Lake bathymetry from Indian Remote Sensing (P6‐LISS III) satellite imagery using artificial neural network model. Issue 2 (19th June 2013) - Moses, Sheela A.; Janaki, Letha; Joseph, Sabu; Gomathi, Jairaj P.; Joseph, Justus
Abstract: 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.