Island Activities Detected by VIIRS and Validation with AIS - Advances in Remote Sensing

ARS > Vol.7 No.3, September 2018

Island Activities Detected by VIIRS and Validation with AIS ()

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Download as PDF (Size: 5267KB) PP. 171-182

DOI: 10.4236/ars.2018.73012 800 Downloads 1,713 Views

Author(s)

Ichio Asanuma, Daisuke Hasegawa, Takashi Yamaguchi, Jong Geol Park, Kenneth James Mackin

Affiliation(s)

Department of Informatics, Tokyo University of Information Sciences, Chiba, Japan.

ABSTRACT

A possibility to monitor the reclamation activities by remote sensing was discussed. The lights observed in the night time by Day Night Band (DNB) of Visible Infrared Imaging Radiometer Suite (VIIRS), ocean color observed in the day time by visible bands of VIIRS were the tools to monitor the surface activities, and the Automated Information System (AIS) was used to verify the types and number of vessels associated with the reclamation activities. The lights as the radiance from the surface were monitored by the object based analysis, where the object was defined as a radius of 5 km from the center of the Mischief Reef in the South China Sea (SCS). The time history of surface lights exhibited the increase of the radiance from January to May 2015 and the radiance was kept in the certain level to December 2016 with some variations. The ocean color, chlorophyll-a concentration as a proxy of sediments, showed an increase from February to June 2015 and returned to a low concentration in August 2015. According to the historical data of AIS, the number of dredgers has increased from February to August 2015 and the maximum number of dredgers was recorded in June 2015. The timing of increase of lights from surface, increase of chlorophyll-a concentration, and increase of number of vessels are consistent.

KEYWORDS

Dredging, Reclamation, VIIRS, DNB, Chlorophyll-a, AIS

Share and Cite:

Asanuma, I. , Hasegawa, D. , Yamaguchi, T. , Park, J. and Mackin, K. (2018) Island Activities Detected by VIIRS and Validation with AIS. Advances in Remote Sensing, 7, 171-182. doi: 10.4236/ars.2018.73012.

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