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Mr. Amitesh Gupta
Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun

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0 Aerosol Science
0 Air Pollution
0 Forest Fire Risks
0 landslide susceptibility
0 Temperature Trend

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Preprint content
Published: 16 July 2021
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Air quality is an important factor for human health conditions. Simultaneously, suitable meteorology poses the circumstances for virus transmission. Hence, we investigated both these two important aspects for the COVID-19 pandemic. We correlated the remote sensing based observations of meteorological parameters and air quality parameters with COVID-19 cases from 657 districts all over the country and found that air quality parameters are playing very crucial role along with a few meteorological parameters for this outbreak. We observed that air temperature, and wind speed were significantly and positively correlated with COVID-19 cases but precipitation and humidity were negatively correlated with confirmed cases. Cloudiness had no significant relation in this aspect. Among the air pollutants, O3 was better correlating with COVID-19 cases. AOD representing the particulate matter concentration also significantly correlated with such cases majorly over Indo-Gangetic plain region. The carbon-pollutants CO was also very high over the same region. Though NO2 and SO2 were reduced during lockdown, due to the power generation and mining activities both these gases were quite highly correlated over eastern India region. We noted the eastern and western coastal districts of India and districts from the low-lying plain areas had more cases during this pandemic. Our study suggests that improving air quality with proper strict regulations and complete lockdown during the peak of pandemic could reduce the misfortune in all over India. Hence, the summer season could be susceptible and might pose a gesture of seasonality for this disease.

ACS Style

Amitesh Gupta. A Correlation based study using air quality and meteorological parameters for the outbreak of COVID-19 on major affected districts in India. 2021, 1 .

AMA Style

Amitesh Gupta. A Correlation based study using air quality and meteorological parameters for the outbreak of COVID-19 on major affected districts in India. . 2021; ():1.

Chicago/Turabian Style

Amitesh Gupta. 2021. "A Correlation based study using air quality and meteorological parameters for the outbreak of COVID-19 on major affected districts in India." , no. : 1.

Preprint content
Published: 29 June 2021
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The present study has investigated the role of meteorology and air quality for recovering from the COVID-19 pandemic in India. Using Pearson’s correlation method, we look into if there is any significant association occurs between the district level recovery case counts and different remote sensing based environmental variables. Among weather parameters, only precipitation and air temperature found to be significantly correlated with recovery situation. However, all the pollutants’ concentration was negatively correlated with count of recovery cases. It depicts that air quality might has greater importance in recovery from this disease. During late monsoon onwards, recovery rate was getting more than the infections which indicate that lesser temperature and good rainfall could help the air to be freshen. Through air pollution was greater during winter and post monsoon than the summer season in India the higher recovery rate was counted during post-monsoon and winter which suggest that patients may require lesser temperate ambient for better recovery. Spatial patterns also suggest that north-eastern hilly region followed by districts located in the northern mountain had better recovery where the pollutants’ concentration was also quite lower during the study period. Therefore, improving air quality with proper preventive precaution could help to combat the pandemic situation in India.

ACS Style

Amitesh Gupta; Labani Saha. Does Environment has any role in recovery from COVID-19 pandemic? A case study from India. 2021, 1 .

AMA Style

Amitesh Gupta, Labani Saha. Does Environment has any role in recovery from COVID-19 pandemic? A case study from India. . 2021; ():1.

Chicago/Turabian Style

Amitesh Gupta; Labani Saha. 2021. "Does Environment has any role in recovery from COVID-19 pandemic? A case study from India." , no. : 1.

Preprint content
Published: 26 June 2021
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The present study has investigated the role of regional meteorology and air quality parameters in the outbreak pattern of COVID-19 pandemic in India. Using the remote sensing based dataset of 12 environmental variables we correlated infective case counts at a district level in India. Our investigation carried out on the circumstantial data from more than 300 major affected districts in India and found that air quality parameters are playing very crucial role in this outbreak. Among the air pollutants, O3 was better correlating with infection counts followed by AOD, CO, NO2, BC and SO2. We also observed that among the weather parameters air temperature, incoming shortwave radiation, wind speed are positively and significantly associate with outbreak pattern and precipitation and humidity are negatively correlated with confirmed cases; only cloud cover has no significant relation. We noted that coastal districts in the both coast of India and districts located in the plain and low-lying areas have experienced bitter situation during this pandemic. Our study suggests that improving air quality with proper strict regulations and complete lockdown during the peak of pandemic might reduce the misfortune in all over India.

