Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Author: search.filters.author.Shetty, A.Has files: No

Search Results

Now showing 1 - 10 of 28
  • No Thumbnail Available
    Item
    Groundwater prospective mapping: Remote sensing and a GIS based index model approach
    (2008) Shetty, A.; Nandagiri, L.; Padmini, R.
    The present study is concerned with the development and test of an integrated remote sensing and GIS based methodology for identification of groundwater potential areas in a humid tropical river basin. Indian Remote Sensing Satellite (IRS 1C-LISS-III) data along with other collateral data such as existing maps and field observations was utilized to extract information on the hydro-geomorphic features of the terrain. The study involves two components: (a) demarcation of groundwater potential zones (b) validation of sites with yield data. In order to demarcate potential groundwater zones, six pertinent thematic layers were integrated and assigned appropriate rankings. Layers considered were: geology, geomorphology, drainage density, slope, rainfall with infiltration factor and land cover map. The layer parameters were also rated according to their importance relative to other classes in the same theme. All the layers were superimposed and analyzed in ARC GIS® environment. A linear additive model based on the DRASTIC model concept was used to find the groundwater potential index (GPI). The map comprised of six categories of groundwater yield. To carry out more focused investigations on the potential zones, lineament maps were superimposed over it. The validity of different potential zones identified using the GIS-based model was compared with available borewell yield data and found to be in good agreement. The map generated can be used in future as a preliminary screening tool in selecting well sites and as a basic tool in land use planning for groundwater protection. © 2008 SPIE.
  • No Thumbnail Available
    Item
    Sub-pixel mineral mapping using EO-1 hyperion hyperspectral data
    (International Society for Photogrammetry and Remote Sensing, 2014) Kumar, C.; Shetty, A.; Raval, S.; Champatiray, P.K.; Sharma, R.
    This study describes the utility of Earth Observation (EO)-1 Hyperion data for sub-pixel mineral investigation using Mixture Tuned Target Constrained Interference Minimized Filter (MTTCIMF) algorithm in hostile mountainous terrain of Rajsamand district of Rajasthan, which hosts economic mineralization such as lead, zinc, and copper etc. The study encompasses pre-processing, data reduction, Pixel Purity Index (PPI) and endmember extraction from reflectance image of surface minerals such as illite, montmorillonite, phlogopite, dolomite and chlorite. These endmembers were then assessed with USGS mineral spectral library and lab spectra of rock samples collected from field for spectral inspection. Subsequently, MTTCIMF algorithm was implemented on processed image to obtain mineral distribution map of each detected mineral. A virtual verification method has been adopted to evaluate the classified image, which uses directly image information to evaluate the result and confirm the overall accuracy and kappa coefficient of 68% and 0.6 respectively. The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, the study demonstrates the feasibility of Hyperion data for sub-pixel mineral mapping using MTTCIMF algorithm with cost and time effective approach.
  • No Thumbnail Available
    Item
    An exploratory analysis of rainfall: A case study on western ghats of India
    (IEOM Society ieom-society@iieom.org, 2018) Rao, P.S.B.; Shetty, S.; Umesh, P.; Shetty, A.
    In this study high resolution 0.250 ×0.250 (approximately 25Km×25Km) gridded daily rainfall data is used to analyze the effect of changing climate on distribution of rainfall in different topographical zones of Western Ghats (WG) of India over the period 1901-2013. The non parametric two tailed Mann- Kendall with Hamed and Rao's method of autocorrelation and Sen's slope estimator for obtaining magnitude of change over time period is used. The rainfall trend in annual, monsoon and post-monsoon is increasing in state of Goa and Coastal region of Karnataka state and significantly decreasing in some part of Kerala and Maharashtra state. Winter season rainfall has seen a declined trend in southern part of the study area and in high elevated region of Kerala state. Even the mean rainfall over the study area is declining from 1951-1960 with disturbance in alternate sequence of flood and drought year from period 1990. The frequency of heavy rainfall events (65mm-124.4mm) are increasing in recent decades with 40-50% contribution from 2000-2013 in regions of Maharashtra state. The trend of heavy rainfall events are increasing in West Coast of India at 5% significance level with no trend in very heavy to extreme rainfall events (>124.5mm). © IEOM Society International.
  • No Thumbnail Available
    Item
    Assessment of spatial variation of soil moisture during maize growth cycle using SAR observations
    (SPIE spie@spie.org, 2019) Gururaj, P.; Umesh, U.; Shetty, A.
