Rapid Mapping of Potential Soil Erosion Using Earth Observation Images Through Google Earth Engine Cloud Platform Muskan Borana, Arpitha Iype, Vanama Venkata Sai Krishna 2023 IEEE India Geoscience and Remote Sensing Symposium Ingarss 2023, 2023 Soil erosion is a significant environmental problem that affects soil productivity, water quality, and ecosystem health. Rapid mapping of potential soil erosion areas is essential for effective soil conservation planning and management. Mapping potential soil erosion risk areas in the Narmada basin, India, using earth observation satellite imagery and Google Earth Engine cloud computing platform, along with estimating the rainfall erosivity factor (R-factor), topographic factor (LS-factor), and crop management factor (C-factor) using remote sensing and geographic information system (GIS) techniques. Then these parameters are incorporated to generate a soil erosion risk map for the study area. The research indicated that the Narmada basin is highly prone to soil erosion, with about 46 percent of the area falling into the moderate to very high soil erosion risk levels. The upper catchment regions were found to be the most vulnerable to soil erosion due to steep slopes and sparse vegetation cover. The study provides important insights into the spatial distribution of soil erosion risk in the Narmada basin and highlights the need for implementing adequate soil conservation measures in high-risk locations. The study’s findings could potentially be used to develop effective soil erosion mitigation techniques in the basin, ensuring the long-term viability of the basin’s soil and water resources.
Integrated geospatial database design for land use pattern analysis and its impact on local governance: A case study of Manesar Urban Complex, Gurugram, India Manuj Dev, Vanama Venkata Sai Krishna, Deepak Kumar, Manas Kumar Jha Journal of Applied and Natural Science, 2023 The absence of an integrated geospatial database hinders effective decision-making among various stakeholders in national projects and programs. The study aimed to create an integrated geospatial database for the Manesar tehsil, District Gurugram, Haryana. Another objective was to create a land use map to analyze the temporal change in land use patterns and its socioeconomic impact along the surrounding areas. The first stage of the study involved creating an integrated database after interviewing different stakeholders. In the second stage, the satellite imagery was georeferenced using Ground Control Points, and the land use patterns were digitized using Landsat imagery for 1987, 2011, 2014, and 2022. The study suggested that the construction area has grown up to 6% while cultivation has dropped significantly from 76% in 1987 to 50% in 2022. Likewise, the industrial area has grown up to 2% and natural vegetation dropped in coverage from 11% to 4%. The area witnessed a population growth rate of 3.2% per year and a decadal growth rate of 39.7%. Less than 50% of the land in the tehsil is used for agricultural purposes. It estimates that 4% of the tehsil's entire surface was covered by natural vegetation in 2019, down from 11% in 1987. The study recommends the creation of an integrated geospatial database that will help reduce duplication and speed up decision-making in government. This will pave the way for improving e-governance in local bodies. The framework created for developing the database system is universally applicable and can be used appropriately by other states, regions, and the entire country.
Appraisal of dual polarimetric radar vegetation index in first order microwave scattering algorithm using sentinel–1A (C - band) and ALOS - 2 (L - band) SAR data Vijay Pratap Yadav, Rajendra Prasad, Ruchi Bala, Prashant K. Srivastava, V. S. K. Vanama Geocarto International, 2022 The dual polarimetric study including degree of polarization (mL) and energy span (λ1+ λ2) for vegetation targets infer the accuracy of vegetation algorithms. The Sentinel − 1 A and ALOS − 2 satellite data were utilized for dual polarimetric radar vegetation index (DPRVI), polarimetric radar vegetation index (PRVI) and radar vegetation index (RVI) computation and simulation of backscattering coefficient (σ0 (dB)) at VV polarization. The DPRVI, PRVI and RVI were computed at VV + VH and HH + HV polarizations, respectively. The simulation of σ0 (dB) at HH + HV can be show better results than VV + VH polarization using derived radar vegetation descriptors (V). The DPRVI, PRVI and RVI showed higher R2 and lower RMSE values at L – band than C - band. The DPRVI at L – band (HH + HV polarization) was indicated highest R2 = 0.91 and lower RMSE = 0.31 (dB) among all proposed V in the algorithm.
