Faculty Publications
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Item Comparison of Different Pan Sharpening Techniques using Landsat 8 Imagery(Institute of Electrical and Electronics Engineers Inc., 2019) Govind, N.R.; Rishikeshan, C.A.; Ramesh, H.Pan sharpening technique is a widely used image processing technique which combines the data available from various sensors and exploits its varied capabilities. In this study, the efficiency of four diverse pan sharpening methods namely High Pass filter, Modified Intensity Hue Saturation, Ehlers fusion and Hyperspectral Colour Sharpening was evaluated. The pan sharpening approaches are applied to Landsat 8 imagery of an urban area. The spatial and spectral quality of the fused images is assessed using different indices like Bias, RMSE, Correlation Coefficient and ERGAS. The fused images obtained have improved spatial resolution and visual appearance compared to the original MS image. The fused images have a spatial resolution comparable to that of the PAN image. According to visual analysis, Modified IHS method yielded a fused image with better visual interpretability. The statistical analysis shows that the high pass filter is the most suitable pan sharpening method for this dataset. On testing for Bias, RMSE, ERGAS and CC, the high pass filter method performed best followed by Modified Intensity Hue saturation, Ehlers fusion and Hyperspectral Colour Sharpening while Ehlers fusion showed a higher correlation, compared to Modified IHS. © 2019 IEEE.Item Assessment of hydropower potential in Nethravathi river basin using SWAT model(CAFET INNOVA Technical Society cafetinnova@gmail.com 1-2-18/103, Mohini Mansion, Gagan Mahal Road, Domalguda, Hyderabad 500029, 2015) Babar, S.; Shobhita, M.P.; Ramesh, H.Hydropower plants have the advantage of producing renewable and clean power, the renewable and reliable energy source that serves national environmental and energy policy objectives. Therefore, the development of hydropower plant and improvements of water management have essential in contributing to sustainable growth and energy production in developing countries like India. The present study is concerned with the development of methodology and assessment of hydropower potential in the Nethravathi River basin with the help of Remote Sensing and GIS. The drainage area covers about 3190 km2 at Bantwal gauging point, and most of the land cover of the basin is dominated by forest. The basin was divided into six sub-basins based on hydrology and topography using GIS tools. The climate over the basin is coastal humid tropical and receives an average annual rainfall of about 4000 mm. sub-basin discharges were estimated using the Soil Conservation Services (SCS) curve number method. To ensure the total discharge from six sub-basins computed from SCS curve number method, the flows were routed and simulated at the gauging location using Soil and Water Assessment Tool (SWAT). SWAT model was calibrated for monthly time steps from 1998–2001, and validated for 2002–2003. Flow-duration curves (FDC) were generated for each sub-basin to assess the dependable yield. The results have shown a good agreement between observed and the simulated flows. The available discharge at 75%, 80% and 90% of time for each sub-basin were extracted from the FDC. This result were used to calculate the hydropower potential in all the six sub-basins at Q75, Q80 and Q90, by integrating thematic layers using ArcSWAT. © 2015 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.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 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.Item An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images(Elsevier B.V., 2018) Rishikeshan, C.A.; Ramesh, H.The detection and extraction of water bodies from satellite imagery is very important and useful for several planning and developmental activities such as shoreline identification, mapping riverbank erosion, watershed extraction and water resource management. Popular techniques for water body extraction like those based on the normalized difference water index (NDWI) require reflectance information in the green and near-infrared (NIR) bands of the light spectrum. Moreover, some commonly used approaches may perform differently according to the spatial resolution of the images. In this regard, mathematical morphological (MM) techniques for image processing have been employed for spatial feature extraction as they preserve edges and shapes. This study proposes a flexible MM driven approach which is very effective for the extraction of water bodies from several satellite images with different spatial resolution. MM provides effective tools for processing image objects based on size and shape and is particularly adapted for water bodies that have typically specific spatial characteristics. In greater details, the proposed extraction algorithm preserves the actual size and shape of the water bodies since it is based on morphological operators based on geodesic reconstruction. Moreover, the choice of the filter size (called structural element (SE) in MM) in the proposed algorithm is done dynamically allowing one to retain the most precise results from different set of inputs images of different spatial resolution and swath. The availability of more than one spectral band of satellite imagery is not necessary for the proposed algorithm as it utilizes only a single band for its computation. This makes it convenient to apply in single band imageries obtained from satellites such as Cartosat thereby making the proposed approach effective over commonly used methods. The accuracy assessment was carried out and compared with the maximum likelihood (ML) classifier and methods based on spectral indices. In all the five test datasets, extraction accuracy of the proposed MM approach was significantly higher than that of spectral indices and ML methods. The results drawn from visual and qualitative assessments indicated its capability and efficiency in water body extraction from different satellite images. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)Item The impact of spatiotemporal patterns of land use land cover and land surface temperature on an urban cool island: a case study of Bengaluru(Springer International Publishing, 2019) Govind, N.R.; Ramesh, H.In most of the developing countries, man-made developments in the environment have led to the growing demand to contextualize the land use land cover (LULC) changes and land surface temperature (LST) variations. Due to the modification in the surface properties of the cities, a difference in energy balance between the cities and its nonurban surroundings is observed. The aim of this study is to analyze the spatial and temporal patterns of LULC and LST and its interrelationship in Bengaluru urban district, India, during the period from 1989 to 2017 using remote sensing data. Intensity analysis was performed for the interval to analyze the LULC change and identify the driving forces. The impact of LULC change on LST was assessed using hot spot analysis (Getis–Ord Gi* statistics). The results of this study show that (a) dominant LULC change experienced is the increase in urban area (approximately 40%) and the rate of land use change was faster in the time period 1989–2001 than 2001–2017; (b) the major transition witnessed is from barren and agricultural land to urban; (c) over the period of 28 years, LST patterns for different land use classes exhibit an increasing trend with an overall increase of approximately 6 °C and the mean LST of urban area increased by about 8 °C; (d) LST pattern change can be effectively analyzed using hot spot analysis; and (e) as the urban expansion occurs, the cold spots have increased, and it is mainly clustered in the urban area. It confirms the presence of an urban cool island effect in Bengaluru urban district. The findings of this work can be used as a scientific basis for the sustainable development and land use planning of the region in the future. © 2019, Springer Nature Switzerland AG.Item Modelling stream flow and soil erosion response considering varied land practices in a cascading river basin(Academic Press, 2020) Venkatesh, K.; Ramesh, H.; Das, P.[No abstract available]