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Research Highlights: This paper provides an alternative approach to contextualize mangrove forest loss by integrating available environmental and socio-economic data sets and products. Background and Objectives: Mangrove forest ecosystems grow in brackish water especially in areas exposed to accumulation of organic matter and tides. This forest type is widely distributed in tropical and subtropical coastal areas. Recent studies have revealed that the mangrove forest ecosystem had significantly degraded due to Land Use and Cover Changes (LUCC) in the recent past. Therefore, contribution of mangrove deforestation drivers has to be assessed to ensure a comprehensive analysis for ecosystem conservation and restoration and facilitate decision making. Materials and Methods: Firstly, a correlation analysis was conducted between individual data products and mangrove deforestation. Each data product was associated with the Dominant Land Use of Deforested Mangrove Patches data for 2012. Next, calculations were performed for specific data combinations to estimate the contributions of anthropogenic factors to mangrove deforestation. Results: In general, our study revealed that 22.64% of the total deforested area was converted into agriculture, 5.85% was converted into aquaculture, 0.69% was converted into infrastructure, and 16.35% was not converted into any specific land use class but was still affected by other human activities. Conclusions: We discovered that the percentage of land affected by these anthropogenic factors varied between countries and regions. This research can facilitate trade-off analysis for natural resources and environmental sustainability policy studies. Diverse management strategies can be evaluated to assess the trade-offs between preserving mangrove forests for climate change mitigation and transforming them for economic purposes.

Indonesia is the world’s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.