Articles | Volume 13, issue 13
https://doi.org/10.5194/bg-13-3991-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/bg-13-3991-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
12 Jul 2016
Research article |
| 12 Jul 2016
Analysing the uncertainty of estimating forest carbon stocks in China
Tian Xiang Yue
CORRESPONDING AUTHOR
State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural
Resources Research, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing
210023, China
Yi Fu Wang
CORRESPONDING AUTHOR
College of Forestry, Beijing Forestry University, Beijing 100083, China
Zheng Ping Du
State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural
Resources Research, Beijing 100101, China
Ming Wei Zhao
State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural
Resources Research, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Li Li Zhang
State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural
Resources Research, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Na Zhao
State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural
Resources Research, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing
210023, China
Ming Lu
State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural
Resources Research, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Guy R. Larocque
Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Québec (QC), G1V 5B9, Canada
John P. Wilson
6Spatial Sciences Institute, Dana and David Dornsife College of Letters, Arts and Sciences, University of Southern
California, Los Angeles, CA 90089-0374, USA
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Cited
12 citations as recorded by crossref.
- Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR X. Sun et al. 10.3390/rs11060722
- A fundamental theorem for eco-environmental surface modelling and its applications T. Yue et al. 10.1007/s11430-019-9594-3
- New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets Z. Chang et al. 10.3390/rs13152892
- High Resolution Forest Masking for Seasonal Monitoring with a Regionalized and Colourimetrically Assisted Chorologic Typology R. Aravena et al. 10.3390/rs15143457
- Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models S. Wu et al. 10.3390/f14101970
- Above-ground carbon storage in Pinus pumila along an alpine altitude in Khingan Mountains, Inner Mongolia of China R. CONG et al. 10.15835/nbha49312389
- Hydrological evaluation of satellite and reanalysis-based rainfall estimates over the Upper Tekeze Basin, Ethiopia K. Reda et al. 10.2166/nh.2022.131
- Adequacy of TRMM satellite rainfall data in driving the SWAT modeling of Tiaoxi catchment (Taihu lake basin, China) D. Li et al. 10.1016/j.jhydrol.2017.01.006
- Proportional allocation with soil depth improved mapping soil organic carbon stocks M. Zhang et al. 10.1016/j.still.2022.105519
- HASM quantum machine learning T. Yue et al. 10.1007/s11430-022-1144-7
- Spatiotemporal patterns of carbon storage in forest ecosystems in Hunan Province, China L. Chen et al. 10.1016/j.foreco.2018.09.059
- Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions Y. Li et al. 10.1038/s41598-017-07732-5
12 citations as recorded by crossref.
- Analyzing the Uncertainty of Estimating Forest Aboveground Biomass Using Optical Imagery and Spaceborne LiDAR X. Sun et al. 10.3390/rs11060722
- A fundamental theorem for eco-environmental surface modelling and its applications T. Yue et al. 10.1007/s11430-019-9594-3
- New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets Z. Chang et al. 10.3390/rs13152892
- High Resolution Forest Masking for Seasonal Monitoring with a Regionalized and Colourimetrically Assisted Chorologic Typology R. Aravena et al. 10.3390/rs15143457
- Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models S. Wu et al. 10.3390/f14101970
- Above-ground carbon storage in Pinus pumila along an alpine altitude in Khingan Mountains, Inner Mongolia of China R. CONG et al. 10.15835/nbha49312389
- Hydrological evaluation of satellite and reanalysis-based rainfall estimates over the Upper Tekeze Basin, Ethiopia K. Reda et al. 10.2166/nh.2022.131
- Adequacy of TRMM satellite rainfall data in driving the SWAT modeling of Tiaoxi catchment (Taihu lake basin, China) D. Li et al. 10.1016/j.jhydrol.2017.01.006
- Proportional allocation with soil depth improved mapping soil organic carbon stocks M. Zhang et al. 10.1016/j.still.2022.105519
- HASM quantum machine learning T. Yue et al. 10.1007/s11430-022-1144-7
- Spatiotemporal patterns of carbon storage in forest ecosystems in Hunan Province, China L. Chen et al. 10.1016/j.foreco.2018.09.059
- Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions Y. Li et al. 10.1038/s41598-017-07732-5
Latest update: 30 Apr 2024
Short summary
The fusion of ground-based forest inventory data with satellite observations achieved with HASM-S provided much more accurate estimates of forest carbon stocks, comparing with either the remote sensing descriptions or the forest inventory data. The HASM-S results show that the forest carbon stocks of China have increased by 2.24 Pg during the period 1984–2008 to a new high of 7.08 Pg C in 2008.
The fusion of ground-based forest inventory data with satellite observations achieved with...
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