. Error bars on horizontal position are directly calculated as the 95% confidence interval on the mean value based on the Student's t distribution of the errors of the regression. The technique was tested using lidar data collected with NASA's Airborne Topographic Mapper (ATM) in September 1997 on the Outer Banks of North Carolina. Estimated lidar-based shoreline position was compared to shoreline position as measured by a ground-based GPS vehicle survey system. The two methods agreed closely with a root mean square difference of 2.9 m. The mean 95% confidence interval for shoreline position was ±1.4 m. The technique has been applied to a study of shoreline change on Assateague Island, Maryland/Virginia, where three ATM data sets were used to assess the statistics of large-scale shoreline change caused by a major 'northeaster' winter storm. The accuracy of both the lidar system and the technique described provides measures of shoreline position and change that are ideal for studying storm-scale variability over large spatial scales.

/pdf/estimation-of-shoreline-position-and-change-using-airborne-4s6g844a2r.pdf

Estimation of Shoreline Position and Change using Airborne Topographic Lidar Data

A simple model for the spatially-variable coastal response to hurricanes

A scanning airborne topographic lidar was evaluated for its ability to quantify beach topography and changes during the Sandy Duck experiment in 1997 along the North Carolina coast. Elevation estimates, acquired with NASA's Airborne Topographic Mapper (ATM), were compared to elevations measured with three types of ground-based measurements-1) differential GPS equipped all-terrain vehicle (ATV) that surveyed a 3-km reach of beach from the shoreline to the dune, 2) GPS antenna mounted on a stadia rod used to intensely survey a different 100 m reach of beach, and 3) a second GPS-equipped ATV that surveyed a 70-km-long transect along the coast. Over 40,000 individual intercomparisons between ATM and ground surveys were calculated. RMS vertical differences associated with the ATM when compared to ground measurements ranged from 13 to 19 cm. Considering all of the intercomparisons together, RMS = 15 cm. This RMS error represents a total error for individual elevation estimates including uncertainties associated with random and mean errors. The latter was the largest source of error and was attributed to drift in differential GPS. The - 15 cm vertical accuracy of the ATM is adequate to resolve beach-change signals typical of the impact of storms. For example, ATM surveys of Assateague Island (spanning the border of MD and VA) prior to and immediately following a severe northeaster showed vertical beach changes in places greater than 2 m, much greater than expected errors associated with the ATM. A major asset of airborne lidar is the high spatial data density. Measurements of elevation are acquired every few m2 over regional scales of hundreds of kilometers. Hence, many scales of beach morphology and change can be resolved, from beach cusps tens of meters in wavelength to entire coastal cells comprising tens to hundreds of kilometers of coast. Topographic lidars similar to the ATM are becoming increasingly available from commercial vendors and should, in the future, be widely used in beach surveying.

https://www.jstor.org/stable/info/4299152

Evaluation of Airborne Topographic Lidar* for Quantifying Beach Changes

A new remote sensing technique based on video image processing has been developed for the estimation of nearshore bathymetry. The shoreward propagation of waves is measured using pixel intensity time series collected at a cross-shore array of locations using remotely operated video cameras. The incident band is identified, and the cross-spectral matrix is calculated for this band. The cross-shore component of wavenumber is found as the gradient in phase of the first complex empirical orthogonal function of this matrix. Water depth is then inferred from linear wave theory's dispersion relationship. Full bathymetry maps may be measured by collecting data in a large array composed of both cross-shore and longshore lines. Data are collected hourly throughout the day, and a stable, daily estimate of bathymetry is calculated from the median of the hourly estimates. The technique was tested using 30 days of hourly data collected at the SandyDuck experiment in Duck, North Carolina, in October 1997. Errors calculated as the difference between estimated depth and ground truth data show a mean bias of-35 cm (rms error - 91 cm). Expressed as a fraction of the true water depth, the mean percent error was 13% (rms error - 34%). Excluding the region of known wave nonlinearities over the bar crest, the accuracy of the technique improved, and the mean (rms) error was-20 cm (75 cm). Additionally, under low-amplitude swells (wave height H _<1 m), the performance of the technique across the entire profile improved to 6% (29%) of the true water depth with a mean (rms) error of-12 cm (71 cm).

/pdf/estimation-of-wave-phase-speed-and-nearshore-bathymetry-from-3qc6ilw0k5.pdf

Hilary F. Stockdon

Papers

Empirical parameterization of setup, swash, and runup

Estimation of Shoreline Position and Change using Airborne Topographic Lidar Data

A simple model for the spatially-variable coastal response to hurricanes

Evaluation of Airborne Topographic Lidar* for Quantifying Beach Changes

Estimation of wave phase speed and nearshore bathymetry from video imagery