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All Journal Jurnal Sains Teknologi dan Lingkungan (JSTL)
Moh Rodiansyah Hambali
Program Studi Kehutanan, Fakultas Pertanian, Universitas Mataram
Agriculture, Biological Sciences & Forestry Computer Science & IT Public Health

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Estimasi Simpanan Karbon Tegakan Menggunakan Citra Sentinel-2A Pada Kawasan Mangrove Labuan Tereng Kabupaten Lombok Barat: Estimation of Standing Carbon Stock Using Sentinel-2A Imagery in the Labuan Tereng Mangrove Area West Lombok Regency Moh Rodiansyah Hambali; Andi Chairil Ichsan; Niechi Valentino; Andrie Ridzki Prasetyo
JURNAL SAINS TEKNOLOGI & LINGKUNGAN Vol. 9 No. 4 (2023): JURNAL SAINS TEKNOLOGI & LINGKUNGAN
Publisher : LPPM Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jstl.v9i4.522

Abstract

The primary worry in addressing climate change problems is the elevation in global temperatures resulting from the growing levels of CO2 emissions in the atmosphere. Mangrove ecosystems contribute to the "blue carbon" plan which is capable of storing carbon well, this research was conducted to assess carbon storage within the mangrove forest ecosystem by combining Sentinel-2A satellite imagery with on-site field measurements. The data analysis findings indicate the presence of six distinct mangrove varieties, namely R. mucronata, A. marina, R. apiculata, S. alba, E. agallocha, and C. decandra. The R. mucronata type is the type that dominates the mangrove area with an average carbon amount of 122.1 tonnes/ha. Correlation analysis shows a strong relationship between IKVm and mangrove forest carbon stocks, with a correlation coefficient value of 80%. In the regression model, the power model provides the best equation for estimating carbon stocks with a coefficient of determination value of 64.4% giving a model equation of y = 109.51x1.2381. Analysis of image carbon reserves obtained the lowest value, namely 0.02-10.46 tonnes/ha which was in the very rare vegetation density type and the highest carbon reserve value was 58.30-59.02 tonnes/ha in the very high density class.
Co-Authors Andi Chairil Ichsan Andrie Ridzki Prasetyo Niechi Valentino