Faculty Publications
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Item Importance of geology and soil survey for mobile communication site planning using RS/GIS technology(2010) Naveenchandra, B.; Lokesh, K.N.; Usha; Gangadhara Bhat, H.G.Geology and Soil survey constitutes a valuable resource inventory linked with the survival of life on the earth. The technological advancements in the field of remote sensing and Geographical Information System have been a boon for such surveys. The present paper describes the role of Remote sensing and Geographical Information System (GIS) technologies for geological mapping and characterizing the importance of soils at various scales for identification of suitable sites for mobile communication network. Cellular network design is becoming more and more important since the network quality is highly dependent on the distribution of base stations. To design a cellular network for a particular region efficiently and accurately, the site suitability is an important determination. The country's mobile services market is forecast to grow by a compound annual rate of 28.3% in next five years. India is a vibrant market from communications point of view. The subscriber base in the wireless market in India, the world's fastest growing telecom market reached another milestone when it surpassed 200 million subscribers in Aug 2008. At present there are around 54000 cell sites operated by different GSM/CDMA operators. This number would further go up to 80,000 in next couple of years. To serve an increasing number of users requires an increasing number of base stations. Thus, operators must carefully plan the deployment and configurations of radio base stations to support voice and data traffic at a level of quality expected by customers. The present study carried out in the Udupi district of Karnataka State based on IRS 1C/1D LISS-III and CARTOSAT-1 satellite data. Various thematic maps like geology, soil, geomorphology, slope and land use/land cover with DEM has helped in understanding the terrain in a better way. The multi spectral satellite data in conjunction with SuperGIS, SuperPad and Getac GPS hardware have helped to formulate suitable plans and strategies for an effective Telecom planning and development in Udupi district. © 2010 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.Item Vegetation dynamics in a tropical river basin inferred from MODIS satellite data(2013) Laxmi, K.; Nandagiri, L.The objective of this study was to analyze temporal and spatial dynamics of vegetation and land use/land cover (LU/LC) characteristics in a humid tropical river basin originating in the forested Western Ghats mountain ranges using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. Both intra-annual and inter-annual variations in the parameters related to vegetation were analyzed in the Netravathi river basin (3314 km2) which is located in Karnataka State, India. MODIS data products on Land Surface Temperature and Reflectance were used as input to map the pixel-wise variations in albedo, Normalized Difference Vegetation Index (NDVI), Fraction of Vegetation (Fr) and Land Surface Temperature (LST) for two dates each (summer and winter) during the years 2002 and 2006. The fact that 2002 experienced a relatively wet summer followed by a relatively dry winter and 2006 experienced opposite conditions, proved useful in interpreting variations as influenced by wetness conditions. Overall results indicated significant variability in the parameters for major LU/LC classes of evergreen /semievergreen forest, scrub forest and agriculture. While albedo values appeared quite sensitive to wetness conditions, NDVI (and Fr) exhibited significant seasonal changes for some LU/LC classes but remained largely unaffected by wetness conditions. LST values corrected for elevation effects (LST*) were influenced by both LU/LC and wetness conditions. Differences in LST* values were as high as 70K between summer and winter of 2006 for some LU/LC classes. Lowest temperatures were recorded for the evergreen/ semievergreen forest class. Similar inferences could be drawn when variations in parameters were analyzed for 20 selected pixels located at different elevations and possessing each of the eight LU/LC classes. The methodology proposed in this research may prove to be useful in regional scale monitoring and mapping of tropical forests and other LU/LC categories in a convenient and cost-effective manner. MODIS satellite data products used in this study provides information on surface characteristics at a reasonable resolution. This permits identification of not only differences in LU/LC classes but also on changes in surface characteristics as influenced by season and wetness conditions. © 2013 CAFET-INNOVA TECHNICAL SOCIETY.Item Latent heat flux estimation using trapezoidal relationship between MODIS land surface temperature and fraction of vegetation-application and validation in a humid tropical region(Taylor and Francis Ltd., 2014) Laxmi, K.; Nandagiri, L.The present study was taken up with the objective of developing a methodology for estimation of actual evapotranspiration (AET) using only satellite data. Accordingly, an algorithm based on the popular Priestley-Taylor method was developed. While previous studies have assumed a triangular relationship between land surface temperature (LST) and fraction of vegetation (FV) to calculate the Priestley-Taylor parameter (?), a trapezoidal relationship was adopted in the present study to enable applications in forested regions in the humid tropics. The developed algorithm was applied to the humid tropical Mae Klong region, Thailand, and latent heat flux (ET) estimates were validated with measurements made at a flux tower located at the centre of the region. Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing satellite data products corresponding to the study area were used to derive various inputs required by the algorithm. Comparison of estimated and measured fluxes on five cloud-free days in 2003 yielded root mean square error (RMSE) of 64.73 W m-2 which reduced to 18.65 W m-2 when one day was treated as an outlier. The methodology developed in this study derived inputs only from satellite imagery and provided reasonably accurate estimates of latent heat flux at a humid tropical location. © 2014 Taylor & Francis.Item Offshore wind power resource assessment using Oceansat-2 scatterometer data at a regional scale(Elsevier Ltd, 2016) Gadad, S.; Deka, P.C.In the offshore region the scarcity of in situ wind data in space proves to be a major setback for wind power potential assessments. Satellite data effectively overcomes this setback by providing continuous and total spatial coverage. The study intends to assess the offshore wind power resource of the Karnataka state, which is located on the west coast of India. Oceansat-2 scatterometer (OSCAT) wind data and GIS based methodology were adopted in the study. The OSCAT data accuracy was assessed using INCOIS Realtime All Weather Station (IRAWS) data. Wind speed maps at 10 m, 90 m and wind power density maps using OSCAT data were developed to understand the spatial distribution of winds over the study area. Bathymetric map was developed based on the available foundation types and demarking various exclusion zones to help in minimizing conflicts. The wind power generation capacity estimation performed using REpower 5 MW turbine, based on the water depth classes was found to be 9,091 MW in Monopile (0-35 m), 11,709 MW in Jacket (35-50 m), 23,689 MW in Advanced Jacket (50-100 m) and 117,681 MW in Floating (100-1000 m) foundation technology. In Indian scenario major thrust for wind farm development in Monopile region is required. Therefore as first phase of development, if 10% of the estimated potential in the region can be developed then, 116% of energy deficit for FY 2011-12 could be met. Also, up to 79% of the anticipated energy deficit for the FY 2014-15 of the Karnataka state could be achieved. © 2016 Elsevier Ltd.Item A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images(Institute of Electrical and Electronics Engineers, 2017) Suresh, S.; Lal, S.; Chintala, C.S.; Kiran, M.S.Owing to the increased demand for satellite images for various practical applications, the use of proper enhancement methods are inevitable. Visual enhancement of such images mainly focuses on improving the contrast of the scene procured, conserving its naturalness with minimum image artifacts. Last one decade traced an extensive use of metaheuristic approaches for automatic image enhancement processes. In this paper, a robust and novel adaptive Cuckoo search based Enhancement algorithm is proposed for the enhancement of various satellite images. The proposed algorithm includes a chaotic initialization phase, an adaptive Levy flight strategy and a mutative randomization phase. Performance evaluation is done by quantitative and qualitative results comparison of the proposed algorithm with other state-of-the-art metaheuristic algorithms. Box-and-whisker plots are also included for evaluating the stability and convergence capability of all the algorithms tested. Test results substantiate the efficiency and robustness of the proposed algorithm in enhancing a wide range of satellite images. © 2008-2012 IEEE.Item The role of atmospheric correction algorithms in the prediction of soil organic carbon from hyperion data(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Minu, S.; Shetty, A.; Minasny, B.; Gomez, C.In this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon (SOC) from spaceborne hyperspectral sensor (Hyperion) visible near-infrared (vis-NIR, 400–2500 nm) data was analysed in fields located in two different geographical settings, viz. Karnataka in India and Narrabri in Australia. Atmospheric correction algorithms, (1) ATmospheric CORection (ATCOR), (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), (3) 6S, and (4) QUick Atmospheric Correction (QUAC), were employed for retrieving spectral reflectance from radiance image. The results showed that ATCOR corrected spectra coupled with partial least square regression prediction model, produced the best SOC prediction performances, irrespective of the study area. Comparing the results across study areas, Karnataka region gave lower prediction accuracy than Narrabri region. This may be explained due to difference in spatial arrangement of field conditions. A spectral similarity comparison of atmospherically corrected Hyperion spectra of soil samples with field-measured vis-NIR spectra was performed. Among the atmospheric correction algorithms, ATCOR corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. ATCOR’s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. This work would open up a great scope for accurate SOC mapping when future hyperspectral missions are realized. © 2017 Informa UK Limited, trading as Taylor & Francis Group.Item Assessment of coastal water quality along south west coast of India using multile regression analysis on satellite data(National Institute of Rural Development Rajendranagar Hyderabad 500 030, 2018) Jose, D.M.; Mandla, V.R.; Subbarao, S.S.V.; Rao, N.S.; Moses, S.A.The coastal waters being the ultimate receiver of all the wastes, shows a declining trend in its quality. It is of immense importance to know the extent of pollution for its monitoringandmanagemenlMeasurementofdissolvedoxygen (DO), biologicaloxygen demand (BOD), pH and fecal coliform (FC) are vital in water quality monitoring and assessment studies. Usually these parameters are determined by analysing water samples collected from various locations. Since this is tedious and expensive, it is limited to small scales. In this paper, an effort has been made to quickly assess the quality of coastal waters of Kerala directly from the satellite imagery by estimating National Sanitation Federation Water Quality Index (NSFWQI) along with DO, BOD, pH and FC. Multiple linear regression is used to develop statistically significant models using Sea Surface Temperature (SST) and Remote Sensing Reflectance (Rrs) from Moderate Resolution Imaging Spectroradiometer (MODIS) and in-situ data available on DO, BOD, pH and FC. The models when validated showed good correlation between in situ values and predicted values with r values ranging from 0.73 (p=0.001) for DO to 0.89 for NSFWQI (p=0.018). Spatial maps are generated showing the distribution of these parameters along the coast. The parameters in the study are checked to see if they are in compliance with the standards. The study gives models to estimate the daily distribution of these parameters along the coast using MODIS data. Thus, appropriate control measures could be adopted to limit the effect on susceptible rural population. © 2019 JPR Solutions. All rights reserved.Item Hybrid atmospheric correction algorithms and evaluation on VNIR/SWIR Hyperion satellite data for soil organic carbon prediction(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Minu, S.; Shetty, A.; Gomez, C.Visible near-infrared and shortwave infrared data acquired by spaceborne sensors contain atmospheric noise, along with target reflectance that may affect its end applications, e.g. geological, vegetation, soil surface studies, etc. Several atmospheric correction algorithms have been already developed to remove unwanted atmospheric components of a spectral signature of Earth targets obtained from airborne/spaceborne hyperspectral image. In spite of this, choosing of an appropriate atmospheric correction algorithm is an ongoing research. In this study, two hybrid atmospheric correction (HAC) algorithms incorporating a modified empirical line (ELm) method were proposed. The first HAC model (named HAC_1) combines (i) a radiative transfer (RT) model based on the concepts of RT equations, which uses real-time in situ atmospheric and climatic data, and (ii) an ELm technique. The second one (named HAC_2) combines (i) the well-known ATmospheric CORrection (ATCOR) model and (ii) an ELm technique. Both HAC algorithms and their component single atmospheric correction algorithms (ATCOR, RT, and ELm) were applied to radiance data acquired by Hyperion satellite sensor over study sites in Australia. The performances of both HAC algorithms were analysed in two ways. First, the Hyperion reflectances obtained by five atmospheric correction algorithms were analysed and compared using spectral metrics. Second, the performance of each atmospheric correction algorithm was analysed for prediction of soil organic carbon (SOC) using Hyperion reflectances obtained from atmospheric correction algorithms. The prediction model of SOC was built using partial least square regression model. The results show that (i) both the hybrid models produce a good spectrum with lower Spectral Angle Mapper and Spectral Information Divergence values and (ii) both hybrid algorithms provided better SOC prediction accuracy, in terms of coefficient of determination (R2), residual prediction deviation (RPD), and ratio of performance to interquartile (RPIQ), with R2 ? 0.75, RPD ? 2, and RPIQ ? 2.58 than single algorithms. HAC algorithms, developed using ELm technique, may be recommended for atmospheric correction of Hyperion radiance data, when archived Hyperion reflectance data have to be used for SOC prediction mapping. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.Item Application of remote sensing and GIS for identification of potential ground water recharge sites in Semi-arid regions of Hard-rock terrain, in north Karnataka, South India(Springer Science and Business Media Deutschland GmbH, 2018) Bhagwat, T.N.; Hegde, V.S.; Shetty, A.Hydro-geomorphological characteristics, together with soil, slope, lineament density and Land use Land cover are signatures of potential ground water recharge areas, and are vital for water harvesting. In the present paper, Fifth order sub-basins in Semi-arid regions of the Varada River basin in South India is studied for selection of suitable area for recharge and prioritize the sub-basins using Indian Remote Sensing satellite (IRS) P6; Linear Imaging Self Scanning Sensor (LISS III) and ArcGIS 9.2. The Fifth order sub-basins of the Varada River spread in Hard-rock terrain and of different agro-climatic zones. The study shows that there are significant spatial variations in the fifth order basins with respect to their morphometric characteristics such as the basin area, drainage density, bifurcation ratio, and circularity ratio, constant of channel maintenance and slope of the basin. These variations reflect the differences in the hydrological process in the different Sub-basins. Based on the variations in the linear, aerial, relief as well as the slope, lineament density, and precipitation pattern rankings are assigned for each parameter with respect to ground water recharge within the Subbasins. Weighted sum overlay for precipitation, Land use, soil and Water table fluctuation are used to select the suitable areas of recharge within the sub-basins. Buffers created for lineaments and drainage networks were intersected with the suitable area of recharge for the probable tank's locations for recharge. The tank locations identified after intersection and having higher stream orders are further filtered for the identification of potential sites for ground water recharge. In the prioritized sub-basins SB-8, SB-10, SB-11 locations have been selected for recharge. © 2018, Springer International Publishing AG, part of Springer Nature.Item Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting(Springer Netherlands rbk@louisiana.edu, 2019) Sulugodu, B.; Deka, P.C.Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models. Daily rainfall data for three decades (1983–2012) over the Nethravathi Basin, Karnataka, India is used for analysis. The analysis is carried out for the monsoon season (June–September), out of which 70% data considered for training the model and remaining for testing. Different input combinations are developed, and soft-computing methods like ANFIS, GRNN, PSO-ANN, and ELM are applied for flow forecasting on a temporal scale. The model performance is evaluated using various statistical indices like NNSE, RRMSE, and MAE. The results indicate that CHIRPS rainfall showed better performance in comparison with IMD data. ELM expressed an enhanced effect when compared to all other methods. The usefulness and effectiveness of CHIRPS data compared to IMD data has been explored. © 2019, Springer Nature B.V.