Remote sensing, ecological water quality modelling and <i>in situ</i> measurements: a case study in shallow lakes

Dekker, A.G.; Zamurović-Nenad, Ž.; Hoogenboom, H.J.; Peters, Steef

doi:10.1080/02626669609491524

Cited by 67 publications

(49 citation statements)

References 9 publications

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“…New calibration methods, such as the use of spectral mixture analysis, combined with the measured spectral reflectance of a range of water±sediment mixtures, the so-called endmembers (Mertes et al, 1993), could improve the accuracy. A similar approach could be the complementary use of optical models and calibration data (Dekker et al, 1996 It has been noted that the fall velocities measured with the VIS are an order of magnitude higher than the velocities which are normally used for such a sediment transport model. This could be explained by the following facts.…”

Section: Conclusion and Discussionmentioning

confidence: 96%

A two-dimensional model for suspended sediment transport in the southern branch of the Rhine-Meuse estuary, The Netherlands

Wijngaarden

1999

Earth Surf. Process. Landforms

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For the southern branch of the Rhine±Meuse estuary, The Netherlands, a two-dimensional horizontal suspended sediment transport model was constructed in order to evaluate the complicated water quality management of the area. The data needed to calibrate the model were collected during a special field survey at high river runoff utilizing a number of techniques: (1) turbidity probes were used to obtain suspended sediment concentration profiles; (2) air-borne remote sensing video recordings were applied in order to obtain information concerning the spatial distribution of the suspended sediment concentration; (3) an acoustic probe (ISAC) was used to measure cohesive bed density profiles and (4) an in situ underwater video camera (VIS) was deployed to collect video recordings of the suspended sediment. These VIS data were finally processed to fall velocity and diameter distributions and were mainly used to improve insight into the relevant transport processes, indicating significant erosion of sand from the upstream Rhine branch. For quantitative calibration of the model, the data from the turbidity profiles were used. Sedimentation and erosion were modelled according to Krone and Partheniades. The model results showed a good overall fit to the measurements, with a mean absolute error of 18 per cent (standard fault = 1 per cent), corresponding to concentrations of about 0Á020 (upstream) to 0Á005 kg m À3 (downstream). The overall correlation between observed and simulated suspended sediment concentrations was 0Á85. The remote sensing video recordings were used for a qualitative calibration of the model. The distribution pattern of the suspended sediment on these photos was reproduced quite well by the model. However, a more accurate calibration technique is needed to enable the use of aerial remote sensing as a quantitative calibration method.

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Section: Conclusion and Discussionmentioning

confidence: 96%

A two-dimensional model for suspended sediment transport in the southern branch of the Rhine-Meuse estuary, The Netherlands

Wijngaarden

1999

Earth Surf. Process. Landforms

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“…일반적으로, 총질소와 총인은 용해성 물질로서 광학적 특 성이 뚜렷하지 않은 것으로 알려져 있다 (Dekker, 1996). 이러한 이유로 인공위성을 이용한 해외 및 국내 연구들에 서 총질소와 총인에 대한 연구는 타 수질인자들(클로로 필, 부유물질, 탁도 등)에 비해 활발히 이뤄지지 않은 실정 이다.…”

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Estimation of Water Quality using Landsat 8 Images for Geum-river, Korea

Lim¹,

Baik²,

Kim³

et al. 2015

J. Korea Water Resour. Assoc.

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In this study, the water quality parameters of Geum-river were estimated using Landsat 8 satellite image data which had launched in March 2013. The goal of this research is to predict HAB and to monitor spatial pattern of total nitrogen (TN) and total phosphorus (TP) because both TN and TP are the dominant factors of the growth of harmful algal blooms (HABs). To investigate the relationship between satellite band reflectance and in situ measurement value, Pearson' correlation coefficient analysis was used. The band2, 3, 4 and 5 reflectance values among 11 bands of Landsat 8 were used which was highly associated with detecting TN and TP. The 20 in situ data set with satellite's overpass time were identified. TN showed positive relation with band 2 (0.48), band3 (0.62), band4 (0.57) at a significance level of p<0.05. TP also showed high correlation for band2 (0.59), band3 (0.59), band4 (0.58) at a significance level of p<0.01.The optimal regression equation models were constructed for TN and TP based on multiple regression equations. The estimated concentration based on derived regression equations of TN and TP were compared with in situ measurement data. Finally, the spatial pattern of the two parameters was able be monitored through mapping on

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“…Time and cost constraints associated with in-situ measurements of lake water quality often limit assessment of spatial and temporal trends of water quality. Remote sensing has a potential to overcome these constraints by providing an alternative means of water quality monitoring over a greater range of temporal and spatial scales (Dekker et al, 1996). In addition, remote sensing can be used as a valuable tool to retrieve water quality information of ungauged lakes from a vast amount of archived remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Application in Lake Water Quality Estimation Using Satellite Imagery

2004

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ABSTRACT. Lake water quality monitoring using traditional water sampling and laboratory analyses is very expensive and time consuming. Application of neural networks to predict water quality using satellite imagery data has a potential to make the water quality determination process cost-effective, quick, and feasible. This paper includes an indirect method of determining the concentrations of chlorophyll-a (chl-a) and suspended matter (SM), two optically active parameters of lake water quality. Radial basis function neural (RBFN) network models are developed to predict the chl-a and SM concentrations in the lake. The low cost commercially available Landsat-TM imagery spectral information was used as the input with chl-a or SM concentrations as output. The model is trained and validated with data from the years 2001, 2002, 2003, and 2004. The model testing resulted in a coefficient of determination (R 2 ) of 0.55, and 0.90, respectively, for actual and predicted chl-a and SM concentrations. The root mean square error (RMSE), standard error of prediction (SEP), and average testing accuracy indicated the merit of the developed models.

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Remote sensing, ecological water quality modelling and in situ measurements: a case study in shallow lakes

Cited by 67 publications

References 9 publications

A two-dimensional model for suspended sediment transport in the southern branch of the Rhine-Meuse estuary, The Netherlands

A two-dimensional model for suspended sediment transport in the southern branch of the Rhine-Meuse estuary, The Netherlands

Estimation of Water Quality using Landsat 8 Images for Geum-river, Korea

Artificial Neural Networks Application in Lake Water Quality Estimation Using Satellite Imagery

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