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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 / 760 840 8660




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

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

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


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


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


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



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




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




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
( .



6 Discussion