PhD in Spatial Informatics - International Institute of Information Technology, Hyderabad and Indian Instistitute of Remote Sensing (ISRO), Deharadun, India
Remote-Sensing Techniques in Solid Waste Management Swati Singh, K. V. Suresh Babu, Shivani Singh Waste Management and Treatment Advances and Innovations, 2024 With the increase in population density and the Industrial Revolution, solid waste is increasing in extreme proportion and being dumped all around us. It presents a tremendous urban problem related to waste generation and the by-product of civilization, which is accumulating on the earth every day. Economic and population expansion are equally contributing to the growth of waste products around the world, which has become a center of significant concern, and comprehensive suggestions are being gathered to deal with this challenge. In this perspective, it is not wrong to say that the production of waste indicates an inefficient use of resources, reducing their value. It is very important to think about the scientific methods of waste management globally. In particular, research on the types of primary sites of waste origin is of utmost importance. Taking into account the waste management requirements, a review analysis available in the literature is detailed in this chapter, along with remote-sensing techniques for precise management at multiple spatio-temporal scales. This will allow for the achievement of proper management of solid waste, thereby strengthening the environment and human health.
Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data K. V. Suresh Babu, Swati Singh, G. Kabdulova, Kabzhanova Gulnara, G. Baktybekov Frontiers in Forests and Global Change, 2024 Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.
Adaptive smart farming system using Internet of Things (IoT) and artificial intelligence (AI) modeling Aiot Technologies and Applications for Smart Environments, 2023
Ecological Risk Assessment of Heavy Metal Pollution in Water Resources Swati Singh, K.V.Suresh Babu Metal Organic Frameworks for Wastewater Contaminant Removal, 2023 Environmental impacts represent a global concern due to various inappropriate sources of pollution. The exceeding limit of heavy metal concentrations can lead to several physical disorders. Their abundance in water ecosystems arises from natural and man-made sources through an extensive range of processes and methods. The accumulation of these heavy metals in existing living beings can be very contaminating and dangerous due to their non-degradable nature. For this reason, water quality management and evaluation in relation to heavy metals is of primary significance. Water quality management involves the monitoring of the inclusive water quality profile and the source of heavy metals. This paper elucidates the use of the heavy metal pollution index (HPI) and factor analysis (FA) to identify the pollutant source to assess water quality status and to define remedial and monitoring strategies. This method is the most appropriate and operative process in reviewing the sources of heavy metal pollution in the aquatic environment, the impact on human health, and evaluation techniques.
Burn area mapping in Google Earth Engine (GEE) cloud platform: 2019 forest fires in eastern Australia K. V Suresh Babu, V. S. K. Vanama Proceedings of the 2020 International Conference on Smart Innovations in Design Environment Management Planning and Computing Icsidempc 2020, 2020 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.
Static Fire Danger Estimation Based on the Historical MODIS Hotspot Data Using Geospatial Techniques for the Uttarakhand State, India K. V. S. Babu, , A. Roy, and Journal of Environmental Informatics Letters, 2020 Forest fires are more frequent in Uttarakhand state, especially during the months from March to June each year, causing a major impact on forest resources, wildlife habitats, economic and environment. The essential factors favourable to fire danger are the ignition factors and spreading factors. Ignition factors are either natural or androgenic activities; spreading factors are type of vegetation, topographic properties and terrain characteristics and dynamic weather variables such as temperature, relative humidity and precipitation. Vegetation, topographic and terrain conditions are static, whereas the dynamic variables change more frequently in a day. In this study, Static Fire danger Index (SFDI) has been developed from the MODIS TERRA, AQUA and ASTER datasets namely, MODIS Land cover type yearly L3 global 500 m SIN grid (MCD12Q1) and ASTER GDEM. LULC danger index, Slope danger index, Aspect danger index, Elevation danger index and Terrain ruggedness danger Index have been generated from the above datasets based on the historical fire data and field observation. The SFDI has been generated by integrating the above-mentioned indices and categorized into 5 fire danger classes from no fire to very high. The results were compared with the MODIS active fire product (MCD14) and the accuracy of SFDI is 90%, 95.9% and 92.5% for the years 2015, 2016 and 2017 respectively. The SFDI is generated each year with updated MODIS land cover type product with a spatial resolution of 1 km and is useful to understand the spatial pattern of fire occurrence and also determine areas of high fire danger due to the static ignition factors.
