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Advances in Meteorology
Volume 2013 (2013), Article ID 964323, 6 pages
http://dx.doi.org/10.1155/2013/964323
Research Article

Examination of the Quantitative Relationship between Vegetation Canopy Height and LAI

1Faculty of Resources and Environmental Science, Hubei University, Hubei, Wuhan 430062, China
2Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Beijing 100101, China
4State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China

Received 16 July 2013; Accepted 1 October 2013

Academic Editor: Xiangzheng Deng

Copyright © 2013 Yongwei Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate estimation of vegetation biophysical variables such as the vegetation canopy height ( ) is of great importance to the applications of the land surface models. It is difficult to obtain the data of at the regional scale or larger scale, but the remote sensing provides the most useful and most effective method. The leaf area index (LAI) is closely related to the , and we analyzed its relationship with the correlation analysis based on the dataset at 86 site-years of field measurements from sites worldwide in this study. The result indicates that there is significant positive exponent correlation between these two parameters and the change of LAI would exert great impacts on . The higher the LAI is, the higher the is, and vice versa. Besides, the coefficients of different land cover types are very heterogeneous, and LAI of the needleleaf forest shows strong correlation with , while that of the cropland shows weak correlation with . The results may provide certain reference information for the extraction of the data of at the regional scale with the remote sensing data.