Bimodal climate control of shoreline change influenced by Interdecadal Pacific Oscillation variability along the Cooloola Sand Mass, Queensland, Australia

Kelly, Joshua T.; McSweeney, Sarah; Shulmeister, James; Gontz, Allen

doi:10.1016/j.margeo.2019.105971

Cited by 18 publications

(19 citation statements)

References 54 publications

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“…To evaluate how sub-pixel waterline extraction performs when the optimal value of the threshold is not known in advance, we also compared performance using two more realistic threshold selection strategies. The simplest 'zero' threshold scenario applied a consistent threshold of zero to all environments and metrics, in an approach which has been widely used in waterline extraction analyses [7,22,64]. Our results indicate a uniform zero threshold was unable to consistently reproduce the absolute reference waterline position across the five contrasting environments or three water indices we assessed.…”

Section: Effect Of Water Index Thresholdmentioning

confidence: 88%

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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Section: Effect Of Water Index Thresholdmentioning

confidence: 88%

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

et al. 2019

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“…However, their study did not include any pre-and post-storm imagery; thus, erosive episodes could not be linked to specific events. Kelly et al (2019) expanded their work with Landsat imagery over 25 years and showed that long-term shoreline change at the Cooloola Sand Mass is controlled by variability in the Interdecadal Pacific Oscillation (IPO). However, Landsat imagery does not have high spatial resolution; thus, the affects of individual storms also could not be discerned, unless the event resulted in shoreline migration of more than~15 m. Other documented shoreline changes occurring in response to large-scale storm events not only include erosion and progradation, but washover fan development (May et al, 2017;Williams, 2015) and new inlet formation (FitzGerald and Pendleton, 2002;Maio et al, 2014).…”

Section: History Of Cyclonesmentioning

confidence: 99%

“…interact and influence a seasonal wave climate that is highly variable with respect to wave heights and direction (Hemer, McInnes, and Ranasinghe, 2013). Recent work has shown that long-term CSM shoreline dynamics are predominantly controlled by IPO phase variability (Kelly et al, 2019).…”

Section: Study Sitementioning

confidence: 99%

“…Advances in satellite technology and geographic information systems provide new mechanisms to examine the effects of episodic events. These techniques couple traditional methods used in aerial photography (Boak and Turner, 2005) and apply new techniques that take advantage of the multispectral capacity of satellite imagery (Kelly and Gontz, 2018;Kelly et al, 2019;van der Werff, 2019;Xu, 2018). High-resolution satellite imagery (,5 m/pixel) with high temporal resolution (daily) allows for an unprecedented capacity to track the effect of a single event over large areas without the logistical planning, time, cost, or combination of factors associated with flying low-altitude aerial imagery (Fellman, 2008), beach profiling (Andrade and Ferreira, 2006;Delgado and Lloyd, 2004), or unmanned aerospace systems/unmanned aerial vehicle surveying (Colomina and Molina, 2014;Gonclaves and Henriques, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Landsat). The daily 3.7-m multispectral imagery represents a significant improvement on Landsat's 30-m imagery acquired in 16-day intervals that has been frequently used to assess shoreline changes (Almonacid-Caballer et al, 2016;Kelly et al, 2019) and could provide much greater insight into storm-induced shoreline changes.…”

Section: Introductionmentioning

confidence: 99%

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

Kelly

Gontz

2019

Journal of Coastal Research

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Using calibrated surface roughness dating to estimate coastal dune ages at K'gari (Fraser Island) and the Cooloola Sand Mass, Australia

Patton

Shulmeister

Rittenour

et al. 2022

Earth Surf Processes Landf

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Here we present a novel application of landscape smoothing with time to generate a detailed chronology of a large and complex dune field. K'gari (Fraser Island) and the Cooloola Sand Mass (CSM) dune fields host thousands of emplaced (relict) and active onlapping parabolic dunes that span 800 000 years in age. While the dune fields have a dating framework, their sheer size (~1930 km2) makes high‐resolution dating of the entire system infeasible. Leveraging newly acquired (n = 8) and previously published (n = 20) optically stimulated luminescence (OSL) ages from K'gari and the CSM, we estimate the age of Holocene dunes by building a surface roughness (σC)–age relationship model. In this study, we define σC as the standard deviation of topographic curvature for a dune area and we demonstrate an exponential relationship (r2 = 0.942, RMSE = 0.892 ka) between σC and timing of dune emplacement on the CSM. This relationship is validated using ages from K'gari. We calculate σC utilizing a 5 m digital elevation model and apply our model to predict the ages of 726 individually delineated Holocene dunes. The timing of dune emplacement events is assessed by plotting cumulative probability density functions derived from both measured and predicted dune ages. We demonstrate that both dune fields had four major phases of dune emplacement, peaking at <0.5, ~1.5, ~4, and ~8.5 ka. We observe that our predicted dune ages did not create or remove major events when compared to the OSL‐dated sequence, but instead reinforced these patterns. Our study highlights that σC–age modelling can be an easily applied relative or absolute dating tool for dune fields globally. This systematic approach can fill in chronological gaps using only high‐resolution elevation data (3–20 m resolution) and a limited set of dune ages.

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Bimodal climate control of shoreline change influenced by Interdecadal Pacific Oscillation variability along the Cooloola Sand Mass, Queensland, Australia

Cited by 18 publications

References 54 publications

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

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

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

Using calibrated surface roughness dating to estimate coastal dune ages at K'gari (Fraser Island) and the Cooloola Sand Mass, Australia

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