Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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.