Uncertainty Analysis of Photogrammetry-Derived National Shoreline

Fang Yao
Master of Science Defense


Monday, Mar. 31, 2014, 10:00am
Chase 130

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 require reliable estimates of the uncertainty in the shoreline position. The National Geodetic Survey (NGS) in the National Oceanic and Atmospheric Administration (NOAA) is responsible for mapping the national shoreline depicted on NOAA nautical charts. Previous studies on modeling uncertainty in shoreline mapping from remote sensing data have focused on airborne light detection and ranging (LiDAR); to date, these methods have not been extended to aerial imagery and photogrammetric shoreline mapping, which remains the primary shoreline mapping method used by NGS. The aim of this research is to develop and test a rigorous total propagated uncertainty (TPU) model for shoreline compiled from both tide-coordinated and non-tide-coordinated aerial imagery using photogrammetric methods. The TPU model is developed using data from a project site in northeast Maine. The results indicate that the main uncertainty components are the offset between the tidal datum (mean high water (MHW) or mean lower low water (MLLW)) and observed water level at the time of imagery acquisition, and human compilation uncertainty, respectively. For the study area, the standard uncertainty was found to be ~3.1-3.3 m, depending on whether the imagery was tide coordinated or not. The TPU model developed in this research can easily be extended from the study area to other areas and may facilitate estimation of uncertainty in inundation models and marsh migration models


Fang Yao received her master's degree from Wuhan University, China in 2011. Her major during undergraduate and graduate studies was photogrammetry and remote sensing.