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    Study of dynamic changes through geoinformatics technique: A case study of Karwar coast, west coast of India
    (Springer, 2019) Yadav, A.; Dodamani, B.M.; Dwarakish, G.S.
    Shoreline is one of the geo-indicators of the coastal zone. Coastal zone is subjected to threats due to change in shoreline. Shoreline change leads to modification and causes for damages of properties, infrastructure around the shoreline region. These modifications, changes of land expands too many issues of the environment under the coastal zone. The present study was carried out by employing remote sensing and GIS techniques for the coastal regime of Karwar, India. LANDSAT-8 remote sensing data was integrated with the GPS data collected during the field survey. The satellite data is processed and analyzed using ERDAS IMAGINE 2014 tool and ArcGIS 10.3 tool, respectively. High Water Line (HWL) is considered for the extraction of shoreline. The visual interpretation of satellite imageries is carried out to distinguish the HWL. Net Shoreline Movement (NSM) was evaluated by adopting Digital Shoreline Analysis System (DSAS) tool. Statistical methods such as Weighted Linear Regression (WLR), Linear Regression Rate (LRR) and End Point Rate (EPR) were used to estimate the changes of shoreline. The present study reveals that shorelines of Karwar Coast, Ravindranath Taghore beach experiences an average erosion rate is −4.61 m/year (EPR), −1.49 m/year (LRR), and 0.19 (WLR) and Devbagh beach experiences an average erosion rate is −9.74 m/year (EPR), −7.53 m/year (LRR), and −11.55 m/year (WLR). © Springer Nature Singapore Pte Ltd. 2019.
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    Drought monitoring for RABI season in upper Krishna river basin using remote sensing and GIS
    (Asian Association on Remote Sensing Sh1939murai@nifty.com, 2015) Chandran, C.; Dodamani, B.M.; Reddy, K.; Naseela, E.K.
    In this study, the upper Krishna river basin, lying in the state of Maharashtra has been chosen as study area. Two drought indices, SPI and NDVI, representing meteorological and agricultural droughts respectively, were calculated and analysed for the study area for a study period of 2000-2012. Using ArcGIS maps of the two types of droughts have been created to represent the spatial extent of the droughts. Further analysing the two indices, relevant relationships have been obtained between them.
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    Assessment of agricultural drought by remote sensing technique
    (SPIE spie@spie.org, 2018) Pathak, A.A.; Dodamani, B.M.
    Drought is commonly occurring natural hazard. It has vicious impact on agricultural production as well as on socioeconomic status of an area. Meteorological drought will induce with the deficit of rainfall and leads to agricultural drought as it prolongs. Rainfall is crucial parameter to assess meteorological drought and NDVI based indices can capture agricultural drought satisfactorily. The present study aims to assess meteorological and agricultural drought in the Ghataprabha river basin using Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI). Monitoring of SPI and VCI will benefits to mitigate drought impacts with the proper water resources managements. Ghataprabha river basin is the sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Average annual rainfall of the basin varies from 600 mm to 2000 mm. Gridded rainfall data was procured from the Indian Meteorological Department for the period of forty three years (1970-2013) and considered same as input for SPI. To calculate SPI with multiple time scale, two parameter gamma distribution was implemented. MODIS NDVI products from 2000-2013 was considered for calculation of VCI. Significant number of meteorological drought episodes were observed during the study period while severe agricultural drought was observed during 2001-2003 and in 2012. SPI and VCI were compared to quantify variation of VCI with respect to SPI. Good agreement between SPI and VCI was observed during drought and non-drought periods. Results indicates that eastern part of the basin was more prone to severe droughts as compare to other part of the basin. This study assistances to formulate drought mitigation strategies and to establish effective water resources policies in the study region. © SPIE. Downloading of the abstract is permitted for personal use only.
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    Spatio-temporal distribution of rainfall and aerosols over urban areas of Karnataka
    (SPIE spie@spie.org, 2018) Nizar, S.; Dodamani, B.M.
