ドローンと衛星画像を用いた再湿潤泥炭採掘地におけるチャンバー測定温室効果ガス排出量のアップスケーリング
Upscaling Chamber-measured Greenhouse Gas Emissions in Rewetted Peat Extraction Sites Using Drone and Satellite Imagery (原題)
Aleksi Isoaho, Milla Niiranen, Eveliina Väyrynen, Aleksi Räsänen, Maarit Liimatainen
🤖 gxceed AI 要約
日本語
再湿潤泥炭採掘地からの温室効果ガス排出量を、チャンバー測定とドローン・衛星画像を組み合わせて空間的にアップスケーリングする手法を開発。フィンランドの3つの再湿潤泥炭地で5つの表面タイプを分類し、ランダムフォレストとXGBoostを用いて表面被覆率を予測。排出フラックスはサイトや時期により変動し、高水位とヨシ被覆率が排出量を低減することを示した。
English
Developed a method to upscale chamber-measured GHG emissions from rewetted peat extraction sites using drone and satellite imagery. Classified surface types with random forest and predicted %-covers with XGBoost on Sentinel-2 data. Fluxes varied by site and time; high water and reed cover reduced emissions. Demonstrates feasibility of combining field, drone, and satellite data for upscaling.
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
日本では泥炭地の再湿潤は限定的だが、農地や湿地のGHG排出算定に応用可能。衛星・ドローンによるモニタリングは、SSBJ対応の排出量算定の効率化に寄与する可能性がある。
In the global GX context
Contributes to global GHG accounting by providing a scalable method for peatland emissions, relevant for national inventories and climate reporting under frameworks like the Paris Agreement. Demonstrates integration of remote sensing with field measurements, useful for improving emission factors and reducing uncertainty in land-sector reporting.
👥 読者別の含意
🔬研究者:Provides a methodological framework for upscaling chamber measurements using remote sensing, applicable to other ecosystems.
🏢実務担当者:Offers a cost-effective approach for monitoring GHG emissions from rewetted peatlands, useful for land managers and carbon credit projects.
🏛政策担当者:Informs national GHG inventory improvements and supports climate mitigation policies for peatland restoration.
📄 Abstract（原文）
Abstract Greenhouse gas (GHG) emissions originating from rewetted peat extraction sites are largely unknown. While the emissions can be quantified with chamber measurements, spatial upscaling is needed to quantify the emissions over the whole rewetted areas. Therefore, we developed a method to upscale chamber-measured GHG emissions with drone and satellite imagery. We measured GHG fluxes in five different surface types within three rewetted peatlands in northern Finland across two growing seasons. We spatially classified surface types within drone mapped areas with random forest classifier and further upscaled the surface type %-covers outside the drone areas with multitemporal Sentinel-2 imagery using extreme gradient boosting regression. With predicted surface type %-covers, we calculated total and area-normalised GHG flux sums for each measurement moment. The drone-based surface type classifiers performed well (class specific F-scores 0.28–0.97, overall accuracies 0.76–0.85) as did the satellite-based surface type %-cover models (R 2 = 0.38–0.99). Predicted surface type covers appeared realistic and followed topographical gradient observed from the high-resolution classification. Upscaled and area-normalised GHG fluxes ranged from 0.24 to 5.91 g CO2-C m –2 d –1 , 0.01 to 0.34 g CH4-C m –2 d –1 and -98 to 61 μg N2O-N m –2 d –1 depending on site and time. High water and reed %-cover reduced overall emissions due to higher emission factors in drier surface types but due to lack of gross primary production estimations, no conclusion could be drawn about net ecosystem carbon balances. Our results indicate that combination of field measurements, drone flights, and satellite imagery can be used to upscale surface area estimates and GHG fluxes.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.1007/s00267-026-02582-2first seen 2026-08-19 05:52:27 · last seen 2026-08-24 05:49:01
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