Journal of Geographical Sciences ›› 2016, Vol. 26 ›› Issue (5): 568-584.doi: 10.1007/s11442-016-1286-z

• Orginal Article • Previous Articles     Next Articles

Uncertainty of forest biomass carbon patterns simulation on provincial scale: A case study in Jiangxi Province, China

Yifu WANG1,2(), Tianxiang *YUE1,2(), Yuancai LEI3, Zhengping DU1, Mingwei ZHAO4   

  1. 1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Chinese Academy of Forestry, Beijing 100091, China
    4. Anhui Center for Collaborative Innovation in Geographical Information Integration and Application, Chuzhou University, Chuzhou 239012, Anhui, China
  • Received:2015-09-23 Accepted:2015-12-29 Online:2016-05-25 Published:2016-08-01
  • About author:

    Author: Wang Yifu (1990-), PhD Candidate, specialized in ecological modeling and system simulation. E-mail: wangyf@lreis.ac.cn

    *Corresponding author: Yue Tianxiang (1963-), PhD and Professor, E-mail: yue@lreis.ac.cn

  • Supported by:
    National Fundamental R&D Program of the Ministry of Science and Technology of the People’s Republic of China, No.2013FY111600-4

Abstract:

Forest vegetation carbon patterns are significant for evaluating carbon emission and accumulation. Many methods were used to simulate patterns of forest vegetation carbon stock in previous studies, however, uncertainty apparently existed between results of different methods, even estimates of same method in different studies. Three previous methods, including Atmosphere-vegetation interaction model 2 (AVIM2), Kriging, Satellite-data Based Approach (SBA), and a new method, High Accuracy Surface Modeling (HASM), were used to simulate forest vegetation carbon stock patterns in Jiangxi Province in China. Cross-validation was used to evaluate methods. The uncertainty and applicability of the four methods on provincial scale were analyzed and discussed. The results showed that HASM had the highest accuracy, which improved by 50.66%, 33.37% and 28.58%, compared with AVIM2, Kriging and SBA, respectively. Uncertainty of simulation of forest biomass carbon stock was mainly derived from modeling error, sampling error and statistical error of forest area. Total forest carbon stock, carbon density and forest area of Jiangxi were 288.62 Tg, 3.06 kg/m2 and 94.32×109 m2 simulated by HASM, respectively.

Key words: forest carbon stock, HASM, AVIM2, Kriging, Satellite-data Based Approach (SBA)