Monitoring interdecadal coastal change along dissipative beaches via satellite imagery at regional scale

Graffin, Marcan; Taherkhani, Mohsen; Leung, Meredith; Vitousek, Sean; Kaminsky, George; Ruggiero, Peter

doi:10.1017/cft.2023.30

Cited by 3 publications

(2 citation statements)

References 63 publications

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“…The definition of the WL in this type of environment is extremely challenging. During low tide conditions, those beaches may present large wet and saturated patches and complex forms [29], sometimes leading to wet/dry boundaries which are optically similar to a waterline interface [42]. Extraction algorithms may experience difficulties in distinguishing the land and the water classes, often creating important errors and biases (e.g., [18,26,30]).…”

Section: Extraction Parameters and Coastal Conditions Affect Sdwl Acc...mentioning

confidence: 99%

“…Particularly, the possibility of using proxies that can be mapped automatically (such as the wet/dry line) constitutes an interesting research issue [13]. The improvement of methods for water level corrections and removal of flawed images also constitutes an interesting challenge in high-energy tidal coasts [42]. Furthermore, although the broad diversity of parameters included in SAET has been tested, new indices and classification alternatives such as k-means or machine learning may help to provide more accurate results [15,23].…”

Section: Future Researchmentioning

confidence: 99%

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Assessing Satellite-Derived Shoreline Detection on a Mesotidal Dissipative Beach

Cabezas-Rabadán,

Almonacid-Caballer,

Benavente

et al. 2024

Remote Sensing

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The accuracy and robustness of the shoreline definition from satellite imagery on different coastal types are crucial to adequately characterising beach morphology and dynamics. However, the generic and widespread application of satellite-derived shoreline algorithms is limited by the lack of robust methods and parameter assessments. This work constitutes a quantitative and comprehensive assessment of the satellite-derived waterlines from Sentinel-2 by using the novel SAET tool (Shoreline Analysis and Extraction Tool) on the exposed and mesotidal beach of La Victoria (Cádiz, SW Spain). The diverse parameters available in SAET, such as water indexes, thresholding methods, morphological filters, and kernel sizes, were combined to define water/land interface positions that were compared against coincident video-derived waterlines. Satellite-derived waterline errors are found to be affected by extraction parameters, as well as by the oceanographic and morphological conditions at the time of the image acquisition. The application of a morphological erosion filter on the water mask, which tends to shift the extracted waterline seawards and reduce bias, is the best solution at the dissipative site of La Victoria Beach. Moreover, using a 3 × 3 kernel size consistently shows higher accuracies than a larger kernel. Although there was no parameter combination showing the best skill for all dates, the employment of the Automated Water Extraction Index for images with no shadows (AWEInsh) with a threshold = 0, erosion morphological filter, and 3 × 3 kernel was, overall, the best combination of extraction parameters for this beach (average waterline RMSE of 5.96 m). The combination of the Modified Normalised Difference Water Index (MDNWI) with the Otsu thresholding also led to similar positions of the resulting waterlines and offered good accuracies. In line with other recent research efforts, our work stresses the lack of generic shoreline extraction solutions that can be applied automatically at a global level and the necessity to adapt and validate the extraction methodologies to the different types of coastlines.

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Section: Extraction Parameters and Coastal Conditions Affect Sdwl Acc...mentioning

confidence: 99%

Section: Future Researchmentioning

confidence: 99%

Assessing Satellite-Derived Shoreline Detection on a Mesotidal Dissipative Beach

Cabezas-Rabadán,

Almonacid-Caballer,

Benavente

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

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Predicting Shoreline Changes Along the California Coast Using Deep Learning Applied to Satellite Observations

Adusumilli,

Cirrito,

Engeman

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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Understanding and predicting changes in shoreline location are critical for coastal planners. In situ monitoring is accurate but not widely available. Satellite observations of shorelines have global coverage, but their accuracy and predictive capacity have not been fully explored. Abundant beach surveys and extensive wave observations in Southern California provide a unique ground truth for the interpretation of satellite‐derived recently wetted waterlines. We combine 23 years of waterline position estimates from satellite imagery with nearshore wave hindcasts and tides to train and test a deep neural network (DNN). The trained DNN uses only tides and waves as predictors at transects with satellite coverage and wave estimates to predict beach width and, for the first time, seasonal average beach slopes. Beach width changes hindcast using DNN have at least fair skill (>0.3) for 50% of transects, where the skill was calculated relative to the mean of extensive new in situ survey data from in San Diego County. The DNN also predicted shoreline changes to within 10 m (the nominal uncertainty in satellite‐derived shorelines) for 64% of transects lacking ground truth. DNN and survey estimates of seasonal beach slope changes also agree qualitatively.

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Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery

Bergsma,

Klotz,

Artigues

et al. 2024

Remote Sensing

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Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

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Monitoring interdecadal coastal change along dissipative beaches via satellite imagery at regional scale

Cited by 3 publications

References 63 publications

Assessing Satellite-Derived Shoreline Detection on a Mesotidal Dissipative Beach

Assessing Satellite-Derived Shoreline Detection on a Mesotidal Dissipative Beach

Predicting Shoreline Changes Along the California Coast Using Deep Learning Applied to Satellite Observations

Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery

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