JOURNAL OF NATURAL RESOURCES ›› 2013, Vol. 28 ›› Issue (7): 1243-1254.doi: 10.11849/zrzyxb.2013.07.016

• Resources Research Methods • Previous Articles     Next Articles

Hyper-spectral Remote Sensing Estimation for the Vegetation Cover

BAO Gang1,2,3, BAO Yu-hai1, QIN Zhi-hao2, ZHOU Yi2, HUANG Ming-xiang1,4, ZHANG Hong-bin3   

  1. 1. Inner Mongolian Key Laboratory of Remote Sensing and Geographic Information System, Inner Mongolia Normal University, Huhhot 01002;
    2. International Institute for Earth System Science, Nanjing University, Nanjing 21009;
    3. Hulunber Grassland Ecosystem Observation and Research Station, Beijing 100081, China;
    4. Information Center of Ministry of Environmental Protection, Beijing 100029, China
  • Received:2012-07-19 Revised:2012-12-06 Online:2013-07-20 Published:2013-07-20

Abstract:

The vegetation cover (VC) and corresponding vegetation canopy reflectance curves were collected in "Remotely sensed loading integrated testing site of non-driving aircraft (North testing site)"of Peking University, and the VC estimation models were developed and compared with each other based on the correlation between the conversion types (wavelet energy coefficient, principal component and vegetation index) of hyper-spectral curves and VC value. The result indicates: The hyper-spectral vegetation index-based model (R2>0.8, RMSE≤0.0188) is the best one of the three conversion types-based models, and EVI-based model is the best one among the other vegetation index-based models; the higher correlation coefficients existed between the second and the fourth single wavelet energy coefficient retrieved from 8-scale wavelet transformation and VC value (R2=0.811 and 0.813; RMSE=0.0199 and 0.0198, respectively); the multi-regression model established between multiple single wavelet energy coefficients and VC works better than the model based on the principal component analysis, the R2 and RMSE were improved by 0.058 and 0.03, respectively; the VC spatial distribution map through combining EVI-based model and TM-EVI indicates that the higher VC is distributed in the northern (75%) and southern (55%) parts of the study site and the lower VC (15%-55%) is distributed in the middle part. The spatial distribution is consistent with the land use/cover characteristics.

Key words: vegetation cover, wavelet energy coefficient, principal component analysis, vegetation index

CLC Number: 

  • TP79