← 論文一覧に戻る

地中海木本作物の地上部バイオマス・炭素推定のためのマルチプラットフォームLiDAR比較評価

Multi-Platform LiDAR Comparative Assessment for Aboveground Biomass and Carbon Estimation in Mediterranean Woody Crops (原題)

Mateo Pastrana, Cristina Velilla, Nelson Mattié, Alfonso Gómez, Sergio Molina

Remote Sensing📚 査読済 / ジャーナル2026-08-19#炭素会計Origin: EU対象セクター: agriculture
DOI: 10.3390/rs18162802
原典: https://doi.org/10.3390/rs18162802

🤖 gxceed AI 要約

日本語

本研究は、スペインの地中海果樹園において、4種類のLiDAR（国営航空機、専用航空機、UAV、モバイル）を用いた地上部バイオマス（AGB）推定を比較した。機械学習モデル（XGBoost）が高い精度を示し、オープンデータの国営LiDARでも競争力があることを確認。TreeQSMによる3次元再構築は低バイオマス地で有効だが、高バイオマス地では過大評価の傾向があった。炭素貯留量のMRV（測定・報告・検証）への応用可能性を示す。

English

This study benchmarks four LiDAR modalities (national airborne, dedicated airborne, UAV, mobile) for aboveground biomass (AGB) estimation in Mediterranean orchards in Spain. Machine learning (XGBoost) achieved high accuracy, and open national LiDAR proved competitive. TreeQSM 3D reconstruction was effective at low-biomass sites but overestimated at high-biomass sites. Results support scalable MRV of carbon stocks in woody crops.

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、果樹園や森林の炭素貯留量算定はJ-クレジット制度やカーボンニュートラル施策に関連する。本研究成果は、リモートセンシングによる効率的なMRV手法の参考となり、特に果樹園の炭素管理に応用可能。ただし、地中海性気候と樹種が異なるため、日本での適用には追加検証が必要。

In the global GX context

Globally, this research contributes to the development of scalable MRV frameworks for agricultural carbon accounting, aligning with the growing demand for accurate carbon credits and climate disclosure. The comparison of open national LiDAR with dedicated sensors provides insights for cost-effective monitoring, relevant to ISSB and CSRD reporting requirements for land-based emissions.

👥 読者別の含意

🔬研究者:Provides a rigorous comparison of LiDAR platforms and machine learning methods for AGB estimation, useful for designing carbon monitoring studies.

🏢実務担当者:Offers guidance on selecting LiDAR technology for orchard carbon inventories, potentially reducing costs by using open national data.

🏛政策担当者:Highlights the feasibility of using open LiDAR data for MRV, supporting policy frameworks for agricultural carbon credits.

📄 Abstract（原文）

Reliable aboveground biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy-cover proxies) were extracted from normalized point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). TreeQSM closely matched the field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops.

🔗 Provenance — このレコードを発見したソース

🔔 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。