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基于高光谱遥感特征参数的樟树幼林叶绿素a含量估算模型 总被引：2，自引：0，他引：2
以ASD公司生产的手持式野外光谱辐射仪测量樟树幼林的冠层光谱,同时对观测叶片进行叶绿素a含量的测定。利用统计相关分析法,分析了高光谱遥感特征参数与叶绿素a含量的相关关系,在此基础上建立了高光谱遥感特征参数的樟树幼林叶绿素a含量估算模型,并对模型进行了精度检验。结果表明:樟树幼林叶绿素a含量与Db、Rg、Rg/Ro、(Rg-Ro)/(Rg Ro)之间的相关系数达到了0.01极显著性检验水平;建立它们与叶绿素a含量的估算模型,通过精度检验,选择出最适合叶绿素a含量估算的高光谱模型:y=exp[1.027 (-348.942)×Db]。 相似文献
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基于BP神经网络的湖泊水体富营养化的短期预测 总被引：1，自引：0，他引：1
选用含三层网络结构的BP神经网络来模拟湖泊水体的富营养化状况,通过软件MATLAB编写了BP神经网络训练和测试程序,测试结果表明此方法成功预测水体中叶绿素a的含量的短期变化趋势。 相似文献
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基于实测大豆冠层高光谱及叶绿素a数据,利用植被指数和三波段方法建立大豆叶绿素a的高光谱反演模型. 通过IDL（interactive data language）实现NDVI和RVI波段的重新选择,提高了基于2种植被指数的模型反演精度. 比较而言,三波段方法建模反演大豆叶绿素a含量的精度较改良后植被指数的更高（R2=0.81）. 研究结果表明,利用波段重新组合的植被指数建立的估算模型可以提高大豆叶绿素a的估算精度;三波段模型法可以筛选更好的波段来构建模型,并在一定程度上提高大豆叶绿素a反演精度. 相似文献
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In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression (PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow. The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October. 相似文献
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