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author:

Guo, Yun (Guo, Yun.) [1] | Tian, Xin (Tian, Xin.) [2] | Li, Zengyuan (Li, Zengyuan.) [3] | Ling, Feilong (Ling, Feilong.) [4] | Chen, Erxue (Chen, Erxue.) [5] | Yan, Min (Yan, Min.) [6] | Li, Chunmei (Li, Chunmei.) [7]

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EI Scopus

Abstract:

Forest biomass reflects the ecological succession and human disturbance of the forest, and can fully embody the quality of forest ecosystem environment. The Qilian Mountain forest reserve at upper reaches of the Heihe River Basin was selected for the study. Landsat Thematic Mapper 5 (TM) images were selected as the source data, which were rectified by SCS + C terrain radiometric correction. Forest above-ground biomass was estimated using k-nearest neighbor (k-NN) method and support vector regression (SVR) method, respectively. The results show that spectral information of remote sensing image was recovered by the sun-canopy-sensor plus the C (SCS+C) terrain correction which can effectively improve the estimation accuracy of the models regardless of k-NN or SVR. The optimal k-NN method (R2=0.54, RMSE=26.62ton/ha) performs better than the optimal SVR method (R2=0.51, RMSE=27.45ton/ha). © 2014 IEEE.

Keyword:

Community:

  • [ 1 ] [Guo, Yun]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry Education, Fuzhou University, Fuzhou, Fujian, China
  • [ 2 ] [Guo, Yun]Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Yiheyuanhou, Beijing, China
  • [ 3 ] [Tian, Xin]Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Yiheyuanhou, Beijing, China
  • [ 4 ] [Tian, Xin]Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengelosestraat 99, Enschede, Netherlands
  • [ 5 ] [Li, Zengyuan]Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Yiheyuanhou, Beijing, China
  • [ 6 ] [Ling, Feilong]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry Education, Fuzhou University, Fuzhou, Fujian, China
  • [ 7 ] [Ling, Feilong]Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Yiheyuanhou, Beijing, China
  • [ 8 ] [Chen, Erxue]Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Yiheyuanhou, Beijing, China
  • [ 9 ] [Yan, Min]Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Yiheyuanhou, Beijing, China
  • [ 10 ] [Li, Chunmei]Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Yiheyuanhou, Beijing, China

Reprint 's Address:

  • 郭云

    [guo, yun]research institute of forest resource information techniques, chinese academy of forestry, yiheyuanhou, beijing, china;;[guo, yun]key laboratory of spatial data mining and information sharing, ministry education, fuzhou university, fuzhou, fujian, china

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Year: 2014

Page: 741-744

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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