Remote Sensing Monitoring of Black Odorous Water Body in Langfang Based on GEE
This paper uses multi-source remote sensing data and random forest classifier in the GEE,Google Earth Engine,platform to extract black odorous water bodies in Langfang City.The results show that the red,green and blue bands in the optical remote sensing data can accurately reflect the water color information,and the radar back scattering data can reflect the geometric information of the sewage surface to a certain extent.Multi-source remote sensing features can help extracting black odorous water information more accurately.Furthermore,the training accuracy of the random forest classifier reaches 96.7％,and the classification accuracy reaches 93.7％,which can basically meet the requirements for monitoring urban black odorous water bodies.Besides,classification errors will occur on the shores of non-black odorous water bodies,dry areas,bridges,etc.These classification errors are caused by the overlap in the feature space of multi-source remote sensing data of black odorous water bodies and non-black odorous water bodies.The number of large sample points and the selection of more feature attributes will help to improve the classification accuracy of this area.Hopefully,free data and algorithms can provide practical solutions for environmental protection departments to regularly monitor black odorous water bodies in urban areas at low cost.
GEELangfangblack and odorous water bodyremote sensingmonitoring
NETL
NSTL
万方数据
