時空間的な土地被覆変化下におけるカリムンジャワ諸島のマングローブ炭素収支動態の評価
Evaluating Mangrove Carbon Balance Dynamics in the Karimunjawa Archipelago under Spatiotemporal Land Cover Change (原題)
M. Arief Rahman Halim, Tri Retnaningsih Soeprobowati, Yoyon Wahyono, Aulia Rahim, Effine Lourinx, Syarif Prasetyo, Dwi Nur Yuliyani, Luri Nurlaila Syahid, Aida Habibah Nurauliyaa, Sari Rahayu, Inda Dwi Solina, Hashfi Hawali Abdul Matin, Setyo Budi Kurniawan, Novy Ariyanto, Anisah Anisah, Virny Zasyana Eka Putri, Sundari Sundari, Muhammad Raihan Farras Hakim
🤖 gxceed AI 要約
日本語
カリムンジャワ諸島の4島を対象に、2000〜2022年のマングローブ土地被覆変化を衛星画像で分析し、炭素ストック変化に基づくCO₂排出量を推計した。カリムンジャワ島・ケムジャン島で排出量が最大となり、2042年までの予測ではケムジャン・ニャムク・パランの各島で炭素損失が拡大する。地形・人為圧力・ガバナンスが動態を規定することを示し、地域固有の適応的管理の必要性を訴える。
English
Satellite imagery tracks mangrove land-cover change across four Karimunjawa islands from 2000–2022, with carbon-stock-based CO₂ emission estimates. Karimunjawa and Kemujan show the largest extents but also the highest emissions from land conversion, while projections to 2042 indicate growing carbon loss on Kemujan, Nyamuk, and Parang. Geomorphology, anthropogenic pressure, and governance shape these divergent trajectories, underscoring the need for site-specific adaptive management.
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
日本企業のブルーカーボン・自然資本開示(TNFD対応やクレジット活用)に関心のある読者にとって、小島嶼マングローブの炭素収支推計手法は参考になる。ただしSSBJ・有報開示との直接の接続は薄い。
In the global GX context
Adds empirical blue-carbon evidence to the growing nature/ biodiversity disclosure agenda (TNFD, ISSB nature-related work), showing how land-cover-driven carbon stock loss can be quantified at island scale. Useful as a methodological reference for ecosystem carbon accounting rather than for corporate disclosure frameworks directly.
👥 読者別の含意
🔬研究者:小島嶼マングローブの炭素収支を土地被覆変化から推計する手法と長期予測の実例を提供する。
🏢実務担当者:ブルーカーボンクレジットや自然資本評価に関わる企業が、生態系炭素推計の限界と地域差を理解する材料になる。
🏛政策担当者:沿岸生態系保全と気候緩和を両立させる地域適応型ガバナンスの必要性を示す。
📄 Abstract(原文)
Mangrove ecosystems play a critical role in coastal resilience and long-term carbon storage, yet their spatiotemporal dynamics in small island systems remain poorly understood. This study analyzes mangrove land-cover change from 2000 to 2022 across four major islands in the Karimunjawa Archipelago (Karimunjawa, Kemujan, Parang, and Nyamuk) and projects future trends to 2042. Using multi-temporal satellite imagery and carbon stock–based estimates of CO₂ emissions resulting from mangrove land-cover change, the results reveal heterogeneous trajectories of degradation and recovery. Estimated CO₂ emissions were calculated by multiplying the change in carbon stock, the molecular weight conversion factor from carbon (12/44), and the area of mangrove land-cover change (ha). In 2015, Karimunjawa and Kemujan maintain the largest mangrove extents but are also associated with the highest estimated CO₂ emissions, reaching 273,899.30 t CO₂ ha-¹ as a consequence of land-use conversion, whereas Nyamuk exhibits relatively stable dynamics with localized carbon stock loss corresponding to estimated CO₂ emissions of up to 1,037.13 t CO₂ ha-¹, and Parang shows persistent decline with estimated CO₂ emissions of up to 587.97 t CO₂ ha-¹. Future projections indicate increasing carbon stock loss and associated estimated CO₂ emissions toward 2042, particularly on Kemujan, Nyamuk, and Parang, reaching 574,188.40, 2,551.95, and 1,953.84 t CO₂ ha-¹, respectively, highlighting growing risks to coastal ecosystem integrity and climate change mitigation efforts. These findings underscore the influence of geomorphology, anthropogenic pressure, and governance in shaping mangrove dynamics on small islands. Integrated, site-specific, and adaptive management strategies are therefore essential to conserve mangrove carbon stocks, minimize carbon losses associated with land-cover change, and strengthen coastal resilience in the Karimunjawa Archipelago.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.24057/2071-9388-2026-4655first seen 2026-11-01 04:36:49 · last seen 2026-11-06 04:35:52
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録(無料・3キーワードまで)。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。