Computation of physical characteristics of a lake system using IRS P6 (LISS-III) imagery

Sheela, A. M.; Letha, J.; Joseph, Sabu; Ramachandran, K. K.; Chacko, Manoj

doi:10.1016/j.jag.2011.09.013

Cited by 7 publications

(8 citation statements)

References 24 publications

(22 reference statements)

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“…Various lake characteristics, namely depth, temperature and turbidity, were successfully measured with this imagery (Sheela et al . ). The extent of sea level rise in a lake system was determined by predicting the concentration of sodium and chloride from satellite imagery (Sheela et al .…”

Section: Introductionmentioning

confidence: 97%

“…A trophic state index based on Secchi depth to determine the trophic status of a lake system has been ascertained successfully with IRS P6 LISS III imagery (Sheela et al 2011). Various lake characteristics, namely depth, temperature and turbidity, were successfully measured with this imagery (Sheela et al 2012). The extent of sea level rise in a lake system was determined by predicting the concentration of sodium and chloride from satellite imagery (Sheela et al 2013a).…”

Section: Introductionmentioning

confidence: 99%

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Determining the spatial variation of phosphorus in a lake system using remote sensing techniques

Moses

Janaki²,

Joseph

et al. 2014

Lakes & Reservoirs

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Lakes are versatile ecosystems, with eutrophication being a serious problem affecting their condition and trophic status. Eutrophication can lead to an over‐abundance of macrophytes in lakes, producing favourable conditions for mosquito larvae. Increased eutrophication is attributed in most to excessive phosphorus concentrations in lake water. Satellite imagery analysis now plays a prominent role for quickly assessing water quality over a large area. The present study is an attempt to illustrate the variation of phosphate and total phosphorus concentrations in Akkulam–Veli Lake, Kerala, India, using Indian Remote Sensing satellite (IRS P6‐ LISS III) imagery. A multiple regression equation derived using radiance in the red and MIR bands in the imagery was found to yield superior results for predicting the phosphate concentration, whereas a simple regression equation using radiance in red band was found to yield good results for the total phosphorus concentration in lake water. Accordingly, the trophic status of the lake system can be determined easily from satellite imagery in this manner.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Determining the spatial variation of phosphorus in a lake system using remote sensing techniques

Moses

Janaki²,

Joseph

et al. 2014

Lakes & Reservoirs

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“…; Sheela et al . ). The area of interest, the water‐spread area of AV Lake, was created using the AOI tool of ERDAS.…”

Section: Methodsmentioning

confidence: 99%

“…Sheela et al . () demonstrated the spatial variation of water depth of Akkulam–Veli Lake (AV Lake), utilizing a multiple regression equation with radiance in green and red bands ( r 2 = 0.30) of IRS P6 LISS III imagery. Because ANNs are rarely used for predicting water depth, the water depth has been measured in this study from IRS (P6‐LISS III) satellite imagery, using remote sensing techniques and ANN.…”

Section: Introductionmentioning

confidence: 99%

Lake bathymetry from Indian Remote Sensing (P6‐LISS III) satellite imagery using artificial neural network model

Moses¹,

Janaki²,

Joseph

et al. 2013

Lakes & Reservoirs

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The remote sensing technique provides a rapid and relatively inexpensive means of identifying silted areas in large water bodies, in order that desilting activities can be effectively conducted. This study developed lake bathymetry for a selected lake system (Akkulam-Veli Lake, Kerala, India) from the Indian Remote Sensing (IRS P6-LISS III) satellite imagery, using an artificial neural network (ANN) model. The water depth was measured for 17 months at different points in the lake on the same date of overpass of the IRS satellite. The satellite imageries obtained for 12 December 2007 and 16 February 2009 were identified as cloud-free images. ANN models were developed with the four input series of radiance values from green, red, NIR and MIR bands observed for the satellite imagery obtained on 12 December 2007 at the sampling sites, with actual water depth measurements also being taken on the same date. A three-layered feed forward neural network with back propagation training algorithm was developed for this study. To train the model, it was run several times by changing the number of neurons, learning rate and the momentum constants until the mean square error was minimum. When the number of neurons is increased to 35, and the logsig function is used as ANN transfer function, the error becomes minimum. To test the model, the developed ANN was run for a new set of input from the satellite imagery taken on 16 February 2009. Comparing the predicted and measured values for the same sites for the same day, it was found that the model is best suited for predicting water depth using ANN and the radiance values for four bands of IRS satellite imagery. The results of this study indicated that, for the shallow lake with lower depth, the difference between the actual and predicted value was considerable. In contrast, this was not the case where the lake water depth was greater, indicating an increased prediction accuracy with ANN with increasing depths for shallow lakes. A bathymetry map prepared with ANN indicated only the lake shoreline, as well as the shallow littoral zones. The approach used in this study requires further refinement, including further of the model based on using more field measurements to obtain a better bathymetry map.

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“…Lake water quality is generally assessed using statistical analysis. Sheela et al (2010Sheela et al ( , 2012Sheela et al ( , 2013aSheela et al ( ,b,c, 2014a provide previous studies focusing on this aspect for tropical lakes.…”

Section: Introductionmentioning

confidence: 99%

Water quality prediction capabilities of WASP model for a tropical lake system

et al. 2015

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Water quality modelling facilitates our better understanding of the processes taking place in a lake system, and conservation plans to address them. The water quality analysis simulation programme (WASP) was used in this study to predict daily variations in water quality parameters, namely dissolved oxygen, nitrate, phosphate and chlorophyll‐a and biochemical oxygen demand concentrations in a tropical lake system. The lake was divided into eight segments with the respective morphological, environmental and flow details being model inputs. The monthly concentration of each water quality parameter also comprised model input. The model output was daily spatiotemporal variation in these parameters over a period of 476 days. This study also indicated that the occurrence of precipitation plays a major role in defining the water quality of a tropical lake. The heavy precipitation after a long gap, especially during the summer season, results in a large quantity of organic matter entering the lake through drains, thereby increasing the organic matter and phosphate in the water body, and subsequently resulting in high chlorophyll‐a concentrations in the lake. A reduced chlorophyll‐a concentration was observed during the heavy rains. The water quality fluctuations are more pronounced with precipitation, especially where polluted drains enter the lake. An improved water quality can be observed downstream, including increased dissolved oxygen and nitrate concentrations. Improved water quality was observed during the postmonsoon period, with increased salinity and dissolved oxygen concentrations, a finding that confirms generalized and specific conclusions can be achieved with the use of the WASP model.

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Computation of physical characteristics of a lake system using IRS P6 (LISS-III) imagery

Cited by 7 publications

References 24 publications

Determining the spatial variation of phosphorus in a lake system using remote sensing techniques

Determining the spatial variation of phosphorus in a lake system using remote sensing techniques

Lake bathymetry from Indian Remote Sensing (P6‐LISS III) satellite imagery using artificial neural network model

Water quality prediction capabilities of WASP model for a tropical lake system

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