Knowledge Management System Of Institute Of Botany,CAS
| New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets | |
Chang, Zhongbing1; Hobeichi, Sanaa2; Wang, Ying-Ping; Tang, Xuli; Abramowitz, Gab2,3; Chen, Yang1; Cao, Nannan1; Yu, Mengxiao; Huang, Huabing5; Zhou, Guoyi6; Wang, Genxu7; Ma, Keping8 ; Du, Sheng9; Li, Shenggong10; Han, Shijie11; Ma, Youxin12; Wigneron, Jean-Pierre; Fan, Lei14; Saatchi, Sassan S.; Yan, Junhua
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| 2021 | |
| 发表期刊 | REMOTE SENSING
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| 卷号 | 13期号:15 |
| 摘要 | Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (-4.6 and -3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales. |
| 关键词 | forest aboveground biomass carbon stock field measurements remote sensing China |
| 学科领域 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| DOI | 10.3390/rs13152892 |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS关键词 | GLOBAL GRIDDED SYNTHESIS ; CARBON-CYCLE MODELS ; TERRESTRIAL ECOSYSTEMS ; FIELD ; SEQUESTRATION ; MISSION ; LIDAR ; BENCHMARK ; REVIEWS ; DENSITY |
| WOS研究方向 | Science Citation Index Expanded (SCI-EXPANDED) |
| WOS记录号 | WOS:000682285300001 |
| 出版者 | MDPI |
| 文献子类 | Article |
| 出版地 | BASEL |
| EISSN | 2072-4292 |
| 资助机构 | National Science Fund for Distinguished Young Scholars [41825020] ; Strategic Priority Research Programof the Chinese Academy of Sciences [XDA05050200] |
| 作者邮箱 | changzb@scbg.ac.cn ; s.hobeichi@unsw.edu.au ; Yingping.Wang@csiro.au ; xltang@scib.ac.cn ; gabriel@unsw.edu.au ; chenyang1025@scbg.ac.cn ; caonan@scib.ac.cn ; yumx@scbg.ac.cn ; huanghb@radi.ac.cn ; gyzhou@scib.ac.cn ; wanggx@imde.ac.cn ; kpma@ibcas.ac.cn ; shengdu@ms.iswc.ac.cn ; lisg@igsnrr.ac.cn ; hansj@iae.ac.cn ; may@xtbg.ac.cn ; jean-pierre.wigneron@inra.fr ; leifan33@swu.edu.cn ; saatchi@jpl.nasa.gov ; jhyan@scib.ac.cn |
| 作品OA属性 | Green Published |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ibcas.ac.cn/handle/2S10CLM1/26766 |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Vegetat Restorat & Management Degraded Ec, South China Bot Garden, Guangzhou 510650, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Univ New South Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia 4.Univ New South Wales, ARC Ctr Excellence Climate Extremes, Sydney, NSW 2052, Australia 5.CSIRO Oceans & Atmosphere, Aspendale, Vic 3195, Australia 6.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Guangzhou 510275, Peoples R China 7.Nanjing Univ Informat Sci & Technol, Sch Appl Meteorol, Nanjing 210044, Peoples R China 8.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China 9.Chinese Acad Sci, Inst Bot, Beijing 100093, Peoples R China 10.Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China 11.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 12.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110016, Peoples R China 13.Chinese Acad Sci, Xishuangbanna Trop Bot Garden, Mengla 666303, Peoples R China 14.INRAE, UMR1391 ISPA, F-33140 Villenave Dornon, France 15.Southwest Univ, Sch Geog Sci, Chongqing 400715, Peoples R China 16.Saatchi, Sassan S.] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA 17.Saatchi, Sassan S.] Univ Calif Los Angeles, Inst Environm & Sustainabil, Los Angeles, CA 91109 USA |
| 推荐引用方式 GB/T 7714 | Chang, Zhongbing,Hobeichi, Sanaa,Wang, Ying-Ping,et al. New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets[J]. REMOTE SENSING,2021,13(15). |
| APA | Chang, Zhongbing.,Hobeichi, Sanaa.,Wang, Ying-Ping.,Tang, Xuli.,Abramowitz, Gab.,...&Yan, Junhua.(2021).New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets.REMOTE SENSING,13(15). |
| MLA | Chang, Zhongbing,et al."New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets".REMOTE SENSING 13.15(2021). |
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| Chang-2021-New Fores（5095KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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