ACS Style

Amitesh Gupta; Labani Saha. Impact of environmental factors on COVID-19 pandemic in India. 2021, 1 .

AMA Style

Amitesh Gupta, Labani Saha. Impact of environmental factors on COVID-19 pandemic in India. . 2021; ():1.

Chicago/Turabian Style

Amitesh Gupta; Labani Saha. 2021. "Impact of environmental factors on COVID-19 pandemic in India." , no. : 1.

Journal article
Published: 24 April 2021 in Geoscience Frontiers
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The Subarnarekha River in east India experiences frequent high magnitude flooding in monsoon season. In this study, we present an in-depth analysis of flood hydrology and GIS-based flood susceptibility mapping of the entire catchment. About 40 years of annual peak discharge data, historical cross-sections of different gauging sites, and 12 flood conditioning factors were considered. Our flood susceptibility mapping followed an expert knowledge-based multi-parametric analytical hierarchy process (AHP) and optimized AHP-VIP methods. Peak hydrology data indicated more than 5 times higher discharge contrasted with the mean streamflow of the peak monsoon month in all hydro-monitoring stations that correspond to possible overbank flooding in the shallow semi-alluvial reaches of the Subarnarekha River. Width-depth ratio revealed continuous changes on the channel cross-sections at decadal scale in all gauging sites. Predicted flood susceptibility map through optimized AHP-VIP method showed a great amount of areas (38%) have a high probability of flooding and demands earnest attention of administrative bodies. The AHP-VIP based flood susceptibility map was theoritically validated through AUC approach and it showed fairly high accuracy (AUC = 0.93). Our study offers an exceptionally cost and time effective solution to the flooding issues in the Subarnarekha basin.

ACS Style

Sumit Das; Amitesh Gupta. Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geoscience Frontiers 2021, 12, 101206 .

AMA Style

Sumit Das, Amitesh Gupta. Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geoscience Frontiers. 2021; 12 (5):101206.

Chicago/Turabian Style

Sumit Das; Amitesh Gupta. 2021. "Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India." Geoscience Frontiers 12, no. 5: 101206.

Research article
Published: 12 November 2020 in SN Applied Sciences
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Satellite-retrieved aerosol optical depth essentially provides an economical option for regular monitoring of particulate matter (PM) concentration; however, the constrains and challenges come in terms of estimation accuracy. In the present study, we estimated PM2.5 and PM10 (PM of aerodynamic diameter lesser than 2.5, 10 µm, respectively) for 11 sites in Bangladesh using different methods. Univariate model showed destitute performance (R2 < 0.1), whereas integrating MODIS-AOD with surface meteorology, multivariate models enhanced accuracy (R2 > 0.6); meanwhile, radial kernel-based ‘eps’-type support vector regression model outperformed rest (R2 > 0.8). Furthermore, we investigated variations in ground concentration of PM2.5, PM10 during 2013–2018 and found annual mean concentration of 76.34 ± 34.12 µg m−3 and 136.25 ± 68.94 µg m−3, respectively. Predominant anthropogenic contribution to elevated pollution is well remarked by PM2.5/PM10 ratio, highest during January (0.65 ± 0.06) and lowest during July (0.48 ± 0.11). Grievous pollution found in Narayanganj (PM2.5: 100.35 ± 56.76 µg m−3, PM10: 200.25 ± 91.79 µg m−3) and slightest in Sylhet (PM2.5: 56.13 ± 26.99 µg m−3, PM10: 103.94 ± 49.37 µg m−3). Intra-annual pattern asserts winter as sternly befouled and least pollution during monsoon, which may indicate significant influence of meteorology on PM pollution. We found that PM divulged negative correlation with air temperature (PM2.5: −0.78, PM10: −0.73), relative humidity (PM2.5: −0.66, PM10: −0.73) and rainfall (PM2.5: −0.59, PM10: −0.61). This study showed outrageous situation of PM pollution in urban areas in Bangladesh and proposed modest pathway for regular monitoring of PM that will help to combat pollution.

ACS Style

Amitesh Gupta; Moniruzzaman; Avinash Hande; Iman Rousta; Haraldur Olafsson; Karno Kumar Mondal. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh. SN Applied Sciences 2020, 2, 1 -15.

AMA Style

Amitesh Gupta, Moniruzzaman, Avinash Hande, Iman Rousta, Haraldur Olafsson, Karno Kumar Mondal. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh. SN Applied Sciences. 2020; 2 (12):1-15.

