Mapping Mangrove Blue Carbon via Unmanned Aerial Vehicles: A Systematic Review of Methodological Trends
DOI:
https://doi.org/10.33394/j-ps.v14i3.21031Keywords:
Biomass, Blue carbon, Machine learning, Mangrove, Unmanned aerial vehiclesAbstract
Accurate quantification of mangrove blue carbon stocks requires precise, non-destructive, and cost-effective methodologies. However, conventional satellite imagery often suffers from moderate resolution and cloud cover, while traditional vegetation indices face optical saturation in dense coastal canopies. To address these core issues, this study conducted a Systematic Literature Review (SLR) using the PRISMA protocol to evaluate the use of Unmanned Aerial Vehicles (UAVs) in coastal carbon estimation. A targeted search of the Scopus database (2017–2026) yielded 37 peer-reviewed articles for thematic and qualitative synthesis. The synthesis reveals a significant paradigm shift toward UAV-based RGB sensors. Scientific findings indicate that advanced visual indices, specifically the Mangrove Vegetation Index (MVI) and Excess Green (ExG), are frequently reported as highly effective for reducing substrate reflectance bias and mitigating optical saturation. Furthermore, 59.5% of the reviewed studies integrated these spatial features with Machine Learning algorithms, primarily Random Forest and Support Vector Machine, to model non-linear biomass relationships and substantially reduce the Root Mean Square Error (RMSE). In conclusion, while UAV-RGB mapping demonstrates high potential, current linear interpolation methods struggle with dense interlocking canopies, causing over-fitted delineations. Future research must transition to discrete spatial algorithms with elevated threshold calibrations to accurately isolate individual tree crowns.
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Copyright (c) 2026 Ludwick Satria Romadoni, Eko Susetyarini, Muhammad Rifky Ardiansyah
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