Implementation of the UN Sustainable Development Goals requires countries to determine targets for the protection, conservation, or restoration of coastal ecosystems such as mangrove forests by 2030. Satellite remote sensing provides historical and current data on the distribution and dynamics of mangrove forests, essential baseline data that are needed to design suitable policy interventions. In this study, Landsat time series were used to map trends and dynamics of mangrove change over a time span of 30 years (1987–2017) in protected areas of Hainan Island (China). A support vector machine algorithm was combined with visual interpretation of imagery and result showed alternating periods of expansion and loss of mangrove forest at seven selected sites on Hainan Island. Over this period, there was a net decrease in mangrove area of 9.3%, with anthropic activities such as land conversion for aquaculture, wastewater disposal and discharge, and tourism development appearing to be the likely drivers of this decline in cover. Long-term studies examining trends in land use cover change coupled with assessments of drivers of loss or gain enable the development of evidence based on policy and legislation. This forms the basis of financing of natural reserves of management and institutional capacity building, and facilitates public awareness and participation, including co-management.

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Mangrove forests are distributed in intertidal regions that act as a “natural barrier” to the coast. They have enormous ecological, economic, and social value. However, the world’s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.