Chicago/Turabian Style

Amitesh Gupta; Moniruzzaman; Avinash Hande; Iman Rousta; Haraldur Olafsson; Karno Kumar Mondal. 2020. "Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh." SN Applied Sciences 2, no. 12: 1-15.

Journal article
Published: 02 September 2020 in Atmospheric Pollution Research
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Ground observations over 60–80 sites in India during 2014–18 reveal that Particulate Matter (PM) concentration exceeded National Ambient Air Quality Standard (NAAQS) by 1.5–2.5 times during Pre-Monsoon (PrM), Post-Monsoon (PoM) and Winter (Win) seasons only. Geographically Weighted Regression technique was employed to estimate PM2.5 and PM10 incorporating Aerosol Optical Depth (AOD) measured from geostationary orbit by Indian National Satellite (INSAT-3D) and ERA-INTERIM derived meteorological parameters. INSAT-3DAOD was well correlated with ground measured AOD (R2 > 0.75). Estimated PM found higher during PrM (PM2.5: 103.16 ± 31.39 μg m−3, PM10: 150.07 ± 43.13 μg m−3) than PoM (PM2.5: 58.72 ± 22.9 μg m−3, PM10: 127.17 ± 48.95 μg m−3) and Win (PM2.5: 71.02 ± 20.25 μg m−3, PM10: 96.86 ± 27.63 μg m−3). PM pollution was severe in West India (PM2.5: 87.44 ± 30.07 μg m−3, PM10: 148.13 ± 42.8 μg m−3), 1.2–1.5 times higher than least polluted North-East India. Spatial variability of PM distinctly discern the major prone areas that experience episodic events like dust (PM10 > 150 μg m−3) and biomass burning (PM2.5 >100 μg m−3). Overall country registered increase in AOD, PM10, PM2.5 by 52.34%, 36.23%, 36.58% respectively. Estimation accuracy produced by the model was satisfactory (R2 > 0.6; NMSE <0.25; −0.01 < FB < 0.09). The human footprint perceived by the PM2.5/PM10 ratio signifies that 60.95% PM10 is attributed to PM2.5 across the country and it was highest during Win (73.45%). The changes in spatial distribution of PM highlight that Indo-Gangetic Plain and coastal area in Southern India had undergone major industrialization, urban expansions which resulted in increase of PM2.5 by 50–125 μg m−3 during all seasons.

ACS Style

Amitesh Gupta; Yogesh Kant; Debashis Mitra; Prakash Chauhan. Spatio-temporal distribution of INSAT-3D AOD derived particulate matter concentration over India. Atmospheric Pollution Research 2020, 12, 159 -172.

AMA Style

Amitesh Gupta, Yogesh Kant, Debashis Mitra, Prakash Chauhan. Spatio-temporal distribution of INSAT-3D AOD derived particulate matter concentration over India. Atmospheric Pollution Research. 2020; 12 (1):159-172.

Chicago/Turabian Style

Amitesh Gupta; Yogesh Kant; Debashis Mitra; Prakash Chauhan. 2020. "Spatio-temporal distribution of INSAT-3D AOD derived particulate matter concentration over India." Atmospheric Pollution Research 12, no. 1: 159-172.

Journal article
Published: 29 July 2020 in Remote Sensing
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Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI.

ACS Style

Iman Rousta; Haraldur Olafsson; Moniruzzaman; Hao Zhang; Yuei-An Liou; Terence Mushore; Amitesh Gupta. Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan. Remote Sensing 2020, 12, 2433 .

AMA Style

Iman Rousta, Haraldur Olafsson, Moniruzzaman, Hao Zhang, Yuei-An Liou, Terence Mushore, Amitesh Gupta. Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan. Remote Sensing. 2020; 12 (15):2433.

Chicago/Turabian Style

Iman Rousta; Haraldur Olafsson; Moniruzzaman; Hao Zhang; Yuei-An Liou; Terence Mushore; Amitesh Gupta. 2020. "Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan." Remote Sensing 12, no. 15: 2433.

