Using machine learning to integrate mangrove restoration with sustainable aquaculture intensification

July 31, 2023 | The Fish Site | Source

A team of experts in academia, conservation, and technology has developed an AI-powered rapid assessment tool to identify and validate suitable sites for Climate Smart Shrimp (CSS) production in Indonesia and the Philippines. The initiative, funded by the 2022 Climate Change AI Innovation Grants program, aims to address the rapid growth of shrimp aquaculture, which has led to the destruction of critical coastal ecosystems like mangroves.

The Climate Smart Shrimp program, developed by Conservation International, supports small- and medium-sized farmers in intensifying production on part of their farms sustainably in exchange for mangrove restoration on the remaining portion. This approach enhances farmers' competitiveness while restoring vital coastal ecosystems.

The team used machine learning and earth observation data, including Planet NICFI satellite imagery and aquaculture pond data, to identify and classify aquaculture farms using extensive production methods. They combined this information with data on sea level rise, flood risk, infrastructure access, historical mangrove cover, and other factors to identify suitable sites for CSS. The result is an interactive web-map tool that analyzes potential aquaculture site suitability based on specific criteria.

This tool not only accelerates the identification of suitable CSS sites but also aids conservation practitioners in decision-making for nature-based solutions. It streamlines the implementation of CSS while supporting efforts to restore mangroves and enhance food security and livelihoods. The tool can potentially be adapted for various coastal and terrestrial restoration applications.

The development of this site assessment tool contributes to the efficient implementation of CSS, which supports both sustainable shrimp aquaculture and climate resilience in coastal communities. CSS is being piloted not only in Southeast Asia but also in Ecuador, demonstrating its applicability across different production systems and geographies.