NDLI: The possibility on estimation of concentration of heavy metals in coastal waters from remote sensing data

Content Provider	IEEE Xplore Digital Library
Author	Chuqun Chen Fenfen Liu Quanjun He Heyin Shi
Copyright Year	2010
Description	Author affiliation: LED, South China Sea Institute of Oceanography, Chinese Academy of Sciences, Guangzhou 510300, China (Chuqun Chen; Fenfen Liu; Quanjun He) \|\| Centre for Monitoring Marine Resources and Environment, Guangzhou, 510235, China (Heyin Shi)
Abstract	The heavy metals in waters cannot be decomposed but can be transferred and accumulated with food chains. Many heavy metals are toxic to human beings. It is very important to measure concentration of heavy metals in coastal waters for water quality research, monitoring, and environmental management. On consideration of geochemistry behavior of heavy metals, their distribution is related with water components which determined waters' optical properties. The in-situ remote sensing reflectance data and heavy metal concentration data at 48 sampling points collected from three cruises in the Pearl River estuary were analysed. For single band among all the 57 bands ranging from 365 to 935 nm, the band centered at 711 nm (B711) has highest correlation coefficient (R=0.51) with concentration of both Cu and Zn. The band ratio, B711/ B406 has the highest correlation coefficient with Cu (R=0.749), and band ration, B711/ B416 has the highest correlation coeficient with Zn (R=0.804). The band and band ratio were employed for algorithm development using the symbolic regression method, and the results showed the possiblity to retrieve concentration of heavy metal from remotely-sensed data.
Starting Page	4216
Ending Page	4219
File Size	254297
Page Count	4
File Format	PDF
ISBN	9781424495658
ISSN	21537003
e-ISBN	9781424495665
e-ISBN	9781424495641
DOI	10.1109/IGARSS.2010.5648845
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Rivers Copper Zinc Sea measurements Lead Pearl River estuary Heavy metals Remote sensing reflectance of water Symbolic regression
Content Type	Text
Resource Type	Article

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