Inundation mapping of Kerala flood event in 2018 using ALOS-2 and temporal Sentinel-1 SAR images V. S. K. Vanama, Mohamed Musthafa, Unmesh Khati, R. Gowtham, Gulab Singh, et al. Current Science, 2021 In August 2018, the southern Indian state of Kerala received unusually heavy rainfall leading to largescale flooding and destruction. Reliable flood inundation maps derived from remote sensing techniques help in flood disaster management activities. The freely available Sentinel-1A/B SAR data have the potential for flood inundation mapping due to its all-weather imaging capability. In this study, temporal dual-pol Sentinel-1 SAR data have been utilized. Single-date ALOS-2/PALSAR-2 commercial SAR data were also used to fill the gap between Sentinel-1 acquisitions during the peak flood-period. Two flood-mapping approaches, viz. rule-based classification in case of temporal SAR data and histogram-based thresholding approach in case of single-date imagery, were utilized in the study. Also, flood inundation mapping with different data constraints, i.e. availability of single-date and multi-date imagery has been analysed and discussed. The obtained results were validated with multiple data sources like survey data and secondary data from government agencies. An overall accuracy of 90.6% and a critical success index of 81.6% were achieved with the proposed rule-based classification approach. This study highlights the potential of the combination of Sentinel-1 and ALOS-2/PALSAR-2 data for flood inundation mapping.
Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform V. S. K. Vanama, Y. S. Rao, C. M. Bhatt European Journal of Remote Sensing, 2021 The new era of cloud platform technologies opens up many opportunities for near real-time dissemination of disaster information to the end-users. The present study utilizes the European Space Agency (ESA) Research and Service Support (RSS) CloudToolbox platform to monitor the spatio-temporal dynamics of a flood event. A collective flood monitoring framework is formulated to rapidly assess cyclone-induced flood in the CloudToolbox platform. The outputs of the framework are spatio-temporal maps of flood extent, depth, and hot spot zones. The framework utilizes Earth Observation (EO) images such as optical and C-band Synthetic Aperture Radar (SAR) images and an automatic Kittler and Illingworth thresholding algorithm for rapid flood mapping. The temporal flood depth maps are created with the Floodwater Depth Estimation Tool (FwDET) which requires only two input parameters, viz. flood extent, and Digital Elevation Model (DEM). Subsequently, flood hotspot zones are also identified. We tested the flood monitoring framework on Amphan cyclone-induced flood event at both regional and local levels. The spatio-temporal flood extent, depth, and hot spot maps are generated for the Amphan cyclone event and a 97% overall accuracy is achieved at the local level. The entire process took less than one hour for regional and local level analysis.
Change detection based flood mapping using multi-temporal Earth Observation satellite images: 2018 flood event of Kerala, India V. S. K. Vanama, Y. S. Rao, C. M. Bhatt European Journal of Remote Sensing, 2021 The future projections of climate change envisage a global increase in extreme precipitation events and subsequent flooding. The reliable and rapid flood maps are the critical parameters in preparing the disaster management plans. This study demonstrated an effective flood mapping framework using freely available multi-temporal Earth Observation (EO) images, including C-band Sentinel-1A & 1B Synthetic Aperture Radar (SAR) images and optical WorldView-3 images, for analyzing the 2018 flood event of Kerala, India. Two Change Detection (CD) techniques, i.e. Ratio Index (RI) and Normalized Change Index (NCI) combined with semi-automatic thresholding are implemented on temporal descending pass SAR images for flood identification. For ascending pass SAR images, the statistical-based thresholding method is implemented. The results indicate that combined use of ascending and descending pass SAR images contributed to a better understanding of flood conditions. It is also inferred that the use of a pre-flood image can enhance flood area estimation and helps in minimizing the overestimation errors. The results also found that NCI outperforms RI for Kerala flood event. Flood area extracted from these techniques is plotted against the Indian Meteorological Department (IMD) rainfall datasets, which showed a similar trend. Field photographs and optical images are used for validation purposes.
Urban area classification with quad-pol L-band ALOS-2 SAR data: A case of Chennai city, India Dhanashri S. Kanade, V. S. K. Vanama, Sanjay Shitole 2020 IEEE India Geoscience and Remote Sensing Symposium Ingarss 2020 Proceedings, 2020 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.