    Rapid increase of population and urban sprawl have an immense impact on local climatic conditions. Urban heat island, increased surface roughness and enhanced aerosol are some of the prominent factors affecting precipitation in such highly populated urban areas. Among these, the complex interaction of aerosol particles with solar radiation have acknowledged their importance in radiation budget and hence climate dynamics. Being cloud condensation nuclei they also influence cloud lifetime and microphysics in turn influencing precipitation. Present investigation emphases on understanding rainfall and aerosol trends and its spatial occurrence pattern with respect to urbanization. An approach where population as an indicator for urbanization is used in this study rather than a profound investigation on the individual factors of urban induced precipitation anomalies. Mann Kendall trend test is carried out at grid level on a 0.25 degree gridded rainfall data and the trends are then related with the distribution of population in the study area. Areas of significant rainfall trends are identified and are analyzed for spatial patterns around urban areas. These identified urban zones are then further analyzed for aerosol variability. Being a monsoon region, a seasonal variation of aerosols are performed. The results shows that during the monsoon season there is a significant increase in rainfall along the Western Ghats, whereas certain grids along the western coast located at the downwind of populated areas such a Mangalore shows a significant decreasing trend. The overall spatial pattern of rainfall trend during pre-monsoon season is indicative of the influence of urban areas on rainfall. This observation during the pre-monsoon season is quantified which shows that 61% of the trends are included within urban influence zones which are only 36% of the size of Karnataka. Further various cloud characteristics and its association with aerosol loading in these urban areas were investigated. The results are indicative of higher aerosol events suppressing rainfall in these urban areas. © 2018 SPIE.
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    Trends in Agro-Meteorological Parameters as Groundwater Exploitation Indicators
    (Institute of Physics Publishing helen.craven@iop.org, 2018) Pathak, A.A.; Nizar, S.; Dodamani, B.M.
    Rainfall being a major component of the hydrologic cycle, influences the agricultural practices in an area. Thus, trends in rainfall as well as rainy days are of major concern to farmers. Present study focusses on understanding the rainfall trends and its spatial distribution along with the trends in vegetation. An approach where Normalized Difference Vegetation Index (NDVI) procured from MODIS NDVI as an indicator for vegetation was used in this study. Mann Kendall trend test was performed on a 0.25-degree gridded data and the trends were then compared with the distribution of groundwater stress map of the study area. The study tries to examine the coupled use of NDVI and rainfall trends to decrypt the groundwater exploitation in the region. Further Ghataprabha river basin being susceptible to drought by hosting most of the significantly decreasing trend was investigated further. The propagation of severe drought return periods within the basin resembles the agro-meteorological trends. Even within the limitations of the present study, the methodology with further modifications promises to portray strong indication of groundwater exploitation. © Published under licence by IOP Publishing Ltd.
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    Application of remotely sensed NDVI and soil moisture to monitor long-term agricultural drought
    (SPIE spie@spie.org, 2019) Pathak, A.A.; Dodamani, B.M.
    The present study aims to assess agricultural drought using remote sensing based NDVI and soil moisture products in a drought prone river basin of India. The study is conducted in the Ghataprabha river basin which is a sub basin of river Krishna, in India and is agriculturally dominated. Major portion of the basin is semiarid and rainfall is the major sources of water for agriculture. Gridded soil moisture data from Modern-Era Retrospective analysis for Research and Applications (MERRA) from 1980 to 2015 is considered to derive Standardized Soil moisture Index (SSI) at different time scales. The Vegetation Condition Index (VCI) was calculated from MODIS NDVI products from 2000-2013. The results of VCI and SSI indicated significant number of drought episodes during the study period while severe agricultural drought was observed during 2001-2003. A Good agreement between SSI and VCI was observed during drought year. © 2019 SPIE.
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    Impacts of dams on sediment yield and coastal processes using swat and dsas tools
    (Springer Science and Business Media Deutschland GmbH, 2021) Athira, K.; Yadav, A.; Dodamani, B.M.; Dwarakish, G.S.
    Soil erosion is considered as one of the major causes of land degradation and reservoir sedimentation. Therefore, modeling of runoff and sediment yield at the catchment level is necessary. In this study, an attempt was made to simulate runoff and sediment yield of hydrologically similar basins of Kali River and Aghanashini River which joins west coast of India. A conceptual, continuous time and semi-distributed SWAT2012 (Soil and Water Assessment Tool) model was selected for the modeling purpose. For the last two decades, Kali river basin experienced a very high rate of soil erosion due to various developmental activities in the basin. Therefore, it is essential to identify the soil loss within the basin. There are five dams constructed across the Kali river basin for various purposes. The presence of these reservoirs regulates stream flow and thus sediment load in the basin. However, the free movement of water across the Aghanashini river catchment leads to the unobstructed passage of sediments to the river mouth, as the catchment is not disturbed by the reservoir. This study deals with the impacts of the dams on stream flow, sediment load and the response of shoreline. Digital Shoreline Analysis System (DSAS) tool was used to analyze the shoreline changes. Simulated and observed values of runoff are compared, and calibration and validation were done for the basins using SWAT-CUP. Analysis of calibration and validation results shows that the model has a good performance. Therefore, the SWAT model can be used to conduct further studies in these study areas. Sediment yield obtained at the catchment outlet was 1.07 t/ha/year and 4.58 t/ha/year for Kali and Aghanashini basins, respectively. Less amount of sediment load in the Kali basin indicate the influence of reservoir operation on stream flow and sediment yield. The shoreline analysis of both the basins concluded that Devbagh beach connecting with Kali river estuary is under erosion and Aghanashini beach is under naturally nourished condition. © Springer Nature Singapore Pte Ltd 2021.