    Spatial Information about Soil moisture over agricultural crops are required for efficient irrigation which in turn helps in saving water and increases crop yield. Soil moisture also useful in prediction of flooded and drought regions. However field measurement of soil moisture is not a practical approach. The main objective of the study is to track soil moisture variation all along the maize growth period in a Semi-Arid region. There are only few studies carried out on soil moisture variation considering whole maize growing period. During the crop growing period soil moisture field investigation are conducted in synchronization with Satellite pass. Sentinel-1a Synthetic Aperture RADAR (SAR) satellite, Interferometric wide swath dual polarized data with 5.405 GHz frequency and central incidence angle of 23ï?° with repeat period of 12 days was used in this study. All in all during growth period 6 satellite pass scenes are acquired and processed by standard procedure using Sentinel Application Platform (SNAP) software. An attempt was made to redeem surface soil moisture for the whole maize growing crop cycle using water cloud model. The whole period of maize crop was divided into 3 parts like seedling, growing and harvesting period and soil moisture is retrieved for each period. The estimated soil moisture was validated with 30 field measured soil moisture samplings. The correlation coefficient of retrieved and actual soil moisture of seedling, growing and harvesting periods are 0.77, 0.72 and 0.6 respectively. The output of this study will be helpful in formulating strategies for irrigation water management. © 2019 SPIE.
  • No Thumbnail Available
    Item
    A QGIS Plug-in for Processing MODIS Data
    (Institute of Electrical and Electronics Engineers Inc., 2019) Aishwarya Hegde, A.; Umesh, U.; Shetty, A.
    In the past few decades number of Earth-observing satellites are continuously gathering information and only about 10 percent of the information is utilized by the users. With so much accessible information the researchers have not explored the datasets completely as there is absence of effective tool to process the information. MODIS data sensors have accessible data at various temporal and spatial resolutions. To productively use these datasets in open-source GIS programming like QGIS, there is a need to pre-process the dataset using a plug-in. The plug-in is built using python and PyQt interface for QGIS.The plug-in operates on MODIS Data (Terra/Aqua/Combined) computerizes and process the functionalities for MODIS products like MOD11, MOD09, MOD21. The processed datasets can be largely used in investigation of time series analysis for some earth resource application. © 2019 IEEE.
  • No Thumbnail Available
    Item
    Static Fire Risk Index for the Forest Resources of Karnataka
    (Institute of Electrical and Electronics Engineers Inc., 2019) Konkathi, P.; Shetty, A.; Venkatesh, V.; Yathish, P.H.; Umesh, U.
    Forest fires are the major cause of degradation of forest. Forest fires have caused substantial damage in the state of Karnataka in terms of economic, social, environmental impacts on humans and also loss of biodiversity. Fire risk indices are important tools for the management of forest fires. They are developed based on static and/or dynamic factors influencing the occurrence of fire and propagation of fire. The objective of the present study was to develop a new static fire risk index based on parameters influencing forest fire such as fuel type, elevation, slope, aspect, terrain ruggedness, proximity to a road, proximity to water bodies and proximity to settlements. MODIS Land cover type yearly L3 global 500m SIN grid(MCD12Q1) was used to compute fuel type index based on historical fire data, SRTM DEM was used to compute slope index, aspect index, elevation index, and terrain ruggedness index. Road index, settlement index, and water body index were developed from the proximity maps generated. A geographic information system (GIS) was utilized adequately to join diverse forest fire causing factors for demarcating static fire risk index. The evaluated exactness was around 87%, i.e., the developed GIS-based static fire risk index of the examination zone was observed to be in solid concurrence with actual fire affected regions. The study area exhibited 32.38% prone to fire risk. © 2019 IEEE.
  • No Thumbnail Available
    Item
    Assessment of Burn Severity using Different Fire Indices: A Case Study of Bandipur National Park
    (Institute of Electrical and Electronics Engineers Inc., 2019) Konkathi, K.; Shetty, A.
