Estimasi Stok Karbon Atas dalam Sebaran Lamun di Pulau Pari, Kepulauan Seribu Menggunakan Citra Satelit Sentinel-2A

Abstract

Padang lamun Indonesia menyimpan sekitar 2% dari karbon laut dunia, namun penelitian mengenai stok karbon lamun khususnya di Pulau Pari masih terbatas. Penelitian ini bertujuan mengkaji tentang estimasi stok karbon atas pada padang lamun di Pulau Pari dengan memanfaatkan data penginderaan jauh. Data yang digunakan adalah data citra satelit Sentinel-2A dan data survei lapang. Pengolahan data dimulai dari pre-processing citra hingga klasifikasi habitat bentik. Klasifikasi habitat bentik dilakukan menggunakan algoritma Maximum Likelihood. Uji akurasi klasifikasi dilakukan dengan Confusion Matrix. Pembagian sebaran lamun berdasarkan kerapatan tutupan dilakukan menggunakan algoritma NDVI. Biomassa atas lamun Enhalus acoroides dan Thalassia hemprichii dihitung dengan mengalikan berat kering sampel lamun dengan kerapatan lamun. Kandungan karbon pada biomassa atas lamun dihitung menggunakan faktor konversi karbon 0,336 dari biomassa. Hasil klasifikasi habitat bentik menunjukkan luasan lamun yang terdeteksi adalah 64,94 ha. Uji akurasi klasifikasi menghasilkan nilai overall accuracy 88,10% dan koefisien kappa 0,85. Kandungan karbon dalam biomassa atas lamun E. acoroides adalah sebesar 0- 13,202 gC/m2 dan T. hemprichii sebesar 0-7,341 gC/m2. Total luasan lamun per kategori kerapatan tutupan NDVI yaitu 62,03 ha, dan total cadangan karbon dari tiga kelas kerapatan lamun yaitu 116,66 tonC. Total cadangan karbon ekosistem lamun di Pulau Pari berkisar 116,66 hingga 122,12 tonC.

Seagrass meadows in Indonesia represent approximately 2% of the global blue carbon reserve. Nevertheless, research on seagrass carbon stocks, particularly in the Pari Island region, remains scarce. The objective of this study is to assess the estimation of aboveground carbon stocks in seagrass meadows on Pari Island by utilizing remote sensing data. The data employed in this study are derived from satellite imagery obtained from the Sentinel-2A satellite and complemented by field survey data. The data processing stage commences with the pre-processing of the image and culminates in the classification of the benthic habitat. Benthic habitat classification was conducted using the Maximum Likelihood algorithm. A Confusion Matrix was employed to assess the accuracy of the classification. The classification of seagrass beds based on the density was done using the NDVI algorithm. The aboveground biomass of Enhalus acoroides and Thalassia hemprichii was calculated by multiplying the dry weight of seagrass samples by the seagrass density. The carbon content of the aboveground biomass of seagrass was calculated using a carbon conversion factor of 0,336. The results of the benthic habitat classification indicated that the area of seagrass detected was 64,94 hectares. The classification accuracy test yielded an overall accuracy value of 88,10% and a kappa coefficient of 0,85. The carbon content of the aboveground biomass of E. acoroides was found to be between 0 and 13,202 gC/m2, while that of T. hemprichii ranged between 0 and 7,341 gC/m2. The total seagrass area per NDVI density category was 62,03 ha, and the total carbon stock of the three seagrass density classes was 116,66 tonsC. The total carbon stock of seagrass ecosystem in Pari Island ranged from 116,66 to 122,12 tonsC.