Shoreline Detection Accuracy from Video Monitoring Systems

Video monitoring has become an indispensable tool to understand beach processes. However, the measurement accuracy derived from the images has been taken for granted despite its dependence on the calibration process and camera movements. An easy to implement self-fed image stabilization algorithm is proposed to solve the camera movements. Georeferenced images were generated from the stabilized images using only one calibration. To assess the performance of the stabilization algorithm, a second set of georeferenced images was created from unstabilized images following the accepted practice of using several calibrations. Shorelines were extracted from the images and corrected with the measured water level and the computed run-up to the 0 m contour. Image-derived corrected shorelines were validated with one hundred beach profile surveys measured during a period of four years along a 1.1 km beach stretch. The simultaneous high-frequency field data available of images and beach surveys are uncommon and allow assessing seasonal changes and long-term trends accuracy. Errors in shoreline position do not increase in time suggesting that the proposed stabilization algorithm does not propagate errors, despite the ever-evolving vegetation in the images. The image stabilization reduces the error in shoreline position by 40 percent, having a larger impact with increasing distance from the camera. Furthermore, the algorithm improves the accuracy on long-term trends by one degree of magnitude (0.01 m/year vs. 0.25 m/year).

Natural and Anthropogenic Factors Shaping the Shoreline of Klaipėda, Lithuania

Port of Klaipėda is situated in a complex hydrological system, between the Curonian Lagoon and the Baltic Sea, at the Klaipėda strait in the South-Eastern part of the Baltic Sea. It has almost 300 m of jetties separating the Curonian Spit and the mainland coast, interrupting the main path of sediment transport through the South-Eastern coast of the Baltic Sea. Due to the Port of Klaipėda reconstruction in 2002 and the beach nourishment project, which was started in 2014, the shoreline position change tendency was observed. Shoreline position measurements of various periods can be used to derive quantitative estimates of coastal process directions and intensities. These data can be used to further our understanding of the scale and timing of shoreline changes in a geological and socio-economic context. This study analyzes long- and short-term shoreline position changes before and after the Port of Klaipėda reconstruction in 2002. Positions of historical shorelines from various sources were used, and the rates (EPR, NSM, and SCE) of shoreline changes have been assessed using the Digital Shoreline Analysis System (DSAS). An extension of ArcGIS K-means clustering was applied for shoreline classification into different coastal dynamic stretches. Coastal development has changed in the long-term (1984–2019) perspective: the eroded coast length increased from 1.5 to 4.2 km in the last decades. Coastal accumulation processes have been restored by the Port of Klaipėda executing the coastal zone nourishment project in 2014.

Citizen-Science with off-the-shelf UAV for Coastal Monitoring

Accurate and repetitive observation and quantification of the shoreline position and the coastal feature are essential aspects of coastal management and planning. Commonly, the dataset associated with coastal observation and quantification is obtained with in-situ coastal surveys. The current methods are mostly quite expensive, time-consuming, and require trained individuals to do the task. With the availability of the off-the-shelf low cost, lightweight, and reliable Unmanned Aerial Vehicle (UAV) with the advances of the algorithms such as structure-from-motion (SfM), UAV-based measurement becomes a promising tool. Open SfM initiative, open topographical database, and UAV communities are the enablers that make it possible to collect accurate and frequent coastal monitoring and democratize data. This paper provides a review and discussions that highlight the possibility of conducting scientific coastal monitoring or collaborating with the public. Literature was examined for the advances in coastal monitoring, challenges, and recommendations. We identified and proposed the use of UAV along with the strategies and systems to encourage citizen-led UAV observation for coastal monitoring while attaining the quality.

Combined Longshore and Cross-Shore Modeling for Low-Energy Embayed Sandy Beaches

The present study focuses on the long-term multi-year evolution of the shoreline position of the Nha Trang sandy beach. To this end an empirical model which is a combination of longshore and cross-shore models, is used. The Nha Trang beach morphology is driven by a tropical wave climate dominated by seasonal variations and winter monsoon intra-seasonal pulses. The combined model accounts for seasonal shoreline evolution, which is primarily attributed to cross-shore dynamics but fails to represent accretion that occurs during the height of summer under low energy conditions. The reason is in the single equilibrium Dean number Ωeq of the ShoreFor model, one of the components of the combined model. This equilibrium Dean number cannot simultaneously account for the evolution of strong intra-seasonal events (i.e., winter monsoon pulses) and the annual recovery mechanisms associated with swash transport. By assigning a constant value to Ωeq, when the surf similarity parameter is higher than 3.3 (occurrence of small surging breakers in summer), we strongly improve the shoreline position prediction. This clearly points to the relevance of a multi-scale approach, although our modified Ωeq retains the advantage of simplicity.

