JOURNAL OF NATURAL RESOURCES ›› 2019, Vol. 34 ›› Issue (3): 613-623.doi: 10.31497/zrzyxb.20190314

• Resource Evaluation • Previous Articles     Next Articles

Assessing canopy structure effect on the estimation of light-use efficiency in a subtropical evergreen forest

QIAN Zhao-hui1,2(), WANG Shao-qiang1,2(), ZHOU Guo-yi3, ZHANG Lei-ming1,2, MENG Ze3   

  1. 1. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. Collaege of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
  • Received:2018-07-08 Revised:2018-11-22 Online:2019-03-28 Published:2019-03-28

Abstract:

Remote sensing is an effective method to assess Light-use Efficiency (LUE) by using Photochemical Reflectance Index (PRI) at leaf level. But when extending this approach to canopy level, we found that the structure of forest canopy is one of the factors that influence the estimation accuracy of LUE. This study calculated the PRI, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Modified Simple Ratio (MSR) respectively, using the spectral reflection data from the spectro-radiometer at the Dinghu Mountain Forest Ecosystem Research Station in Guangdong, southern China. We compared and analyzed the influence of different canopy structural vegetation indexes (NDVI, EVI and MSR) on PRI used to track LUE as measured by eddy covariance at the canopy level. A multivariate linear regression model was built to improve the fitting accuracy of LUE seasonal dynamics in this subtropical evergreen forest. The results show that: (1) Canopy structural vegetation index of subtropical evergreen forest has a significant correlation with PRI at canopy scale, and the correlation between MSR and PRI was the strongest (R2=0.40, P<0.01); (2) The estimation accuracy of LUE is better when high NDVI and MSR are observed because of larger canopy density and higher LAI; (3) Multivariate regression model between LUE and PRI constructed by NDVI, EVI and MSR improves the estimation accuracy of LUE by 18.14% in the observation period, and 54% from January to May. The LUE estimation method modified by the canopy structure can improve assessment of LUE in the light-use efficiency model, and the ability of remote sensing to accurately assess subtropical evergreen forest productivity.

Key words: light-use efficiency, spectral observation, canopy structure, subtropical evergreen forest, light-use efficiency, spectral observation