Browsing by Author "Avtar, Ram"

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    Performance assessment of phased array type L-band Synthetic Aperture Radar and Landsat-8 used in image classification
    (Elsevier, 2022-09-02T00:00:00) Suman, Swati; Srivastava, Prashant K.; Petropoulos, George P.; Avtar, Ram; Prasad, Rajendra; Singh, Sudhir Kumar; Mustak, S.K.; Faraslis, Ioannis N.; Gupta, Dileep Kumar
    Owing to its large spatial and periodic temporal coverage, satellite remote sensing has emerged for formulating and implementing strategies for natural resources management. This study focuses on an appraisal of satellite sensors and artificial intelligence techniques such as kernels-based support vector machines (SVMs) and artificial neural networks (ANNs). These methods are used for land cover classification on multispectral and microwave satellite images acquired from Landsat-8 and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR) over Varanasi, India. The analysis shows comparable the performance of the microwave-classified image compared with the multispectral Landsat-8 image. ANNs and SVMs performed best with an overall accuracy of 97.3% and kappa coefficient of 0.97 for the Landsat-8 image, whereas SVM radial basis function was the best classifier for the ALOS PALSAR image with 94% overall accuracy. Other statistical indices such as kappa total disagreement and allocation disagreement scores revealed similar performances. The analysis demonstrated the ability of microwave data in land cover classification studies with satisfactory performance. These data can be used in nearly all weather and environmental conditions for broad image classification purposes when optical and infrared imagery such as Landsat are unavailable. � 2022 Elsevier Inc. All rights reserved.
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    Substantial changes in Gaseous pollutants and health effects during COVID-19 in Delhi, India
    (PeerJ Inc., 2023-01-09T00:00:00) Singh, Bhupendra; Pandey, Puneeta; Wabaidur, Saikh Mohammad; Avtar, Ram; Kumar, Pramod; Rahman, Shakilur
    Background. Coronavirus disease has affected the entire population worldwide in terms of physical and environmental consequences. Therefore, the current study demonstrates the changes in the concentration of gaseous pollutants and their health effects during the COVID-19 pandemic in Delhi, the national capital city of India. Methodology. In the present study, secondary data on gaseous pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and ozone (O3) were collected from the Central Pollution Control Board (CPCB) on a daily basis. Data were collected from January 1, 2020, to September 30, 2020, to determine the relative changes (%) in gaseous pollutants for pre-lockdown, lockdown, and unlockdown stages of COVID-19. Results. The current findings for gaseous pollutants reveal that concentration declined in the range of 51%�83% (NO), 40%�69% (NOx), 31%�60% (NO2), and 25%�40% (NH3) during the lockdown compared to pre-lockdown period, respectively. The drastic decrease in gaseous pollutants was observed due to restricted measures during lockdown periods. The level of ozone was observed to be higher during the lockdown periods as compared to the pre-lockdown period. These gaseous pollutants are linked between the health risk assessment and hazard identification for non-carcinogenic. However, in infants (0�1 yr), Health Quotient (HQ) for daily and annual groups was found to be higher than the rest of the exposed group (toddlers, children, and adults) in all the periods. Conclusion. The air quality values for pre-lockdown were calculated to be ��poor category to ��very poor�� category in all zones of Delhi, whereas, during the lockdown period, the air quality levels for all zones were calculated as ��satisfactory,�� except for Northeast Delhi, which displayed the ��moderate�� category. The computed HQ for daily chronic exposure for each pollutant across the child and adult groups was more than 1 (HQ > 1), which indicated a high probability to induce adverse health outcomes. � Copyright 2023 Singh et al.