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Abstract Barrier islands are landscape features that protect coastlines by reducing wave energy and erosion. Quantifying vegetation-topographic interactions between adjacent habitats are essential for predicting long-term island response and resilience to sea-level rise and disturbance. To understand the effects of dune dynamics on adjacent interior island ecosystem processes, we quantified how sediment availability and previous disturbance regime interact with vegetation to influence dune building and ease of seawater and sediment movement into the island interior on two US mid-Atlantic coast barrier islands. We conducted field surveys of sediment accretion, vegetative cover, and soil characteristics in dune and swale habitats. Digital elevation models provided assessment of water flow resistance from the mean high water mark into the island interior. We found that geographic location impacted sediment accretion rates andPanicum amarum(a species increasing in abundance over time in the Virginia barrier islands) accreted sediment at a significantly lower rate compared to other dune grasses. Dune elevation impacted the ease of seawater flow into the island interior, altering soil chlorides, annual net primary productivity, and soil carbon and nitrogen. Our work demonstrates the importance of incorporating biological processes and cross-island connectivity into future scenario modeling and predictions of rising sea-levels and increased disturbance.
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Predicting Barrier Island Shrub Presence Using Remote Sensing Products and Machine Learning Techniques
Abstract Barrier islands are highly dynamic coastal landforms that are economically, ecologically, and societally important. Woody vegetation located within barrier island interiors can alter patterns of overwash, leading to periods of periodic‐barrier island retreat. Due to the interplay between island interior vegetation and patterns of barrier island migration, it is critical to better understand the factors controlling the presence of woody vegetation on barrier islands. To provide new insight into this topic, we use remote sensing data collected by LiDAR, LANDSAT, and aerial photography to measure shrub presence, coastal dune metrics, and island characteristics (e.g., beach width, island width) for an undeveloped mixed‐energy barrier island system in Virginia along the US mid‐Atlantic coast. We apply decision tree and random forest machine learning methods to identify new empirical relationships between island geomorphology and shrub presence. We find that shrubs are highly likely (90% likelihood) to be present in areas where dune elevations are above ∼1.9 m and island interior widths are greater than ∼160 m and that shrubs are unlikely (10% likelihood) to be present in areas where island interior widths are less than ∼160 m regardless of dune elevation. Our machine learning predictions are 90% accurate for the Virginia Barrier Islands, with almost half of our incorrect predictions (5% of total transects) being attributable to system hysteresis; shrubs require time to adapt to changing conditions and therefore their growth and removal lags changes in island geomorphology, which can occur more rapidly.
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- Award ID(s):
- 1832221
- PAR ID:
- 10548366
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Earth Surface
- Volume:
- 129
- Issue:
- 5
- ISSN:
- 2169-9003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Rahman, Md Naimur (Ed.)Barrier islands provide a first line of defense against ocean flooding and storm surge. Biogeomorphic interactions are recognized as important in coastal system processes, but current barrier island models are primarily dominated by physical processes. Recent research has demonstrated different biogeomorphic states that influence response to sea level rise and other disturbance. Building on this understanding, we present a cellular model utilizing biotic and abiotic processes and their interactions for barrier island evolution. Using the literature and field derived parameters, we model barrier island evolution and compare to three decades of change for Smith Island, a Virginia Coast Reserve barrier island. We conduct simulations that show the impact of biogeomorphic states on island migration under different sea level rise scenarios. We find that migration is highest in areas with low topography and light vegetation cover (i.e. disturbance reinforcing) compared to areas with greater topographic complexity and high cover of woody vegetation i.e. disturbance resisting). This study demonstrates the importance of biogeomorphic interactions for barrier island evolution with sea level rise and will aid future predictions for these important ecosystems with climate change.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2023JF007465
