Faculty Publications
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Item Assessment of Changes inWetland Storage in Gurupura River Basin of Karnataka, India, Using Remote Sensing and GIS Techniques(Springer Science+Business Media, 2018) Kundapura, S.; Kommoju, R.; Verma, I.In view of the significant importance of wetlands in the ecosystem and regional economy, an attempt has been made to analyze the impact of land use/land cover dynamics and other contributing factors on spatial status of Gurupura river basin wetland ecosystem located in Karnataka region. The impact assessment has been carried out by analyzing the multi-temporal changes in the storage capacities of wetlands in the watershed, by using remote sensing data of LISS-III. The multi-temporal land use/land cover statistics will reveal the significant changes that have taken place over time in the watershed. The runoff generated can be easily calculated from this information which gives an idea of the total input into the system. In response to these upstream watershed changes, wetland has exhibited changes in spatial extension, structure, and hydrological characteristics. As a consequence of continuously changing land use/land cover characteristics and unpredictability of the monsoon, the wet land ecosystems have exhibited considerable changes in spatial extent and their storage capacities. Overall, there has been degradation in the storage capacities of the wetland ecosystems of the region causing a multitude of adverse effects such as increase in floods and submergence of mainland. © Springer Nature Singapore Pte Ltd. 2019.Item High-resolution mapping of soil properties using aviris-ng hyperspectral remote sensing data—a case study over lateritic soils in mangalore, india(Springer Science and Business Media Deutschland GmbH, 2021) Chitale, M.M.; Kundapura, S.Quick and accurate mapping of properties of soil is considered to be critical for agriculture and environmental management. Rapid assessment of soil properties is a daunting task in monitoring the environment. The conventional field sampling is a laborious as well as time-consuming job. The conventional methods are restricted to a specific region but there is a need to analyses the soil properties at landscape levels. Hence, this study emphasises on hyperspectral remote sensing which to some extent helps in rapid assessment of the properties. The hyperspectral data used for the study is AVIRIS-NG data. The study explored the potential of AVIRIS-NG hyperspectral data in mapping soil properties which were analysed by in situ laboratory methods and compared with them by geostatistical method of spatial interpolation. Hence, the method adopted for this purpose is the study on spatial variability of soil properties by using Kriging interpolation technique. Also, a review study is carried out on the visible and near-infrared analysis (VNIRA), multiple regression analysis approach and spectral angle mapper supervised classification technique on the high-resolution AVIRIS-NG Hyperspectral data, which will yield as an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples and classifies the data accordingly. © Springer Nature Singapore Pte Ltd 2021.Item Mapping of Flood-Inundated Urban Regions Using Sentinel-1 SAR Imagery for the 2018 and 2019 Kerala Floods(Springer Science and Business Media Deutschland GmbH, 2023) Kulithalai Shiyam Sundar, K.S.S.; Kundapura, S.Floods are a common natural calamity causing an immense impact on the natural and human ecosystems around the world. A combination of unfavorable meteorological, hydrological, and physical conditions causes it. The study area is the Vembanad Lake System in Kerala, India comprising six watersheds: Periyar, Muvattupuzha, Meenachil, Manimala, Pamba, and Achenkovil that drains into the lake. The state faced severe flooding in 2018 and 2019 due to torrential rainfall. Thus, this study focuses on assessing flood inundation mapping utilizing Sentinel-1 SAR imagery in Google Earth Engine (GEE) for 2018 and 2019 since it simplifies and streamlines the complicated and time-consuming pre-processing of Sentinel-1 SAR images. These images are pre-processed, and the flooded areas are delineated. Change detection by image ratio method is utilized to identify the flood inundated and the most frequently flooded areas. The results show that 4% and 3.21% of the entire region were flooded in 2018 and 2019, respectively. In addition, 14.7 Km2 of the urban area flooded in 2018, whereas 7.26 Km2 of urban land flooded in the 2019 floods. Hence, these inundation maps can be utilized for risk assessment and primary preventive measures. It also serves as a tool to warn the residents in that region about the hazards and the possibility of inundations at the time of heavy downpours in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Mapping of 2018 Flood and Estimation of Future Flood Inundation Region for Vembanad Lake System in Kerala, India Using Sentinel-1 SAR Imagery(Springer Science and Business Media Deutschland GmbH, 2024) Kulithalai Shiyam Sundar, K.S.S.; Kundapura, S.Floods have claimed the lives of countless people and caused significant property damage, jeopardizing their livelihoods. The study area is the Vembanad Lake System in Kerala, India has faced severe flooding in 2018 due to torrential rainfall. Considering that Google Earth Engine (GEE) streamlines and simplifies the complex and time-consuming pre-processing of SAR images, this paper evaluates flood inundation mapping using Sentinel-1 SAR data for 2018. The flood inundation zone for the study is calculated using the Land Use Land Cover (LULC) map for 2018 and the forecasted LULC for 2035 and 2050. Hence, the research assesses the areas affected by floods in 2018 and those that may experience flooding of a similar degree in the near future. Thus, the extent of flood inundation during the 2018 floods and the potential flood inundation region for future LULC in 2035 and 2050 are determined. From the analysis, 14.7 km2 of built-up area was inundated during the 2018 floods. The 2018 flood event is used to quantify the flood that may inundate the future LULC in 2035 and 2050; it is found that the flood will affect about 19.87 km2 and 23.32 km2 of the built-up region, respectively. According to the study, the built-up area impacted by the flooding will increase by 34.99% and 58.4% from 2018 to 2035 and 2050, respectively. Examining the flood-prone areas and potential flood-affected areas in the future will be of great use to planners in their efforts to forewarn of an impending tragedy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Groundwater Potential Mapping for Mangaluru in India, a Coastal Urban Environment using Convolutional Neural Networks(Springer, 2025) Kundapura, S.; Venkatesh, A.K.; Kandpal, U.Groundwater is vital for sustaining life, particularly in regions facing water scarcity. Effective management of groundwater resources requires accurate mapping of potential groundwater zones. This research incorporates Convolutional Neural Networks (CNN) to map precisely Groundwater Potential (GWP) zones in Mangaluru, a coastal taluk in Karnataka, India. Suitability of ten GWP conditioning factors: Elevation, Slope, Aspect, Rainfall, Geology, Geomorphology, Soil, Land Use and Land Cover (LULC), Drainage Density, and Topographic Wetness Index (TWI) is considered using Multicollinearity analysis. The CNN model performance was compared with Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models, and it outperformed by achieving an overall accuracy of 94.23% and an Area under the Receiver Operating Characteristic (AUC-ROC) of 94%. The resulting GWP map was classified into three zones: high (74.98%), moderate (17.13%), and low (7.88%) potential. Validation using groundwater level data from twenty-nine monitoring wells yielded an accuracy of 77%. The findings demonstrate the effectiveness of CNN for GWP mapping and provide valuable insights for sustainable groundwater resource management, policy and decision-making. © The Institution of Engineers (India) 2025.