<![CDATA[This study aims to evaluate the surface carbon stock estimation model in Kendari City working area using Geographic Information Systems (GIS) and remote sensing approaches. Spatial analysis was carried out using Sentinel-2A satellite imagery with a resolution of 10 meters per pixel, employing the vegetation index method as the primary indicator for estimating carbon stocks. Field data were used as reference and validation for image interpretation results, which were processed using spatial statistical methods to produce an accurate and reliable surface carbon distribution model. The research findings indicate that the Kendari City area has a significantly varied distribution of carbon stocks, with values ranging from low to high at 203.669 tons of carbon per pixel. Areas with high vegetation cover, such as urban forests and green open spaces, exhibited higher carbon concentrations compared to settlement areas and densely urbanized regions. Model evaluation conducted through field validation methods revealed a high correlation between model predictions and actual field conditions. This research demonstrates that the integration of GIS and remote sensing is effective for rapidly and accurately mapping and evaluating potential surface carbon stocks. Spatial information about carbon reserves is crucial as a basis for formulating sustainable environmental management policies and climate change mitigation strategies in Kendari City. The results of this study recommend wider adoption of this method to support spatial data-based environmental management in other regions across Indonesia]]>
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