Spatial-temporal changes of China’s mangrove forests over the past 50 years: An analysis towards the Sustainable Development Goals (SDGs)

Mangrove forests are tropical trees and shrubs that grow in sheltered coastlines, mudflats, and river banks in many parts of the world. These forests are rated amidst the most productive natural ecosystems on the earth, and are ecologically and socioeconomically important because of their crucial roles in coastal ecosystem protection. However, these forests are declining at an alarming rate, which is possibly more rapid than that of inland tropical forests. This serious loss has prompted a worldwide movement to protect and promote the sustainable use of mangrove forests. Recently, many governments adopted the United Nations' Sustainable Development Goals (SDGs). The SDGs present an opportunity for nations to set realistic targets for achieving sustainable use of natural resources and environmental capital. Relevant to mangrove conservation, a range of targets were established for implementation by the year 2020, including Targets 6.6, 14.2, 14.5, and 15.2. To date, mangrove forests have been protected and restored for decades in China. However, little is known about achievements of China's SDGs implementation on mangrove forests. The issue highlighted the need for a long-term holistic view of China's mangrove forests dynamics. Although there have been multiple national datasets of China's mangrove forests, few studies focused specifically on mangrove forests and their surrounding land covers. Thus, the objectives of this study are: (1) to apply a systematic remote sensing method across the entire coast of China, and build a new dataset of long-term China's mangrove forests and surrounding land covers in 1973, 1980, 1990, 2000, 2010, 2015 (the first year of SDGs), and 2020 (the complete year of mangrove related SDGs); (2) to quantify the spatial-temporal changes of mangrove forests and conversion between mangrove forests and other coastal land covers; and (3) to discuss the achievements of China's SDGs implementation on mangrove forests. In this study, we applied a hybrid object-based and hierarchical classification method to Landsat series imagery and achieved a high accuracy dataset of China's mangrove forests and surrounding land covers. Results showed that: (1) on national scale, area of mangrove forests declined from 48801 to 18602 ha between 1973 and 2000, then partially recovered to 28010 ha in 2020; (2) the lost mangrove forests were mainly changed to croplands and aquaculture ponds, while the restored mangrove forests were mainly converted from tidal flats; and (3) during 2015−2020, China government restored 25% of national mangrove forests. To Sep. 2020, the area of mangrove nature reserves accounted for 16% of mangrove growth zone, and 77% of China's mangrove forests grew inside these nature reserves. A batch of relevant laws and regulations has been formulated to prohibit mangrove forests destruction. The protection and restoration of mangrove forests in China have already met Targets 6.6, 14.2, 14.5, and 15.2. However, since illegal logging is strictly prohibited and the awareness of protecting mangrove ecosystem has been increased continuously, losses of mangrove forests in some areas were mainly caused by natural disasters, such as extremely low temperature, hurricane, biological invasions, and insect outbreaks. For example, according to the Guangxi Mangrove Research Center, in March 2008 numbers of Avicennia plants along the coasts of Guangxi were killed by extremely low temperature, and in Guangxi Shankou Mangrove Nature Reserve, more than 167 ha of Spartina alterniflora (an invasive species) were discovered in 2005. The classification method and datasets of this study can support the evaluation of SDG 6.6 implementation, and provide important information for SDGs 13, 14, and 15 evaluation. In addition, the results of this study can serve as an important scientific basis and fundamental data for formulating China's mangrove protection and restoration strategies.