ORCID Profile
0000-0002-0205-3814
Current Organisation
US Geological Survey
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Publisher: Cambridge University Press (CUP)
Date: 28-11-2022
DOI: 10.1017/CFT.2022.4
Abstract: 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 have 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 % 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, that is, (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.
Publisher: American Geophysical Union (AGU)
Date: 07-2023
DOI: 10.1029/2023JF007135
Abstract: The northern California littoral cell of the Klamath River, which is a mixed rocky and sandy system with significant shoreline curvature, was investigated by examining ∼40 yr of satellite‐derived shoreline positions and historical records. We find that an accretion wave of sediment was initiated near the Klamath River mouth in the late 1980s and translated downcoast over the subsequent decades. The wave passed rapidly (∼2,500 m/yr) through a rocky coastal reach with more oblique wave directions and slowly through a sandy reach (∼200 m/yr) where wave crests approach at more normal angles. Within the sandy reach, the accretion wave extended over 200 m offshore, was ∼10 km long, incorporated 20 ± 6 million m 3 of sediment, and averaged 1.3 ± 0.4 million m 3 /yr of longshore sediment transport over a 20‐yr interval. Diffusion of the accretion wave was observed, but the diffusivity coefficient ( ε obs ∼0.01 m 2 /s) was lower than values predicted by theory, which we attribute to net sediment transport convergence in the study area caused by the curvature of the shoreline. Examining historical records, we find that increased sediment discharge in the Klamath River occurred during the 20th century from industrial‐scale logging and climatic extremes. Thus, we hypothesize that increased river sediment discharge introduced new sediment to the littoral cell that initiated the observed accretion wave. These hypotheses can be tested with stratigraphic and mineralogic investigations of the broad study area beach that has formed during the past 150 years.
Publisher: California Digital Library (CDL)
Date: 28-07-2023
DOI: 10.31223/X5W66T
Abstract: Almar and colleagues (2023) are correct in stating that, “understanding and predicting shoreline evolution is of great importance for coastal management.” Amongst the different timescales of shoreline change, the interannual and decadal timescales are of particular interest to coastal scientists as they reflect the integrated system response to the Earth’s climate and its natural modes of variability. Therefore, establishing the links between shoreline change and climate variability at the global scale would be a major achievement. However, we find that the work of Almar et al.1 does not achieve this goal because: (i) the satellite-based method does not meet the current standards of practice and produces inaccurate results, (ii) the spatial coverage of the shoreline dataset is not adequate for a global analysis, (iii) the relevance of the statistical analyses between the shoreline data and independent variables is questionable, and (iv) the findings do not capture physical patterns of shorelines developed from field-based observations.
Publisher: American Geophysical Union (AGU)
Date: 09-2021
DOI: 10.1029/2021EA001896
Abstract: Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for ex le, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
Publisher: Public Library of Science (PLoS)
Date: 18-01-2011
Location: United States of America
Location: United States of America
No related grants have been discovered for Jonathan Warrick.