VIIRS Boat Detection (VBD)

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About VIIRS Boat Detection (VBD)


SNPP is the Suomi National Polar Partnership satellite flown by NASA and NOAA. It is the next generation polar orbiting satellite, collecting both daytime and nighttime data worldwide each day. The primary imager on SNPP is the Visible Infrared Imaging Radiometer Suite (VIIRS). At night the VIIRS day / night band (DNB) collects low light imaging data in the visible to enable detection of moonlit clouds. In addition to clouds, the DNB detects lights sources present at the earth’s surface. This includes the detection of boats, especially thos using light to attract catch. NOAA’s Earth Observation Group (EOG) has worked since 2014 on algorithms to report the locations of boat detected based on lights with low temporal latency. The service started in S.E. Asia and has now been expanded to global.

VIIRS Info from National Oceanic and Atmospheric Administration

Nightly Data

Boat detection results older than 45 days can be accessed by general public free of charge.

Monthly Summary Grid

Boat detection are aggregated into 15 asec grid in GeoTIFF format. The pixel values represent the number of detection fall within the pixel footprint.

Annual Summary Grid

Similar to monthly summary grid, annual summary grid aggregates boat detection counts for the whole year.

Reference

  • C. Elvidge, M. Zhizhin, K. Baugh, and F.-C. Hsu, “Automatic Boat Identification System for VIIRS Low Light Imaging Data,” Remote Sensing, vol. 7, no. 3, pp. 3020–3036, Mar. 2015.
  • C. D. Elvidge et al., “Rating the Effectiveness of Fishery Closures With Visible Infrared Imaging Radiometer Suite Boat Detection Data,” Frontiers in Marine Science, vol. 5, Apr. 2018.

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VBD Data Browser


CREDIT

When using the data please credit the product generation to the Earth Observation Group, Payne Institute for Public Policy, with proper citations as below.

 Elvidge, Christopher D., Mikhail Zhizhin, Kimberly Baugh, and Feng-Chi Hsu. “Automatic boat identification system for VIIRS low light imaging data.” Remote sensing 7, no. 3 (2015): 3020-3036.

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