Ground truth mapping with multioral earth observation data in ESA CloudTool box: A case of Kerala flood event occurred in 2018 V. S. K. Vanama, K. V. S. Babu, Y. S. Rao 2020 International Conference on Emerging Smart Computing and Informatics Esci 2020, 2020 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.
Mapping of forest fire burned severity using the sentinel datasets International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ISPRS Archives, 2018
A novel framework for fire risk assessment in Kazakhstan: integrating machine learning and remote sensing KVS Babu, S Singh, G Kabdulova, K Gulnara Frontiers in Forests and Global Change 8, 1680856 , 2025 2025
Evaluation of Forest Fire Susceptibility in Mizoram State Utilizing Analytical Hierarchy Processes and Frequency Ratios KV Suresh Babu, RCP Pillutla Forest Fire and Climate Change: Insights into Science, 401-414 , 2025 2025 Citations: 2
Advancing Wildfire Detection Through Enhanced Satellite Technologies SB KV, S Singh MDPI , 2024 2024
Remote-Sensing Techniques in Solid Waste Management S Singh, KVS Babu, S Singh Waste Management and Treatment, 294-306 , 2024 2024 Citations: 1
Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data KV Suresh Babu, S Singh, G Kabdulova, K Gulnara, G Baktybekov Frontiers in Forests and Global Change 7, 1296100 , 2024 2024 Citations: 1
Mapping burned areas in Kazakhstan using KazEOSat 1 datasets SB KV, S Singh, K Gulzhiyan, G Kabzhanova MDPI , 2024 2024
Weather-based rice yield prediction in Kerala using ANN, SMLR and normal regression PL Davis, B Ajithkumar, KR Riya, A Vysakh, K Babu JOURNAL OF KRISHI VIGYAN Учредители: Diva Enterprises Private Limited 12 (4 … , 2024 2024
Ecological Risk Assessment of heavy metal pollution in water resources S Singh, KVS Babu Metal Organic Frameworks for Wastewater Contaminant Removal, 281-297 , 2023 2023 Citations: 4
Enhancing Maritime Safety Through IoT-Integrated High-Pressure Water Mist Fire Fighting System KV Babu, M Gowtham, A Soorya 2023 3rd International Conference on Pervasive Computing and Social … , 2023 2023
Enhancing the Safety of LPG Storage Through the Implementation of Safety Instrumented Systems with IoT Technology M Sanjay, KV Babu, A Yoonus, AJ Anto 2023 3rd International Conference on Pervasive Computing and Social … , 2023 2023 Citations: 1
Forest fire emissions and their impact on global climate change L Goparaju, RCP Pillulata, KV Suresh Babu, HB Tecimen Frontiers in Forests and Global Change 6, 1188632 , 2023 2023 Citations: 2
Adaptive smart farming system using Internet of Things (IoT) and artificial intelligence (AI) modeling S Singh, KVS Babu AIoT Technologies and Applications for Smart Environments 57, 113 , 2023 2023 Citations: 1
Machine learning approach for climate change impact assessment in agricultural production S Singh, KVS Babu, S Singh Visualization techniques for climate change with machine learning and … , 2023 2023 Citations: 11
Developing Forest Fire Danger index using NASA MODIS TERRA Near Real Time satellite datasets SB KV, A Roy Authorea Preprints , 2022 2022
Role of hyperspectral imaging for precision agriculture monitoring S Singh, SB KV ADBU Journal of Engineering Technology 11 (1) , 2022 2022 Citations: 9
A machine learning algorithm approach to map wildfire probability based on static parameters SB KV, V Visser, G Moncrieff, J Slingsby, R Altwegg Environmental Sciences Proceedings 13 (1), 10 , 2021 2021 Citations: 1
Forest fire susceptibility mapping for Uttarakhand state by using geospatial techniques S Singh, KV Suresh Babu Recent technologies for Disaster Management and Risk reduction: Sustainable … , 2021 2021 Citations: 14
