Venkata Sai Krishna Vanama
@iitb.ac.in
Indian Institute of Technology Bombay
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Gautam Dadhich, Venkata Sai Krishna Vanama, Hiroyuki Miyazaki, and Indrajit Pal
Elsevier
V. S. K. Vanama, Y. S. Rao, and C. M. Bhatt
Informa UK Limited
Vijay Pratap Yadav, Rajendra Prasad, Ruchi Bala, Prashant K. Srivastava, and V. S. K. Vanama
Informa UK Limited
V. S. K. Vanama and Y. S. Rao
Springer Singapore
V. S. K. Vanama, Y. S. Rao, and C. M. Bhatt
Informa UK Limited
Dhanashri S. Kanade, V. S. K. Vanama, and Sanjay Shitole
IEEE
Globally, 55% of the population lives in urban areas in 2018, and this number is expected to hit 68% by 2050. Earth Observation (EO) images based mapping of the urban regions is a critical parameter in the sustainable urban planning process. In recent years, rapid urban growth is experienced in the coastal metropolitan city of India-Chennai. The two land regions, having heterogeneous land uses, as high-rise high-density and medium-rise low-density of the Chennai city are taken as study area. The fully-polarimetric L-band ALOS-2 Synthetic Aperture Radar (SAR) data is used for rapid identification of the urban regions. With respect to this, a comparative assessment of the two supervised classification algorithms such as Wishart and Support Vector Machine (SVM) is presented. The same training data set is used for both algorithms, and a confusion matrix is created algorithm wise. The results of classification with the two classes as urban and non urban indicate that the SVM outperformed the Wishart supervised classification algorithm.
K. V Suresh Babu and V. S. K. Vanama
IEEE
Forest fires occur throughout the year in rainforests and deserts of Australia. The disastrous bush fire event occurred during November 2019, and lasted until February 2020, destroying more than 46 million acres of land. Burn area mapping is a major parameter in carrying out mitigation measures and regrowth activities by forest officials or fire managers post fire event. In this study, multi-temporal satellite datasets such as images acquired from Sentinel-2 (S2) and Landsat-8 (L8) missions are used to map the burn areas. Two thematic indices such as Differenced Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) are implemented on the study area. The entire analysis, i.e., accessing the datasets, preprocessing, and calculation of indices for brunt area mapping is carried out on Google Earth Engine cloud platform. Rather than ground survey, the active fire product VIIRS product (VNP14IMGTDL) is used as a proxy for the actual fire indices in accuracy assessment. Results revealed that RBR showed better accuracy than dNBR for both the datasets (S2 and L8). S2 burn severity maps of dNBR and RBR showed better accuracy than L8 burn severity maps because of S2 having a higher spatial resolution. Thus, S2 datasets can be useful for rapid mapping of burn areas with improved spatial as well as temporal resolution.
Sanjav Shitole, Vijay Jain, and V. S. K. Vanama
IEEE
The role of Synthetic Aperture Radar (SAR) images is inevitable in remote sensing applications. One of the major concern in SAR imagery is that basic textures are generally affected by multiplicative speckle noise. Speckle noise is a consequence of image formation under coherent radiation, but it often carries useful information about the scene being imaged. However, speckle noise is considered undesirable as it damages the resolution and affects the tasks of human interpretation. The proposed framework uses Boxcar filter for reduction of speckle-noise whilst retaining the point targets, edges and texture with the inclusion of Discrete Fourier Transform (DFT) in the speckle reduction framework. A novel technique is propounded, which aims at having a fine control on the high-frequency components by tuning the radius of the frequency rectangle.
V. S. K. Vanama, Sanjay Shitole, U. Khati, and Y. S. Rao
IEEE
The Kerala state of India experienced a devastating flood during Aug 2018, which incurred huge socio-economic losses and human fatalities. ALOS-2 L-band SAR image acquired during the peak flood was used in this study. A split-window approach combined with thresholding algorithm was used for analyzing the 2018 flood event of Kerala, India. The SAR image splitting and tile selection was carried out based on two parameters, namely the Coefficient of Variation (CV) and ratio to the scene. Kittler and Illingworth's thresholding algorithm was implemented on the selected split images. Euclidean distance was used to shortlist the split images with large variation in the data representing both thematic classes (flood/non-flood). An independent split based analysis (ISBA) was implemented in which respective threshold values obtained from the split images are averaged to get an optimum threshold value. From the results, we observe that an underestimation of flood area in urban land use due to double bounce, volume scattering and shadow effects. Validation is carried out on a small subset area for which the field data was available, and an accuracy of 73 % was obtained.
