Essential for students, researchers, practitioners, and policymakers, this guide equips readers with the tools necessary to assess ecosystem health, inform management strategies, and support sustainable coastal development in a rapidly changing environment.
Table of Contents
Part I: Basics of Blue Carbon and Remote Sensing1. Introduction to Blue Carbon Ecosystems
2. Emerging Contaminants in Coastal Ecosystems
3. Foundations of Remote Sensing for Coastal Ecosystem
4. Monitoring Geographic Ecological Land Processing
Part II: Mapping and Monitoring Blue Carbon Ecosystems
5. UAV and Drone-Based Remote Sensing for Coastal Contaminant Monitoring
6. Remote Sensing Data Collection Methods in Mangrove Ecosystems
7. Remote Sensing Applications in Mangrove & Blue Carbon Monitoring
8. Environmental Monitoring Using Spatio-temporal Patterns of Contaminants
9. Impact of Emerging Contaminants on Mangrove Ecosystems
10. Intelligent Deep Learning Methods for Ecological Land-change Monitoring
11. Segmentation Models in Agricultural Image Classification
12. Geospatial Data Integration for Coastal Ecosystems
13. Case Studies in Mangrove Monitoring Using Remote Sensing
14. Monitoring Coastal Ecosystem Health with UAVs & Drones
15. Challenges in Remote Sensing of Coastal Ecosystems
Part III: Quantifying Carbon and Verifying Impact
16. Blue Carbon Sequestration in Mangroves
Part IV: Future Horizons and Global Impact
17. Emerging Trends & Technologies in Remote Sensing
18. Future Directions in Mangrove & Blue Carbon Monitoring
Authors
Uzair Aslam Bhatti Hainan University, Haikou, China.Uzair Aslam Bhatti is a researcher focused on applying machine learning to medical and signal processing problems, with additional interest in broader artificial intelligence applications. He completed his PhD at Hainan University, where he received two Best Research Paper Awards and a Chinese Government Scholarship. After his PhD, he worked as a Postdoctoral Researcher at Nanjing Normal University (School of Geography), in the Remote Sensing and Signal Processing area. During this period, he contributed as first author to multiple publications in SCI-indexed journals and conferences, including work in IEEE Transactions on Geoscience and Remote Sensing and Chemosphere. His work also contributed to recognition as an Excellent Postdoctoral Candidate by Nanjing Normal University. He has participated in research projects supported by the National Natural Science Foundation of China, the National Key R&D Program, and the Hainan Provincial Major Science and Technology Program.
Sibghat Ullah Bazai Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS), Pakistan. Sibghat Ullah Bazai received the Ph.D. degree in information technology with a specialization in cyber security from Massey University, Auckland, New Zealand. Currently, he is an Assistant Professor with the Department of Computer Engineering, Faculty of Information and Communication Technology, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS). His research interests include the application of cybersecurity and privacy in computer-aided diagnosis, disease identification using deep learning, local language sentiment corpus design, and smart city planning. He also serves as a Guest Editor and a Reviewer for special issues in journals, such as MDPI, Hindawi, CMC, PLOS One, and Frontier. He was a recipient of the HEC HRDI-UESTP Faculty Ph.D. Scholarship Muhammad Aamir Huanggang Normal University, Huanggang, China. Muhammad Aamir earned his Ph.D. in Computer Science and Technology from Sichuan University (Chengdu, China) in 2019. He is currently an Associate Professor with the Department of Computer Science at Huanggang Normal University (Huanggang, China).He received the B.E. in Computer Systems Engineering from Mehran University of Engineering and Technology (Jamshoro, Sindh, Pakistan) in 2008 and the M.E. in Software Engineering from Chongqing University (Chongqing, China) in 2014. His research interests include pattern recognition, computer vision, image processing, deep learning, and fractional calculus.
