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Kathryn M. Weber

Publications and source records attributed to Kathryn M. Weber.

5 recordsLinked to original sources

Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Alaska, Florida, Massachusetts, Washington↗

Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data

The U.S. Geological Survey Coastal and Marine Geology Program uses three methods to derive a datum-based, mean high water shoreline on open-ocean coasts from light detection and ranging (lidar) elevation surveys. This work compared the shorelines produced by the three methods for two different surveys: one survey with simple beach morphology, and one survey with complex beach morphology. For the survey with simple beach morphology, the three methods gave very similar results. The mean differences were less than 0.1 meter, and the root mean square differences were all less than 1.0 meter. For the survey of a beach with complex morphology, the quality control used in the Profile method and Smoothed Contour/Manual Hybrid method produced cleaner shorelines than the Grid method. Only the Profile method can extrapolate if there is no data around mean high water. The Grid and Profile methods produce a point by point estimate of uncertainty which is needed for some applications. Only the Contour method can be easily transferred to external users.

Open-File Report↗

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

Massachusetts↗

Using topographic lidar data to delineate the North Carolina Shoreline

In North Carolina, shoreline change rates are an important component of the state's coastal management program. To enhance methods of measuring shoreline change, the NC Division of Coastal Management (DCM) is considering using mean high water (MHW) shorelines extracted from lidar data together with traditional wet/dry shorelines digitized from aerial photography. To test their compatibility, a wet/dry line and MHW shoreline derived from a concurrent 2004 oceanfront photography and lidar dataset were compared along a distance of 244 km. Results show that the MHW shoreline was seaward of the wet/dry shoreline by 2.82 m on average, and that this offset biased shoreline change rates by an average of 0.05 m/yr. The offset was greatest on low-sloping beaches experiencing higher water levels at the time of photography, but overall was small enough to suggest that the MHW shoreline can be a reliable substitute for the wet/dry shoreline.

North Carolina↗