Pulau Pari, yang terletak di Kepulauan Seribu, merupakan kawasan konservasi mangrove. Ekosistem mangrove memiliki peran penting dalam penyimpanan karbon, dengan kapasitas mencapai 30-40%. Namun, pemetaan stok karbon di atas permukaan ekosistem mangrove berdasarkan kerapatan vegetasi di Pulau Pari masih belum banyak tersedia. Penelitian ini bertujuan untuk menganalisis distribusi spasial kerapatan vegetasi serta membangun model estimasi stok karbon di atas permukaan berdasarkan kerapatan vegetasi di kawasan mangrove. Pendekatan yang digunakan dalam estimasi stok karbon melibatkan indeks vegetasi dan analisis regresi statistik. Indeks vegetasi yang digunakan meliputi SAVI, ARVI, EVI, TVI, dan CMRI, yang diperoleh dari pengolahan citra Sentinel-2. Hasil penelitian menunjukkan bahwa dari lima model yang diuji, model regresi linear berbasis TVI memberikan hasil terbaik dengan nilai RMSE sebesar 54,24 kg/piksel atau setara dengan 0,5424 kg/m². Namun, model ini masih memiliki keterbatasan karena nilai koefisien determinasi yang rendah. Selain itu, analisis distribusi spasial kerapatan vegetasi di kawasan mangrove selama periode 2018–2024 mengindikasikan adanya perubahan pola persebaran. Pada periode 2018–2022, wilayah mangrove didominasi oleh kelas kerapatan tinggi, sedangkan pada 2022–2024 lebih banyak didominasi oleh kelas kerapatan rendah. Perubahan ini sejalan dengan dinamika luasan ekosistem mangrove di Pulau Pari.

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Pari Island, located in the Seribu Islands, is a designated mangrove conservation area. The mangrove ecosystem plays a crucial role in carbon storage, with a capacity of approximately 30–40%. However, surface carbon stock mapping based on vegetation density in the mangrove ecosystem of Pari Island remains limited. This study aims to analyze the spatial distribution of vegetation density and develop a model for estimating surface carbon stock based on vegetation density in the mangrove area. The estimation approach involves the use of vegetation indices and statistical regression analysis. The vegetation indices utilized include SAVI, ARVI, EVI, TVI, and CMRI, derived from Sentinel-2 imagery processing. The results indicate that among the five tested models, the linear regression model based on TVI performed the best, with an RMSE value of 54.24 kg/pixel, equivalent to 0.5424 kg/m². However, this model still has limitations due to its low coefficient of determination. Furthermore, the spatial distribution analysis of vegetation density in the mangrove area from 2018 to 2024 reveals changes in distribution patterns. During the 2018–2022 period, the mangrove area was predominantly classified as high-density vegetation, whereas in the 2022–2024 period, it was largely classified as low-density vegetation. These changes align with the dynamics of mangrove ecosystem coverage on Pari Island.