基于高光谱的辽河口盐地碱蓬生态化学计量特征反演研究

Research on Hyperspectral Data of Ecological Stoichiometric Characteristics of Suaeda salsa in Liaohe Estuary based on Machine Model

通过植物反射光谱信息反演植物体内营养元素含量可以实现快速监测湿地植物状态。以辽河口湿地盐地碱蓬(Suaeda salsa)为研究对象,基于冠层高光谱数据选择随机森林(RF)、支持向量机回归(SVR)、反向传播神经网络(BPNN)3种机器模型建模反演碳、氮、磷元素含量以及生态化学计量特征,并通过一阶微分(FD)、相关性分析提取敏感波段、主成分分析等方法提高反演模型的精度。结果表明,一阶微分处理可以明显提升高光谱信息对元素和生态化学计量特征的敏感度,对建模精度也有一定提升;测试集交叉验证结果与建模集反演结果对比得出BPNN建模时出现过度拟合现象,SVR模型在两次反演中精度最差,RF模型反演效果最为稳定、精度最高。研究结果可为湿地植物元素及生态化学计量特征反演提供依据。

The inversion of nutrient element content in plants through plant reflectance spectrum information can quickly monitor the states of wetland plants.Taking Suaeda salsa in the Liaohe Estuary Wetland as the research object,three machine models including Random Forest(RF),Support Vector Machine Regress(SVR)and Backpropagation Neural Network(BPNN)are selected based on canopy hyperspectral data to model inversing carbon,nitrogen,phosphorus element content and ecological stoichiometry,and by the First Derivative(FD),correlation analysis sensitive bands extraction method,principal component analysis to improve the accuracy of the inversion model.The results showed that the first-order differential processing can significantly improve the sensitivity of hyperspectral information to element and ecological stoichiometric characteristics,and also improve the modeling accuracy;comparing the cross-validation result of the test data with the inversion result of the train data show that over-fitting were had occurred during BPNN modeling,the SVR model has the worst accuracy in the two inversions,and the RF model has the most stable inversion effect and the highest accuracy.The research results can provide a basis for the inversion of wetland plant elements and ecological stoichiometric characteristics.