AI-Enabled Ocean Health Monitoring for Environmental Remote Sensing and Geospatial Sustainability Intelligence

Introduction:
Remote sensing has become central to environmental monitoring, yet ocean health assessment often remains fragmented across ecological, socioeconomic, and policy indicators. This study develops an interpretable artificial intelligence framework for transforming longitudinal Ocean Health Index data into a geospatial sustainability intelligence system for monitoring marine environmental trajectories, identifying vulnerable coastal systems, and forecasting future ocean health performance.

Methods:
The study analyses 341,124 Ocean Health Index score observations across 220 coastal countries and territories, 19 goals or subgoals, six score dimensions, and annual assessments from 2012 to 2025. The framework integrates data harmonisation, longitudinal trajectory modelling, K-means country grouping, risk stratification, Random Forest next-year forecasting, goal-level importance analysis, and country-specific policy diagnostics. Forecast predictors were constructed from lagged-goal and dimension-level components, while the contemporaneous overall Index was excluded to avoid circular prediction.

Results:
The global Ocean Health Index increased from 71.85 in 2012 to 74.94 in 2019, declined sharply to 69.50 in 2024, and partially recovered to 72.31 in 2025, remaining 2.63 points below its 2019 level. Goal-level changes were highly uneven, with Tourism and Recreation declining by 12.42 points, while Natural Products and Lasting Special Places increased by 7.67 and 7.38 points, respectively. Country trajectory modelling identified two regimes with mean 2025 Index scores of 65.07 and 74.47, separated by 9.40 points. Risk stratification classified 51 countries and territories as low risk, 98 as moderate risk, and 71 as high risk. The Random Forest model predicted next-year OHI scores with R² = 0.723, MAE = 3.07, and RMSE = 4.03, identifying Habitat, Carbon Storage, and Fisheries as the strongest predictors of future ocean health.

Conclusions:
This study advances AI-enabled environmental monitoring by demonstrating that multidimensional ocean health indicators can be converted into forecastable, interpretable, and policy-relevant geospatial intelligence. The framework complements remote sensing-based environmental observation by providing a scalable analytical layer for marine sustainability assessment, coastal system prioritisation, and ocean health decision support.