Volume 32 Issue 3
May  2015
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Article Navigation >  Journal of Zhejiang A&F University  > 2015  >  32(3): 384-391

WANG Yueting, ZHANG Xiaoli, YANG Huiqiao, et al. Forest volume estimation based on spectral and textural information from the Landsat 8 satellite[J]. Journal of Zhejiang A&F University, 2015, 32(3): 384-391. DOI: 10.11833/j.issn.2095-0756.2015.03.008
Citation: WANG Yueting, ZHANG Xiaoli, YANG Huiqiao, et al. Forest volume estimation based on spectral and textural information from the Landsat 8 satellite[J]. Journal of Zhejiang A&F University, 2015, 32(3): 384-391. DOI: 10.11833/j.issn.2095-0756.2015.03.008
shu

Forest volume estimation based on spectral and textural information from the Landsat 8 satellite

DOI: 10.11833/j.issn.2095-0756.2015.03.008
  • 1.

    Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China;

  • 2.

    Forestry College, Beijing Forestry University, Beijing 100083, China

  • Received Date: 2014-07-17
  • Rev Recd Date: 2014-11-03
  • Publish Date: 2015-06-20
  • On the Jiangle State Forest Farm of Fujian Province forest volume was obtained by field investigation and by Landsat 8 observations that utilized band spectral values, vegetation indexes, derivatives of bands, and optimal textural measurements derived from the panchromatic band using varied window sizes. Through multiple regression analysis, volume estimation models were produced with independent variables of 1） only spectral factors and 2） combined spectral factors and textural volume. Then, validation was conducted using field survey data to test and compare model prediction accuracy. Experimental results showed R2 = 0.727 6 for the spectrally based volume estimation model and R2 = 0.857 5 for the combined model. Model prediction accuracy was 79.8% for the single spectral based volume estimation model and 86.0% for the combined model. Therefore, the improved prediction accuracy using textural information from the panchromatic band with images of Landsat 8 for forest volume estimation and application of this procedure should be considered when determining forest volume.［Ch, 3 fig. 6 tab. 22 ref.］
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Forest volume estimation based on spectral and textural information from the Landsat 8 satellite

doi: 10.11833/j.issn.2095-0756.2015.03.008
  • 1. Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China;
  • 2. Forestry College, Beijing Forestry University, Beijing 100083, China
  • Received Date: 2014-07-17
  • Rev Recd Date: 2014-11-03
  • Publish Date: 2015-06-20

Abstract: On the Jiangle State Forest Farm of Fujian Province forest volume was obtained by field investigation and by Landsat 8 observations that utilized band spectral values, vegetation indexes, derivatives of bands, and optimal textural measurements derived from the panchromatic band using varied window sizes. Through multiple regression analysis, volume estimation models were produced with independent variables of 1） only spectral factors and 2） combined spectral factors and textural volume. Then, validation was conducted using field survey data to test and compare model prediction accuracy. Experimental results showed R2 = 0.727 6 for the spectrally based volume estimation model and R2 = 0.857 5 for the combined model. Model prediction accuracy was 79.8% for the single spectral based volume estimation model and 86.0% for the combined model. Therefore, the improved prediction accuracy using textural information from the panchromatic band with images of Landsat 8 for forest volume estimation and application of this procedure should be considered when determining forest volume.［Ch, 3 fig. 6 tab. 22 ref.］

WANG Yueting, ZHANG Xiaoli, YANG Huiqiao, et al. Forest volume estimation based on spectral and textural information from the Landsat 8 satellite[J]. Journal of Zhejiang A&F University, 2015, 32(3): 384-391. DOI: 10.11833/j.issn.2095-0756.2015.03.008
Citation: WANG Yueting, ZHANG Xiaoli, YANG Huiqiao, et al. Forest volume estimation based on spectral and textural information from the Landsat 8 satellite[J]. Journal of Zhejiang A&F University, 2015, 32(3): 384-391. DOI: 10.11833/j.issn.2095-0756.2015.03.008
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