リモートセンシングを用いた炭素隔離の空間的推定と炭素クレジット評価への応用：包括的レビュー

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

🔬研究者:リモートセンシング・機械学習と炭素会計を橋渡しする研究課題と不確実性評価の枠組みを整理しており、学際的研究の出発点となる。

🏢実務担当者:炭素クレジット創出やScope3・カーボンオフセット戦略を検討する企業が、MRV要件やデータ品質・不確実性の留意点を把握するのに有用。

🏛政策担当者:デジタルMRVや炭素クレジット制度の設計において、空間データとAI活用の可能性および検証・透明性確保の論点を提供する。

Carbon sequestration is fundamental to climate-change mitigation because terrestrial and aquatic ecosystems store carbon in vegetation, biomass, soils, and the Earth’s crust. Reliable estimation of carbon stocks and their temporal and spatial changes is essential for the formulation of climate policy, ecosystem management, carbon accounting, and carbon-credit assessment. Conventional field measurements provide detailed information but are constrained by sampling requirements, cost, labour, spatial heterogeneity, and limitations in continuous monitoring. Remote sensing provides a complementary approach by enabling spatially continuous observations of vegetation characteristics, land-cover change, biomass, and other carbon-related variables across different spatial and temporal scales. Advances in multispectral, hyperspectral, synthetic aperture radar, LiDAR, thermal, and high-resolution satellite observations have extended the capability for carbon estimation. Their integration with geographic information systems, field observations, statistical techniques, and machine-learning algorithms enables precise spatial assessment of above-ground biomass, below-ground biomass, soil organic carbon, forest carbon, agricultural carbon, mangrove carbon, and blue-carbon resources. However, uncertainties associated with sensor characteristics, field-data quality, model selection, spatial variability, temporal dynamics, and scaling remain acknowledged limitations. Converting spatial carbon estimates into carbon credits requires consideration of baseline conditions, additionality, permanence, leakage, uncertainty, verification, and measurement, reporting, and verification requirements. This review critically examines remote sensing technologies, carbon-estimation models, spatial mapping approaches, machine-learning techniques, carbon-accounting frameworks, and their integration into carbon-credit assessment. It places particular emphasis on multisource data fusion, emerging Earth-observation technologies, artificial intelligence, uncertainty assessment, and digital MRV systems. Major methodological, technological, and policy gaps are identified, along with opportunities for developing transparent, spatially explicit, scientifically robust, and scalable carbon-credit assessment frameworks.

• openalex https://doi.org/10.9734/jgeesi/2026/v30i91125first seen 2026-09-27 04:42:58