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New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

文献类型：期刊论文

作者Chang, Zhongbing1,2; Hobeichi, Sanaa3,4; Wang, Ying-Ping4,5; Tang, Xuli1; Abramowitz, Gab3,4; Chen, Yang1,2; Cao, Nannan1,2; Yu, Mengxiao1; Huang, Huabing6; Zhou, Guoyi1,7
刊名REMOTE SENSING
出版日期2021-08-01
卷号13期号:15页码:20
关键词forest aboveground biomass carbon stock field measurements remote sensing China
DOI10.3390/rs13152892
通讯作者Yan, Junhua(jhyan@scib.ac.cn)
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.
WOS关键词GLOBAL GRIDDED SYNTHESIS ; CARBON-CYCLE MODELS ; TERRESTRIAL ECOSYSTEMS ; FIELD ; SEQUESTRATION ; MISSION ; COVER ; LIDAR ; BENCHMARK ; REVIEWS
资助项目National Science Fund for Distinguished Young Scholars[41825020] ; Strategic Priority Research Programof the Chinese Academy of Sciences[XDA05050200]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000682285300001
出版者MDPI
资助机构National Science Fund for Distinguished Young Scholars ; Strategic Priority Research Programof the Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/164699]  
专题中国科学院地理科学与资源研究所
通讯作者Yan, Junhua
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
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):20.
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),20.
MLA Chang, Zhongbing,et al."New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets".REMOTE SENSING 13.15(2021):20.

入库方式： OAI收割

来源：地理科学与资源研究所

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