Spatial Estimation of Mangrove Carbon Reserves Using Random Forest Algorithm and NDVI Index from Sentinel-2 Imagery

Rudy Fermana; M. Junaidi; Markum Markum

doi:10.55927/eajmr.v5i4.67

Authors

Rudy Fermana Universitas Mataram
M. Junaidi Universitas Mataram
Markum Markum Universitas Mataram

DOI:

https://doi.org/10.55927/eajmr.v5i4.67

Keywords:

mangrove carbon stock, remote sensing, NDVI, Random Forest, Sentinel-2

Abstract

This study aims to evaluate the effectiveness of the Random Forest (RF) algorithm in classifying mangrove cover using Sentinel-2 imagery, to develop a regression model between NDVI values and carbon stock, and to estimate the total spatial carbon stock and its economic value. A quantitative approach was employed by combining RF-based classification of Sentinel-2 imagery with field data collected from 35 sample plots to estimate above- and below-ground biomass using species-specific allometric equations. This study concludes that the integrative method is effective for spatial estimation of mangrove carbon stocks and supports NDVI as a reliable predictor. Future implications include the development of temporal analysis and integration with drone-based imagery for higher spatial resolution.

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