On August 12th, the State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Resources, Chinese Academy of Sciences released the 10-meter resolution data product of global mangrove distribution, which is the highest resolution and latest product in global mangrove data at present. The product is produced by Su Fenzhen's team, which aims to provide basic data support for the research of carbon neutrality, global change and sustainable development by using the intelligent remote sensing extraction method of big data.

Mangrove is a sensitive ecosystem with global changes, which has the functions of maintaining coastal ecological balance, preventing wind and reducing disasters, protecting banks, purifying environmental pollution and protecting biodiversity. Its changes directly affect the sustainable development of the global environment and regions. Accurate and efficient monitoring is the foundation of mangrove protection. Based on the field visits conducted by China and five Southeast Asian countries and the sample points collected in other parts of the world, the team established a global mangrove classification sample bank. Using the advantages of big data and cloud platform, combined with multi-source remote sensing data and other spatial data related to mangrove growth conditions, the mangrove was extracted in 2018-2020 by the pixel-based and object-oriented composite classification method and the deep learning method combining space and spectrum, and the global 10-meter resolution mangrove distribution data product was produced.

Data shows that the area of mangroves in the world is 14,348,400 hectares at present, which is about 8.82% less than that of 35 years ago. This data has been shared in the Scientific Data Bank.

Source from:

[1] http://www.igsnrr.cas.cn/news/kyjz/202108/t20210819_6159241.html

[2] Xiao Han, Su Fenzhen, Fu Dongjie, Yu Hao, Ju Chengyuan, Pan Tingting, Kang Lu. 10-M GLOBAL MANGROVE CLASSIFICATION PRODUCTS OF 2018-2020 BASED ON BIG DATA. V1. Science Data Bank. http://www.doi.org/10.11922/sciencedb.01019. (2021-08-12).