Photogrammetry-Derived National Shoreline: Uncertainty and Sensitivity Analyses

TitlePhotogrammetry-Derived National Shoreline: Uncertainty and Sensitivity Analyses
Publication TypeConference Abstract
AuthorsYao, F, Parrish, CE, Calder, BR, Pe'eri, S, Rzhanov, Y
Conference Name2013 Fall Meeting, American Geological Union (AGU)
Conference DatesDec 9-13, 2013

Tidally-referenced shoreline data serve a multitude of purposes, ranging from nautical charting, to coastal change analysis, wetland migration studies, coastal planning, resource management and emergency management. To assess the suitability of the shoreline for a particular application, end users need not only the best available shoreline, but also reliable estimates of the uncertainty in the shoreline position. NOAA’s National Geodetic Survey (NGS) is responsible for mapping the national shoreline depicted on NOAA nautical charts. Previous studies have focused on modeling the uncertainty in NGS shoreline derived from airborne lidar data, but, to date, these methods have not been extended to aerial imagery and photogrammetric shoreline extraction methods, which remain the primary shoreline mapping methods used by NGS. The aim of this study is to develop a rigorous total propagated uncertainty (TPU) model for shoreline compiled from both tide-coordinated and non-tide-coordinated aerial imagery and compiled using photogrammetric methods. The project site encompasses the strait linking Dennys Bay, Whiting Bay and Cobscook Bay in the “Downeast” Maine coastal region. This area is of interest, due to the ecosystem services it provides, as well as its complex geomorphology. The region is characterized by a large tide range, strong tidal currents, numerous embayments, and coarse-sediment pocket beaches. Statistical methods were used to assess the uncertainty of shoreline in this site mapped using NGS’s photogrammetric workflow, as well as to analyze the sensitivity of the mapped shoreline position to a variety of parameters, including elevation gradient in the intertidal zone. The TPU model developed in this work can easily be extended to other areas and may be facilitate estimation of uncertainty in inundation models and marsh migration models.