Satellite based drought assessment over Latur, India using soil moisture derived from SMOS International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2018
Examining the effect of the physical characteristics of the urban green & blue spaces in heat mitigation: A case study of Pune 38th Asian Conference on Remote Sensing Space Applications Touching Human Lives Acrs 2017, 2017
RECENT SCHOLAR PUBLICATIONS
Appraisal of dual polarimetric radar vegetation index in first order microwave scattering algorithm using sentinel–1A (C-band) and ALOS-2 (L-band) SAR data VP Yadav, R Prasad, R Bala, PK Srivastava, VSK Vanama Geocarto International 37 (21), 6232-6250 , 2022 2022 Citations: 20
Real-Time Flood Mapping with Temporal SAR Images Using ESA CloudToolbox Service VSK Vanama, YS Rao Urban Science and Engineering: Proceedings of ICUSE 2020, 133-141 , 2021 2021
Inundation mapping of Kerala flood event in 2018 using ALOS-2 and temporal Sentinel-1 SAR images VSK Vanama, M Musthafa, U Khati, R Gowtham, G Singh, YS Rao Current Science 120 (5), 915-925 , 2021 2021 Citations: 20
Flood damage assessment with multitemporal earth observation SAR satellite images: A case of coastal flooding in Southern Thailand G Dadhich, VSK Vanama, H Miyazaki, I Pal Disaster Resilience and Sustainability, 265-276 , 2021 2021 Citations: 2
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Change detection based flood mapping using multi-temporal Earth Observation satellite images: 2018 flood event of Kerala, India VSK Vanama, YS Rao, CM Bhatt European Journal of Remote Sensing 54 (1), 42-58 , 2021 2021 Citations: 73
Urban area classification with quad-pol L-band ALOS-2 SAR data: A case of Chennai city, India DS Kanade, VSK Vanama, S Shitole 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 58-61 , 2020 2020 Citations: 2
Burn area mapping in Google Earth Engine (GEE) cloud platform: 2019 forest fires in eastern Australia KVS Babu, VSK Vanama 2020 International Conference on Smart Innovations in Design, Environment … , 2020 2020 Citations: 5
Burn area mapping in google earth engine (GEE) cloud platform: 2019 forest fires in eastern Australia KV Suresh Babu, VSK Vanama 2020 International Conference on Smart Innovations in Design, 26 , 2020 2020 Citations: 3
De-speckling of synthetic aperture radar using discrete fourier transform S Shitole, V Jain, VSK Vanama IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium … , 2020 2020 Citations: 5
Split-Window Based Flood Mapping with L-Band ALOS-2 SAR Images: A Case of Kerala Flood Event in 2018 VSK Vanama, S Shitole, U Khati, YS Rao IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium … , 2020 2020 Citations: 5
GEE4FLOOD: rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth Engine cloud platform VSK Vanama, D Mandal, YS Rao Journal of Applied Remote Sensing 14 (3), 034505-034505 , 2020 2020 Citations: 87
Ground truth mapping with multi-temporal earth observation data in ESA CloudTool box: A case of Kerala flood event occurred in 2018 VSK Vanama, KVS Babu, YS Rao 2020 International Conference on Emerging Smart Computing and Informatics … , 2020 2020 Citations: 3
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Sentinel-1 SLC preprocessing workflow for polarimetric applications: A generic practice for generating dual-pol covariance matrix elements in SNAP S-1 toolbox D Mandal, DS Vaka, NR Bhogapurapu, VSK Vanama, V Kumar, YS Rao, ... Preprints , 2019 2019 Citations: 50
Change detection based flood mapping of 2015 flood event of Chennai city using sentinel-1 SAR images VSK Vanama, YS Rao IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium … , 2019 2019 Citations: 41
Satellite based Drought Assessment Over Latur, India Using Soil Moisture Derived From SMOS D Kolekar, VSK Vanama, YS Rao The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2018 2018 Citations: 4
Rapid detection of regional level flood events using AMSR-E satellite images VSK Vanama, C Praveen Kumar, YS Rao Proceedings of International Conference on Remote Sensing for Disaster … , 2018 2018 Citations: 1
Rapid Detection of Regional Level Flood Events Using AMSR-E Satellite VSK Vanama, CP Kumar, YS Rao Proceedings of International Conference on Remote Sensing for Disaster … , 2018 2018
Fire Detection in a Varying Topography Using Landsat-8 for Nainital Region, India BKV Suresh, VSK Vanama 2018 4th International Conference for Convergence in Technology (I2CT), 165 , 2018 2018 Citations: 2
MOST CITED SCHOLAR PUBLICATIONS
