Analysis of Vegetation Changes in Siak Regency's Oil Palm Plantations
DOI:
https://doi.org/10.21063/jtif.2026.V14.1.24-46Kata Kunci:
Google Earth Engine, Land Cover Change, Multitemporal Classification, Oil Palm Monitoring, Sentinel-2, Vegetation IndexAbstrak
Land cover change driven by the expansion of oil palm plantations has become a critical environmental issue in Siak Regency, Riau Province, necessitating periodic vegetation monitoring for sustainable land management. This study aimed to integrate Google Earth Engine and Quantum Geographic Information System to analyze vegetation changes in oil palm plantations using Sentinel-2 imagery from the 2022 to 2024 period. The methods involved processing Sentinel-2 Level-2A imagery through cloud masking, generating annual median-based composites, and calculating vegetation indices, including the Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Soil Adjusted Vegetation Index. These indices were classified into several density classes to map land cover conditions, while changes in oil palm and non-oil palm areas were identified annually. All image processing stages were performed on a cloud computing platform, and the results were exported for spatial visualization and further analysis. The findings indicated that Siak Regency remained predominantly characterized by moderate to dense vegetation, particularly in the central and eastern regions. However, annual vegetation dynamics were detected in the western region, which was dominated by residential and industrial activities. In Koto Gasib District, there was a measurable increase in non-oil palm classes and expanding land cover changes over time. Overall, the integration of cloud-based processing and desktop geographic information systems proved effective in producing accurate spatial information for multitemporal analysis to support land use planning and oil palm plantation management in Siak Regency.
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