Coastal ecosystems such as mangroves, salt marshes, and seagrass meadows play a central role in carbon storage, shoreline protection, and habitat provision. Restoration efforts have expanded globally, but systematic methods to evaluate their effectiveness remain limited. Field surveys provide accurate data at local scales but are restricted in coverage and continuity. This paper reviews approaches that integrate remote sensing, isotopic proxy analysis, and machine learning for assessing coastal vegetation restoration. Remote sensing enables spatial and temporal monitoring of vegetation change. Isotopic proxies provide indicators of nutrient dynamics and carbon sequestration. Machine learning supports the integration of heterogeneous datasets and the development of predictive models. The combination of these methods allows assessment of structural and functional recovery at multiple scales. A framework is outlined for applying these approaches in restoration monitoring and management. Research directions are identified in relation to sampling design, data integration, and policy applications.
