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Chinese Journal of Applied Ecology ›› 2018, Vol. 29 ›› Issue (1): 44-52.doi: 10.13287/j.1001-9332.201801.011

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Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

MAO Xue-gang*, DU Zi-han, LIU Jia-qian, CHEN Shu-xin, HOU Ji-yu   

  1. School of Forestry, Northeast Forestry University, Harbin 150040, China
  • Received:2017-06-05 Online:2018-01-18 Published:2018-01-18
  • Contact: * E-mail: maoxuegang@aliyun.com
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (31300533) and the National Undergraduate Innovative Training Program.

Abstract: Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RAor) and intersection area of segmented object (RAos) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RAor was equal to RAos. The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.