林冠構造がLiDAR由来のマングローブバイオマス推定と炭素会計に与える影響
How canopy structure influences LiDAR-derived mangrove biomass estimates and carbon accounting (原題)
Yaya Ihya Ulumuddin, Bayu Prayudha, Meizani Irmadhiany, Hanggar Prasetio, Siti Maryam Yaakub, Victor Nikijuluw, Burhanuddin, Agustin Rustam, Anang Dwi Purwanto, Devi Dwiyanti Suryono, Early Septiningsih, Hadiwijaya Lesmana Salim, Joko Prihantono, Mariska Astrid Kusumaningtyas, Nasir Sudirman, Restu Nur Afi Ati, Rita Rachmawati, Yusmiana Puspitaningsih Rahayu, Viga A. Wicaksono, Agus F. Faisol
🤖 gxceed AI 要約
日本語
インドネシア東ジャワのマングローブ林を対象に、高解像度LiDARデータから個別樹冠ベースと面積ベース（Lorey平均樹高）の2経路で地上部バイオマス（AGB）を推定し比較した。個別樹冠法は平均153.8 Mg/haと低く抑えられたのに対し、面積ベース法は167.3〜183.6 Mg/haと10〜20%高く、差は地下部・生態系全体バイオマスにも比例的に波及した。LiDAR由来の炭素推定値のばらつきは、生態学的差異よりも林冠構造の表現・集約に関する仮定に由来する部分が大きいことを示す。
English
Using high-resolution airborne LiDAR over a fringing mangrove system in eastern Java, Indonesia, this study compares individual-crown and area-based (Lorey's mean height) pathways for estimating above-ground biomass. Individual-crown estimates averaged 153.8 Mg/ha, while area-based methods yielded 167.3–183.6 Mg/ha—a 10–20% difference that propagated into below-ground and total ecosystem biomass. The findings show that LiDAR-derived mangrove carbon estimates are highly sensitive to how canopy structure is represented and aggregated, underscoring the need to document structural assumptions explicitly.
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
日本企業はブルーカーボン・クレジットや自然関連財務情報開示（TNFD）への関心を高めており、マングローブ炭素会計の手法感度はJ-クレジットや海外炭素プロジェクトの信頼性評価に直結する。LiDAR推定の構造的仮定の明示は、日本発のブルーカーボン計測・認証スキームの頑健性確保に示唆を与える。
In the global GX context
As blue-carbon credits and nature-related disclosure (TNFD) gain traction under ISSB and CSRD frameworks, this paper highlights a methodological blind spot: mangrove carbon estimates vary 10–20% purely by canopy-structure assumption. It contributes to global disclosure scholarship by showing that remote-sensing-based carbon accounting needs transparent structural metadata to be audit-ready for carbon markets and corporate nature reporting.
👥 読者別の含意
🔬研究者:LiDARベースのバイオマス推定における林冠構造仮定の感度を定量化した点が、炭素会計研究の再現性・不確実性評価に有用。
🏢実務担当者:ブルーカーボン・クレジットや自然資本開示を検討する企業は、LiDAR由来の炭素量推定値の手法依存性を理解し、第三者検証時の前提条件確認に活用できる。
🏛政策担当者:炭素クレジット認証や国家温室効果ガスインベントリにおいて、リモートセンシング推定の構造的仮定の文書化を義務付ける根拠となりうる。
📄 Abstract（原文）
Mangrove forests are among the most carbon-dense coastal ecosystems, yet estimates of mangrove biomass and carbon stocks derived from remote sensing remain sensitive to methodological choices. In particular, how canopy structure is represented and aggregated from LiDAR data can systematically influence biomass estimates, with direct implications for carbon accounting and climate mitigation assessments. Few studies, however, have directly contrasted individual-crown and area-based biomass pathways within a single LiDAR dataset, leaving it unclear how much of the reported variability in mangrove carbon stocks reflects structural assumptions rather than genuine ecological difference. This study examines the structural sensitivity of LiDAR-derived mangrove biomass estimates by comparing two pathways within a consistent spatial framework: (i) individual-crown above-ground biomass (AGB) estimated from diameter at breast height (DBH) proxies derived from LiDAR-based canopy diameter, and (ii) stand-level AGB estimated from area-based-weighted Lorey’s mean canopy height. High-resolution airborne LiDAR data were acquired over a fringing mangrove system in eastern Java, Indonesia, and used to derive digital surface models, digital terrain models, and canopy height models. The individual-crown-based AGB estimates aggregated to 10 m × 10 m grid cells yielded lower and more constrained biomass values (mean = 153.8 ± 74.7 Mg ha − 1 ; median = 162.73 Mg ha − 1 ), reflecting a tree-level structural perspective. In contrast, the area-based-weighted Lorey’s pathways produced higher biomass estimates at the same spatial scale, with mean AGB values of 167.3 ± 121.7 Mg ha − 1 for the canopy-cover-weighted equation and 183.6 ± 132.5 Mg ha − 1 for the height- weighted equation, representing a mean difference of approximately 10–20% relative to Individual-crown-based estimates. Differences in AGB estimates propagated proportionally into BGB and total ecosystem biomass. Overall, the results indicate that LiDAR-derived mangrove biomass estimates can vary depending on how canopy structure is represented and aggregated, highlighting the need to explicitly document structural assumptions when interpreting LiDAR-based biomass and carbon estimates.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1186/s13021-026-00508-3first seen 2026-09-27 04:34:11
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