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AI and Machine Learning in Carbon Sequestration: Transforming Climate Mitigation Strategies
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Author(s): Sarita Singh (Raj Kumar Goel Institute of Technology, Ghaziabad, India), Samridhi Gulati (Raj Kumar Goel Institute of Technology, Ghaziabad, India), Deepak Kumar (Raj Kumar Goel Institute of Technology, Ghaziabad, India)and Pratima Sharma (Raj Kumar Goel Institute of Technology, Ghaziabad, India)
Copyright: 2025
Pages: 18
Source title:
Advanced Systems for Monitoring Carbon Sequestration
Source Author(s)/Editor(s): Hari Mohan Pandey (Bournemouth University, UK), Pawan Kumar Goel (Raj Kumar Goel Institute of Technology, India), Vipin Balyan (Cape Peninsula University of Technology, South Africa)and Satya Prakash Yadav (Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India)
DOI: 10.4018/979-8-3373-2091-5.ch018
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Abstract
Carbon sequestration plays a crucial role in mitigating climate change by capturing and storing atmospheric carbon dioxide in various natural and engineered systems. The chapter Advanced Systems for Monitoring Carbon Sequestration explores state-of-the-art technologies and methodologies for tracking, quantifying, and verifying carbon sequestration in terrestrial, oceanic, and geological reservoirs. It provides an in-depth analysis of remote sensing, geospatial modeling, sensor networks, and AI-driven analytics that enhance the accuracy and efficiency of monitoring processes. Additionally, the chapter discusses the integration of satellite imagery, LiDAR, eddy covariance flux towers, and soil carbon measurement techniques. The role of blockchain and data transparency in carbon credit verification is also examined. By addressing challenges such as data uncertainty, cost-effectiveness, and scalability, the chapter highlights future directions for advancing monitoring frameworks to support global carbon management strategies.
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