Faculty Publications
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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 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.Item 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.Item A NDVI Based Approach To Detect The Landslides By Using Google Earth Engine(Institute of Electrical and Electronics Engineers Inc., 2023) Vishnu Vardhan, M.; Harish Kumar, S.; Mohan Kumar, S.; Kundapura, S.Detection of landslide-prone areas plays an important role in planning urban connectivity like roads, bridges, etc. Landslides are generally caused by a variety of factors, the most important of which is rainfall. In this paper, the detection is carried out in four taluks of Chikkamagaluru district, namely Koppa, Sringeri, Mudigere, and Narashimarajpur; these four taluks are located in the Western Ghat region. Landslides are primarily caused by heavy rainfall during the monsoon season. For the detection of landslides, Sentinel optical and SAR data are used because of their 10metre resolution and revisiting period of two to five days. The entire methodology for detecting landslides is carried out in Google Earth Engine due to its large collection of data, which aids in multi-temporal studies. This paper attempts to investigate the capabilities of remote sensing and GIS techniques in the detection of landslides. For the detection of landslides, Normalized Difference Vegetation Index (NDVI) is used for Sentinel-2 data and the SAR backscatter change approach is used for Sentinel-1 images, and I thresholding is applied to both methods to detect areas where landslides had occurred. The main thing is that no previous landslide inventory data is used for detection. The previous landslide inventory is used for validation purposes only. Finally, the performance of both approaches was compared using accuracy assessment properties such as overall accuracy and kappa coefficient to determine which approach is superior. © 2023 IEEE.Item Quantification and Assessment of The Virtual Water Content of Rice Crop: A Case Study of Mysore District, India(Institute of Electrical and Electronics Engineers Inc., 2023) Saicharan, V.; Shwetha, H.R.Agriculture is the largest consumer of freshwater among all sectors. Currently, there are minimal studies to quantify a crop's water consumption and virtual water content of crops in India, especially using geospatial products. To address this issue, the current study employed a geospatial approach to quantify and assess the virtual content of rice crop. The actual evapotranspiration, NDVI and crop yield datasets are used in this study to quantify the virtual water content (VWC) of rice crops in the Mysore district from 2015 to 2019. The results show that the rice crop's water consumption (CWC) and VWC are higher in Kharif than in summer. The rice crop yield in Mysore is reducing, but the CWC was increasing with respect to time during the study period. The maximum VWC was observed in the 2018 Kharif season, i.e., 5228.9 m3/ton, and the lowest VWC (962.7 m3/ton) was observed in the summer of 2016. The findings will make it easier to comprehend how much water rice crops need over the course of various seasons and years, allowing for more effective water management. It will also assist officials and water planners in determining which seasons to minimise supply to achieve sustainable water management, especially in arid and semi-arid regions. © 2023 IEEE.