Application of Remote Sensing and GIS to Identifying Marine Fisheries off the Coasts of Kenya and Tanzania

Abstract

10°S 38°E 38°E 38°E 42°E 42°E 42°E 40°E 40°E 40°E 44°E 44°E 44°E 0 100 200 300 Kilometers Two case studies demonstrate that the application of satellite remote sensing and GIS techniques can inform the development and improvement of fishing policies and fishery management in Kenya and Tanzania. Artisanal coastal fishing communities in both countries still rely on traditional methods to identify fishing grounds. The rudimentary techniques they use are based on conservative hunting methods that rely on recurrent experiences and evidence gathering among fisherfolk. However, multiple environmental factors determine the spatial structure and distribution of pelagic fisheries (Planque et al., 2011), and marine organisms are highly vulnerable to the rapid variations in oceanographic conditions that are being accelerated by global changes. These changes contribute to the broad diversity in species distribution and assemblages in space and time, further complicating fishers’ quests for productive grounds. Biophysical indicators of the sea surface environment such as temperature and chlorophyll concentration may serve as important determinants of the presence of marine life. Physical processes in the upper ocean such as currents, waves, and tides stimulate biological processes that ultimately determine the distribution of pelagic fish (Solanki et al., 2005). A thorough understanding of key environmental parameters and their influence on pelagic fish distribution can inform exploration for prospective fishing zones. Chlorophyll-a (Chl-a) concentration is a measure of the algae present in seawater and can be used as an indicator of fish production. The microscopic algae form the top of the marine food web and are consumed by zooplankton and small fish, which are then consumed by larger fish. Similarly, sea surface temperature (SST) is a significant physical factor that strongly influences the physiology and growth of ocean life, including phytoplankton and all other organisms at higher trophic levels (Tang et al., 2003), and can be used to help identify fishing grounds. Collecting measurements of oceanographic parameters from boats over large areas is time consuming and expensive and can be impractical for identifying commercially viable fishing areas due to the dynamic nature of the ocean. Consequently, there is a need for more effective methods that can capture changes instantaneously over broad regions. Satellite sensors can be used to gather information on global ocean SST and Chl-a concentration at relatively high resolutions over broad regions and long time periods. Geographic Information System (GIS) techniques can then be used to integrate satellite images with spatial databases (e.g., Microsoft SQL Server, Oracle, PostgreSQL) and statistical techniques to inform fisheries management. A pilot case study in Kenya involved the discovery of potential yellowfin tuna fishing grounds using satellite data on oceanographic parameters selected based on their relevance as descriptors of tuna habitat. SST, sea surface Chl-a, and mean sea level anomalies obtained from Application of Remote Sensing and GIS to Identifying Marine Fisheries off the Coasts of Kenya and Tanzania