Optimization Model to Estimate Mount Tai Forest Biomass Based on Remote Sensing

  • Yanfang Diao
  • Chengming Zhang
  • Jiping Liu
  • Yong Liang
  • Xuelian Hou
  • Xiaomin Gong
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 370)


The development of low-carbon economy and the promotion of energy conservation are becoming a basic consensus of all countries. Therefore, global carbon cycle becomes a widespread concern research topic in scientific community. About 77% of the vegetation carbon stores in forest biomass in terrestrial ecosystems. So forest biomass is the most important parameter in terrestrial ecosystem carbon cycle. In this paper, for estimating the forest biomass of Mount Tai, a support vector machine (SVM) optimization model based on remote sensing is proposed. The meteorological data, terrain data, remote sensing data are taken into account in this model. In comparison the results of SVM with that of regressive analysis method, both the training accuracy and testing accuracy of regressive analysis method are lower than those of SVM, so SVM could obtain higher accuracy.


forecast biomass remote sensing support vector machine (SVM) 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Yanfang Diao
    • 1
  • Chengming Zhang
    • 2
    • 3
  • Jiping Liu
    • 2
  • Yong Liang
    • 3
  • Xuelian Hou
    • 4
  • Xiaomin Gong
    • 1
  1. 1.College of Water Conservancy and Civil EngineeringShandong Agricultural UniversityTaianChina
  2. 2.Chinese Academy of Surveying and MappingBeijingChina
  3. 3.School of Information Science and EngineeringShandong Agricultural UniversityTaianChina
  4. 4.Information Center of Shandong Electric Power SchoolTaianChina

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