Mataram — A 2026 study by Rudy Fermana, M. Junaidi, and Markum from Universitas Mataram reports that satellite-based analysis using the Random Forest algorithm and NDVI vegetation index can accurately estimate mangrove carbon reserves in the Gili Lawang–Gili Sulat Marine Tourism Park, East Lombok. Published in the East Asian Journal of Multidisciplinary Research, the study demonstrates that integrated remote sensing and field measurements provide reliable spatial carbon estimates that can support climate mitigation strategies and blue carbon development programs in Indonesia.
Mangrove ecosystems play a critical role in reducing greenhouse gas concentrations because they store significantly more carbon than most terrestrial forest systems. In Indonesia, the forestry and land-use sector contributes a major share of national emissions, making coastal ecosystem conservation increasingly important for achieving long-term climate commitments. Accurate carbon mapping in mangrove areas is therefore essential for strengthening conservation planning and supporting participation in global carbon markets.
The Gili Lawang–Gili Sulat Marine Tourism Park in East Lombok is one of the most important mangrove conservation zones in West Nusa Tenggara Province. The area covers approximately 10,000 hectares, with dense mangrove vegetation occupying around 80 percent of the protected coastal landscape. Besides serving as habitat for coastal biodiversity and protection against shoreline erosion, the ecosystem holds strong potential for blue carbon storage and climate mitigation initiatives.
The research conducted by Rudy Fermana, M. Junaidi, and Markum from Universitas Mataram combined satellite remote sensing data with direct field observations to estimate total carbon reserves across the conservation area. Sentinel-2 satellite imagery from 2025 provided vegetation reflectance data, while field surveys collected biomass measurements from 35 sampling plots representing different vegetation density classes.
Tree diameter measurements taken in each sampling plot were used to calculate both above-ground and below-ground biomass using species-specific allometric equations. These biomass values were then statistically linked with NDVI vegetation index values derived from satellite imagery to build a spatial carbon prediction model covering the entire mangrove landscape.
The study shows that the Random Forest classification method achieved very high mapping accuracy. Land-cover classification results reached an overall accuracy of 95.87 percent and a Kappa coefficient of 0.9175, indicating strong agreement between satellite interpretation and field verification data. This level of accuracy confirms that machine learning-based mapping can support reliable environmental monitoring in complex coastal ecosystems.
Satellite analysis also revealed that mangrove vegetation dominates approximately 974.94 hectares, representing about 85.4 percent of the analyzed conservation area. This extensive coverage highlights the ecological importance of the Gili Lawang–Gili Sulat ecosystem as a major coastal carbon storage zone in eastern Indonesia.
Statistical regression results demonstrated a strong relationship between NDVI values and total mangrove carbon reserves. The model explained about 76.7 percent of variation in carbon stock distribution across the study area, confirming that vegetation index data can serve as a reliable predictor of mangrove carbon storage at landscape scale.
The total estimated carbon stock of mangrove ecosystems in the Gili Lawang–Gili Sulat Marine Tourism Park reached approximately 73,513.84 tons of carbon, equivalent to 269,553.20 tons of carbon dioxide equivalent. Within this total, Gili Lawang contributed about 28,738.78 tons of carbon, while Gili Sulat contributed approximately 44,775.06 tons.
Carbon storage levels varied according to vegetation density. Areas classified as very dense mangrove stands stored more than 120 tons of carbon per hectare, while lower-density zones stored significantly smaller amounts. These variations reflect differences in tree diameter, species composition, and stand structure across the conservation landscape.
Beyond ecological significance, the study also estimated the economic value of carbon stored in the mangrove ecosystem. Using an average voluntary carbon market price of 3.61 US dollars per ton of carbon dioxide equivalent, the total carbon value of the study area reached approximately 970,000 US dollars. This estimate highlights the economic potential of mangrove-based carbon trading initiatives in coastal regions of Indonesia.
According to Rudy Fermana from Universitas Mataram, integrating Sentinel-2 satellite imagery with Random Forest classification enables faster and more efficient monitoring of mangrove carbon reserves compared with traditional field-only survey approaches. The method supports long-term spatial monitoring systems for coastal ecosystem management and climate mitigation planning.
Markum from Universitas Mataram explained that combining NDVI-based satellite indicators with field biomass measurements produces more representative carbon estimates than approaches relying on a single data source. This integrated model provides stronger scientific support for conservation policy development and regional climate action strategies.
The findings are particularly relevant for policymakers responsible for coastal ecosystem protection, as well as for environmental planners developing blue carbon initiatives. Spatial carbon reserve mapping allows conservation agencies to identify priority restoration areas, monitor ecosystem changes over time, and design more effective emission-reduction strategies based on measurable environmental data.
For research institutions and universities, the study demonstrates how machine learning and satellite remote sensing technologies can strengthen environmental monitoring capabilities across Indonesia’s coastal regions. Similar approaches can be applied to other mangrove ecosystems to improve national carbon accounting systems and support international climate commitments.
Rudy Fermana is affiliated with Universitas Mataram. M. Junaidi is affiliated with Universitas Mataram. Markum is affiliated with Universitas Mataram.
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