Rapid Assessment of Shoreline Changes Induced by Tropical Cyclone Oma Using CubeSat Imagery in Southeast Queensland, Australia

Kelly, Joshua T.; Gontz, Allen

doi:10.2112/jcoastres-d-19-00055.1

Cited by 19 publications

(10 citation statements)

References 62 publications

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“…The inclusion of time‐varying parametrizations (and their uncertainty) offers the opportunity to ensure consistency between modeled coastal evolution drivers and the underlying physical processes (Toimil et al, 2020) and now warrants the EnKF application as a method to explore parameter changes and investigate strategies to improve shoreline models in view of climate variability. This is motivated by the advent of newly available global‐scale shoreline detection methods using satellite remote sensing (e.g., Kelly & Gontz, 2019; Vos, Harley, et al, 2019; Vos, Splinter, et al, 2019) and the increasing public availability of high‐resolution long‐term shoreline data sets (e.g., Ludka et al, 2019; Turner et al, 2016). It is anticipated that the approach presented here will be useful for exploring cross‐shore parameter variability as a first step for training model parameters and empirically relating their variability to natural changes in forcing (e.g., Splinter et al, 2014) to ensure model transferability during forecast periods.…”

Section: Discussionmentioning

confidence: 99%

Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates

Ibaceta

Splinter

Harley

et al. 2020

Geophysical Research Letters

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A novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model-free parameters can vary in time, adjusting to potential nonstationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (dual state-parameter ensemble Kalman filter) within the established shoreline evolution model ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real-world shoreline data set, revealing that time-varying model-free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modeling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate. Plain Language Summary Understanding and predicting future changes along sandy coastlines worldwide is highly relevant for coastal management in the context of climate change. In the future, the changing occurrence of storms-and over longer time scales, rising sea levels-are expected to result in new patterns of shoreline erosion. It is very common for shoreline change models to use past records of measured shorelines and waves to match mathematical equations to these existing observations. However, the validity of these types of shoreline models to predict the future is questionable, when waves and storm patterns around the world in coming decades are expected to be different to those observed in the past. A new methodology is presented to address this issue by exploring how a mathematical shoreline model can self-adjust to wave climates that vary through time. The proposed methodology is shown to be successful at improving shoreline predictions.

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Section: Discussionmentioning

confidence: 99%

Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates

Ibaceta

Splinter

Harley

et al. 2020

Geophysical Research Letters

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“…In recent decades, a number of studies have used optical satellite imagery (including the infrared portions of the electromagnetic spectrum) to map changes in shoreline position with increasing levels of automation. While manual digitalization of shoreline position is a reliable and accurate method, particularly on high-resolution images (Ford, 2013;Kelly and Gontz, 2019), it remains time-consuming and impractical when employed for long stretches of coastline with hundreds of revisits. Instead, modern methods exploit the contrast in spectral signature between water and land to automatically identify the shoreline.…”

Section: Optical Satellitesmentioning

confidence: 99%

The future of coastal monitoring through satellite remote sensing

Vitousek

Buscombe

Vos

et al. 2022

Camb. prisms Coast. futures

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Satellite remote sensing is transforming coastal science from a 'data-poor' field into a 'data-rich' field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change has outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in-situ surveys. The longterm coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, i.e., (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer-vision to fully leverage this massive treasure-trove of data. Extensive

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Section: Optical Satellitesmentioning

confidence: 99%

The future of coastal monitoring through satellite remote sensing

Vitousek¹,

Buscombe²,

Vos³

et al. 2022

Preprint

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Satellite remote sensing is transforming coastal science from a ‘data-poor’ field into a ‘data-rich’ field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change has outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in-situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, i.e., (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer-vision to fully leverage this massive treasure-trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earth-observing satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems.

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Rapid Assessment of Shoreline Changes Induced by Tropical Cyclone Oma Using CubeSat Imagery in Southeast Queensland, Australia

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 19 publications

References 62 publications

Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates

Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates

The future of coastal monitoring through satellite remote sensing

The future of coastal monitoring through satellite remote sensing

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