Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong

Chen, Xiaoling; Li, Yok Shueng; Liu, Zhigang; Yin, Kedong; Li, Zhilin; Wai, Onyx W. H.; King, B. V.

doi:10.1016/j.csr.2004.06.010

Cited by 63 publications

(31 citation statements)

References 39 publications

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“…The changes of river water discharge are mostly related to rainfall in this region [33]. During the wet season, the discharge of the Pearl River accounts for 80% of the yearly total [30], and the discharges of major tributaries (West River, North River, and East River) are much higher than those in dry season [48]. As the increase of rainfall and river flow from upstream, a large number of pollutants from farmland and street were carried into rivers.…”

Section: Water Quality Variation and Correlation Of Water Quality Parmentioning

confidence: 99%

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Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Fan

Cui

Zhang

et al. 2012

CLEAN Soil Air Water

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In the Pearl River Delta (PRD), river water quality deteriorates continually due to the population increase and ongoing industrialization and urbanization. In this study, a water quality management paradigm based on the seasonal variation is proposed. For better exploring the seasonal change of water quality, wavelet analysis was used to analyze the division of dry and wet seasons in the PRD during 1952PRD during -2009. Then water quality seasonal variation in 2008 and relevant impact factors were analyzed by multivariate statistic methods as a case to make some management measures. The results show that there are some differences of dry and wet seasons division among different years. Wet season mainly appear from April to September, which occupy the largest proportion among the 58 years (about 70%) and then followed by the wet season from May to October (about 13.8% of the total years). As to the water quality of 2008, significant differences exist between dry and wet seasons for 17 water quality parameters except TP, NO À 3 , Fe 2þ , and Zn 2þ . Levels of parameters pH, EC, COD Mn , BOD 5 , NH þ 4 , SO 2À 4 , and Cl À in dry season are much higher than those in wet season. In dry season the variations of river water quality are mainly influenced by domestic sewage, industrial effluents, and salt water intrusion. While in wet season, except the aforementioned pollution sources, drainages from cultivated land and livestock farm are also the main factors influencing water pollution. Thus, water quality management measures are proposed in dry and wet seasons, respectively. The results obtained from this study would further facilitate water quality protection and water resources management in the PRD.

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Section: Water Quality Variation and Correlation Of Water Quality Parmentioning

confidence: 99%

“…Most of the studies have been conducted on water quality evaluation and related pollution factors [18,[30][31][32]. Unfortunately, studies on water quality improvement based on seasonal variations are comparatively rare.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Fan

Cui

Zhang

et al. 2012

CLEAN Soil Air Water

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“…As one of the important parameters of marine ecology, the chlorophyll-a (Chl-a) concentration has been used as an indicator of phytoplankton abundance, a marker for bioturbation and carbon flux, and a crucial variable for estimating the carbon budget (Boon and Duineveld 1998). This parameter is also important for understanding the ecological system and global environmental change (Venrick et al 1987;Liu et al 2002;Chen et al 2004;Peñaflor et al 2007). The necessity for studying the spatio-temporal variations in Chl-a concentration across the SCS is further intensified because of the seriously deteriorated marine environment.…”

Section: Introductionmentioning

confidence: 99%

“…Satellite observed ocean color imagery has been widely used to retrieve the Chl-a concentration (Tang et al 1998;Chen et al 2004;Peñaflor et al 2007;Siegel et al 2013;Tilstone et al 2013). A variety of remote sensing data, including Coastal Zone Color Scanner (CZCS), Sea-viewing Wide Field of View Sensor (SeaWiFS), and Advanced Very High Resolution Radiometer (AVHRR), have been utilized to investigate the SCS marine biological processes and environment (Tang et al 1998(Tang et al , 1999Liu et al 2002;Tseng et al 2005;Siegel et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Surface Chlorophyll-a Concentration Spatio-Temporal Variations in the Northern South China Sea Detected Using MODIS Data

Fang¹,

Ju²,

Liu³

et al. 2015

Terr. Atmos. Ocean. Sci.

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This study examines the spatio-temporal variations in the sea-surface chlorophyll-a (Chl-a) concentration and their associated underlying driving forces in the northern South China Sea (SCS) from 2004 -2010. A stratified analysis method and the Moderate Resolution Imaging Spectroradiometer (MODIS) Chl-a product, the National Centers for Environmental Prediction (NCEP) reanalysis data, with maps of monsoon winds and current systems were used in this work. Validation indicated that the MODIS-derived Chl-a is in good agreement with in-situ measurements (R 2 = 0.815), accurate enough for analyzing the temporal-spatial variations of Chl-a in the northern SCS. The results show that the spatial heterogeneity of the Chl-a concentration was lowest in winter and increased in spring. The seasonal Chl-a concentration dynamics varied spatially and depended predominantly on the distance from the coastline. The most important factors that affect the Chl-a concentration spatial variability were identified as the monsoon, surface current and sea surface temperature (SST).

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“…The related research has used supervised classifiers, such as decision trees, maximum likelihood, supervised neural networks, or support vector machines [19,30] and unsupervised classification, based on K-means or fuzzy c-means (FCM) clustering [27,[31][32][33][34], hierarchical algorithms [11,25], unsupervised neural networks [35], or eigenvector classifiers using variance analysis [2,36]. Unsupervised classification is based on the data alone and characterized by less human interference than supervised classification.…”

Section: Introductionmentioning

confidence: 99%

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

Shen

Zhang

et al. 2015

Remote Sensing

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Determining the dominant optically active substances in water bodies via classification can improve the accuracy of bio-optical and water quality parameters estimated by remote sensing. This study provides four robust centroid sets from in situ remote sensing reflectance (Rrs (λ)) data presenting typical optical types obtained by plugging different similarity measures into fuzzy c-means (FCM) clustering. Four typical types of waters were studied: (1) highly mixed eutrophic waters, with the proportion of absorption of colored dissolved organic matter (CDOM), phytoplankton, and non-living particulate matter at approximately 20%, 20%, and 60% respectively; (2) CDOM-dominated relatively clear waters, with approximately 45% by proportion of CDOM absorption; (3) nonliving solids-dominated waters, with approximately 88% by proportion of absorption of nonliving particulate matter; and (4) cyanobacteria-composed scum. We also simulated spectra from seven ocean color satellite sensors to assess their classification ability. OPEN ACCESSRemote Sens. 2015, 7 14732POLarization and Directionality of the Earth's Reflectances (POLDER), Sentinel-2A, and MEdium Resolution Imaging Spectrometer (MERIS) were found to perform better than the rest. Further, a classification tree for MERIS, in which the characteristics of Rrs (709)/Rrs (681), Rrs (560)/Rrs (709), Rrs (560)/Rrs (620), and Rrs (709)/Rrs (761) are integrated, is also proposed in this paper. The overall accuracy and Kappa coefficient of the proposed classification tree are 76.2% and 0.632, respectively.

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Integration of multi-source data for water quality classification in the Pearl River estuary and its adjacent coastal waters of Hong Kong

Cited by 63 publications

References 39 publications

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Surface Chlorophyll-a Concentration Spatio-Temporal Variations in the Northern South China Sea Detected Using MODIS Data

Classification of Several Optically Complex Waters in China Using in Situ Remote Sensing Reflectance

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