Mapping Mangrove Blue Carbon via Unmanned Aerial Vehicles: A Systematic Review of Methodological Trends
無人航空機によるマングローブブルーカーボンのマッピング：方法論的トレンドの系統的レビュー (AI 翻訳)
Ludwick Satria Romadoni, Eko Susetyarini, Muhammad Rifky Ardiansyah
🤖 gxceed AI 要約
日本語
本レビューは、無人航空機（UAV）を用いたマングローブブルーカーボン推定の方法論的傾向をPRISMAプロトコルで分析。37件の論文から、RGBセンサーと機械学習（主にRandom Forest、SVM）の統合が主流で、MVIやExG指標が有効であることが明らかになった。しかし、密な樹冠に対する線形補間の限界も指摘されている。
English
This systematic review analyzes methodological trends in mangrove blue carbon estimation using UAVs, following PRISMA. Among 37 papers, RGB sensors integrated with machine learning (Random Forest, SVM) dominate, with MVI and ExG indices effective. However, linear interpolation struggles with dense canopies.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではブルーカーボン生態系の評価手法が注目されており、本レビューの知見は国内のマングローブ・海草藻場調査に応用可能。ただし、日本の気候・地形に適したパラメータ調整が必要。
In the global GX context
Globally, blue carbon is gaining traction for climate mitigation. This review provides a methodological synthesis for UAV-based carbon mapping, relevant for countries with mangrove ecosystems. It highlights the need for improved algorithms for dense canopies.
👥 読者別の含意
🔬研究者:Methodological trends for UAV-based blue carbon estimation, with emphasis on RGB-ML integration and saturation issues.
🏢実務担当者:Guidance on deploying UAVs for mangrove carbon monitoring, though local calibration is required.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.33394/j-ps.v14i3.21031first seen 2026-06-30 05:14:00
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