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Spatiotemporal Patterns of Carbon Storage in Hainan Bamen Bay Mangroves Based on a Decision Tree Classification

海南バーメン湾のマングローブにおける炭素貯留の時空間パターン：決定木分類に基づく解析 (AI 翻訳)

Yiwen Wang, Xiyu Guo, Hui Zhu, Fengxia Wang

Forests📚 査読済 / ジャーナル2026-04-29#気候科学Origin: CN
DOI: 10.3390/f17050540
原典: https://doi.org/10.3390/f17050540

🤖 gxceed AI 要約

日本語

本研究は、決定木法（SWIR1、NDVI、NDMI）を用いて海南バーメン湾のマングローブを高精度にマッピングし、2000～2020年の時空間動態と炭素貯留量を分析した。その結果、マングローブが大幅に拡大し、炭素貯留量が増加したことが明らかになった。シナリオ分析では、Green Revivalシナリオが最も高い炭素貯留を達成した。この研究は、マングローブのモニタリングと炭素評価のためのフレームワークを提供し、地域の保全とカーボンニュートラル目標を支援する。

English

This study used a decision tree method to map mangroves in Hainan Bamen Bay with high accuracy, analyzing spatiotemporal dynamics and carbon storage from 2000 to 2020. Results show significant mangrove expansion and increased carbon storage. Scenario analysis indicates that the Green Revival scenario achieves the highest carbon storage. The study provides a framework for mangrove monitoring and carbon assessment, supporting regional conservation and carbon neutrality goals.

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

日本でもブルーカーボン生態系の評価が進んでおり、本手法は日本のマングローブや海草藻場の炭素貯留評価に応用可能である。

In the global GX context

This study offers a robust methodology for blue carbon stock assessment using remote sensing and scenario analysis, which is globally relevant for coastal conservation and carbon accounting, especially under emerging frameworks like the Paris Agreement.

👥 読者別の含意

🔬研究者:Provides a reproducible framework for mangrove carbon storage estimation using open data and models.

🏢実務担当者:Can inform blue carbon offset project design for coastal ecosystems.

🏛政策担当者:Supports national carbon accounting for coastal wetlands under UNFCCC reporting.

📄 Abstract（原文）

Mangroves play a vital role in climate change mitigation due to their exceptional carbon sequestration capacity, as a highly productive blue carbon ecosystem. Current research on mangroves in Bamen Bay has been limited to short-term observations, lacking systematic analysis of long-term spatiotemporal dynamics and carbon storage. This study developed a decision tree method integrating SWIR1, NDVI, and NDMI, achieving high-accuracy mangrove mapping. Spatiotemporal dynamics from 2000 to 2020 were analyzed using the dynamic degree model, standard deviation ellipse, centroid model, and landscape pattern indices. Carbon storage was quantified through the InVEST model, grey prediction model, and scenario analysis. The results reveal significant mangrove expansion, with substantial net growth. Spatial aggregation strengthened despite persistent fragmentation, characterized by a shrinking standard deviation ellipse and northeastward centroid migration. Carbon storage increased considerably over the two decades. Under the baseline scenario, carbon storage would continue to grow by mid-century. Among alternative scenarios, the Green Revival scenario achieves the highest carbon storage, outperforming the baseline, while the Hard Preservation scenario achieves slightly above the baseline. The Missed Opportunity and Ecological Collapse scenarios project declines. This study provides a valuable framework for mangrove monitoring, carbon assessment, and ecological restoration, supporting regional conservation and carbon neutrality goals.

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