Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery

Vos, Kilian; Harley, Mitchell D.; Splinter, Kristen D.; Simmons, Joshua A.; Turner, Ian L.

doi:10.1016/j.coastaleng.2019.04.004

Cited by 234 publications

(222 citation statements)

References 49 publications

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“…The pre-processing of images from different sensors in order to make it consistent with other images by normalizing them is very important in shoreline change detection studies [4,7,31,53,54]. The image based Dark Object Subtraction (DOS) model was used to convert the digital number recorded by the MSS sensor to generate a surface reflectance product [55][56][57]. Then, the surface reflectance products of L2 MSS images with a 60 m spatial resolution were resampled at 30 m to conform to the spatial resolution of other Landsat sensors (TM, ETM+, and OLI) [16,56].…”

Section: Atmospheric Correction Of Satellite Imagerymentioning

confidence: 99%

“…The image based Dark Object Subtraction (DOS) model was used to convert the digital number recorded by the MSS sensor to generate a surface reflectance product [55][56][57]. Then, the surface reflectance products of L2 MSS images with a 60 m spatial resolution were resampled at 30 m to conform to the spatial resolution of other Landsat sensors (TM, ETM+, and OLI) [16,56].…”

Section: Atmospheric Correction Of Satellite Imagerymentioning

confidence: 99%

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Coastline Vulnerability Assessment through Landsat and Cubesats in a Coastal Mega City

et al. 2020

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According to the Intergovernmental Panel on Climate Change (IPCC), global mean sea levels may rise from 0.43 m to 0.84 m by the end of the 21st century. This poses a significant threat to coastal cities around the world. The shoreline of Karachi (a coastal mega city located in Southern Pakistan) is vulnerable mainly due to anthropogenic activities near the coast. Therefore, the present study investigates rates and susceptibility to shoreline change using a 76-year multi-temporal dataset (1942 to 2018) through the Digital Shoreline Analysis System (DSAS). Historical shoreline positions were extracted from the topographic sheets (1:250,000) of 1942 and 1966, the medium spatial resolution (30 m) multi-sensor Landsat images of 1976, 1990, 2002, 2011, and a high spatial resolution (3 m) Planet Scope image from 2018, along the 100 km coast of Karachi. The shoreline was divided into two zones, namely eastern (25 km) and western (29 km) zones, to track changes in development, movement, and dynamics of the shoreline position. The analysis revealed that 95% of transects drawn for the eastern zone underwent accretion (i.e., land reclamation) with a mean rate of 14 m/year indicating that the eastern zone faced rapid shoreline progression, with the highest rates due to the development of coastal areas for urban settlement. Similarly, 74% of transects drawn for the western zone experienced erosion (i.e., land loss) with a mean rate of −1.15 m/year indicating the weathering and erosion of rocky and sandy beaches by marine erosion. Among the 25 km length of the eastern zone, 94% (23.5 km) of the shoreline was found to be highly vulnerable, while the western zone showed much more stable conditions due to anthropogenic inactivity. Seasonal hydrodynamic analysis revealed approximately a 3% increase in the average wave height during the summer monsoon season and a 1% increase for the winter monsoon season during the post-land reclamation era. Coastal protection and management along the Sindh coastal zone should be adopted to defend against natural wave erosion and the government must take measures to stop illegal sea encroachments.Remote Sens. 2020, 12, 749 2 of 24 developments in coastal areas [1,2]. Therefore, shoreline indicators are used for a consistent representation of a shoreline. The most common shoreline indicators are the tidal datum (e.g., a specific elevation at the land-ocean boundary) and visually discernible features (e.g., a revetment structure, an erosion scarp, a previous high-tide high-water level, low-tide low-water level, or dune vegetation line, etc.) [3][4][5][6][7][8]. The shoreline is an indicator of the ecological health of coastal areas [3,[9][10][11]. In past decades, the development of mega-infrastructure projects in coastal areas for urbanization/industrialization has placed more stress on the coastal ecosystem and changed ocean hydrodynamics [12][13][14][15][16]. Unplanned coastal development coupled with the natural action of ocean processes has led to the increasing vulnerability of low lying coasta...

