International Journal of Marine Science, 2025, Vol.15, No.4, 220-232 http://www.aquapublisher.com/index.php/ijms 230 In terms of vegetation biomass, indicators include the breast diameter, plant height, growth amount of trees, and the biomass carbon contained in xylem and roots per unit area. The commonly used method is to conduct forest inventory every year or every few years in a fixed sample, record the growth of a certain number of standard wood, and estimate the living carbon storage of the entire forest land through the biomass equation. For ecosystems such as mangroves where a large amount of carbon is distributed underground, both above-ground and underground biomass should be included in the monitoring system. Finally, in order to make monitoring comparable and popular, unified technical standards and specifications should be followed. There are already some blue carbon monitoring guidelines internationally, which divide carbon storage monitoring into different levels (Tier 1/2/3), and encourage the gradual improvement of accuracy based on capabilities. A complete mangrove carbon sink monitoring index system will provide scientific basis for the certification of carbon trading projects, the compilation of national greenhouse gas inventory, etc., and is also an important tool for evaluating the effectiveness of management measures and adjusting strategies in a timely manner. 7.3 The Philippines' remote sensing-based mangrove carbon sink monitoring system The Philippines has a vast mangrove coast and is actively committed to the assessment and improvement of its carbon sink capacity. In order to efficiently monitor the country's mangrove carbon sink monitoring system, the Philippines has built a mangrove carbon sink monitoring system based on remote sensing technology in recent years. The system was developed by the Philippine Space Agency (PhilSA) and a university research organization. It integrates satellite remote sensing, geographic information systems and artificial intelligence analysis to dynamically monitor the area changes and carbon reserves of mangroves across the country. In terms of data acquisition, the system makes full use of free images of Sentinel series satellites and data of domestic micro-nano satellites to achieve regular coverage of mangroves in more than 7 000 islands in the Philippines. Through machine learning algorithms, the project team has developed a mangrove classification model suitable for diverse environments in the Philippines, which can not only quickly identify mangrove distribution, but also determine whether mangrove forests are in a healthy, growing or degenerate state (Magalona et al., 2023). In terms of carbon storage estimation, the monitoring system combines remote sensing and ground survey data. The researchers selected representative mangrove sample sites from different regions, measured the stand structure and carbon content, and established a correlation model with satellite image characteristics. Based on the model, the system can calculate the changes in biomass carbon storage and soil carbon storage of mangroves in various places based on the latest remote sensing data. At the global level, the Philippines is also actively sharing its experience with other Southeast Asian countries to promote the construction of regional blue carbon monitoring networks. This case shows that using modern remote sensing and information technology can build an efficient mangrove carbon sink monitoring and evaluation system on a national scale, thereby supporting the needs of refined management and international carbon emission reduction reporting, and escorting the continuous improvement of mangrove carbon sink functions. Acknowledgements During this study, we would like to sincerely thank our colleagues for their support and help in the collation and discussion of the data. At the same time, we would like to thank the two review experts for their valuable revisions and suggestions on this article to make the article more perfect. Conflict of Interest Disclosure The authors confirm that the study was conducted without any commercial or financial relationships and could be interpreted as a potential conflict of interest. References Adame M., Connolly R., Turschwell M., Lovelock C., Fatoyinbo T., Lagomasino D., Goldberg L., Holdorf J., Friess D., Sasmito S., Sanderman J., Sievers M., Buelow C., Kauffman J., Bryan-Brown D., and Brown C., 2020, Future carbon emissions from global mangrove forest loss, Global Change Biology, 27: 2856-2866. https://doi.org/10.1111/gcb.15571
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