Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
6 results
Search Results
Item Examining the effects of vented dams on land use and land cover in the Shambhavi Catchment: a multitemporal sentinel imagery analysis(Elsevier Ltd, 2024) Chandana, S.; Aishwarya Hegde, A.; Umesh, P.; Chandan, M.C.The rapid expansion of the global economy has given rise to concerning ecological consequences, notably a dramatic increase in land cover change (LCC). This section presents how to use the Google Earth Engine (GEE) cloud platform to explore the administrative divisions of the Southern Indian Dakshina Kannada (DK) district, which were chosen for their LCC susceptibility. Leveraging GEE, we generated a time series dataset tracking LCC over a 4-year period (2019–22). Our findings demonstrate an impressive overall accuracy (OA) of 96.30% for 2019 and 95.47% for 2022. A significant revelation in our study is the 13.64% reduction in forested areas, accompanied by a 0.68% increase in urban development within the district. This research attempt offers vital insights into the impact of dam construction on LCC, aiding informed decisions on water resource management. This research promotes a sustainable and ecologically conscious approach to holistic development in the study region and beyond. © 2024 Elsevier B.V.Item Predictive Model for Enhancing Water Quality Monitoring leveraging Satellite Data(Institute of Electrical and Electronics Engineers Inc., 2024) Prakash, P.; Sowmya Kamath, S.; Bhattacharjee, S.; Umesh, P.; Gangadharan, K.V.Remote sensing data can be used instead of conventional methods to collect image data from multiple satellites with acceptable spatial and temporal coverage. The proposed study makes use of Landsat 8 Operational Land Imager (OLI) data. The relationship between reflectance retrieved from Landsat 8 OLI data and in-situ data is established through the application of machine learning model. The dataset is made up of Landsat8 band extractions for water quality features. Water with high turbidity is predicted and verified using in-situ data that was gathered within the chosen temporal and spatial limits. © 2024 IEEE.Item Future transition in climate extremes over Western Ghats of India based on CMIP6 models(Springer Science and Business Media Deutschland GmbH, 2023) Shetty, S.; Umesh, P.; Shetty, A.The effect of climate change on the tropical river catchments in the Western Ghats of India is studied using the Coupled Model Intercomparison Project-6 data (CMIP-6). Multi-model ensembles of rainfall and temperature are constructed using the Random Forest ensemble technique for bias-corrected GCMs in the near future (2014–2050) and far future (2051–2100) horizons. For the two catchments each in the southern, central, and northern Ghats, the trend in minimum and maximum temperatures, precipitation, and other indices are calculated. By 2100, dry sub-humid and humid catchments will see a higher increase in mean annual temperature than per-humid central catchments. In future decades, the warm days and nights increase by 45–50% and 40–70%, respectively, with twofold warming in the winter season. Under a climate change scenario, annual rainfall increases in Vamanapuram, Ulhas, and Purna, while Chaliyar, Netravati, and Aghanashini catchments experience a decrease in rainfall in the far future with an increase in pre-monsoon rainfall. The southern catchments are anticipated to have contrasting variations in the rainfall extremes; northern catchments face a substantial increase in very wet to extremely wet days and medium to heavy rainfall. In all catchments (excluding Vamanapuram), cumulative wet days increase with a decrease in cumulative dry days. After the mid-twenty-first century, humid to per-humid catchments encompass an increase in cool nights, whereas it disappears in dry sub-humid catchments of the Ghat. Interestingly, warming tendencies begin to slow down after 2050. This investigation can assist in comprehending the regional climate extremes in the Western Ghats to formulate better climate risk planning and adaptation strategies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item The effectiveness of machine learning-based multi-model ensemble predictions of CMIP6 in Western Ghats of India(John Wiley and Sons Ltd, 2023) Shetty, S.; Umesh, P.; Shetty, A.The popularity of cutting-edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top-performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi-Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo-climatic basins. Precipitation is best simulated by EC-Earth3 and BCC-CSM2-MR. Maximum temperature by MPI-ESM1-2-HR, EC-Earth3-Veg, INM-CM5-0 and MPI-ESM1-2-LR. Minimum temperature by INM-CM5-0 and MPI-ESM1-2-LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per-humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms. © 2023 Royal Meteorological Society.Item Comparison of Neural Networks for Binary Spatial Classification of Rice Field by Studying Temporal Pattern using Dual Polarimetric SAR Measurements(Springer, 2024) Aishwarya Hegde, A.; Umesh, P.; Tahiliani, M.P.Timely and precise information on rice cultivation plays a pivotal role in reshaping the global food and agricultural system. Synthetic aperture radar, with its capability to observe around the clock and in all weather conditions, is an invaluable tool for monitoring rice distribution. Such comprehensive cropland data at vast spatial scales not only enhances crop management but also provides critical support to governmental decision-making processes. The paper focuses on Binary classification by learning the temporal pattern of the Rice pixel. Time series curves of VV, VH, VV+VH, and VV/VH polarization and major rice varieties, MO4 and Kaje Jaya, cultivated in the area are analyzed to study the similarity of the curves the similarities in the curves, which could influence the temporal pattern recognition capacity of deep learning models. The study underscores the superior performance of RNN models, particularly BiLSTM and the proposed Dual Branch BiLSTM, over their CNN counterparts, such as 3DCNN and 3DUNET, especially for the VH and VV+VH polarizations. Specifically, the Dual Branch BiLSTM emerged as a standout, exhibiting an accuracy rate of 99.92% for combination of VH and VV+VH polarization. This model adeptly combined features from both VH and VV+VH polarizations, ensuring robust rice field mapping. Our results present a promising avenue for enhanced rice mapping, especially in tropical or subtropical zones, through the nuanced application of deep learning models. © Indian Society of Remote Sensing 2024.Item Multi-season rice mapping using deep learning models with multitemporal Sentinel-1 SAR data in the Kuttanad Delta, Kerala(Taylor and Francis Ltd., 2025) Aishwarya Hegde, A.; Nair, M.K.; Umesh, P.; Tahiliani, M.P.Timely and precise monitoring of rice paddies is essential for sustaining production, ensuring food security, and addressing climate challenges, as rice is a significant contributor to greenhouse gas emissions. Accurate rice mapping, facilitated by Sentinel-1 SAR, unaffected by weather is used in Machine learning (ML) and Deep learning (DL) models for multiclass classification of rice cropping seasons by analysing temporal backscatter patterns. A modified Dual-Branch BiLSTM model is developed to capture VH backscatter variations across the homogeneous and heterogeneous rice-growing landscapes. The study compares the performance of ML models, Random Forest (RF) and Support Vector Machine (SVM), with DL models, BiGRU and BiLSTM-BiGRU, for mapping Rabi, Kharif, double-cropping rice fields, and non-rice areas in the Kuttanad Delta region. A thorough evaluation of the proposed models was conducted using metrics like Precision, Recall, and F1 Score to assess their effectiveness. The results show that the Modified Dual-branch BiLSTM model attains F1 scores as high as 0.97 in homogeneous regions and 0.94 in heterogeneous rice-growing landscapes, highlighting its robustness and strong generalisation in mapping rice in varied landscape areas, particularly in the cloudy tropical and subtropical regions where optical data are often not consistently available during the rice cultivation season. © 2025 Informa UK Limited, trading as Taylor & Francis Group.