Faculty Publications
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Item Parameter estimation and vulnerability assessment of coastal unconfined aquifer to saltwater intrusion(2012) Mahesha, A.; Vyshali; Lathashri, U.A.; Ramesh, H.The focus of the present work is to characterize a tropical, coastal aquifer and to carry out its vulnerability to saltwater intrusion using hydrogeological parameters. The characterization of the aquifer involves pumping tests, vertical electrical sounding, and water quality analysis carried out at 41 monitoring wells. The area under investigation lies between two tropical, seasonal, tidal rivers, i.e., Pavanje and Gurpur rivers, joining the Arabian on the west coast of India. The aquifer is predominantly shallow and unconfined, having moderate to good groundwater potential with transimissivity and specific yield ranging from 49.2 to 461:4 m2/day and 0.00058 to 0.2805, respectively. The electrical resistivity tests indicated that the thickness of the aquifer ranges from 18 to 30 m. The study also investigates the saltwater affected areas in the region the vertical electrical sounding and water quality analysis. The resistivity results revealed several probable isolated saltwater intruded pockets in the region with resistivity less than 70 Om. From the salinity analysis of water, the locations that are affected during February to May (summer) and throughout the year are identified. The wells that are located close to the coast (< 350 m) and at lower elevations (well bottom < +1 m) were found to be saline throughout the year. Also, wells along the banks of the river show considerable salinity (> 200 ppm) during the summer period from tidal inflow along the rivers. The water samples were also analyzed for chloride to bicarbonate ratios during December to May at all the monitoring wells and were found to be exceeding the allowable limit at several locations. The saltwater vulnerability maps are derived for the area by the index-based method using the hydrogeological parameters. The method was found to be effective while compared to the field observations. The results from the analysis indicate that the aquifer is medium to highly vulnerable to saltwater intrusion at majority of the locations. The impact of projected sea level rise by 0.25 and 0.50 m from the climate change is also assessed on the vulnerability of the region to saltwater intrusion. © 2012 American Society of Civil Engineers.Item Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin(Elsevier B.V., 2016) Ganasri, B.P.; Ramesh, H.Soil erosion is a serious problem arising from agricultural intensification, land degradation and other anthropogenic activities. Assessment of soil erosion is useful in planning and conservation works in a watershed or basin. Modelling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions. In the present study, the soil loss model, Revised Universal Soil Loss Equation (RUSLE) integrated with GIS has been used to estimate soil loss in the Nethravathi Basin located in the southwestern part of India. The Nethravathi Basin is a tropical coastal humid area having a drainage area of 3128 km2 up to the gauging station. The parameters of RUSLE model were estimated using remote sensing data and the erosion probability zones were determined using GIS. The estimated rainfall erosivity, soil erodibility, topographic and crop management factors range from 2948.16 to 4711.4 MJ/mm·ha? 1hr? 1/year, 0.10 to 0.44 t ha? 1·MJ? 1·mm? 1, 0 to 92,774 and 0 to 0.63 respectively. The results indicate that the estimated total annual potential soil loss of about 473,339 t/yr is comparable with the measured sediment of 441,870 t/yr during the water year 2002–2003. The predicted soil erosion rate due to increase in agricultural area is about 14,673.5 t/yr. The probability zone map has been derived by the weighted overlay index method indicate that the major portion of the study area comes under low probability zone and only a small portion comes under high and very high probability zone. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Nethravathi Basin. © 2015 China University of Geosciences (Beijing) and Peking UniversityItem EXhype: A tool for mineral classification using hyperspectral data(Elsevier B.V., 2017) Adep, R.N.; Shetty, A.; Ramesh, H.Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named ‘EXhype (Expert system for hyperspectral data classification)’ to map minerals. The system incorporates human expertise at several stages of it's implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding misclassification between target classes when applied on minerals. © 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)Item A novel mathematical morphology based algorithm for shoreline extraction from satellite images(Taylor and Francis Ltd., 2017) Rishikeshan, C.A.; Ramesh, H.Shoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to accomplish the complex task of automatic extraction of features, such as shoreline, has considerably increased. Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating. Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps. Here, we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries. The salient features of this work are the preservation of actual size and shape of the shorelines, run-time structuring element definition, semi-automation, faster processing, and single band adaptability. The proposed approach is tested with various sensor-driven images with low to high resolutions. Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach. The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments. © 2017 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.