INTEGRATING FIELD INVENTORY AND SENTINEL-2 IMAGERY TO ASSESS CARBON STOCK AND BIOMASS DYNAMICS OF GYMNOSPERMS IN AYUBIA NATIONAL PARK, PAKISTAN
パキスタンのアユビア国立公園における裸子植物の炭素蓄積量とバイオマス動態評価のための現地調査とSentinel-2画像の統合 (AI 翻訳)
Mehreen Ghazal, Asad Ullah, Muhammad Nauman Khan
🤖 gxceed AI 要約
日本語
本研究は、パキスタンのアユビア国立公園において、現地調査とSentinel-2画像を統合し、裸子植物の地上部・地下部バイオマス炭素蓄積量を推定した。NDVIは強い相関を示したが、ステップワイズマルチインデックスモデルにより精度が向上した。結果はREDD+炭素会計に貢献する。
English
This study integrated field inventory and Sentinel-2 imagery to estimate above- and below-ground biomass carbon stocks of gymnosperms in Ayubia National Park, Pakistan. NDVI showed strong correlation, but a stepwise multi-index model improved prediction accuracy (R²=0.915). The findings support REDD+ carbon accounting protocols.
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
森林炭素計測の手法として、日本における山林の炭素蓄積量推定にも応用可能な知見を提供する。ただし、対象地域はパキスタンであり、直接的な政策連動はない。
In the global GX context
This study offers a cost-effective method for forest carbon accounting using remote sensing, relevant for REDD+ programs and national greenhouse gas inventories globally. The stepwise model improves biomass estimation accuracy and can be adapted to other ecosystems.
👥 読者別の含意
🔬研究者:Researchers in forest ecology and remote sensing can apply the integrated methodology to improve biomass estimates in other regions.
🏢実務担当者:Practitioners involved in REDD+ projects can leverage the multi-index regression model for more accurate carbon stock assessments.
🏛政策担当者:Policymakers in forest carbon accounting can consider the use of Sentinel-2 and field data integration for national inventory reporting.
📄 Abstract（原文）
This study presents an integrated assessment of above-ground biomass (AGB) and below-ground biomass (BGB) carbon stocks of dominant gymnosperms in Ayubia National Park, Pakistan. Sixty-three circular plots (0.1 ha each; 17.84 m radius) were established to estimate the carbon sequestration potential of key conifer species, quantify carbon stocks, validate AGB estimates using Sentinel-2 satellite imagery, and examine the correlation between spectral vegetation indices and biomass. A suite of regression models simple, multiple, and stepwise was employed to identify optimal predictors. Biomass estimates were further evaluated for their applicability to REDD (Reducing Emissions from Deforestation and Forest Degradation) + carbon accounting protocols. The maximum diameter at breast height (DBH) and height recorded for Pinus wallichiana (Wall. ex D. Don) A.B. Jacks., Abies pindrow (Royle ex D. Don) Royle, and Picea smithiana (Wall.) Boiss. were 74.00 cm and 33.95 m; 72.13 cm, and 34.65 m; and 70.45 cm and 32.00 m, respectively. AGB and BGB varied significantly among species: P. wallichiana (196.13–6.17 t/ha, 92.18–1.27 t/ha), A. pindrow (175.46–10.92 t/ha, 45.62–8.67 t/ha), and P. smithiana (174.63–5.03 t/ha, 45.40–3.99 t/ha). Mean AGB and above-ground carbon (AGC) ranged from 17.56 to 312.39 t/ha and 8.25 to 146.82 t/ha, respectively. Among spectral indices, NDVI (Normalized Difference Vegetation Index) demonstrated the strongest individual correlation with AGB (R² = 0.622, RMSE = 39.7 t/ha). However, a stepwise multi-index regression model significantly improved prediction accuracy (R² = 0.915, RMSE = 20.2 t/ha), reducing estimation error nearly fivefold. In contrast, the multi-band model performed poorly (R² = 0.37, RMSE = 80 t/ha), likely due to overfitting. These results confirm that NDVI is a strong standalone predictor of biomass, while the stepwise index model offers the most reliable estimation method for carbon stock assessment.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.36899/japs.2026.4.0085first seen 2026-05-14 23:56:45
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