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You are here : eLibrary : IAHR World Congress Proceedings : 35th IAHR Congress - Chengdu (2013) : THEME 4 - HYDRO-ENVIRONMENT : Retrieving Water Quality from High Resolution IKONOS Multispectral Imagery by Multiple Regression an...
Retrieving Water Quality from High Resolution IKONOS Multispectral Imagery by Multiple Regression and Artificial Neural Networks in Lake Cihu, Huangshi
Author : Jiaming Liu , Yanjun Zhang, Xingyuan Song, Yang Kuang and Di Yuan
This paper presents different methodologies to estimate water quality parameters which are chemical oxygen demand (COD), ammonia nitrogen (NH -H), total nitrogen (TN) and total phosphorus 3 (TP) concentration in Lake Cihu, Huangshi, from high resolution satellite remote sensing data, based on multiple regression and artificial neural networks (ANNs). Image procedure including radiometric calibration and atmospheric correction converts digital numbers into surface reflectance. Then multiple regression and ANNs are applied to the visible and near-infrared bands of IKONOS in order to determine a relationship between the surface reflectance of the lake and the water quality parameters obtained by in situ measurements. Statistical analysis using determination coefficients and error estimation is employed, aiming to evaluate the most accurate methodology. The results show that the estimated accuracy of water quality parameters using ANNs is higher than the accuracy using multivariate regression approaches, and 2 the measured and estimated values for water quality parameters are in good consistency (R > 0.9). The spatial distribution maps of water quality parameters generated by ANNs model present apparent spatial variations and inform the decision makers of water quality variations in Lake Cihu.
File Size : 2,022,780 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 35th IAHR Congress - Chengdu (2013)
Date Published : 18/07/2016
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