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Hanif Amrulloh
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Journal of Multidisciplinary Applied Natural Science
Published by Pandawa Institute
ISSN : -     EISSN : 27743047     DOI : 10.47352/jmans
Core Subject : Science, Education, Agriculture, Social, Engineering,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Energy Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics
Journal of Multidisciplinary Applied Natural Science (abbreviated as J. Multidiscip. Appl. Nat. Sci.) is a double-blind peer-reviewed journal for multidisciplinary research activity on natural sciences and their application on daily life. This journal aims to make significant contributions to applied research and knowledge across the globe through the publication of original, high-quality research articles in the following fields: 1) biology and environmental science 2) chemistry and material sciences 3) physical sciences and 4) mathematical sciences. The J. Multidiscip. Appl. Nat. Sci. is an open-access journal containing original research articles, review articles, and short communications in the areas related to applied natural science. The J. Multidiscip. Appl. Nat. Sci. publishes 2 issues in a year on January (first issue) and July (second issue). This journal has adopted a double-blind reviewing policy whereby both the referees and author(s) remain anonymous throughout the process.
Arjuna Subject : Umum - Umum
Articles 1 Documents
 1 
Search results for , issue "Vol. 6 No. 1 (2026): Journal of Multidisciplinary Applied Natural Science" : 1 Documents clear
Mapping Aboveground Carbon in Rehabilitated Mangrove: Evaluating the Performance of Regression Modelling with Satellite-Derived Vegetation Indices and Kriging Interpolation Sumarga, Elham; Syamsudin, Tati Suryati; Sarah, Sarah; Qalbi, Mutiara Putri; Tansy, Belinda Calista; Basyuni, Mohammad; Larekeng, Siti Halimah
Journal of Multidisciplinary Applied Natural Science Vol. 6 No. 1 (2026): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.311

Abstract

Mangroves are vital ecosystems in combating climate change, primarily through their exceptional capacity for carbon sequestration and long-term storage. To effectively manage and conserve these valuable resources, accurate carbon stock mapping is crucial. Given the inherent variability of mangrove biophysical characteristics, selecting appropriate mapping methodologies is essential. This study aimed to evaluate two distinct approaches: regression modeling using satellite-derived vegetation indices and kriging interpolation, within the Angke Kapuk mangrove area of Jakarta. Regression models were constructed utilizing forest canopy density (FCD) and its constituent indices (derived from Landsat 8), alongside normalized difference vegetation index (NDVI), advanced vegetation index (AVI), and soil adjusted vegetation index (SAVI) from Sentinel 2A, as predictor variables. Field-based carbon stock data, obtained from 50 square plots (10 m × 10 m) using established allometric models, served as the response variable. The study revealed substantial heterogeneity in carbon storage, ranging from 34.76 to 236.87 tons/ha, with a mean of 135.31 tons/ha and a standard deviation of 50.09 tons/ha. Regression modelling, however, demonstrated limited predictive power, achieving a maximum R² value of only 0.03, indicating a poor fit between the predictor variables and observed carbon stocks. Kriging interpolation yielded moderate accuracy, as evidenced by a coefficient of variation of root mean square error (CV RMSE) of 0.39. This disparity in performance can be attributed to several factors, including the homogeneity of the rehabilitated mangrove canopy, which limited the ability of vegetation indices to accurately represent carbon stock variations. Furthermore, kriging's capacity to model spatial autocorrelation proved advantageous in this context. Based on these findings, this paper discusses the influence of mangrove characteristics on modelling performance and provides practical recommendations for area managers regarding future carbon stock mapping initiatives.

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