Global coastal geomorphology – integrating earth observation and geospatial data

Mao, Yongjing; Harris, Daniel L.; Xie, Zunyi; Phinn, Stuart

doi:10.1016/j.rse.2022.113082

Cited by 24 publications

(11 citation statements)

References 54 publications

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“…Using ICESat-2 ATL03 and ATL08 products, this study obtains the coastal slope results of sandy beach in Texas, USA. Along the 599 km coastline of Texas, beach coastline is ∼581 km, accounting for ∼97% (Mao et al 2022). In figures 3(a) and (b), 713 coastal slope transects perpendicular to the directions of the coastline are calculated from 713 valid ground tracks in 397 ATL03/ATL08 datasets.…”

Section: Resultsmentioning

confidence: 99%

“…Near Texas coast, the Mississippi River brings a large amount of sediments into the bay, forming a huge estuary delta and large underwater alluvial fan (Nittrouer and Viparelli 2014). Texas has approximately 599 km of open bay shoreline (Mao et al 2022), which is dominated by narrow muddy and sandy beach (https://tpwd.texas.gov/ business/grants/wildlife/cwcs/media/docs/coastal/ coastal_final.doc). Coastal wetlands in Texas include intertidal saline marshes, seagrass, and mangroves.…”

Section: Study Area and Satellite Datamentioning

confidence: 99%

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Estimating coastal slope of sandy beach from ICESat-2: a case study in Texas

Wang²,

Xu³

et al. 2023

Environ. Res. Lett.

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Coastal slope is a fundamental land characteristic that can influence the hydrodynamic and morphological processes, which is the essential parameter to calculate the wave setup and wave runup for further estimating extreme coastal water levels. Slope information of coastal zones also plays a key role in estimating the coastline erosion and evaluating the coastal vulnerability under sea level rise. However, accurate estimates of coastal slopes are currently limited, especially over sparsely populated and remote areas. The recent ICESat-2 photon-counting lidar provides unprecedented along-track dense and accurate height measurements in coastal zones. This study aims to demonstrate the potential of ICESat-2 measurements to estimate coastal slope of sandy beach at a large scale, and the proposed method is tested in Texas, USA. The validation with local airborne lidar data (with an average slope of 0.023 in Texas) indicates that, the ICESat-2 derived coastal slopes (0.026) have much better accuracy than current large-scale coastal slopes (0.0032) derived from SRTM and MERIT DEMs. With globally covered ICESat-2 datasets, this method can be expanded to estimate coastal slopes even at a global scale.

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Section: Resultsmentioning

confidence: 99%

Section: Study Area and Satellite Datamentioning

confidence: 99%

Estimating coastal slope of sandy beach from ICESat-2: a case study in Texas

Wang²,

Xu³

et al. 2023

Environ. Res. Lett.

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“…We started by drawing 25 km long transects perpendicular to the coastline with an interspacing of 1 km. We used the coastline of OpenStreetMap, which indicates the mean high-water springs line at a resolution comparable to shoreline estimates derived from Sentinel-1 with a 10-m spatial resolution 56 – 58 . We then selected the transects where the coast was classified as sandy 2 .…”

Section: Methodsmentioning

confidence: 99%

A global analysis of how human infrastructure squeezes sandy coasts

Lansu,

Reijers,

Höfer

et al. 2024

Nat Commun

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Coastal ecosystems provide vital services, but human disturbance causes massive losses. Remaining ecosystems are squeezed between rising seas and human infrastructure development. While shoreline retreat is intensively studied, coastal congestion through infrastructure remains unquantified. Here we analyse 235,469 transects worldwide to show that infrastructure occurs at a median distance of 392 meter from sandy shorelines. Moreover, we find that 33% of sandy shores harbour less than 100 m of infrastructure-free space, and that 23–30% of this space may be lost by 2100 due to rising sea levels. Further analyses show that population density and gross domestic product explain 35–39% of observed squeeze variation, emphasizing the intensifying pressure imposed as countries develop and populations grow. Encouragingly, we find that nature reserves relieve squeezing by 4–7 times. Yet, at present only 16% of world’s sandy shores have a protected status. We therefore advocate the incorporation of nature protection into spatial planning policies.

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“…Image segmentation, whereby each pixel is classified into predetermined classes (e.g., 'sand,' 'water,' 'vegetation'; see Figure 3), facilitates spatially explicit mapping of coastal environments. Image segmentation methods have been useful for estimating landcover types (Buscombe and Ritchie, 2018), backshore morphology (Mao et al, 2022), roughness for flood models (Sherwood et al, 2021), mapping flood extents (Erdem et al, 2021, Bentivoglio et al, 2021, flood damage (Adriano et al, 2021), and economic impacts (Donaldson and Storeygard, 2016). Segmentation and classification methods can also be slightly modified into so-called "image regression" methods, which provide continuous estimates of quantities of interest (e.g., wave height - Buscombe et al, 2020) or water depth (Collins et al, 2020).…”

Section: Satellite Image Classification Segmentation and Regressionmentioning

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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Global coastal geomorphology – integrating earth observation and geospatial data

Cited by 24 publications

References 54 publications

Estimating coastal slope of sandy beach from ICESat-2: a case study in Texas

Estimating coastal slope of sandy beach from ICESat-2: a case study in Texas

A global analysis of how human infrastructure squeezes sandy coasts

The future of coastal monitoring through satellite remote sensing

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