New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets
文献类型：期刊论文
| 作者 | Chang, Zhongbing17; Hobeichi, Sanaa16; Wang, Ying-Ping; Tang, Xuli; Abramowitz, Gab15,16; Chen, Yang17; Cao, Nannan17; Yu, Mengxiao; Huang, Huabing13; Zhou, Guoyi12 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021 |
| 卷号 | 13期号:15页码:- |
| 关键词 | forest aboveground biomass carbon stock field measurements remote sensing China |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs13152892 |
| 英文摘要 | 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. |
| 学科主题 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000682285300001 |
| 源URL | [http://ir.xtbg.org.cn/handle/353005/12320]  |
| 专题 | 西双版纳热带植物园_2012年后新成立研究组 |
| 作者单位 | 1.Saatchi, Sassan S.] Univ Calif Los Angeles, Inst Environm & Sustainabil, Los Angeles, CA 91109 USA 2.Saatchi, Sassan S.] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA 3.Southwest Univ, Sch Geog Sci, Chongqing 400715, Peoples R China 4.INRAE, UMR1391 ISPA, F-33140 Villenave Dornon, France 5.Chinese Acad Sci, Xishuangbanna Trop Bot Garden, Mengla 666303, Peoples R China 6.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110016, Peoples R China 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 8.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 9.Chinese Acad Sci, Inst Bot, Beijing 100093, Peoples R China 10.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China |
| 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):-. |
入库方式： OAI收割
来源：西双版纳热带植物园
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