Study of sediment distribution in the area of the Panuco river plume by means of remote sensing

Lira, Jorge; Morales, A.; Zamora, Francisco

doi:10.1080/014311697219349

Cited by 25 publications

(17 citation statements)

References 5 publications

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“…Three kinds of models can be summarized, analysis model, Semi-analysis model and empirical model. The empirical method including lineal, logarithmic, minus exponential relation and other algorithms, has been widely discussed (Mertes et al, 1993;Tassan, 1994;Lira et al, 1997;Mishra, 2004;Van der Lee et al, 2009;Griet et al, 2009).…”

mentioning

confidence: 98%

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Chen

Cui

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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mentioning

confidence: 98%

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Chen

Cui

et al. 2010

International Journal of Applied Earth Observation and Geoinfor

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“…Many researchers (Curran and Novo, 1988;Forget and Ouillon, 1998;Lira et al, 1997;Miller and Mckee, 2004;Binding et al, 2005) had conducted studies to determine SSC in various water bodies by remote sensing methods and developed some inversion algorithms of SSC by analyzing the relationship between SSC measured synchronously or quasisynchronously and reflectance from spectral wave bands or combinations of wave bands of different satellite images such as Thematic Mapper (TM), High Resolution Visible (HVR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-ofView Sensor (SeaWiFS). A number of researchers have worked on the methods of extracting the sediment information and established quantitative inversion models of SSC for highly turbid waters such as the Changjiang (Yangtze) River and the Zhujiang (Pearl) River estuaries in China by remote sensing technology.…”

Section: Introductionmentioning

confidence: 98%

Experimental research on quantitative inversion models of suspended sediment concentration using remote sensing technology

et al. 2007

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Research on quantitative models of suspended sediment concentration (SSC) using remote sensing technology is very important to understand the scouring and siltation variation in harbors and water channels. Based on laboratory study of the relationship between different suspended sediment concentrations and reflectance spectra measured synchronously, quantitative inversion models of SSC based on single factor, band ratio and sediment parameter were developed, which provides an effective method to retrieve the SSC from satellite images. Results show that the b 1 (430-500nm) and b 3 (670-735nm) are the optimal wavelengths for the estimation of lower SSC and the b 4 (780-835nm) is the optimal wavelength to estimate the higher SSC. Furthermore the band ratio B 2 /B 3 can be used to simulate the variation of lower SSC better and the B 4 /B 1 to estimate the higher SSC accurately. Also the inversion models developed by sediment parameters of higher and lower SSCs can get a relatively higher accuracy than the single factor and band ratio models.

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“…Several attempts to segment specific spectral classes, as open water bodies, have been reported in the literature [7] [8] [9]. Nezlin et al [10] used satellite images of SeaWiFS collected during 1997-2003 to analyze the spatio-temporal dynamics of plumes in the San Pedro Shelf (California) and identified the factors that influence the incidence and dispersal patterns of plumes.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Douro River plume dimension based on image segmentation of MERIS scenes

Teodoro¹,

Gonçalves²,

Gomes³

et al. 2008

SPIE Proceedings

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Plumes are a mixture of fresh water and river sediment load, with some dilution caused by currents. Spatial and temporal variation of the river plumes can be studied by remote sensing techniques. The main objectives of this work were modeling the Douro River Plume (DRP) dimension based on image segmentation of MERIS data and to establish a relationship between the DRP dimension and different input parameters. Two different segmentation techniques were applied (watershed and region-based) in order to estimate the DRP dimension of twenty-five MERIS scenes (from 2003 to 2005). Firstly, we considered a simple linear regression model of the DRP dimension on the water volume, considering seasonal effects (summer period and the rest of the year), where a significant correlation of 0.664 was found (watershed segmentation) ignoring summer period. The second proposed model consisted in the incorporation of several parameters (last available plume, water volume, tide height and wind speed), presumed to be related to the DRP dimension. A determination coefficient of 62.2% was found for watershed segmentation excluding the summer period, regarding the multiple linear regression branch of the second proposed model.

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Study of sediment distribution in the area of the Panuco river plume by means of remote sensing

Cited by 25 publications

References 5 publications

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Coupling remote sensing retrieval with numerical simulation for SPM study—Taking Bohai Sea in China as a case

Experimental research on quantitative inversion models of suspended sediment concentration using remote sensing technology

Estimation of the Douro River plume dimension based on image segmentation of MERIS scenes

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