Faculty Publications
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Publications by NITK Faculty
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Item 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.Item A QGIS Plug-in for Processing MODIS Data(Institute of Electrical and Electronics Engineers Inc., 2019) Aishwarya Hegde, A.; Umesh, U.; Shetty, A.In the past few decades number of Earth-observing satellites are continuously gathering information and only about 10 percent of the information is utilized by the users. With so much accessible information the researchers have not explored the datasets completely as there is absence of effective tool to process the information. MODIS data sensors have accessible data at various temporal and spatial resolutions. To productively use these datasets in open-source GIS programming like QGIS, there is a need to pre-process the dataset using a plug-in. The plug-in is built using python and PyQt interface for QGIS.The plug-in operates on MODIS Data (Terra/Aqua/Combined) computerizes and process the functionalities for MODIS products like MOD11, MOD09, MOD21. The processed datasets can be largely used in investigation of time series analysis for some earth resource application. © 2019 IEEE.Item Static Fire Risk Index for the Forest Resources of Karnataka(Institute of Electrical and Electronics Engineers Inc., 2019) Konkathi, P.; Shetty, A.; Venkatesh, V.; Yathish, P.H.; Umesh, U.Forest fires are the major cause of degradation of forest. Forest fires have caused substantial damage in the state of Karnataka in terms of economic, social, environmental impacts on humans and also loss of biodiversity. Fire risk indices are important tools for the management of forest fires. They are developed based on static and/or dynamic factors influencing the occurrence of fire and propagation of fire. The objective of the present study was to develop a new static fire risk index based on parameters influencing forest fire such as fuel type, elevation, slope, aspect, terrain ruggedness, proximity to a road, proximity to water bodies and proximity to settlements. MODIS Land cover type yearly L3 global 500m SIN grid(MCD12Q1) was used to compute fuel type index based on historical fire data, SRTM DEM was used to compute slope index, aspect index, elevation index, and terrain ruggedness index. Road index, settlement index, and water body index were developed from the proximity maps generated. A geographic information system (GIS) was utilized adequately to join diverse forest fire causing factors for demarcating static fire risk index. The evaluated exactness was around 87%, i.e., the developed GIS-based static fire risk index of the examination zone was observed to be in solid concurrence with actual fire affected regions. The study area exhibited 32.38% prone to fire risk. © 2019 IEEE.Item Monitoring Harmful Algal Blooms in the Indian Ocean Region Using Satellite Remote Sensing Data(Institute of Electrical and Electronics Engineers Inc., 2023) Ramesh, N.V.K.; Spandana, A.P.; Sri, N.N.; Kumar, D.R.; Ratnam, D.V.; Vani, B.V.Nowadays, fishermen and other people dependent on marine life and seafood face severe problems due to HABs (harmful algal blooms). HABs are severely affecting climate change, fishermen, and the Indian economy in many ways. Therefore, detecting harmful algal blooms in various coastal regions is necessary to increase economic growth and save aquatic animal life. The present study summarizes the algal bloom events analysis reported in India during of January 2004, September 2004, March 2008, and March 2013. The detection of algal blooms in Asian Pacific Data Research Center(APDRC) using Chlorophyll, sea surface temperature of Moderate Resolution Imaging Spectroradiometer(MODIS) satellite data is considered for the significant algal blooms events that occurred over the Indian Ocean in January 2004, September 2004, March 2008, and March 2013. In addition, the corresponding zonal total surface currents from OSCAR satellite data are analysed to investigate the HAB characteristics. It is evident from the results that various factors for such algal blooms are based on the environmental factors prevalent during the blooms period. The outcome of this work would help understand the spatial and temporal variability of Algal blooms and for developing algal bloom detection and prediction algorithm using satellite and ground-based observations. © 2023 IEEE.Item High-resolution Soil Moisture Prediction from SMOS using Machine Learning Models(Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Maheshwari, A.; Periasamy, M.; Bhattacharjee, S.Soil moisture is essential for the land carbon cycle, surface and groundwater circulation, heat transport, energy exchange between these systems and other processes. SMOS's (Soil Moisture and Ocean Salinity) 36-kilometer spatial resolution and 3-day temporal resolution offer valuable insights into soil moisture dynamics. This research paper introduces an innovative approach to enhance our understanding and prediction of SMOS values by applying advanced machine learning models. Our research focuses on developing and implementing advanced downscaling techniques, leveraging advanced machine learning algorithms. The primary objective is to establish a robust framework for estimating soil moisture levels at multiple geographic locations within the study region of Oklahoma, USA. To achieve this, three years of SMOS (Soil Moisture and Ocean Salinity) data was integrated with remotely captured images spanning the full range of the electromagnetic spectrum, from visible to infrared wavelengths. The LSTM model performed significantly better in predicting soil moisture values with 0.041 RMSE (m3/m3) and 0.869 (R2) than the other models. © 2025 IEEE.Item Predicting High-Resolution Soil Moisture Using MODIS Bands: A Fusion-Regression Approach(Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Pais, S.M.; Bhattacharjee, S.Soil moisture (SM) is a crucial biophysical parameter that plays a significant role in predicting food security. Monitoring SM is essential as it provides valuable insights into agricultural productivity and the impacts of climate change. Remote sensing platforms retrieve SM data from various satellites, enabling global monitoring. However, the coarse resolution of these datasets limits their ability to capture fine-scale variations in SM. To address this challenge, this study aims to generate a high-resolution SM product using data from the Soil Moisture Active Passive (SMAP) mission. The SMAP data is fused with MODIS spectral bands to create a multi-source dataset, which is then used as input for different regression models to achieve downscaling of SM. Experimental results demonstrate that the Extremely Randomized Trees (ERT) model achieves the minimum error, with RMSE 0.0113 m3/m3, MAE 0.0172 m3/m3, and R2 0.9725. The study focuses on Karnataka, covering the temporal window from 2015 to 2022. © 2025 IEEE.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 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 Spatiotemporal distribution of aerosols over the Indian subcontinent and its dependence on prevailing meteorological conditions(Springer Netherlands rbk@louisiana.edu, 2019) Nizar, S.; Dodamani, B.M.The prevailing meteorological conditions that influence the advection and diffusion of the atmosphere govern the distribution of atmospheric particles from its sources. The present study explores the spatiotemporal distribution of atmospheric aerosols over the Indian subcontinent (5°–40° N, 65°–100° E) and its dependence on the prevailing meteorological conditions. Eleven years (2002–2012) of Aerosol Optical Depth (AOD) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with meteorological parameters extracted from reanalysis data are analysed at monthly timescales. Wind speed, wind divergence and planetary boundary layer height (PBLH) are studied as parameters for advection and diffusion of atmospheric aerosols. The result shows higher aerosol loading during the monsoon season with increased spatial variability. Wind speed and divergence correlate with AOD values both over land (R = 0.75) and ocean (R = 0.82) with increased aerosol loading at higher wind speeds, which are converging in nature. Owing to the varied climatology of the Indian subcontinent, land and ocean areas were classified into subregions. Analysis was carried out over these subregions to infer the influence of meteorological conditions on aerosol loading. Results are indicative of a distinct characteristic in the prevailing meteorological conditions that influence the distribution of certain aerosol types. Further, the PBLH was analysed as an indicator of atmospheric diffusion to infer its importance in aerosol distribution. The results indicate that PBLH explains almost 30 to 90% of the total variance in AOD over the subregions which is particularly evident during the winter and pre-monsoon seasons. © 2019, Springer Nature B.V.