Deriving Sediment Information for Lake Athabasca Using Principal Components Analysis of Landsat Data

Lodwick, G.D.; Harrington, R.

doi:10.1080/07038992.1985.10855074

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…Recently several linear models have been developed (Carpenter and Carpenter 1983, Khorram and Cheshire 1985, Rimmer et al 1987, Ritchie et al 1990, Mertes et al 1993 ). Munday and Alfoldi (1979 ) suggested a non-linear model and Lodwick and Harrington (1985 ) applied principal component analysis to assess the sediment distribution pattern in Lake Athabasca.…”

Section: Introductionmentioning

confidence: 99%

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

Choubey¹

1998

International Journal of Remote Sensing

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Section: Introductionmentioning

confidence: 99%

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

Choubey¹

1998

International Journal of Remote Sensing

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“…In this mode of operation each line of the image is electronically scanned by a linear array of detectors (charge-coupled devices) located in the focal plane of the optical system, and successive lines of the image are produced by satellite movement.This method maximizes the exposure time for each ground point and ensures photogrammetric quality and geometric fidelity along the line-scan axis. Each detector array provides data in a single spectral band and the IRS-1A imaging system has four spectral bands in the visible and the infrared regions (1RS Data User Handbook, 1986 The land and water interface was identified and the land masked (Lodwick & Harrington, 1985) so that only water would be analysed. Digital data for each spectral band were extracted from the CCT by locating the sample point coordinate on a CCT generated grey level map, then averaging 9 pixels (3x3 array) centred around the coordinate, which corresponds to a 217.5 x 217.5 m square area, encompassing each of the 47 sampling locations.…”

Section: (C) Irs-1a-liss-i Datamentioning

confidence: 99%

“…Satellite-borne sensors can provide repetitive, low cost multispectral coverage over wide areas and have the potential to monitor water quality. Many investigators have successfully used Landsat, Skylab and Coastal Zone Color Scanner data for water quality monitoring in reservoirs and estuaries (Khorram & Cheshire, 1985;Amos & Topliss, 1985;Lodwick & Harrington, 1985;Ritchie & Cooper, 1988). Carpenter & Carpenter (1983) and Khorram & Cheshire (1985) suggested that shorter wavelength bands of the visible region are useful for the monitoring of water quality in reservoirs and estuaries.…”

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confidence: 98%

Correlation of turbidity with Indian Remote Sensing Satellite-1A data

Choubey

1992

Hydrological Sciences Journal

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Indian Remote Sensing Satellite-1A Linear Imaging Self Scanning System (IRS-1A-LISS-I) multispectral digital data acquired over the Tawa reservoir region have been analysed to evaluate the turbidity in the surface water. Tawa reservoir water samples were collected on 20 October 1988 concurrent with an IRS-1A overpass. The turbidity was measured at each sampling point in the field with a nephelometer. Reservoir water samples were analysed for total suspended matter, grain size and mineralogy. IRS-1A-LISS-I computer-compatible tapes were obtained from the National Remote Sensing Agency (NRSA), India. The relationship between the LISS-I digital data of 20 October 1988 and the measured values of turbidity was established. The results indicate that, in the turbidity range between 15-45 NTU (nephelometric turbidity units), a positive relationship exists between the turbidity and the visible wavelength band 1, 2 and 3 (0.45 to 0.68 fxm) mean pixel values. Shorter wavelengths, especially in band 3 (0.62-0.68 /un), are more useful than longer wavelengths (near infrared) in quantifying turbidity. The concentration, mineralogy and grain size of the suspended matters are the main factors which influence the reflected radiance at low concentration levels (15-45 NTU) of turbidity. There is a positive correlation between reflectance, turbidity and suspended sediment concentration. It can be concluded that, as turbidity increases in the 15-45 NTU range, the spectral response increases. It appears that IRS-1A-LISS-I data could be developed as a practical tool in water quality monitoring. La corrélation de la turbidité avec les données de télédétection fournis par le satellite Indien IRS-1A Résumé Les échantillons d'eau, correspondant aux données fournis par IRS-1A-LISS-I multispectral digital, de la région du réservoir de Tawa ont été analysées pour évaluer la turbidité de l'eau de surface. Les échantillons on été recueillis le 20 Octobre 1988 pendant que IRS-1A passait sur la région de Tawa. La turbidité était mesurée sur les lieux à chaque point d'échantillonnage par le néphélémètre. Les échantillons de l'eau du réservoir étaient analysées pour la matière en suspension, la granulométrie et la minéralogie. Les bandes de IRS-1 A-LISS-I compatibles avec l'ordinateur étaient obtenues de NSRA d'Inde. On a établi le relation entre les données de LISS-I du 20 Octobre et la valeur mesurée de la turbidité. Les résultats indiquent que la turbidité varie de 15 à 45 NTU et qu'une relation existe entre la turbidité et la bande visible 1, 2 et 3 (de 0.45 à 0.68 /xm) de valeurs moyennes pixel. Les ondes courtes, spécialement la bande 3 (de 0.62 à 0.68 /im), sont plus utiles que les plus grandes longueurs d'onde (prés de l'infrarouge) pour quantifier la turbidité. La concentration, la minéralogie, la granulométrie des matières en suspension, toutes les trois Open for discussion until I October 1992 Hydrological Sciences Journal 1992.37:129-140. 130 V. K. Choubey sont les facteurs principaux qui ont une forte influence sur la radiance réfléc...

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Monitoring turbidity with IRS-1A data

Choubey

1997

Hydrol. Process.

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Deriving Sediment Information for Lake Athabasca Using Principal Components Analysis of Landsat Data

Cited by 11 publications

References 3 publications

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

Laboratory experiment, field and remotely sensed data analysis for the assessment of suspended solids concentration and secchi depth of the reservoir surface water

Correlation of turbidity with Indian Remote Sensing Satellite-1A data

Monitoring turbidity with IRS-1A data

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