Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass
文献类型：期刊论文
| 作者 | Ahmad, Naveed13; Ullah, Saleem12; Zhao, Na11; Mumtaz, Faisal9,10; Ali, Asad8; Ali, Anwar7; Tariq, Aqil5,6; Kareem, Mariam4; Imran, Areeba Binte1; Khan, Ishfaq Ahmad3 |
| 刊名 | FORESTS
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 14期号:2 |
| 关键词 | REDD plus Sentinel-2 above-ground biomass carbon sequestration climate change forest degradation |
| ISSN号 | 1999-4907 |
| DOI | 10.3390/f14020379 |
| 文献子类 | Article |
| 英文摘要 | Accurately characterizing carbon stock is vital for reporting carbon emissions from forest ecosystems. We studied the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests in the Galies region of Abbottabad Pakistan. Above-ground biomass (AGB), estimated from 60 field plots, was correlated with vegetation indices obtained from Sentinel-2 image-to-map AGB using regression models. Furthermore, additional explanatory variables were also associated with AGB in the geo-statistical technique, and kriging interpolation was used to predict AGB. The results illustrate that the atmospherically resistant vegetation index (ARVI) is the best index (R-2 = 0.67) for estimating AGB. In spectral reflectance, Band 1(Coastal Aerosol 443 nm) performs better than other bands. Multiple linear regression models calibrated with ARVI, NNIR and NDVI yielded better results (R-2 = 0.46) with the lowest RMSE (48.53) and MAE (38.42) and were therefore considered better for biomass estimation. On the other hand, in the geo-statistical technique, distance to settlements, ARVI and annual precipitation were significantly correlated with biomass compared to others. In the stepwise regression method, the forward selection resulted in a very significant value (less than 0.000) for ARVI. Therefore, it can be considered best for prediction and used to interpolate AGB through kriging. Compared to the geo-statistical technique, the remote sensing-based models performed relatively well. Regarding potential sites for REDD+ implementation, temporal analysis of Landsat images showed a decrease in forest area from 8896.23 ha in 1988 to 7692.03 ha in 2018. Therefore, this study concludes that the state-of-the-art open-source sensor, the Sentinel-2 data, has significant potential for forest biomass and carbon stock estimation and can be used for robust regional AGB estimation with acceptable accuracy and frequent availability. |
| WOS关键词 | ABOVEGROUND BIOMASS ; SPATIAL-DISTRIBUTION ; CARBON STOCKS ; VEGETATION INDEX ; AIRBORNE LIDAR ; GEOSPATIAL TECHNIQUES ; TEMPERATE FORESTS ; INVENTORY ; IMAGERY ; COVER |
| WOS研究方向 | Forestry |
| WOS记录号 | WOS:000941214400001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190320]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Govt Coll Univ, Dept Geog, Faisalabad 38000, Pakistan 2.Univ Putra Malaysia, Fac Forestry & Environm, Dept Forest Sci & Biodivers, Upm Serdang 43400, Selangor, Malaysia 3.PMAS Arid Agr Univ, Dept Forestry & Range Management, Rawalpindi 46300, Pakistan 4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan 430072, Peoples R China 5.Mississippi State Univ, Dept Wildlife Fisheries & Aquaculture, 775 Stone Blvd, Starkville, MS 39762 USA 6.Pakistan Forest Inst, Forestry Res Div, Peshawar 25120, Pakistan 7.Inst Space Technol, Dept Appl Math & Stat, Islamabad 44000, Pakistan 8.Univ Chinese Acad Sci UCAS, Beijing 101408, Peoples R China 9.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 10.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ahmad, Naveed,Ullah, Saleem,Zhao, Na,et al. Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass[J]. FORESTS,2023,14(2). |
| APA | Ahmad, Naveed.,Ullah, Saleem.,Zhao, Na.,Mumtaz, Faisal.,Ali, Asad.,...&Shakir, Muhammad.(2023).Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass.FORESTS,14(2). |
| MLA | Ahmad, Naveed,et al."Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass".FORESTS 14.2(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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