Mapping above-ground carbon of mangroves using Sentinel-2 satellite imagery in the east coast of North Sumatra Province, Indonesia

ACHMAD SIDDIK THOHA (Faculty of Forestry, Universitas Sumatera Utara. Jl. Lingkar USU, Durin Tunggal, Pancur Batu, Deli Serdang 20353, North Sumatra, Indonesia)
PURWANTO PURWANTO (Department of Geography, Faculty of Social Sciences, Universitas Negeri Malang. Jl. Semarang No. 5, Lowokwaru, Sumbersari, Malang 65145 East Java, Indonesia)
PIPIT WIJAYANTI (Faculty of Teacher Training and Education, Universitas Sebelas Maret. Jl. Ir. Sutami No.36 A, Kentingan, Jebres, Surakarta 57126, Central Java, Indonesia)
RITA NOVIANI (Faculty of Teacher Training and Education, Universitas Sebelas Maret. Jl. Ir. Sutami No.36 A, Kentingan, Jebres, Surakarta 57126, Central Java, Indonesia)
RAVINESH ROHIT PRASAD (Department of Social Sciences, Fiji National University. Lautoka, Fiji)
SHOFIA PUTRI NASIBAH (Faculty of Forestry, Universitas Sumatera Utara. Jl. Lingkar USU, Durin Tunggal, Pancur Batu, Deli Serdang 20353, North Sumatra, Indonesia)

Biodiversitas Journal of Biological Diversity
Vol. 26 No. 8 (2025)

Abstract. Thoha AS, Purwanto, Wijayanti PW, Noviani R, Prasad RR, Nasibah SP. 2025. Mapping above-ground carbon of mangroves using Sentinel-2 satellite imagery in the east coast of North Sumatra Province, Indonesia. Biodiversitas 26: 3916-3925. Accurate estimation of carbon stocks in mangrove ecosystems is essential for assessing blue carbon potential. Conventional methods using field surveys to estimate Above-Ground Carbon (AGC) require significant amount of time and costs. Mangrove carbon stock can be calculated quickly and cost-effectively by utilizing satellite technology and publicly available data. The purpose of this study is to determine the best model for estimating AGC in mangrove forests managed by community in east coast of North Sumatra, Indonesia, using Sentinel-2 Imagery. The study was conducted on two different mangrove conditions following community-led restoration efforts, i.e. mangrove forest planted with a single species of Rhizophora mucronata, and restored mangrove forest consisted of 6 tree species. Field-measured AGC was estimated using the allometric equation of tree Diameter at Breast Height (DBH) and used as dependent variable. Sentinel-2 satellite imagery was used to derive several vegetation indices using reflectance values, which were used as independent variables. Several carbon estimation models were developed and validated with the best model was those with the lowest error and the highest coefficient of determination (R²). It was discovered that the model that employed Normalized Difference Vegetation Index (NDVI) and Total Ratio Vegetation Index (TRVI) was the best for estimating AGC in monospecies mangroves, while the model that employed NDVI and Inverted Red Edge Chlorophyll Index (IRECI) was the most accurate when estimating AGC in multispecies mangroves. In monospecies mangrove forest, AGC estimate ranged 0-200 Mg/Ha, while in multispecies mangrove forest, the estimate ranged 0-400 Mg/Ha. The higher AGC estimates in multispecies forests indicate that a more diverse mangrove species has a higher carbon sequestration capacity than homogeneous forests. The findings of this study indicate that AGC mapping using Sentinel-2 Imagery also supports blue carbon initiatives, mangrove ecosystem service assessments, and conservation-based policy-making.