GEE4FLOOD: rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth Engine cloud platform VSK Vanama, D Mandal, YS Rao Journal of Applied Remote Sensing 14 (3), 034505-034505 , 2020 2020 Citations: 87
Change detection based flood mapping using multi-temporal Earth Observation satellite images: 2018 flood event of Kerala, India VSK Vanama, YS Rao, CM Bhatt European Journal of Remote Sensing 54 (1), 42-58 , 2021 2021 Citations: 73
Sentinel-1 SLC preprocessing workflow for polarimetric applications: A generic practice for generating dual-pol covariance matrix elements in SNAP S-1 toolbox D Mandal, DS Vaka, NR Bhogapurapu, VSK Vanama, V Kumar, YS Rao, ... Preprints , 2019 2019 Citations: 50
Change detection based flood mapping of 2015 flood event of Chennai city using sentinel-1 SAR images VSK Vanama, YS Rao IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium … , 2019 2019 Citations: 41
Appraisal of dual polarimetric radar vegetation index in first order microwave scattering algorithm using sentinel–1A (C-band) and ALOS-2 (L-band) SAR data VP Yadav, R Prasad, R Bala, PK Srivastava, VSK Vanama Geocarto International 37 (21), 6232-6250 , 2022 2022 Citations: 20
Inundation mapping of Kerala flood event in 2018 using ALOS-2 and temporal Sentinel-1 SAR images VSK Vanama, M Musthafa, U Khati, R Gowtham, G Singh, YS Rao Current Science 120 (5), 915-925 , 2021 2021 Citations: 20
Geospatial multicriteria approach for solid waste disposal site selection in Dehradun city, India VVS Krishna, K Pandey, H Karnatak Current Science, 549-559 , 2017 2017 Citations: 17
Assessment of forest fire danger using automatic weather stations and MODIS TERRA satellite datasets for the state Madhya Pradesh, India KVS Babu, VSK Vanama, A Roy, PR Prasad 2017 International Conference on Advances in Computing, Communications and … , 2017 2017 Citations: 11
Flood modelling with global precipitation measurement (GPM) satellite rainfall data: a case study of Dehradun, Uttarakhand, India SK VV, AK Dikshit, K Pandey Multispectral, hyperspectral, and ultraspectral remote sensing technology … , 2016 2016 Citations: 10
Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform VSK Vanama, YS Rao, CM Bhatt European Journal of Remote Sensing 54 (1), 589-609 , 2021 2021 Citations: 5
Burn area mapping in Google Earth Engine (GEE) cloud platform: 2019 forest fires in eastern Australia KVS Babu, VSK Vanama 2020 International Conference on Smart Innovations in Design, Environment … , 2020 2020 Citations: 5
De-speckling of synthetic aperture radar using discrete fourier transform S Shitole, V Jain, VSK Vanama IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium … , 2020 2020 Citations: 5
Split-Window Based Flood Mapping with L-Band ALOS-2 SAR Images: A Case of Kerala Flood Event in 2018 VSK Vanama, S Shitole, U Khati, YS Rao IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium … , 2020 2020 Citations: 5
Satellite based Drought Assessment Over Latur, India Using Soil Moisture Derived From SMOS D Kolekar, VSK Vanama, YS Rao The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2018 2018 Citations: 4
Burn area mapping in google earth engine (GEE) cloud platform: 2019 forest fires in eastern Australia KV Suresh Babu, VSK Vanama 2020 International Conference on Smart Innovations in Design, 26 , 2020 2020 Citations: 3
Ground truth mapping with multi-temporal earth observation data in ESA CloudTool box: A case of Kerala flood event occurred in 2018 VSK Vanama, KVS Babu, YS Rao 2020 International Conference on Emerging Smart Computing and Informatics … , 2020 2020 Citations: 3
Flood damage assessment with multitemporal earth observation SAR satellite images: A case of coastal flooding in Southern Thailand G Dadhich, VSK Vanama, H Miyazaki, I Pal Disaster Resilience and Sustainability, 265-276 , 2021 2021 Citations: 2
Urban area classification with quad-pol L-band ALOS-2 SAR data: A case of Chennai city, India DS Kanade, VSK Vanama, S Shitole 2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS), 58-61 , 2020 2020 Citations: 2
Urban flood mapping with C-band RISAT-1 SAR Images: 2016 flood event of Bangalore city, India VSK Vanama, S Shitole, YS Rao 2020 International Conference on Convergence to Digital World-Quo Vadis … , 2020 2020 Citations: 2
Fire Detection in a Varying Topography Using Landsat-8 for Nainital Region, India BKV Suresh, VSK Vanama 2018 4th International Conference for Convergence in Technology (I2CT), 165 , 2018 2018 Citations: 2
