冠层结构对亚热带常绿林光能利用效率估算的影响

利用遥感方法可以使用光化学植被指数(PRI)在叶片尺度表征光能利用效率(LUE)的动态变化,但在冠层尺度上,森林植被冠层结构是影响LUE估算精度的关键因素之一。利用2014-2015年中国科学院广东省鼎湖山森林生态试验站自动多角度高光谱观测系统的光谱反射数据,分别计算常绿阔叶林PRI、归一化植被指数(NDVI)、增强型植被指数(EVI)和优化比值植被指数(MSR)。基于通量观测计算的LUE,分析不同表征冠层结构的植被指数对于LUE与PRI拟合精度的影响,并利用不同类型植被指数的组合,构建多元线性回归模型。研究结果表明:(1)亚热带常绿阔叶林冠层结构型植被指数与冠层尺度PRI具有显著的相关性,其中MSR与PRI相关性较为显著(R^2=0.40,P<0.01);(2)在植被冠层密度较大、LAI较高(即高NDVI和MSR)时,PRI对于表征LUE的动态变化更具优势;(3)利用NDVI、EVI、MSR和PRI所构建的估算LUE的多元回归模型,能将LUE估算精度提高18.14%,对于冠层结构变化活跃期(1-5月),能将LUE估算精度提高54%。研究认为利用冠层结构参数能够进一步改进PRI对LUE的估算精度,提升遥感精确评估亚热带常绿林生产力的能力。

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

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.