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Section: Atmospheric Correction Of Satellite Imagerymentioning

confidence: 99%

Section: Atmospheric Correction Of Satellite Imagerymentioning

confidence: 99%

Coastline Vulnerability Assessment through Landsat and Cubesats in a Coastal Mega City

et al. 2020

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“…Using only those areas of identified water and sand and removing all other features, Ostu's method [99] was used to delineate the shoreline interface. Vos et al [98] then compared their enhanced shoreline method to five long-term shoreline datasets from around the world including Narrabeen, where 502 individual satellite images between 1987-2018 are available. They report a cross-shore RMSE between satellite-derived and in situ (Emery method and RTK-GPS) shoreline measurements of 8.2 m ( Figure 9B) at Narrabeen, comparable to the method of Liu et al [96].…”

Section: Satellite-derived Datamentioning

confidence: 99%

“…Most recently, Vos et al [98] applied new machine learning techniques to extend the methods of Liu et al [96] to automatically detect shorelines from satellite images that are now freely available from the cloud (i.e., Landsat, ASTER, Sentinel-2) via the Google Earth Engine (https://earthengine.google.com/). Specifically, an image classification technique was applied to refine the sand/water interface.…”

Section: Satellite-derived Datamentioning

confidence: 99%

Remote Sensing Is Changing Our View of the Coast: Insights from 40 Years of Monitoring at Narrabeen-Collaroy, Australia

2018

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Narrabeen-Collaroy Beach, located on the Northern Beaches of Sydney along the Pacific coast of southeast Australia, is one of the longest continuously monitored beaches in the world. This paper provides an overview of the evolution and international scientific impact of this long-term beach monitoring program, from its humble beginnings over 40 years ago using the rod and tape measure Emery field survey method; to today, where the application of remote sensing data collection including drones, satellites and crowd-sourced smartphone images, are now core aspects of this continuing and much expanded monitoring effort. Commenced in 1976, surveying at this beach for the first 30 years focused on in-situ methods, whereby the growing database of monthly beach profile surveys informed the coastal science community about fundamental processes such as beach state evolution and the role of cross-shore and alongshore sediment transport in embayment morphodynamics. In the mid-2000s, continuous (hourly) video-based monitoring was the first application of routine remote sensing at the site, providing much greater spatial and temporal resolution over the traditional monthly surveys. This implementation of video as the first of a now rapidly expanding range of remote sensing tools and techniques also facilitated much wider access by the international research community to the continuing data collection program at Narrabeen-Collaroy. In the past decade the video-based data streams have formed the basis of deeper understanding into storm to multi-year response of the shoreline to changing wave conditions and also contributed to progress in the understanding of estuary entrance dynamics. More recently, 'opportunistic' remote sensing platforms such as surf cameras and smartphones have also been used for image-based shoreline data collection. Commencing in 2011, a significant new focus for the Narrabeen-Collaroy monitoring program shifted to include airborne lidar (and later Unmanned Aerial Vehicles (UAVs)), in an enhanced effort to quantify the morphological impacts of individual storm events, understand key drivers of erosion, and the placing of these observations within their broader regional context. A fixed continuous scanning lidar installed in 2014 again improved the spatial and temporal resolution of the remote-sensed data collection, providing new insight into swash dynamics and the often-overlooked processes of post-storm beach recovery. The use of satellite data that is now readily available to all coastal researchers via Google Earth Engine continues to expand the routine data collection program and provide key insight into multi-decadal shoreline variability. As new and expanding remote sensing technologies continue to emerge, a key lesson from the long-term monitoring at Narrabeen-Collaroy is the importance of a regular re-evaluation of what data is most needed to progress the science.

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“…This produces visually intuitive smooth waterlines that closely reproduce the true waterline position and shape without the distracting blocky pixel artefacts that affect whole-pixel approaches. While early applications of the technique applied waterline extraction to soft-classified layers where the exact proportion of water and land within each pixel was known (e.g., [35][36][37][38]), recent applications have instead used remote sensing water indices such as NDWI which can be calculated directly from open source remote sensing imagery [28,38,39]. These approaches have proven able to extract waterline positions with high levels of accuracy in sandy beach environments (e.g., up to 5.7 m against a 30 year validation dataset at Narrabeen Beach in eastern Australia, [38]).…”

Section: Introductionmentioning

confidence: 99%

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

et al. 2019

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Accurately mapping the boundary between land and water (the 'waterline') is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu's method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50-3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.

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Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery

Cited by 234 publications

References 49 publications

Coastline Vulnerability Assessment through Landsat and Cubesats in a Coastal Mega City

Coastline Vulnerability Assessment through Landsat and Cubesats in a Coastal Mega City

Remote Sensing Is Changing Our View of the Coast: Insights from 40 Years of Monitoring at Narrabeen-Collaroy, Australia

Sub-Pixel Waterline Extraction: Characterising Accuracy and Sensitivity to Indices and Spectra

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