Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26506
Title: Modeling of Chlorophyll-A concentration for the coastal waters of Hong Kong
Authors: Nazeer, M
Nichol, JE 
Keywords: Chlorophyll-a
Coastal water
Fuzzy clustering
HJ-1 A/B
Landsat
Neural network
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2015 Joint Urban Remote Sensing Event, JURSE 2015, 2015, 7120460 How to cite?
Abstract: In coastal waters, accurate remote sensing retrieval of Chlorophyll-A (Chl-a) is challenging. In a spatially complex urban coastal region like Hong Kong, the development of a single Chl-A estimation algorithm over whole region is unrealistic. In such case the best strategy will be to develop an individual algorithm for each water type to precisely estimate Chl-A concentration. Therefore, to define the effective water zones in the region, Fuzzy c-Means (FCM) clustering was applied to surface reflectance derived from the first four bands of the Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) and HJ-1 A/B Charge Couple Device (CCD) sensors for 76 Hong Kong Environmental Protection Department (EPD) water monitoring stations. The FCM clustering results suggested the existence of five optically different water types in the region. Cluster specific algorithms were then developed for the retrieval of Chl-A concentrations using Neural Network (NN) and Regression Modeling (RM) techniques. Twenty seven Landsat TM/ETM+ (January 2000-December 2012) and thirty HJ-1 A/B CCD (September 2008-December 2012) cloud free images paired with in situ Chl-A data were used for development and validation of the NNs and RMs. The performance of the cluster specific NNs and RMs suggested that NN can efficiently estimate and map Chl-A concentrations with greater confidence as compared to band ratio algorithms developed using regression modeling. Overall, the validation results showed a correlation of 0.63 to 0.85 between the NN estimated and in situ measured Chl-A concentrations compared to a correlation of 0.26 to 0.54 between the RM estimated and in situ measured Chl-A concentrations.
Description: 2015 Joint Urban Remote Sensing Event, JURSE 2015, 30 March-1 April 2015
URI: http://hdl.handle.net/10397/26506
ISBN: 9781480000000
DOI: 10.1109/JURSE.2015.7120460
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