TIME: Monday, 8:00-12:20AM

PAPER NUMBER: IN11B-1278 

TITLE: Global Coastal and Marine Spatial Planning (CMSP) from Space Based AIS Ship Tracking

AUTHORS: Kurt D Schwehr1, 2, Jenifer Austin Foulkes2, Dino Lorenzini3, Mark Kanawati3

INSTITUTIONS: 1. CCOM 24 Colovos Road, Univ. of New Hampshire,
  Durham, NH, United States. 2. Google, Mountain View, CA, United
  States. 3. SpaceQuest, Fairfax, VA, United States. 

All nations need to be developing long term integrated strategies for
how to use and preserve our natural resources. As a part of these
strategies, we must evalutate how communities of users react to
changes in rules and regulations of ocean use. Global characterization
of the vessel traffic on our Earth’s oceans is essential to
understanding the existing uses to develop international Coast and
Marine Spatial Planning (CMSP). Ship traffic within 100-200km is
beginning to be effectively covered in low latitudes by ground based
receivers collecting position reports from the maritime Automatic
Identification System (AIS). Unfortunately, remote islands, high
latitudes, and open ocean Marine Protected Areas (MPA) are not covered
by these ground systems. Deploying enough autonomous airborne (UAV)
and surface (USV) vessels and buoys to provide adequate coverage is a
difficult task. While the individual device costs are plummeting, a
large fleet of AIS receivers is expensive to maintain. The global AIS
coverage from SpaceQuest’s low Earth orbit satellite receivers
combined with the visualization and data storage infrastructure of
Google (e.g. Maps, Earth, and Fusion Tables) provide a platform that
enables researchers and resource managers to begin answer the question
of how ocean resources are being utilized. Near real-time vessel
traffic data will allow managers of marine resources to understand how
changes to education, enforcement, rules, and regulations alter usage
and compliance patterns. We will demonstrate the potential for this
system using a sample SpaceQuest data set processed with libais which
stores the results in a Fusion Table. From there, the data is imported
to PyKML and visualized in Google Earth with a custom gx:Track
visualization utilizing KML’s extended data functionality to
facilitate ship track interrogation. Analysts can then annotate and
discuss vessel tracks in Fusion Tables.

http://vislab-ccom.unh.edu/~schwehr/papers/2011-agu/