INTEGRATION OF REMOTE SENSING DATA AND GEOGRAPHIC INFORMATION SYSTEM FOR PRECISE MAPPING OF CARBON SEQUESTRATION POTENTIAL IN FOREST
Indonesia Indonesia 
China 
Abstract
Accurate assessment of forest carbon sequestration potential has become increasingly important for climate change mitigation, sustainable forest management, and environmental policy development. Advances in Remote Sensing and Geographic Information System (GIS) technologies offer new opportunities to improve the precision and efficiency of carbon mapping across large and heterogeneous forest landscapes. This study aimed to examine the effectiveness of integrating remote sensing data and GIS techniques for precise mapping of carbon sequestration potential in forest ecosystems and to identify the environmental factors influencing carbon distribution patterns. A quantitative geospatial approach was employed using multisource satellite imagery, vegetation indices, biomass estimates, topographic variables, land-cover data, and field validation measurements. Spatial modeling, statistical analysis, and GIS-based overlay techniques were applied to evaluate carbon sequestration potential across the study area. Results revealed substantial spatial variation in carbon storage capacity, with high-carbon zones concentrated in dense and ecologically intact forests. Vegetation density, biomass accumulation, forest cover percentage, and topographic characteristics showed significant positive relationships with carbon sequestration estimates. Integrated modeling achieved high predictive accuracy and demonstrated strong agreement with field observations. Findings indicate that combining remote sensing and GIS technologies provides a reliable framework for identifying carbon-rich forest areas, supporting evidence-based conservation planning, improving carbon accounting practices, and strengthening climate change mitigation strategies through more accurate spatial assessment of forest carbon resources.
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Copyright (c) 2026 Obed Patiung, Nilam Atsirina Krisnaputri, Li Wei
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