Static fire danger estimation based on the historical Modis hotspot data using geospatial techniques for the Uttarakhand state, India KVS Babu, A Roy Int. Soc. Environ. Inf. Sci 4, 11-21 , 2020 2020 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
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
MOST CITED SCHOLAR PUBLICATIONS
Forest fire risk modeling in Uttarakhand Himalaya using TERRA satellite datasets S Babu KV, A Roy, PR Prasad European Journal of Remote Sensing 49 (1), 381-395 , 2016 2016 Citations: 94
Mapping of forest fire burned severity using the sentinel datasets KV Suresh Babu, A Roy, R Aggarwal The International Archives of the Photogrammetry, Remote Sensing and Spatial … , 2018 2018 Citations: 36
Developing the forest fire danger index for the country kazakhstan by using geospatial techniques KVS Babu, G Kabdulova, G Kabzhanova Journal of Environmental Informatics Letters 1 (1), 48-59 , 2019 2019 Citations: 21
Chemical properties, soil moisture status and litter production influenced by the growth of MPTS. T Mathew, KVS Babu, KU Maheswaran, BM Kumar 1997 Citations: 19
Forest fire susceptibility mapping for Uttarakhand state by using geospatial techniques S Singh, KV Suresh Babu Recent technologies for Disaster Management and Risk reduction: Sustainable … , 2021 2021 Citations: 14
Machine learning approach for climate change impact assessment in agricultural production S Singh, KVS Babu, S Singh Visualization techniques for climate change with machine learning and … , 2023 2023 Citations: 11
Developing forest fire danger index using geo-spatial techniques KVS Babu International Institute of Information Technology, Hyderabad , 2019 2019 Citations: 11
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
Developing the static fire danger index using geospatial technology KVS Babu, A Roy, PR Prasad 2016 2nd International Conference on Contemporary Computing and Informatics … , 2016 2016 Citations: 10
Role of hyperspectral imaging for precision agriculture monitoring S Singh, SB KV ADBU Journal of Engineering Technology 11 (1) , 2022 2022 Citations: 9
Agroforestry practices of central Kerala in a socio-economic milieu BM Kumar, KVS Babu, NK Sasidharan, T Mathew Proceedings of the seminar on socioeconomic research in forestry. Kerala … , 1992 1992 Citations: 9
Forest fire danger index based on modifying Nesterov Index, fuel, and anthropogenic activities using MODIS TERRA, AQUA and TRMM satellite datasets SB KV, A Roy Land Surface and Cryosphere Remote Sensing III 9877, 188-196 , 2016 2016 Citations: 6
Static fire danger estimation based on the historical Modis hotspot data using geospatial techniques for the Uttarakhand state, India KVS Babu, A Roy Int. Soc. Environ. Inf. Sci 4, 11-21 , 2020 2020 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
Fire potential index for Uttarakhand using daily MODIS TERRA satellite datasets SB KV, ARPR Prasad Organized By Department of Civil Engineering, Indian Institute of Technology … , 2015 2015 Citations: 5
Ecological Risk Assessment of heavy metal pollution in water resources S Singh, KVS Babu Metal Organic Frameworks for Wastewater Contaminant Removal, 281-297 , 2023 2023 Citations: 4
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
Evaluation of Forest Fire Susceptibility in Mizoram State Utilizing Analytical Hierarchy Processes and Frequency Ratios KV Suresh Babu, RCP Pillutla Forest Fire and Climate Change: Insights into Science, 401-414 , 2025 2025 Citations: 2
Forest fire emissions and their impact on global climate change L Goparaju, RCP Pillulata, KV Suresh Babu, HB Tecimen Frontiers in Forests and Global Change 6, 1188632 , 2023 2023 Citations: 2
Remote-Sensing Techniques in Solid Waste Management S Singh, KVS Babu, S Singh Waste Management and Treatment, 294-306 , 2024 2024 Citations: 1
