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Mangroves store significant carbon content that, when managed properly, will contribute to combating the climate crisis. Despite having the largest mangrove forest, Indonesia’s mangrove annual damage rate turns out to be the highest globally, one of the most significant factors is extensive plastic waste exposure, exacerbated by mangroves’ deforestation for conversion into agricultural land. Many efforts initiated by the government and other stakeholders have been targeting mangrove rehabilitation and plastic waste abatement. Labor-intensive and time-consuming ground checking have been the main source of information to determine priority areas for mangrove rehabilitation so far. This study aims to introduce a more effective and efficient identification of priority areas for rehabilitation. The study utilizes vulnerability index by optimizing remote sensing satellite data modeling. The study covers all mangroves in Indonesia, and for the purpose of this study, four mangrove vulnerability classes are formed to help categorize the severity of the damage. The classes are formed through integration, scoring, and classifying plant health, water turbidity, land temperature, plant carbon sequestration capability, and plastic waste distribution in Indonesian coastal area data. The modeling demonstrates its ability to distinguish the classes through machine learning. This study identifies that 65.74% of Indonesia’s coastal mangroves are highly exposed to plastic waste. Bali and Surabaya are two of the most severely damaged areas. This study, along with further analysis of socio-cultural, economic, and development priorities, will enable decision-makers to prioritize and mobilize necessary resources to rehabilitate the mangroves guided by a suitable mangrove management regime for each class.
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