Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset

Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal, state, and local sources. First, a decision tree-based dataset is built that describes the fabric or integrity of the coastal landscape and includes landcover, elevation, slope, long-term (&gt;150 years) shoreline change trends, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (e.g., flooding, wave power, and probability of storm overwash) and persistent hazards (e.g., relative sea-level rise rate, short-term (about 30 years) shoreline erosion rate, and storm recurrence interval). The fabric dataset is then merged with the coastal hazards databases and a training dataset made up of hundreds of polygons is generated from the merged dataset to support a supervised learning classification. Results from this pilot study are location-specific at 10-meter resolution and are made up of four raster datasets that include (1) quantitative and qualitative information used to determine the resistance of the landscape to change, (2 &amp; 3) the potential coastal hazards that act on it, (4) the machine learning output, or Coastal Change Likelihood (CCL), based on the cumulative effects of both fabric and hazards, and (5) an estimate of the hazard type (event or persistent) that is the likely to influence coastal change. Final outcomes are intended to be used as a first order planning tool to determine which areas of the coast may be more likely to change in response to future potential coastal hazards, and to examine elements and drivers that make change in a location more likely.

Tags: acadia-national-park, arcgis-pro, arcpy, autoclassification, automation, bathymetry, cape-cod-national-seashore, coastal-and-marine-hazards-mission-area, coastal-change-hazard-assessment, coastal-fabric, coastal-hazards, coastal-processes, coastal-vulnerability-index, connecticut, decision-tree-framework, delaware, elevation, gateway-national-recreation-area, george-washington-birthplace-national-monument, geoscientificinformation, hazards, high-tide-flooding, interpretation, land-cover, land-use-and-land-cover, landcover, machine-learning, maine, marine-geology, maryland, massachusetts, new-hampshire, new-jersey, new-york, northeast-us, oceans, rhode-island, scientific-interpretation, sea-level-change, sea-level-rise, shoreline-change, st-croix-island-international-historic-site, storm-overwash, storm-recurrence, supervised-classification, support-vector-machine, topography, training-samples, u-s-geological-survey, unvegetated-vegetated-ratio, usgs, usgs-61781f88d34e4c6b7fe2a444, uvvr, virginia, wave-power, woods-hole-coastal-and-marine-science-center
Organization: Department of the Interior