Open Sources Geospatial Intelligence Dashboard for Mangrove Monitoring Using Machine Learning and Remote Sensing Data

Mangroves’ role in global climate mitigation has gained increased attention. From 2000 to 2016, about 62% of global mangrove forests were lost. One significant mangrove area in Indonesia is Sembilang National Park, South Sumatra. Despite its importance, this eco-system has suffered major degradation. Between 2009 and 2017, primary mangrove forest area in the park decreased from 83,447 to 70,263 hectares—a loss of 13,184 hectares. The research focuses on the Sembilang National Park in South Sumatra, Indonesia, over the period of 2019–2023. This study developed a mangrove biomass estimation model using two machine learning algorithms: Random Forest (RF) and Gradient Boosting (GB). RF outperformed GB with an R² score of 0.701 vs. 0.601, highlighting its robustness and better generalization. The biomass (AGB) graph displays total biomass carbon in tonnes, with gradient colouring helping to visualize temporal trends. AGB Trend Overview is imple-mented as a comprehensive metrics dashboard, displaying key information on changes in mangrove biomass. The developed open sources dashboard can provide temporal data analysis of man-grove forest condition, including biomass changes and predictions for the coming year, supporting more targeted data-driven decision-making in mangrove forest conservation and management efforts in Sembilang National Park Indonesia.