GEDIを活用した機械学習によるベトナム中部クアダイ河口域の地上部ブルーカーボンマッピング
Global Ecosystem Dynamics Investigation-enabled machine learning for aboveground blue-carbon mapping in the Cua Dai estuary, central Vietnam (原題)
Vu Thi Hoai Thu, Dang Thi Kieu Oanh, Trieu Anh Ngoc
🤖 gxceed AI 要約
日本語
本研究は、GEDI LiDAR、Sentinel-1/2、SRTM、機械学習を統合し、ベトナム中部クアダイ河口域の地上部ブルーカーボン量を高精度に推定した。ランダムフォレストが最良で、平均AGC 13.659 Mg C/ha、総量109,472 Mg Cを算出。マングローブと河岸沖積帯に高炭素ホットスポットを特定し、生態系サービス評価やMRVシステムへの応用可能性を示した。
English
This study integrated GEDI LiDAR, Sentinel-1/2, SRTM, and machine learning to estimate aboveground blue-carbon stocks in the Cua Dai estuary, Vietnam. Random Forest performed best, yielding mean AGC of 13.659 Mg C/ha and total 109,472 Mg C. High-carbon hotspots were in mangroves and riparian zones, supporting restoration targeting and MRV systems.
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炭素貯留量評価が気候変動適応策や国土強靱化の文脈で注目されている。本手法は、日本の沿岸域やアジアのマングローブ林における迅速な炭素評価に応用可能であり、SSBJやTCFD開示における自然関連情報の充実にも寄与する。
In the global GX context
Globally, blue-carbon ecosystems are increasingly recognized for their climate mitigation potential, and accurate mapping is critical for carbon accounting and MRV. This study demonstrates a scalable, remote-sensing-based approach that can support countries in meeting their NDCs and aligning with ISSB and TNFD disclosure frameworks.
👥 読者別の含意
🔬研究者:Provides a robust machine-learning workflow for blue-carbon mapping that can be adapted to other estuarine regions.
🏢実務担当者:Offers a rapid, scalable method for carbon stock assessment to support restoration projects and ecosystem-service valuation.
🏛政策担当者:Highlights the potential of remote sensing for national carbon inventories and MRV systems, aiding climate policy and reporting.
📄 Abstract（原文）
Accurate blue-carbon assessment in fragmented estuarine landscapes remains challenging because multispectral optical data may saturate over dense canopies and plot observations are often sparse. This study developed a Google Earth Engine workflow integrating Global Ecosystem Dynamics Investigation (GEDI) spaceborne Light Detection and Ranging (LiDAR), Sentinel-1 C-band synthetic aperture radar (SAR), Sentinel-2 multispectral imagery, Shuttle Radar Topography Mission (SRTM) topography, principal component analysis (PCA), and machine-learning models to estimate aboveground blue-carbon stocks in the Cua Dai estuary, central Vietnam. After strict quality, sensitivity, and land-water screening, 2,781 GEDI top-of-canopy height (TCH) footprints and 6,711 GEDI aboveground biomass density (AGBD) footprints were retained as spaceborne reference observations. Random Forest (RF), Gradient Boosted Trees (GBT), Support Vector Machine (SVM), and Classification and Regression Trees (CART) were compared. RF produced the most stable performance, with root mean square error (RMSE) values of 17.32 Mg ha -1 for AGBD and 5.45 m for TCH. The resulting map estimated a mean aboveground carbon (AGC) stock of 13.659 Mg C ha -1 and a total AGC pool of 109,472.12 Mg C across the vegetated landscape. High-carbon hotspots were concentrated in mangrove and riparian alluvial zones in the southwestern sector. Because no local plot-based biomass inventory was available, the outputs should be interpreted as GEDI-calibrated, aboveground carbon estimates rather than fully field-validated total blue-carbon stocks. The workflow provides a rapid and scalable basis for restoration targeting, ecosystem-service valuation, and future measurement, reporting, and verification systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.cscee.2026.101465first seen 2026-08-22 04:59:11
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