Primary drivers of multidecadal spatial and temporal patterns of shoreline change derived from optical satellite imagery

Castelle, Bruno; Ritz, Auguste; Marieu, Vincent; Lerma, Alexandre Nicolae; Vandenhove, Marine

doi:10.1016/j.geomorph.2022.108360

Cited by 29 publications

(39 citation statements)

References 68 publications

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“…However, Castelle et al. (2022) showed that using raw, uncorrected satellite‐derived shorelines is generally sufficient for long‐term trend analyses.…”

Section: Resultsmentioning

confidence: 99%

“…Castelle et al. (2022) showed that even raw satellite‐derived shorelines (which are not corrected for tide or wave setup) can reproduce long‐term shoreline trends obtained from traditional methods (e.g., manually digitized shorelines from orthorectified and georeferenced aerial photographs in their study). Avoiding the need to apply tide and wave corrections is a particularly attractive benefit to simplified shoreline‐trend analysis efforts.…”

Section: Discussionmentioning

confidence: 99%

“…Although we apply unweighted regressions to all available shoreline observations from different sources (e.g., Lidar, GPS, satellite) at each transect, 99.9% of the observations come from satellites; hence, they dominate the trend analysis, as expected. Trends fit to satellite-derived shoreline observations have been repeatedly shown to reproduce observed trends (derived from traditional sources of shoreline data for overlapping time periods) in many different settings (e.g., Castelle et al, 2022;Smith et al, 2021). In the current application, the shoreline trends are fit to the full set of tide-, wave-, and-level-corrected satellite-derived shoreline observations (see Section 2.3).…”

Section: Long-term Shoreline Change Ratesmentioning

confidence: 93%

“…In the current application, the shoreline trends are fit to the full set of tide-, wave-, and-level-corrected satellite-derived shoreline observations (see Section 2.3). However, Castelle et al (2022) showed that using raw, uncorrected satellite-derived shorelines is generally sufficient for long-term trend analyses.…”

Section: Long-term Shoreline Change Ratesmentioning

confidence: 99%

“…Yet, integration of satellite‐data with large‐scale, dynamic coastal‐change models has remained underdeveloped, until now. Most applications of satellite‐derived shorelines investigate shoreline trends (e.g., Calkoen et al., 2021; Castelle et al., 2022; Luijendijk et al., 2018) or interannual variability (Vos et al., 2023), rather than synoptic shoreline variability with wave and storm events. Recently, Alvarez‐Cuesta et al.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

et al. 2023

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Satellite‐derived shoreline observations combined with dynamic shoreline models enable fine‐scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite‐data‐assimilated, “littoral‐cell”‐based, ensemble Kalman‐filter shoreline model to predict coastal change and uncertainty due to waves, sea‐level rise (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water‐level‐corrected, satellite‐derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in situ surveyed shorelines (e.g., mean sea‐level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20‐year hindcast period (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) compared to model calibration with monthly in situ observations at Ocean Beach. When comparing model‐predicted shoreline positions to satellite‐derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi‐decadal simulations of shoreline change due to projected wave and sea‐level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. The satellite‐data‐assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.

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“…However, Castelle et al. (2022) showed that using raw, uncorrected satellite‐derived shorelines is generally sufficient for long‐term trend analyses.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Long-term Shoreline Change Ratesmentioning

confidence: 93%

Section: Long-term Shoreline Change Ratesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

et al. 2023

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Improved geospatial analysis of shoreline modification using a weighted‐average‐based novel formulation

Mohanty

Rajani

Majumdar

et al. 2023

Earth Surf Processes Landf

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This study presents a novel weighted-average method (WAM) that captures the detailed nuances of shoreline change patterns over long and short periods by considering shorelines belonging to five different time frames. Using the WAM, the average shoreline modification for the longer period (136 years with intervals of $34 years) and for the shorter period (annual changes over a period of 5 years) have been calculated for the 9 km stretch of the Kollam coast in Kerala, India. Historically, Kollam has been a prominent seaport on the Malabar Coast of India and was extensively mapped due to its importance for political, strategic, and trade reasons. The coastal information available in these older maps is unique as there are no comparable data for the coastline of that period that can be used to assess changes along the coast. The WAM demonstrated the efficacy of the historical maps as pivotal reference points. It reliably captured the different shoreline activities (i.e., growing, stable, and receding regions) of natural and anthropogenic origins within the study area, and the findings are consistent with results obtained by using the linear regression rate (LRR) and the end point rate (EPR) methods. This work presents both qualitative and quantitative analyses to evaluate shoreline change using various statistical and mathematical methods. The findings should serve as baseline information for coastal management, notably mitigating coastal hazards-whether of natural or anthropogenic originwhich could result in the enhancement of ecosystem services for coastal communities.

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Spatial gradients in bank stability response in macrotidal environments: Inferences from the Severn estuary (UK)

Gasparotto,

Darby,

Leyland

et al. 2024

Earth Surf Processes Landf

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In this study, we present a synthetic model summarising the main sedimentary and morphologic factors that drive spatial–temporal variations in bank stability through an exemplar macrotidal estuary. In contrast to previous studies that tend to only consider localised variations in the stability of small‐scale banks, here the focus is on understanding the bank stability patterns at the scale of the whole Severn Estuary (UK). The results show that during falling tides, the bank sediments persist in a near‐saturated state giving elevated bank pore pressures that coincide in time with declines in the hydrostatic confining pressure, leading to destabilisation of the bank. In contrast, bank stabilisation predominantly occurs during rising tides when the hydrostatic confining pressure is able to dominate over the destabilisation processes. Cohesive macrotidal estuaries similar to the Severn Estuary, tend to present a generalised decrease in the instability moving from the outer estuary where the tidal oscillations are more significant, to the inner part of the system where such oscillations are reduced and coupled with less high banks.

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Primary drivers of multidecadal spatial and temporal patterns of shoreline change derived from optical satellite imagery

Cited by 29 publications

References 68 publications

A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions

Improved geospatial analysis of shoreline modification using a weighted‐average‐based novel formulation

Spatial gradients in bank stability response in macrotidal environments: Inferences from the Severn estuary (UK)

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