バイオマスおよび炭素蓄積量推定アプローチの系統的レビュー：手法、不確実性、新たな機会

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:森林炭素推定手法の比較と不確実性評価の最新動向を体系的に把握できる。

🏢実務担当者:オフセット調達や吸収源開示における推定精度・不確実性の限界を理解する参考になる。

🏛政策担当者:森林吸収源の国別報告やクレジット制度設計における手法選択と不確実性管理に示唆を与える。

Accurate quantification of forest biomass and carbon stocks is essential for understanding terrestrial carbon dynamics, climate change mitigation, and improving forest monitoring and greenhouse gas reporting. However, biomass estimation approaches vary considerably in accuracy, scalability, cost, and uncertainty, creating challenges for selecting appropriate methods across ecosystems and spatial scales. This systematic review synthesizes recent advances in forest biomass and carbon stock estimation and evaluates the strengths, limitations, and future directions of major methodological approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, 147 peer-reviewed studies published between 2000 and 2025 were reviewed. The studies encompassed field-based measurements, allometric modelling, remote sensing, machine learning, carbon stock models, and integrated multi-source frameworks. The evidence reveals a clear transition from conventional field inventories toward multi-source approaches that integrate ground observations with optical, radar, and Light Detection and Ranging data, advanced modelling, and artificial intelligence. Field measurements and locally calibrated allometric equations remain indispensable for model development and validation, while remote sensing and machine learning enhance spatial coverage and predictive capability. However, estimation accuracy remains strongly influenced by vegetation structure, environmental conditions, data quality, and model transferability. The review demonstrates that no single approach is universally optimal across all ecosystem conditions. Reliable biomass and carbon stock assessments require integrated frameworks that balance accuracy, scalability, cost, and uncertainty through the complementary use of field observations, remote sensing, and advanced modelling techniques. Future research should prioritize expanding biomass reference datasets in underrepresented regions, strengthening model validation and uncertainty quantification, and developing transparent, ecosystem-specific estimation frameworks. Such advances will improve forest carbon accounting, climate mitigation, and evidence-based forest management.

• openalex https://doi.org/10.1371/journal.pclm.0000935first seen 2026-10-10 05:01:18