Other
Published: 17 June 2020
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The COVID-19 pandemic has outspread obstreperously in India. As of June 04, 2020, more than 2 lakh cases have been confirmed with a death rate of 2.81%. It has been noticed that, out of each 1000 tests, 53 result positively infected. In order to investigate the impact of weather conditions on daily transmission occurring in India, daily data of Maximum (TMax), Minimum (TMin), Mean (TMean) and Dew Point Temperature (TDew), Diurnal Temperature range (TRange), Average Relative Humidity, Range in Relative Humidity, and Wind Speed (WS) over 9 most affected cities are analysed in several time frames: weather of that day, 7, 10, 12, 14, 16 days before transmission. Spearman’s rank correlation (r) shows significant but low correlation with most of the weather parameters, however, comparatively better association exists on 14 days lag. Diurnal range in Temperature and Relative Humidity shows non-significant correlation. Analysis shows, COVID-19 cases likely to be increased with increasing air temperature, however role of humidity is not clear. Among weather parameters, Minimum Temperature was relatively better correlate than other. 80% of the total confirmed cases were registered when TMax, TMean, TMin, TRange, TDew, and WS on 12-16 days ago vary within a range of 33.6-41.3° C, 29.8-36.5° C, 24.8-30.4° C, 7.5-15.2° C, 18.7-23.6° C, and 4.2-5.75 m/s respectively, hence, it gives an idea of susceptible weather conditions for such transmission in India. Using Support Vector Machine based regression, the daily cases are profoundly estimated with more than 80% accuracy, which indicate that coronavirus transmission can’t be well linearly correlated with any single weather parameters, rather multivariate non-linear approach must be employed. Accounting lag of 12-16 days, the association found to be excellent, thus depict that there is an incubation period of 14 ± 02 days for coronavirus transmission in Indian scenario.

ACS Style

Amitesh Gupta; Biswajeet Pradhan. Impact of Daily Weather on COVID-19 outbreak in India. 2020, 1 .

AMA Style

Amitesh Gupta, Biswajeet Pradhan. Impact of Daily Weather on COVID-19 outbreak in India. . 2020; ():1.

Chicago/Turabian Style

Amitesh Gupta; Biswajeet Pradhan. 2020. "Impact of Daily Weather on COVID-19 outbreak in India." , no. : 1.

Short communication
Published: 17 June 2020 in Modeling Earth Systems and Environment
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Recently, the large outbreak of COVID-19 cases all over the world has whacked India with about 30,000 confirmed cases within the first 3 months of transmission. The present study used long-term climatic records of air temperature (T), rainfall (R), actual evapotranspiration (AET), solar radiation (SR), specific humidity (SH), wind speed (WS) with topographic altitude (E) and population density (PD) at the regional level to investigate the spatial association with the number of COVID-19 infections (NI). Bivariate analysis failed to find any significant relation (except SR) with the number of infected cases within 36 provinces in India. Variable Importance of Projection (VIP) through Partial Least Square (PLS) technique signified higher importance of SR, T, R and AET. However, generalized additive model fitted with the log-transformed value of input variables and applying spline smoothening to PD and E, significantly found high accuracy of prediction (R2 = 0.89), and thus well-explained complex heterogeneity among the association of regional parameters with COVID-19 cases in India. Our study suggests that comparatively hot and dry regions in lower altitude of the Indian territory are more prone to the infection by COVID-19 transmission.

ACS Style

Amitesh Gupta; Sreejita Banerjee; Sumit Das. Significance of geographical factors to the COVID-19 outbreak in India. Modeling Earth Systems and Environment 2020, 6, 2645 -2653.

AMA Style

Amitesh Gupta, Sreejita Banerjee, Sumit Das. Significance of geographical factors to the COVID-19 outbreak in India. Modeling Earth Systems and Environment. 2020; 6 (4):2645-2653.

Chicago/Turabian Style

Amitesh Gupta; Sreejita Banerjee; Sumit Das. 2020. "Significance of geographical factors to the COVID-19 outbreak in India." Modeling Earth Systems and Environment 6, no. 4: 2645-2653.

