Welcome to Chinese Journal of Applied Ecology! Today is Share:

Chinese Journal of Applied Ecology ›› 2019, Vol. 30 ›› Issue (10): 3385-3394.doi: 10.13287/j.1001-9332.201910.016

• Original Articles • Previous Articles     Next Articles

Estimation of forest vegetation carbon storage in Hunan Province, China based on k-NN method and domestic high-resolution data

ZHANG Qin-yu1, WANG Hai-bin2, PENG Dao-li1*, XIA Chao-zong3, CHEN Jian3, LIU Wen-jie4   

  1. 1College of Forestry, Beijing Forestry University, Beijing 100083, China;
    2Academy of Forest Industry Planning and Design, National Forestry and Grassland Administration, Beijing 100010, China;
    3Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China;
    4Inner Mongolia Daxing-anling Inventory and Planning Institute, Yakeshi 022150, Inner Mongolia, China
  • Received:2018-12-17 Online:2019-10-20 Published:2019-10-20
  • Contact: *E-mail: dlpeng@bjfu.edu.cn
  • Supported by:
    This work was supported by the National Key R&D Program of China (2016YFD0600205) and the High-resolution Forestry Remote Sensing Application Demonstration System (Phase II) Project.

PDF (PC)

172

Abstract: To promote the application of domestic high-resolution satellite data in large-scale carbon storage estimation and measurement, a total of 206 high-resolution remote sensing images covering Hunan Province were used as the data source, and the estimated minimum unit was fixed as a 0.06 hm2 square composed of multiple pixels. Through the establishment and purification of the interpretation marks, in the extraction of forest information, the pixel-based method and object-oriented classification method were used to compare. In the estimation of carbon storage of arbor forest, the robust estimate, partial least squares method and k-NN estimate were used to compare. Finally, we estimated forest carbon storage in Hunan Province and generated the distribution map of carbon density levels. The results showed that the interpretation mark based on the automatic extraction of plots could increase the extraction accuracy of arbor forest after purification. For the estimation of forest carbon storage at large-scale, the k-NN algorithm embodied a large advantage in forest information extraction and arbor forest carbon storage modeling. The average classification accuracy of the 206 scene images was 76.8%, the average RMSE was 8.95 t·hm-2, the average RRMSE was 19.1%, and the total carbon stock in Hunan Province was 22.28 Mt. The results provided effective reference for the estimation and measurement of forest carbon storage at the provincial and national scales.