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Global High-resolution Shoreline Project

John J. Helly

San Diego Supercomputer Center & Scripps Institution of Oceanography
University of California, San Diego
hellyj@ucsd.edu / 760 840 8660

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Contents

List of Figures

1 Introduction

As a result of efforts at SIO and the US Navy to develop a sea-level rise risk analysis framework, we have been wrestling with the lack of a high-resolution shoreline that has sound provenance and sufficient local geodetic control to be used as a reference for the integration of modern multi-source bathymetry and topography data. These data are primarily LIDAR data with sub-meter horizontal resolution and claimed 0.15 m vertical precision in some cases (need references here). However, when data from multiple providers of LIDAR are combined, problems with vertical control become immediately apparent and problematic. It should be mentioned that there is also a need to validate the horizontal accuracy and precision in projects employing geospatial data data fusion but this is commonly done by visual inspection using imagery and vector data to look for visually apparent anomalies.

After discussions with experts in geodesy and coastal engineering, it seems that an approach that provides independent, localized geodetic control, beyond the relatively sparse set of US national tidal datums and gauges, would contribute significantly to better geospatial data products. However, it is clear that there is no current or near-future national or international effort to develop this type of information. So this is a proposal to establish a grass-roots effort to collect those data and publish them in the public domain for the benefit of entire spectrum of geospatial data users. The basic idea is simple. For a given locale, for example the City of Encinitas in San Diego, establish a set of locations that are surveyed in using the best available technology (nominally differential GPS with post-processing using nearby base stations) and upload the data to the local coastal atlas for the area. This would create local groups with vested interest in the quality of the data and facilitate the development of interoperability across atlases. It distributes the cost and labor across an large population of motivated providers and avoids the complexities of a centralized bureaucracy. However, to achieve coherence and high quality, it requires an organization that can oversee and provide guidance to the collective efforts of the various providers to ensure a known level of data quality, with the ability to establish and maintain standards and conventions, and with the ability to conduct peer-review of the data as well as to put a ’seal-of-approval’ on it.

Toward this goal, it has been suggested that we establish an ICAN (International Coastal Atlas Network) working group on high-resolution shorelines. The ICAN group is a community of coastal atlas developers that have been working together for about five years with a well-established and growing international constituency.

 

2 ICAN Shoreline Task Force (ISTF)

 

3 Standards and Procedures

To establish the things that require coordination, here is a preliminary list of toolkit parts:

3.1 Workflow Specification including Quality Control Procedures

 

  1. Each Atlas provider will be responsible for the collection and publication for the data within its locale.

 

3.2 Sampling station criteria (surface type, structure type, spacing between stations, metadata blocks)

 

3.3 Data criteria (number of samples, post-processing criteria, data format, metadata blocks)

 

3.4 Interoperability Criteria (metadata specification, access protocol, data format)

 

4 Data Publication Process

 

  1. Digital Object Identifiers will be used by each data provider to enable reliable cross-referencing, versioning and attribution of data to its publisher and author(s).

 

5 Software Repository

ICAN will designate a software repository (e.g., svn repository) where authorized developers can contribute tools to accomplish post-processing of shoreline data according to ICAN interoperability conventions.

 


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Figure 1: Sample of LIDAR dataset used to estimate shoreline in San Diego emphasizing the realism of the engineered infrastructure.

 


 


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Figure 2: San Diego shoreline extracted from LIDAR with high-resolution beach and shoreline flooded to a given sea-level rise scenario.

 


 


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Figure 3: Comparative plot of San Diego open ocean coastline dataset to draft coastline provided by NOAA. This is an example of variability in comparable sources and need for intercalibration between three-different estimates of the shoreline. The third shoreline estimate is from the GSHHS (Global Self-consistent Hierarchical High-resolution Shoreline database from NOAA (http://www.ngdc.noaa.gov/mgg/shorelinesgshhs.html) .

 


 

6 Discussion