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Title: Water quality management in the estuary of Pearl River and Hong Kong's coastal waters based on SeaWiFs, Landsat TM sensor data and in situ water quality sampling data
Authors: Chen, X
Li, YS
Liu, Z
Li, Z 
Wai, WHO
King, B 
Keywords: In situ measurements
Landsat TM
Methods of maximum likelihood
Neural network
Ocean color classification
Pearl River estuary
Support vector machine
Water quality
Issue Date: 2003
Publisher: SPIE-International Society for Optical Engineering
Source: Proceedings of SPIE : the International Society for Optical Engineering, 2003, v. 4892, p. 589-599 How to cite?
Journal: Proceedings of SPIE : the International Society for Optical Engineering 
Abstract: The Pearl River estuary and Hong Kong's coastal waters were selected to study the ocean color categories related to water quality. Three ocean color sensitive parameters: turbidity, suspended sediments (SS) and chlorophyll-a concentration (Chl-a), in 58 monitoring stations were selected to evaluate the water quality. A dataset with 88 samples was picked up from the monitoring stations and the successfully retrieved points of SS and Chl-a from SeaWiFS, 66 of the 88 samples were used as training data and the other 22 as testing data. The normalized difference water index was extracted from the Landsat TM image on Dec. 22, 1998 and the threshold segmentation was used to retrieve the waters from the image for further analysis. The methods of maximum likelihood, neural network and support vector machine were employed for ocean color classification of the selected Landsat TM image. Five classes of water quality could be well interpreted for all the methods. The results showed spatial variation from the west turbid waters to the east relative clear waters and suggested that the turbid waters could be well classified using Landsat TM data.
Description: Ocean Remote Sensing and Applications, Hangzhou, 24-26 October 2002
ISSN: 0277-786X
EISSN: 1996-756X
DOI: 10.1117/12.467325
Appears in Collections:Conference Paper

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