Gillen Megan N.

No Thumbnail Available
Last Name
Gillen
First Name
Megan N.
ORCID
0000-0002-2375-6519

Filters

2021 2
2
2
Reset filters

Settings

Search Results

Now showing 1 - 2 of 2
  • Article
    Biophysical controls of marsh soil shear strength along an estuarine salinity gradient
    (European Geosciences Union, 2021-05-25) Gillen, Megan N. ; Messerschmidt, Tyler C. ; Kirwan, Matthew L.
    Sea-level rise, saltwater intrusion, and wave erosion threaten coastal marshes, but the influence of salinity on marsh erodibility remains poorly understood. We measured the shear strength of marsh soils along a salinity and biodiversity gradient in the York River estuary in Virginia to assess the direct and indirect impacts of salinity on potential marsh erodibility. We found that soil shear strength was higher in monospecific salt marshes (5–36 kPa) than in biodiverse freshwater marshes (4–8 kPa), likely driven by differences in belowground biomass. However, we also found that shear strength at the marsh edge was controlled by sediment characteristics, rather than vegetation or salinity, suggesting that inherent relationships may be obscured in more dynamic environments. Our results indicate that York River freshwater marsh soils are weaker than salt marsh soils, and suggest that salinization of these freshwater marshes may lead to simultaneous losses in biodiversity and erodibility.
  • Article
    Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement
    (American Geophysical Union, 2021-09-03) Goldstein, Evan B. ; Buscombe, Daniel ; Lazarus, Eli ; Mohanty, Somya D. ; Rafique, Shah Nafis ; Anarde, Katherine A. ; Ashton, Andrew D. ; Beuzen, Tomas ; Castagno, Katherine ; Cohn, Nicholas ; Conlin, Matthew P. ; Ellenson, Ashley ; Gillen, Megan N. ; Hovenga, Paige A. ; Over, Jin-Si ; Palermo, Rose V. ; Ratliff, Katherine M. ; Reeves, Ian R. B. ; Sanborn, Lily H. ; Straub, Jessamin A. ; Taylor, Luke A. ; Wallace, Elizabeth J. ; Warrick, Jonathan ; Wernette, Phillipe ; Williams, Hannah E.
    Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, 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.