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Article

New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

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Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
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College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
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ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW 2052, Australia
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CSIRO Oceans and Atmosphere, Aspendale, VIC 3195, Australia
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School of Geospatial Engineering and Science, Sun Yat-Sen University, Guangzhou 510275, China
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School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China
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Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
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Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
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State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
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Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
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Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
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INRAE, UMR1391 ISPA, F-33140 Villenave d’Ornon, France
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School of Geographical Sciences, Southwest University, Chongqing 400715, China
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Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
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Institute of the Environment and Sustainability, University of California, Los Angeles, CA 91109, USA
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Author to whom correspondence should be addressed.
Academic Editor: Stefano Tebaldini
Remote Sens. 2021, 13(15), 2892; https://doi.org/10.3390/rs13152892
Received: 15 June 2021 / Revised: 1 July 2021 / Accepted: 14 July 2021 / Published: 23 July 2021
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. View Full-Text
Keywords: forest aboveground biomass; carbon stock; field measurements; remote sensing; China forest aboveground biomass; carbon stock; field measurements; remote sensing; China
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MDPI and ACS Style

Chang, Z.; Hobeichi, S.; Wang, Y.-P.; Tang, X.; Abramowitz, G.; Chen, Y.; Cao, N.; Yu, M.; Huang, H.; Zhou, G.; Wang, G.; Ma, K.; Du, S.; Li, S.; Han, S.; Ma, Y.; Wigneron, J.-P.; Fan, L.; Saatchi, S.S.; Yan, J. New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets. Remote Sens. 2021, 13, 2892. https://doi.org/10.3390/rs13152892

AMA Style

Chang Z, Hobeichi S, Wang Y-P, Tang X, Abramowitz G, Chen Y, Cao N, Yu M, Huang H, Zhou G, Wang G, Ma K, Du S, Li S, Han S, Ma Y, Wigneron J-P, Fan L, Saatchi SS, Yan J. New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets. Remote Sensing. 2021; 13(15):2892. https://doi.org/10.3390/rs13152892

Chicago/Turabian Style

Chang, Zhongbing, Sanaa Hobeichi, Ying-Ping Wang, Xuli Tang, Gab Abramowitz, Yang Chen, Nannan Cao, Mengxiao Yu, Huabing Huang, Guoyi Zhou, Genxu Wang, Keping Ma, Sheng Du, Shenggong Li, Shijie Han, Youxin Ma, Jean-Pierre Wigneron, Lei Fan, Sassan S. Saatchi, and Junhua Yan. 2021. "New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets" Remote Sensing 13, no. 15: 2892. https://doi.org/10.3390/rs13152892

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