Satellite remote sensing techniques for mapping and estimating mangrove carbon stocks in the small island of Gili Meno, West Nusa Tenggara, Indonesia

I WAYAN GEDE ASTAWA KARANG (Department of Marine Sciences, Faculty of Marine Science and Fisheries, Universitas Udayana. Jl. Kampus Bukit Jimbaran, South Kuta 80361, Badung, Bali, Indonesia)
I WAYAN NUARSA (Department of Marine Sciences, Faculty of Marine Science and Fisheries, Universitas Udayana. Jl. Kampus Bukit Jimbaran, South Kuta 80361, Badung, Bali, Indonesia)
I GEDE HENDRAWAN (Department of Marine Sciences, Faculty of Marine Science and Fisheries, Universitas Udayana. Jl. Kampus Bukit Jimbaran, South Kuta 80361, Badung, Bali, Indonesia)
NI MADE NIA BUNGA SURYA DEWI (Doctoral Program of Environmental Science, Universitas Udayana. Jl. Panglima Besar Sudirman, Denpasar 80234, Bali, Indonesia)
PUTU KUMARA YASA (Department of Marine Sciences, Faculty of Marine Science and Fisheries, Universitas Udayana. Jl. Kampus Bukit Jimbaran, South Kuta 80361, Badung, Bali, Indonesia)
I MADE DWITA KRISNANDA (Department of Marine Sciences, Faculty of Marine Science and Fisheries, Universitas Udayana. Jl. Kampus Bukit Jimbaran, South Kuta 80361, Badung, Bali, Indonesia)

Biodiversitas Journal of Biological Diversity
Vol. 25 No. 9 (2024)

Abstract. Karang IWGA, Nuarsa IW, Hendrawan IG, Dewi NMNBS, Yasa PK, Krisnanda IMD. 2024. Satellite remote sensing techniques for mapping and estimating mangrove carbon stocks in the small island of Gili Meno, West Nusa Tenggara, Indonesia. Biodiversitas 25: 3189-3200. Estimating mangrove carbon stocks is crucial for effective conservation and management but presents challenging, particularly on small islands. Satellite remote sensing offers a powerful tool for assessing mangrove ecosystems, though its application in small island environments remains underutilized. This study aims to explore and evaluate the effectiveness of satellite remote sensing techniques, specifically using Sentinel-2, for mapping and estimating mangrove carbon stocks on the small island of Gili Meno, West Nusa Tenggara, Indonesia. The Random Forest technique was employed to distinguish between mangrove and non-mangrove areas by analyzing multiple parameters. Additionally, a semi-empirical method was used to evaluate and map the Above Ground Carbon (AGC) of mangroves, with general allometric equations applied to calculate AGC values. Six vegetation indices were assessed to develop a model for estimating mangrove AGC using linear regression equations. The accuracy of the model predictions was evaluated using the Root Mean Square Error. The study identified that the mangrove forest area in Gili Meno covers approximately 6.88 ha. Notably, the research revealed that the IRECI model, with an R² value of 0.76 and RMSE of 17.14 ton/ha, was the most effective for AGC estimation when utilizing red edge bands.