青海瑞香狼毒叶绿素含量高光谱预测模型  

Hyperspectal predicting model of chlorophyll content of Stellera chamaejasme in Qinghai Province

作　　者：凯楠 刘咏梅[1] 李京忠[1,2] 常伟[1] 谢小燕[1] 

机构地区：[1]西北大学城市与环境学院,西安710127 [2]许昌学院城乡规划与园林学院,河南许昌461000

出　　处：《生态学杂志》2017年第4期1150-1157,共8页Chinese Journal of Ecology

基　　金：农业部公益性行业科研专项(201203062);国家自然科学基金项目(41171225)资助

摘　　要：叶绿素含量是植物生长状况的重要指标,狼毒叶绿素含量预测可为狼毒长势监测及危害防控提供科学依据。本文选取青海省兴海县瑞香狼毒分布的典型退化草甸,利用全光谱的偏最小二乘法(PLS)、基于连续投影算法的多元线性回归法(SPA-MLR)、基于连续投影算法的偏最小二乘法(SPA-PLS)、红边参数以及植被指数共5种方法对狼毒叶片SPAD值进行预测和对比分析,构建青海省瑞香狼毒叶绿素含量的最优预测模型。结果表明:利用SPA算法筛选出9个特征波长建立的PLS模型对狼毒SPAD值的预测结果最好,预测相关系数为0.778,预测均方根误差为1.895;与全光谱PLS模型相比,SPA-PLS模型明显减少计算量,提高了建模效率;与SPA-MLR模型相比,SPA-PLS模型有效解决了变量之间的共线问题,显著提高了预测精度,是狼毒叶绿素含量的最佳预测模型;基于红边参数和植被指数建立的预测模型中,MCARI指数构建的模型对狼毒SPAD值的预测精度最高,预测相关系数为0.808,预测均方根误差为1.969,可作为反演狼毒叶绿素含量的最优植被指数。Chlorophyll content is an important indicator of plant growth. The chlorophyll content of Stellera chamaejasme can provide a basis for both monitoring the growth and controlling the hazard of S. chamaejasme. A typical degraded meadow, which was dominated by S. chamaejasme in Xinghai County, Qinghai Province, was chosen for the experiment. Five methods were adopted to predict, contrast and analyze the SPAD values so as to construct the optimal prediction model of the chlorophyll content of S. chamaejasme in Qinghai Province, which included partial least squares （PLS） in the whole wavelength region of 400-1000 nm, multiple linear regression （MLR） and PLS based on successive projections algorithm （SPA）, the red edge parameters and vegetation index. Results indicated that the optimal prediction performance was achieved by SPA-PLS model that was established by 9 characteristic wavelengths with SPA algorithm, and the correlation coefficient was predicted as 0.778, while the root mean square error was 1.895. Compared with the PLS model built on the full spectrum, the SPA-PLS model significantly reduced the computational complexity and improved the modeling efficiency. Compared with the SPA-MLR model, SPA-PLS model effectively solved the collinear problem among variables and also improved the forecasting accuracy, thus, it was the best model for predicting chlorophyll content of S. chamaejasme. Among predicting models built on the red edge parameters and vegetation index, a model constructed by MCARI index possessed the highest predicting accuracy with a correlation coefficient of 0.808 and a root mean square error of 1.969. Consequently, it could be the optimal vegetation index for inversing chlorophyll content of S. chamaejasme.

关 键 词：狼毒 叶绿素含量 SPAD 回归模型 光谱参量 

分 类 号：Q945[生物学—植物学]