Venkata Sai Krishna Vanama, Dipankar Mandal, and Yalamanchili Subrahmanyeswara Rao
SPIE-Intl Soc Optical Eng
Abstract. The present state of the art technologies for flood mapping are typically tested on small geographical regions due to limitation of resources, which hinders the implementation of real-time flood management activities. We proposed a unified framework (GEE4FLOOD) for rapid flood mapping in Google Earth Engine (GEE) cloud platform. With the unexpected spells of extreme rainfall in August 2018, many parts of Kerala state in India experienced a major disastrous flood. Therefore, we tested the GEE4FLOOD processing chain on August 2018 Kerala flood event. GEE4FLOOD utilizes multitemporal Sentinel-1 synthetic aperture radar images available in GEE catalog and an automatic Otsu’s thresholding algorithm for flood mapping. It also utilizes other remote sensing datasets available in GEE catalog for permanent water body mask creation and result validation. The ground truth data collected during the Kerala flood indicates promising accuracy with 82% overall accuracy and 78.5% accuracy for flood class alone. In addition, the entire process from data fetching to flood map generation at a varying geographical extent (district to state level) took ∼2 to 4 min.
V. S. K. Vanama, K. V. S. Babu, and Y. S. Rao
IEEE
Kerala received an extensive rainfall during Jul-Aug 2018 which led to disastrous flood condition in many places of Kerala state. Optical Remote Sensing (RS) images acquired during the flood event with low cloud cover are used for ground truth mapping. Earth Observation (EO) images from three satellite, i.e., Sentinel-2, Landsat-8 and World View-3 are used. Two indices, i.e. Green Normalized Difference Vegetation Index (GNDVI) and Modified Normalized Difference Water Index (MNDWI) are used to analyze the flood pattern in the study area. A hybrid approach involving thresholding the indices combined with manual digitization is carried out for ground truth flood area extraction. Here, we used the CloudToolbox service provided by the European Space Agency (ESA) to process and analyze the Earth Observation (EO) data. From the images, it is quite evident that the flood water was increased from 12 Aug - 22 Aug 2018 and started receding by 27 Aug 2018.
V.S. K. Vanama, Sanjay Shitole, and Y. S. Rao
IEEE
Flood mapping in urban areas is a rigorous and crucial task in disaster management. Bangalore, one of the Indian megacities, has experienced severe flooding in July 2016. To analyze this flood event, RISAT-1 satellite images were acquired before and after the flood. Various change detection methods were applied to the processed SAR images to identify the flood area. Horizontal like polarized data (HH) is highly sensitive to identify permanent water bodies and also flood affected areas. Permanent water bodies and high elevated areas extracted from DEM were masked out form the results for accurate urban flood mapping. The results show that the spatial distribution of flood was better identified by Normalized Change Index (NCI) method. The results reveal that difference and ratio change detection methods ensued in over and underestimation of flood area, which may be due to the use of moderate resolution RISAT-1 SAR images. In urban areas, the use of images acquired with RISAT FRS mode may give better results due to its high spatial resolution.
Venkata Sai Krishna Vanama, Ch. Praveen Kumar, and Y. S. Rao
Springer International Publishing
Remote sensing plays a prominent role in the rapid detection of the flood event at a regional level. In this paper, the potential of AMSR-E images in regional level flood detection was identified. The study area of the research covers a part of Krishna river basin in the Andhra Pradesh state of India. Spatio-temporal database of daily Land Surface Water Coverage (LSWC) was developed by using Normalized Difference Polarization Index (NDPI). NDPI is calculated using AMSR-E brightness temperature of vertical and horizontal polarizations at 36.5 GHz frequency. The flood anomaly identified from the LSWC database is in strong agreement with actual flood events like Ogni cyclone. To extract the hidden information and similarities in the temporal images, image similarity was calculated by using Bhattacharya distance. Based on the similarity values, all the images in the database are ranked which helps in rapid flood information extraction. Among the various flood events identified by the database, Ogni cyclone is chosen for in-depth analysis. The SAR images acquired during the Ogni cyclone was used to validate the results of AMSR-E outputs.
