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

Geronimo, R. C.; Franklin, E. C.; Brainard, R. E.; Elvidge, C. D.; Santos, M. D.; Venegas, R.; Mora, C.

doi:10.3390/rs10101604

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

2018

By Geronimo, R. C. ; Franklin, E. C. ; Brainard, R. E. ; ...

Source: Remote Sensing, 10(10), 23.

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Geronimo, R. C. ; Franklin, E. C. ; Brainard, R. E. ; Elvidge, C. D. ; Santos, M. D. ; ... More +

NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; PIFSC (Pacific Islands Fisheries Science Center) ; JIMAR (Joint Institute for Marine and Atmospheric Research) ; NESDIS (National Environmental Satellite, Data, and Information Service) ; NCEI (National Centers for Environmental Information) ;

Description:

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 km(2) 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.

Source:

Remote Sensing, 10(10), 23.

DOI:

http://dx.doi.org/10.3390/rs10101604

Document Type:

Journal Article ;

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CC BY

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