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

Xiaoling Chen, Yok Sheung Li, Zhigang Liu, Zhilin Li, Wing Hong Onyx Wai, Bruce King

Research output: Journal article publicationConference articleAcademic researchpeer-review

6 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)589-599
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4892
DOIs
Publication statusPublished - 8 Aug 2003
EventOcean Remote Sensing and Applications - Hangzhou, China
Duration: 24 Oct 200226 Oct 2002

Keywords

  • In situ measurements
  • Landsat TM
  • Methods of maximum likelihood
  • Neural network
  • Ocean color classification
  • Pearl River estuary
  • SeaWiFS
  • Support vector machine
  • Water quality

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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