Retrieving seawater turbidity from Landsat TM data by regressions and an artificial neural network

Gan, Thian Yew; Kalinga, Oscar Anthony; Ohgushi, Koichiro; Araki, Hiroyuki

doi:10.1080/01431160410001655921

Cited by 17 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These attempts require a number of water sampling from a set of conveniently distributed locations which are time consuming and require a lot of field and laboratory efforts in addition to budget restriction problems. At this point, it can be mentioned that remote sensing has an important and effective role in water quality management, processing time reduction and requirement of several ground measurements (Gan et al 2004; satellites provide both spatial and temporal information needed to understand changes in water quality parameters necessary for developing better management practices to improve water quality (Ellis 1999;Jensen 2000).…”

Section: Introductionmentioning

confidence: 98%

Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in Istanbul, Turkey

Ekercin

2007

Water Air Soil Pollut

View full text Add to dashboard Cite

This paper presents an application of high resolution satellite remote sensing data for mapping water quality in the Goldon Horn, Istanbul. It is an applied research emphasizing the present water quality conditions in this region for water quality parameters; secchi disc depth (SDD), chlorophyl-a (chl-a) and total suspended sediment (TSS) concentration. The study also examines the retrievals of these parameters through high resolution IKONOS multispectral data supported by in situ measurements. Image processing procedure involving radiometric correction is carried out for conversion from digital numbers (DNs) to spectral radiance to correlate water quality parameters and satellite data by using multiple regression technique. The retrieved and verified results show that the measured and estimated values of water quality parameters in good agreement (R 2 >0.97). The spatial distribution maps are developed by using multiple regression algorithm belonging to water quality parameters. These maps present apparent spatial variations of selected parameters and inform the decision makers of water quality variations in a large water region in the Istanbul metropolitan area.

show abstract

Section: Introductionmentioning

confidence: 98%

Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in Istanbul, Turkey

Ekercin

2007

Water Air Soil Pollut

View full text Add to dashboard Cite

show abstract

“…Optical remote sensing has been employed successfully to map water turbidity (e.g., Fraser, 1998;Gan et al, 2004;Vignolo et al, 2006). However, it is difficult to combine SAR with optical remote sensing simultaneously, because the overpass time of the platforms carrying these sensors is not synchronous.…”

Section: Introductionmentioning

confidence: 99%

Performance of Landsat TM in ship detection in turbid waters

Wu¹,

Leeuw²,

Skidmore³

et al. 2009

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

“…Furthermore, in order to confirm the accuracy of the re-determined Sváb et al (2005) Lake Balaton, Hungary. 596,0 10 60 Gan et al (2004) Ariake Sea, Japan 1700,0 33 52 Zhang et al (2003) Gulf of Finland 29600,0 53 559 Brivio et al (2001) Lake Garda 368 5 74 Giardino et al (2001) Lake …”

Section: Introductionmentioning

confidence: 99%

“…Although traditional water quality monitoring methods can be precise, they are usually costly and time consuming (Östlund et al 2001;Wang and Ma 2001;Gan et al 2004), but above all, it is not possible to obtain from them the spatial overview required for global assessment and monitoring of water quality . Therefore, in order to monitor the environmental state and pollution of such bodies of water, a viable and economical approach must be based on satellite data (Gan et al 2004;Ekercin 2007). Owing to their potential for mapping, modelling and quantifying quality parameters of water bodies, extensive use has been made of remote sensing techniques (Wang and Ma 2001;Liu et al 2003;Sváb et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Selection of appropriate sampling stations in a lake through mapping

Karabörk

2009

Environ Monit Assess

View full text Add to dashboard Cite

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.

show abstract

Retrieving seawater turbidity from Landsat TM data by regressions and an artificial neural network

Cited by 17 publications

References 0 publications

Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in Istanbul, Turkey

Water Quality Retrievals from High Resolution Ikonos Multispectral Imagery: A Case Study in Istanbul, Turkey

Performance of Landsat TM in ship detection in turbid waters

Selection of appropriate sampling stations in a lake through mapping

Contact Info

Product

Resources

About