Remote Monitoring Of The Weihe River Water Quality Study Based On High Resolution Remote Sensing Images

With the development of remote sensing technology and the fine-scale of remote sensing images,remote sensing technology is used more and more in water quality research.And the requirement of remote sensing of water quality monitoring is changed from qualitative to quantitative monitoring with more exactitude.Because the optical models of the main material in large-scale water,such as oceans,lakes etc.,are relatively mature,and the research of these materal is wide,currently the studies of the quantitative remote sensing of water quality mostly make in the area of oceans,inland lakes and large rivers.But few people do quantitative remote sensing research about water quality of Weihe River based on SPOT data.Weihe River flows across the cental of Guanzhong in Shaanxi Province,including Xi'an,Baoji,Xianyang,where are the cental of political, economic,cultural,financial and information in Shaanxi Province,because of its industrial concentration,population density,agricultural development.Besides those,Weihe River is the Main River and surface water in the area.Thus it is significant to make remote sensing monitoring of water quality of Weihe River.According to the reasons above,quantitative remote sensing study of the water quality using the French SPOT-5 remote sensing image is done,based on the Weihe River in Shaanxi Province.As to the structure of this paper,we firstly systematically introduce the relative researches and methods of remote sensing monitoring of water quality.Secondly,we find perfect methods in various processes and analyze these experimental results in detail.In the paper,we focus on atmospheric radiation correction methods of remote sensing image and the applications of Support Vector Regression (SVR) in the Weihe River water quality in quantitative remote sensing based on parameter optimization.For the method of correction of atmospheric radiation,the current methods are analyzed firstly,and then we choose an appropriate method to finish SPOT-5 remote sensing atmospheric radiation image correction based on its own remote-sensing images and the experimental conditions.For the application of Support Vector Regression based on parameter optimization in the Weihe River water quality in quantitative remote sensing,after analyzing the shortcomings of the Traditional Multiple Regression,we introduce the Support Vector Machine (SVM) and its promotion,which called SVR,in the field of regression based on Statistical Learning Theory(SLT).Finally the SVR is applied to the inversion of Weihe River water quality remote sensing.The selection of SVM model and its parameters(such as penalty coefficient C,kernel function and the parameter of the kernel functionσ²,and the parameter of insensitive loss functionε) can greatly affect the accuracy of the model and there is absence of the guidance in theory.Thus we choose the Radial Basis Function as kernel function of SVR according to experimental analysis,use Cross-Validation(CV) to estimate the promote error and use Genetic Algorithm(GA) to optimize the parameters of SVR model.In the end,the SVR model we have built is used to inverse some water quality variables of Wei River in Shaanxi Province.The main works in this thesis are as follows:(1) The water quality field data of Weihe River in Shaanxi Province is analyzed and chose to get the eligible and representative water quality variables,including Permanganate Index(CODmn), ammonia(NH₃-N),Dissolved Oxygen(DO),and Chemical Oxygen Demand(COD).Besides that, we concentrate on analyzing the preprocessing of SPOT-5 remote sensing images,especially the correction of atmospheric radiation.Based on analyzing current research of it,we comprehensively use seven methods to finish the correction of atmospheric radiation in remote sensing images of this paper.And ERDAS software is used to realize the Geometric Correction of remote sensing images.(2) The role and significance of correlation analysis in water quality monitoring is clarified.We respectively analyze the relevance among the various water quality variables,among the wave bands of remote sensing data,between water quality variables and single-band remote sensing data, between the water quality variables and the wave bands combination of remote sensing data,based on which the multiple regression models of various water quality variables are built by using the method of traditional statistical multivariate regression.And finally the model is checked and analyzed.(3) The SVR theory which is based on the SLT is introduced and its chatecters are analyzed, including the construction of kernel function and the optimization method of model parameters.We use CV to estimate the promote error and use GA to optimize the parameters of SVR model.The SVR regression model which is based on GA optimization parameters is used to do the remote sensing of water quality retrieval of Weihe River in Shaanxi Province and then various remote sensing retrieval models of water quality variables are built.Compared with the Traditional Multiple Regression,SVR model which is based on GA optimization parameters can predict various water quality variables in higher accuracy.