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.