Spatial Modeling and Analysis for Shoreline Change Detection and Coastal Erosion Monitoring

Abstract

Coastal erosion presents a serious problem throughout U.S. coastal areas. The OhioGeological Survey estimates that more than 3,200 acres of Ohio's Lake Erie shore have been lost to erosion since the 1870s, resulting in economic losses exceeding tens of millions of dollars per year. This article presents research results of a project that monitors shoreline erosion using high-resolution imagery and examines erosion causes. Spatial modeling and analysis methods are applied to the project area along the south shore of Lake Erie. The shoreline is represented as a dynamically-segmented linear model that is linked to a large amount of data describing shoreline changes. A new method computes an instantaneous shoreline using a digital water level model, a coastal terrain model, and bathymetric data. This method provides an algorithm for deriving the Mean-Lower Low Water (MLLW) and the Mean High Water (MHW) shorelines that are essential to navigation charts. The results describe a part of our effort towards a coastal spatial information infrastructure to support management and decision-making in the dynamic coastal environment.Erosion is de ned as the gradual wearing away of the earth's surface by the action of natural forces of wind and water. Lake Erie, the great body of fresh water forming Ohio's north coast, is the fourth largest of the ve Great Lakes (Figure 1). Lake Erie provides an unlimited fresh water supply to communities along its shore and is an unmatched recreational and sport-shing area. It provides signi cant quantities of sand and gravel for construction. On the other hand, Lake Erie is also a dynamic body of water noted for the ferocity of its storm waves and the havoc they wreak along the lakeshore (MacKey and Guy 1994; Highman 1997). Over much of this region, erosion rates have been less than 1 m per year. However, local rates may exceed 2 m per year (Highman 1997). Even where rates are slow, the highly developed nature of the coast makes recession a serious property-damage problem. Furthermore, about 95% of Ohio's Lake Erie shore is eroding and thus nearly 2,500 structures are within 15 m of destruction. Figure 2 shows a stretch of eroded Lake

Applying ArcGIS to Estimate the Rates of Shoreline and Back-Shore Area Changes along the Nile Delta Coast, Egypt

“…Many methods have been proposed to estimate coastline change. Among them are 1) the baseline approach [6]; 2) the dynamic segmentation approach [7] [8]; 3) the area-based approach [9]; and 4) the buffering and nonlinear least squares estimation approach [1]. Approach of baseline is used in the present work.…”

Automatic shoreline detection and future prediction: A case study on Puri Coast, Bay of Bengal, India

“…Several methods have been used for prediction of shoreline position as a function of time, rate of erosion and deposition or sea-level rise such as non-linear mathematical models e.g. higher order polynomial, exponential model, cyclic series models [Li et al, 2001]. Among them, the most simple and useful ones are the End Point Rate (EPR) and the Linear Regression (LR) models.…”

“…In the present study, the EPR model has been adopted to predict the future shoreline. The model is based on the assumption that the observed periodical rate of change of shoreline position is the best estimate for prediction of the future shoreline [Fenster et al, 1993] and no prior knowledge regarding the sediment transport or wave interference is required because the cumulative effect of all the underlined processes is assumed to be captured in the position history [Li et al, 2001]. The position of the future shoreline for a given data is estimated using the rate of shoreline movement (slope), time interval between observed and predicted shoreline and model intercept which can be expressed as…”

A Review: Applications of Geographic Information Systems (Gis) in Marine Areas

“…(2000) used GIS to rank the environmentally sensitive areas of the UK coastline based on assessments of pollution risks associated with shipping (description of different hazards, vessels routing patterns and historical frequencies of shipping accidents). Li et al (2001) applied various spatial modelling and analysis methods in high-resolution imagery to detect shoreline changes along the South shore of Lake Erie by representing the shoreline as a dynamically segmented linear model linked to a large amount of data describing shoreline changes.…”