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Wang Qing, Liu Jian, Yu Kun-Yong. Inversion of Masson pine forest LAI by multiple-perspective vegetation index[J]. Plant Science Journal, 2017, 35(1): 48-55. DOI: 10.11913/PSJ.2095-0837.2017.10048
Citation: Wang Qing, Liu Jian, Yu Kun-Yong. Inversion of Masson pine forest LAI by multiple-perspective vegetation index[J]. Plant Science Journal, 2017, 35(1): 48-55. DOI: 10.11913/PSJ.2095-0837.2017.10048
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Inversion of Masson pine forest LAI by multiple-perspective vegetation index

  • 1. Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Fuzhou 350002, China;

  • 2. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China;

  • 3. Institute of Geomatics Application, Fujian Agriculture and Forestry University, Fuzhou 350002, China;

  • 4. Sanming University, Sanming, Fujian 365000, China

Funds: 

This work was supported by a grant from the National Natural Science Foundation of Young Scientists of China (41401385):Reconstruction of vegetation cover and management factor (C factor) based on remote sensing in southern red soils water and soil loss region.

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  • Received Date: May 18, 2016
  • Available Online: October 31, 2022
  • Published Date: February 27, 2017
    Graphical Abstract
    • Abstract
  • CHRIS/PROBA is a multiple-angle sensor providing hyper-spectral data with 17 m×17 m spatial resolution that can be applied for data inversion of vegetation canopy structure parameters, such as tree height and leaf area index (LAI). We used a four-scale geometrical optics model to simulate anisotropy distribution regulation of the normalized difference vegetation index (NDVI) of a Pinus massoniana (Masson pine) forest canopy. By extracting the red and near infrared characteristic spectral bands from the 18 bands in CHRIS, a new multi-angle normalized hotspot-dark-spot difference vegetation index (NHDVI) was applied to the estimation of the LAI of Pinus massoniana forest using CHRIS data. The results showed that:(1) Compared with the NDVI and soil adjusted vegetation index (SAVI), NHDVI well integrated the spectral information and angle information with the ground measured LAI, and the coefficient of determination reached 0.7278; (2) The LAI was calculated by statistical regression of NHDVI-LAI. The correlation between the LAI and the measured values was 0.8272, significantly higher than that of SAVI, and the root mean square error (RMSE) was 0.1232. Thus, these findings indicate that angular information is important for improving the retrieval accuracy of LAI.
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    • References (17)
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