A Shoreline Change Detection (2012-2021) and forecasting Using Digital Shoreline Analysis System (DSAS) Tool: A Case Study of Dahej Coast, Gulf of Khambhat, Gujarat, India

Shoreline is one of the coastal landforms which continuously changing in nature. Hence, monitoring of shoreline change is very obligate to understand and manage the coastal process. The objectives of the present study were i) to identify the shoreline change detection (2012 to 2021) based on various statistical methods along Dahej coast, Gujrat. ii) to forecast the shoreline position after 10 years. DSAS tool and Multi-dated satellite images (Sentinel-2 and LISS-IV) were used in present study. The result shows that, the pattern of rate of change was more or less similar with little variation in the values for the 3 different methods. Highest erosion rate was for End Point Rate, Linear Regression Rate and Weighted Linear Regression rate found -33m, -31m, -31m respectively at transect no 54. Highest accretion rate was 38m (EPR), 50m (LRR), 51m (WLR) along a particular transect. The forecast of shoreline position for the year 2032 observed through Kalman Filter Model. Seasonal analysis for 3 years (2016, 2017, 2018) shows the region not having any seasonal pattern.

Forecasting oceanfront shoreline position to evaluate physical vulnerability for recreational and infrastructure resilience at Cape Hatteras National Seashore

The Cape Hatteras National Seashore (Seashore) is located along the Outer Banks of eastern North Carolina, and is renowned for its prominent historical landmarks and world-class recreation. Seashore managers maintain hundreds of assets that support visitor use. Additionally, and primary to the mission of the National Park Service (NPS), managers steward natural and cultural resources located on public and protected lands. The portfolio of assets managed by NPS within the Seashore carries a high level of risk due to its exposure to both coastal erosion and storm surge inundation. The impacts of Hurricane Dorian demonstrated the importance of examining the physical vulnerability of the entire portfolio managed by NPS within the Seashore. The purpose of this study was to 1) evaluate the functionality of the beta forecast tool available in the Digital Shoreline Analysis System (v 5.0); and 2) explore options for using the output to assess the potential physical vulnerability of NPS assets. The study determined that using the 10- and 20-year oceanfront shoreline position forecast provides decision makers with a first order screening tool that can be used to prioritize mitigation and adaptation strategies given the unpredictable nature of tropical and extra-tropical cyclones and uncertainty associated with sea level rise.

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

VEdge_Detector: Automated coastal vegetation edge detection using a convolutional neural network

<p>Coastal communities, land covers and intertidal habitats are vulnerable receptors of erosion, flooding or both in combination. This vulnerability is likely to increase with sea level rise and greater storminess over future decadal-scale time periods. The accurate, rapid and wide-scale determination of shoreline position, and its migration, is therefore imperative for future coastal risk adaptation and management. Developments in the spectral and temporal resolution and availability of multispectral satellite imagery opens new opportunities to rapidly and repeatedly monitor change in shoreline position to inform coastal risk management decisions. This presentation discusses the development and application of an automated tool, VEdge_Detector, to extract the coastal vegetation line from high spatial resolution (Planet's 3 – 5 m) remote sensing imagery, training a very deep convolutional neural network (Holistically-Nested Edge Detection) to predict sequential vegetation line locations on annual/decadal timescales. The VEdge_Detector outputs were compared with vegetation lines derived from ground-referenced positional measurements and manually digitised aerial photographs, revealing a mean distance error of <6 m (two image pixels) and > 84% producer accuracy at six out of the seven sites. Extracting vegetation lines from Planet imagery of the rapidly retreating cliffed coastline at Covehithe, Suffolk, UK identified a mean landward retreat rate >3 m a<sup>-1</sup> (2010 - 2020). Plausible vegetation lines were successfully retrieved from images of other global locations, which were not used to train the neural network; although significant areas of exposed rocky coastline proved to be less well recovered by VEdge_Detector. The method therefore promises the possibility of generalising to estimate retreat of sandy coastlines in otherwise data-poor areas, which lack ground-referenced measurements. Vegetation line outputs derived from VEdge_Detector are produced rapidly and efficiently compared to more traditional non-automated methods. These outputs also have the potential to inform upon a range of future coastal risk management decisions, including hazard and risk mapping considering future shoreline change.</p>