Babu K.V. Suresh and Venkata Sai Krishna Vanama
IEEE
Forest fires are the most frequent phenomenon during the summer season in India, and especially in the hilly terrains of Uttarakhand forests. Remote sensing sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Visible Infrared Imaging Radiometer Suite (VIIRS) with coarse spatial resolution on board different satellites were used to detect the forest fires across the world. Landsat-8 Operational Land Imager (OLI) data has the better spatial resolution (30m) as compared with the MODIS and VIIRS, therefore useful to detect the smaller fires. Nainital district in Uttarakhand state was severely affected by the massive forest fire events occurred during April-May, 2016. The main objective of the study is to identify the potential of Landsat-8 data in detecting the forest fire for varying topographic region like Nainital. Landsat-8 data acquired on 28th April 2016 and 1st May 2016 has been used in this study. The results obtained from Landsat-8 data are compared with the MODIS fire products and showed an improvement in the detection of small fires.
K.V. Suresh Babu, Venkata Sai Krishna Vanama, Arijit Roy, and P. Ramachandra Prasad
IEEE
Forest fires are the most frequently occurred phenomenon during summer seasons in the state Madhya Pradesh. Monitoring and assessment of forest fires are the crucial steps in effective forest fire management. Forest fire danger estimation helps the disaster management authorities to take necessary mitigation measures for minimizing the losses and to evacuate the local people. Fire danger rating systems predict the fire danger based on the meteorological station parameters and ground datasets. McArthur Forest Fire Danger Index (FFDI) is the most popularly used fire danger rating systems using in the country Australia. This index requires large amount of ground datasets for the computation of drought parameter. In India, it is very difficult to compute the drought parameter due to the unavailability of instruments and man power. In the present research, McArthur Fire Danger Index was modified by inducing Normalized multiband drought index (NMDI) that was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA surface reflectance product MOD09GA as a substitute for fuel availability parameter. To test the robustness of modified FFDI, the research was carried out on Madhya Pradesh state for the assessment of forest fire danger. The results obtained from modified McArthur fire danger index were validated by using MODIS active fire hot spot location data (MOD14) and achieved an overall accuracy of 82%. The research concludes that modified FFDI can be used for assessing the forest fire danger in case of unavailability of fuel availability data for a particular forest.
V. V. Sai Krishna, Kamal Pandey, and Harish Karnatak
Current Science Association
Solid waste generation is increasing rapidly in urban areas of India as well as globally. As land resources for waste disposal are limited in highly populated countries like India, identification of solid waste disposal sites in urban centres is a challenging task, as this involves physical, socio-economic and environmental factors. Dehradun, the capital city of Uttarakhand at present has only one disposal site which is not having good spatial accessibility for all the locations in the city and also it is an environmentally vulnerable site. The present study aims to find suitable sites for decentralized solid waste disposal using geospatial techniques with multi spatio-temporal remote sensing data. A geospatial multicriteria analysis was performed with weighted overlay technique by considering various criteria such as physical, social and demographic aspects of the city for locating the solid waste disposal site(s).
Sai Krishna V. V., Anil Kumar Dikshit, and Kamal Pandey
SPIE
Urban expansion, water bodies and climate change are inextricably linked with each other. The macro and micro level climate changes are leading to extreme precipitation events which have severe consequences on flooding in urban areas. Flood simulations shall be helpful in demarcation of flooded areas and effective flood planning and preparedness. The temporal availability of satellite rainfall data at varying spatial scale of 0.10 to 0.50 is helpful in near real time flood simulations. The present research aims at analysing stream flow and runoff to monitor flood condition using satellite rainfall data in a hydrologic model. The satellite rainfall data used in the research was NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), which is available at 30 minutes temporal resolution. Landsat data was used for mapping the water bodies in the study area. Land use land cover (LULC) data was prepared using Landsat 8 data with maximum likelihood technique that was provided as an input to the HEC-HMS hydrological model. The research was applied to one of the urbanized cities of India, viz. Dehradun, which is the capital of Uttarakhand State. The research helped in identifying the flood vulnerability at the basin level on the basis of the runoff and various socio economic parameters using multi criteria analysis.