    Forest fires are the significant catastrophic events which affect the landscape and vegetation in forested lands. They cause loss of biodiversity, land degradation & ecological imbalance. As the forest fires cause high damage to the habitat, it is of utmost necessity to assess the impact of fire. Burn severity mapping contributes to the evaluation of fire severity and extent of burnt areas. In this article, recently occurred forest fire (21st to 25th February 2019) in Bandipur national park was assessed using remote sensing techniques for mapping burnt area and burn severity as on-site estimations are highly impossible during forest fires. Three established fire severity indices differenced normalized burn ratio (dNBR), relativized burn ratio (RBR) and relativized dNBR (RdNBR) are assessed and compared based on number of active fire points provided by MODIS & VIIRS. The RdNBR resulted in an accuracy of 89.14% whereas RBR and dNBR produced an efficiency of 52.48% and 60.633% respectively. The burnt area under high severity was around 4099 hectares. Post-fire assessment is an essential element for finding the effects of fire on vegetation and implementing mitigation strategies. © 2019 IEEE.
  • No Thumbnail Available
    Item
    Experiential Learning of Strength of Materials and Fluid Mechanics using Virtual Labs
    (Institute of Electrical and Electronics Engineers Inc., 2020) Shetty, S.; Shetty, A.; Aishwarya Hegde, A.; Salian, A.B.; Akshaya; Umesh, P.; Gangadharan, K.V.
    Massive Open Online Courses (MOOCs) have revolutionized the teaching and learning process. It provides personalized learning while being cost effective and highly scalable. Furthermore, the advancements in Information and Communication Technology (ICT) have made it possible to deploy high fidelity, interactive web applications that provide seamless learning experience. However, the paucity of synergetic and adequate instructional support has demanded the quest for interactivity in MOOCs. Virtual Labs, an initiative by Government of India, aims to provide an interactive web interface to perform laboratory experiments (besides theoretical understanding of the subject) without affecting the experiential learning that is otherwise gained in the actual laboratory. This paper describes the design and development of Virtual Labs for two fundamentals courses of Civil and Mechanical engineering: Strength of Material (SOM) and Fluid Mechanics (FM). Subsequently, the outcomes of this work are discussed by analyzing the data collected from past four years, which reveals that these labs are an useful means to provide easy, cost effective and scalable solutions for online experiential learning. © 2020 IEEE.
  • No Thumbnail Available
    Item
    Quality assessment of dimensionality reduction techniques on hyperspectral data: A neural network based approach
    (International Society for Photogrammetry and Remote Sensing, 2020) C, C.; Shetty, A.; Narasimhadhan, A.V.
    Dimensionality reduction of hyperspectral images plays a vital role in remote sensing data analysis. The rapid advances in hyperspectral remote sensing has brought in a lot of opportunities to researchers to come up with advanced algorithms to analyse such voluminous data to better explore earth surface features. Modern machine learning algorithms can be applied to explore the underlying structure of high dimensional hyperspectral data and reduce the redundant information through feature extraction techniques. Limited studies have been carried out on dimensionality reduction for mineral exploration. The current study mainly focuses on the application of autoencoders for dimensionality reduction and provides a qualitative (visual) analysis of the obtained representations. The performance of autoencoders are investigated on Cuprite scene. Coranking matrix is used as evaluation criteria. From the obtained results it is evident that, deep autoencoders provide better results compared to single layer autoencoders. An increase in the number of hidden layers provides a better embedding. The neighborhood size K ≥ 40 of deep autoencoders provides a better transformation compared to autoencoders which shows an improved embedding only after K ≥ 80. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
  • No Thumbnail Available
    Item
    Experiential Learning of Physio-Chemical and Bacteriological Properties of Water using Virtual Labs
    (Institute of Electrical and Electronics Engineers Inc., 2020) Shetty, S.; Shetty, A.; Aishwarya Hegde, A.; Salian, A.B.; Akshaya; Umesh, P.; Gangadharan, K.V.
    Virtual Labs, an initiative by the Government of India under the National Mission on Education through Information and Communication Technology (NMEICT), has revolutionized the teaching and learning process for laboratory courses in the science and engineering disciplines. The web-based laboratories provided by the Virtual Labs project enable personalized learning while being cost effective and highly scalable. This approach helps to quickly learn the fundamental concepts of science and engineering through virtual experimentation, fosters curiosity and innovation among students, and prevents laboratory hazards. In this paper, we describe the design and development of two web-based virtual laboratories that simulate the fundamental concepts of Civil Engineering and Environmental Engineering. The proposed virtual labs provide a detailed explanation of the experiments in the respective engineering domains, and reagents and apparatuses involved while performing the experiments. The outcomes of this work are evaluated by analyzing the feedback collected from the users of these virtual labs, which reveals that the labs are an useful means to provide easy, cost effective and scalable solutions for online experiential learning. © 2020 IEEE.