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Remote Sens. 2018, 10(10), 1604; https://doi.org/10.3390/rs10101604

Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling

1
Department of Geography, University of Hawai’i at Mānoa, Honolulu, HI 96822, USA
2
Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, U.S. National Oceanic and Atmospheric Administration, Honolulu, HI 96818, USA
3
Hawaii Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai’i at Mānoa, Kaneohe, HI 96744, USA
4
Earth Observation Group, National Oceanic and Atmospheric Administration’s National Centers for Environmental Information, Boulder, CO 80305, USA
5
National Fisheries Research and Development Institute, Quezon City 1103, Metro Manila, Philippines
*
Author to whom correspondence should be addressed.
Received: 13 August 2018 / Revised: 27 September 2018 / Accepted: 28 September 2018 / Published: 9 October 2018
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Abstract

Fisheries surveys over broad spatial areas are crucial in defining and delineating appropriate fisheries management areas. Yet accurate mapping and tracking of fishing activities remain largely restricted to developed countries with sufficient resources to use automated identification systems and vessel monitoring systems. For many countries, the spatial extent and boundaries of fishing grounds are not completely known. We used satellite images at night to detect fishing grounds in the Philippines for fishing gears that use powerful lights to attract coastal pelagic fishes. We used nightly boat detection data, extracted by U.S. NOAA from the Visible Infrared Imaging Radiometer Suite (VIIRS), for the Philippines from 2012 to 2016, covering 1713 nights, to examine spatio-temporal patterns of fishing activities in the country. Using density-based clustering, we identified 134 core fishing areas (CFAs) ranging in size from 6 to 23,215 km2 within the Philippines’ contiguous maritime zone. The CFAs had different seasonal patterns and range of intensities in total light output, possibly reflecting differences in multi-gear and multi-species signatures of fishing activities in each fishing ground. Using maximum entropy modeling, we identified bathymetry and chlorophyll as the main environmental predictors of spatial occurrence of these CFAs when analyzed together, highlighting the multi-gear nature of the CFAs. Applications of the model to specific CFAs identified different environmental drivers of fishing distribution, coinciding with known oceanographic associations for a CFA’s dominant target species. This case study highlights nighttime satellite images as a useful source of spatial fishing effort information for fisheries, especially in Southeast Asia. View Full-Text
Keywords: VIIRS; fisheries; maximum entropy; mapping VIIRS; fisheries; maximum entropy; mapping
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Geronimo, R.C.; Franklin, E.C.; Brainard, R.E.; Elvidge, C.D.; Santos, M.D.; Venegas, R.; Mora, C. Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling. Remote Sens. 2018, 10, 1604.

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