Remote Sensing Recognition And Classification Of Urban Black And Odourous Water Body Based On PlanetScope Images

Urban black and odorous water body is essentially caused by the imbalance development between environment protection and city construction,and it has been one of the focuses of ecological environmental protection work in China these years.There have been many achievements about formation mechanism and ecological restoration technology of black and odorous water bodies,while the monitoring method still rely on field work,which is high-cost and time consuming.Moreover,the monitoring results of water quality by manpower likely tend to inaccuracy and it is hard to show a comprehensive water condition.This paper focuses on monitoring and evaluation methodology of urban black and odorous water body based on remote sensing technology,which has advantages of monitoring on a large scale,low-cost and high-frequency.This paper takes advantages of remote sensing technology to realize dynamic monitoring and fully analysis of spatial and temporal change of urban black and odorous water body,which makes significance for the renovation of urban black and odorous water.In this paper,4 arithmetic of recognizing black and odorous water body are proposed which are NIR reflectance threshold method,HI,EHI,NDBWI and saturation based on CIE based on field work samples,in Qinzhou,and PlanetScope image.In order to quantify the process of the variation of black-odorous water quality,a comprehensive water quality evaluation method that is generated from an improved Nemerow index.This modified evaluation method makes the results of black-odorous rank be continuous,that is mean there is a difference among same black-odorous rank.Moreover,a correlation analysis and multivariate linear regression model between remote sensing reflectance and water quality index is conducted,and the regression model is used to analyze the characteristics of the spatial and temporal distribution of black and odorous water body in Qinzhou.The major conclusions of this paper are as follow:(1)The similarity of normal water and black-odorous water body is low reflectance in the blue,green and red bands(455 nm-670 nm).The diversity is that the slope of the spectral curve in 455 nm-670 nm bands of the general water body is higher than black-odorous water.The high peak reflectance of normal water shows at red band and goes down in the range of red and NIR bands.The spectrum of black-odorous water appears a high peak at red band and goes up in the range of red and NIR bands.(2)Five arithmetic of recognizing black and odorous water body are proposed based on field work samples and PlanetScope image,which are NIR reflectance threshold method,H,EH,NDBWI and saturation based on CIE.The recognition results show that the NIR reflectance threshold method has low recognition accuracy and large deviation,and the HI index recognition accuracy is 57.14%.The recognition accuracy of EHI index and saturation method for black odor water are 78.57%.The NDBWI index has the highest recognition accuracy rate of over 90%.(3)According to the classification of the black and odor level of the water body in the Guide to Remediation of Black and Odorous Waters,the commonly used comprehensive water quality evaluation method,the Nemerow index method,is revised to achieve continuous evaluation of water quality result and quantify the black and odorous in the same level.The black odorous water body in the main district of Qinzhou has different black odor levels in the same river channel.Due to the influence of the commercial area,residential area and food city on the bank side,the phenomenon of partial black odor is very common.(4)Based on the correlation analysis between black odor water evaluation index and remote sensing reflectivity,the improved Nemerow black odor index is used as the dependent variable,and the remote sensing reflectivity is used to establish multiple linear regression equations.Six kinds of correlations with significant correlation are proposed.The equation is analyzed and evaluated by the measured data.The results show that the simulation results of model 6(the independent variable is the combination of the first three bands)are more in line with the actual situation,and the deviation of the fitting effect is smaller.