Carbon Sequestration in Coastal Ecosystems: A Review of Modeling Techniques and Applications

Abstract

Coastal ecosystems, including mangroves, salt marshes, and seagrass meadows, play a crucial role in global carbon sequestration by capturing and storing atmospheric carbon dioxide within their biomass and sediments. Traditional methods for assessing carbon sequestration in these ecosystems rely on field-based measurements and empirical models, which often struggle with spatial limitations and inconsistencies in data accuracy. This study investigates the integration of artificial intelligence (AI) and remote sensing technologies to enhance the accuracy, scalability, and efficiency of carbon sequestration monitoring. Using a case study approach with ten case studies spanning diverse coastal ecosystems, this research examines the application of machine learning models, such as Random Forest, Support Vector Machines (SVM), Deep Neural Networks (DNNs), and Long Short-Term Memory (LSTM) networks, in processing multi-sensor datasets from satellite, LiDAR, and UAV sources. The findings reveal that AI-enhanced models improve biomass estimation accuracy by up to 28%, outperforming conventional remote sensing approaches. UAV-based LiDAR assessments achieved error margins within ±5%, demonstrating superior precision in carbon stock estimations. Additionally, AI-driven models successfully detected carbon sequestration trends over a 10-year period, enabling the identification of sequestration fluctuations with up to 93% predictive accuracy. The study further highlights the cost effectiveness of AI models, which reduced the need for manual field validation by 50% while maintaining high correlation with ground-truth measurements. These results underscore the
transformative potential of AI in automating blue carbon ecosystem assessments, improving long-term carbon forecasting, and informing adaptive conservation policies. By leveraging AIdriven remote sensing, this study establishes a robust framework for advancing climate mitigation efforts and sustainable environmental management in coastal carbon sequestration research.