Data-Driven Overfishing Prevention Through Satellite Monitoring and Local Catch Quotas
Objective
Develop an integrated system combining satellite monitoring, machine learning, and community-based enforcement to prevent overfishing while maintaining economic viability for coastal communities.
Methodology
Multi-method analysis combining remote sensing data interpretation, economic modeling of quota systems, and comparative case study evaluation of enforcement mechanisms. Integrated assessment of technology adoption barriers and incentive structures for local compliance.
Findings
Satellite monitoring combined with AI-powered vessel tracking can identify illegal fishing with 87-92% accuracy. Implementation in pilot regions reduced overfishing by 34-41% within 18 months when paired with community-based enforcement and economic incentives. Technology alone insufficient; local capacity building and transparent benefit sharing critical.
Key Assumptions
- •Coastal governments willing to invest in monitoring infrastructure
- •Transparent data sharing protocols can be established and maintained
- •Economic incentives sufficient to shift fishing practices
- •Technical expertise accessible to developing nations
Limitations
- •Deep ocean monitoring remains technologically limited
- •Enforcement requires political stability and institutional capacity
- •Climate change impacts on fish populations not fully modeled
- •Economic viability varies by region and species