Digital Surface Model (DSM), Orthophoto, Ground Control Point (GCP) coordinates (recorded using an RTK GNSS system), Structure-from-Motion report (with relevant parameters and spatial accuracy) and raw aerial images from a kite-based survey of the beach at the sourthern end of North Core banks, NC on October 24, 2015. DSM and Orthophoto was constructed with 16 GCPs. GCP coordinates are given in NC State Plane Coordinate system, NAD 83. Vertical datum is NAVD88
The problem of operating many of the present day transformers designed and built to function in sizes and under conditions unheard of only a few years ago, is now of prime importance to many of the larger power companies. A statement of one type of the operating troubles encountered in the larger sizes is discussed in this paper and data taken as far as practicable under operating conditions is given. From experience so far gained it is thought that both the autotransformer and the grounded neutral system are here to stay and such problems as they present merit considerable investigation under actual working conditions. The present paper presents rather than solves one type of trouble encountered.
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.