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    Temporal crop monitoring with sentinel-1 sar data
    (Springer Science and Business Media Deutschland GmbH, 2021) Salma, S.; Dodamani, B.M.
    Spatial and temporal analysis of crops and other land surface features is the major application of the present spaceborne sensors. Among most of the spaceborne sensors, synthetic aperture radar (SAR) is having the advantage of all-weather capability with low-frequency bands. SAR data is useful for decompositions, crop classifications, etc. In this study, paddy fields are classified using Sentinel-1 ground range detection. Synthetic aperture radar data with the combination of vertical polarization with the horizontal receiver (VV and VH) is selected for the temporal variation analysis and classification analysis of paddy fields along with the plantations. Multi-temporal classification analysis is done using random forest classifier, and correlation obtained is 0.78 and 0.45 in VH and VV polarization, respectively, and the error rate shows significant variation in both the polarizations, i.e., 0.05 and 0.25 (in VH and VV polarizations, respectively), which indicates more error rate in VV polarization band. In this study area, VH polarization shows better classification and correlation compared to VV polarization due to double bounce effect of urban features, paddy and plantation at the stem elongation and booting stage in VV polarization. © Springer Nature Singapore Pte Ltd 2021.
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    Assessment of meteorological drought return periods over a temporal rainfall change
    (Springer Science and Business Media Deutschland GmbH, 2021) Datta, R.; Pathak, A.A.; Dodamani, B.M.
    Investigation of the rainfall homogeneity along with bivariate frequency analysis of drought considering change points in long-term annual precipitation series has been carried out in this study. Nonparametric Pettitt’s test was applied for detecting change points of annual precipitation series at different grid locations over the Ghataprabha River Basin. Depending on the results of change point analysis, we divided the entire period of 1950–2013 into two subperiods: from 1950 to 1980 and 1981 to 2013. Characterization of meteorological drought is performed with the help of the Standardized Precipitation Index (SPI) at a time scale of three months for the period before the change point (1950–1980), after the change point (1981–2013) and for the entire period of 1950–2013. Three Archimedean copulas, namely Clayton, Gumbel–Houggard, and Frank, were tested for joint distribution modeling. The Akaike’s and Bayesian information criteria have been implemented for selecting the best copula; the Gumbel–Hougaard copula performed comparatively better for all three periods. Drought return periods were calculated using the joint distribution of drought characteristics. The study gives valuable insight into drought risk management on a regional scale. © Springer Nature Singapore Pte Ltd 2021.
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    Hybrid Deep Learning-Based Potato and Tomato Leaf Disease Classification
    (Springer Science and Business Media Deutschland GmbH, 2024) Patil, M.A.; Manur, M.; Laxuman, C.; Parane, K.; Dodamani, B.M.; Sunkad, G.
    Predicting potato and tomato leaf disease is vital to global food security and economic stability. Potatoes and tomatoes are among the most important staple crops, providing essential nutrition to millions worldwide. However, many tomato and potato leaf diseases can seriously reduce food productivity and yields. We are proposing a hybrid deep learning model that combines optimized CNN (OCNN) and optimized LSTM (OLSTM). The weight values of LSTM and CNN models are optimized using the modified raindrop optimization (MRDO) algorithm and the modified shark smell optimization (MSSO) algorithm, respectively. The OCNN model is used to extract potato leaf image features and then fed into the OLSTM model, which handles data sequences and captures temporal dependencies from the extracted features. Precision, recall, F1-score, and accuracy metrics are used to analyze the output of the proposed OCNN-OLSTM model. The experimental performance is compared without optimizing the CNN-LSTM model, individual CNN and LSTM models, and existing MobileNet and ResNet50 models. The presented model results are compared with existing available work. We have received an accuracy of 99.25% potato and 99.31% for tomato. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.