Preprint content
Published: 15 June 2020
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The COVID-19 pandemic has outspread obstreperously in India. Within a period of 95 days, from March 02 to June 04, India surpassed 2 lakh in count of infected cases. Approximately 3 out of each 1000 people in India has been tested till date and 53 per 1000 tests results positively infected. During the first week of March, only 14 out of each 1000 tests were resulting as positively infected and it has been extended at a rate of 71/1000 tests in the first week of June, which may indicate a sign of community spread of this disease. Mann-Kendall test denotes that the count of daily confirmed cases is significantly increasing with estimated Sen’s slope of ~ 76 persons/day in entire country. This trend has escalated from ~ 5 persons/day in March to ~ 249 persons/day in the very first week of June. Among major affected cities, Mumbai and Delhi are noted with extremely high rate of increase. In the 3 out of 5 megacities in India: Delhi, Mumbai, and Chennai, the count of daily transmission have reached beyond of 1200 after the third week of May which indicate that the allowance to the migrants might make an easy-way of coronavirus transmission. Additionally, Pettitt test indicates an abrupt change in increasing trend over entire country on April 17, 2020. The nationwide transmission rate was ~ 22 persons/day before April 17 and afterward it amplified to ~ 174 persons/day. Moreover, all the major affected cities also registered multi-fold increase in transmission rate after the evaluated change point over that city; explicitly, this increment was more than 20 times over Pune, Chennai and Ahmedabad. Therefore, the nationwide imposed lockdown in India might have very less impact on flattening the curve of daily confirmed case.

ACS Style

Amitesh Gupta; Biswajeet Pradhan. Assessment of temporal trend of COVID-19 outbreak in India. 2020, 1 .

AMA Style

Amitesh Gupta, Biswajeet Pradhan. Assessment of temporal trend of COVID-19 outbreak in India. . 2020; ():1.

Chicago/Turabian Style

Amitesh Gupta; Biswajeet Pradhan. 2020. "Assessment of temporal trend of COVID-19 outbreak in India." , no. : 1.

Preprint content
Published: 05 May 2020
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Very recently, large outbreak of COVID-19 cases all around the world has also whacked India since approximately 30,000 cases confirmed within first three months of transmission. The present study used long-term climatic records of air temperature (T), rainfall (R), actual evapotranspiration (AET), solar radiation (SR), specific humidity (SH), wind speed (WS) with topographic altitude (E) and population density (PD) at regional level to investigate the spatial association with number of COVID-19 infections (NI). Bivariate analysis failed to find any significant relation (except SR) with the number of infected cases within 36 provinces in India. Variable Importance of Projection (VIP) through Partial Least Square (PLS) technique signified higher importance of SR, T, R and AET. However, Generalized Additive Model (GAM) fitted with log-transformed value of input variables and applying spline smoothening to PD and E, significantly found high accuracy of prediction (R2=0.89), thus, well explained the complex heterogeneity among association of regional parameters with COVID-19 cases in India. Our study suggests that comparatively hot and dry regions in lower altitude of the Indian territory are more prone to the infection by COVID-19 transmission.

ACS Style

Amitesh Gupta; Sreejita Banerjee; Sumit Das. Significance of geographical factors (climatic, topographic and social) to the COVID-19 outbreak in India. 2020, 1 .

AMA Style

Amitesh Gupta, Sreejita Banerjee, Sumit Das. Significance of geographical factors (climatic, topographic and social) to the COVID-19 outbreak in India. . 2020; ():1.

Chicago/Turabian Style

Amitesh Gupta; Sreejita Banerjee; Sumit Das. 2020. "Significance of geographical factors (climatic, topographic and social) to the COVID-19 outbreak in India." , no. : 1.

Journal article
Published: 14 April 2020 in Atmospheric Research
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The Doon valley located in the foothills of the Indian Himalayan region, is recently undergoing rapid urbanisation. Hence, in this study, we investigate the variation of black carbon mass concentration (BC) and biomass burning generated black carbon mass concentration (BCbb) for one year (October 2017 to September 2018) using ground-based observations. We also present here the relationship of BC with meteorological parameters and effect of transport. Using three different case studies we explain the dominant role of local meteorological conditions and effect of long-range transported aerosols impacting the study site in different seasons. Satellite and reanalysis datasets were also used to strengthen the analysis. A seven channel Aethalometer (AE-33) was used to measure real-time BC at the study site. Seasonal analysis indicates a winter maximum (9.45 ± 2.65 μg m−3) followed by post-monsoon (6.94 ± 1.52 μg m−3), pre-monsoon (5.35 ± 1.46 μg m−3), and lowest in the monsoon season (3.36 ± 0.62 μg m−3). The daily mean ground-based BC had a moderately positive correlation with Modern-Era Retrospective analysis for Research and Applications, version-2 (MERRA2) BC (r = 0.52) and Copernicus Atmosphere Monitoring Service (CAMS) BC (r = 0.74), these correlations get better when compared with monthly datasets. Effect of local emissions and long-range transport was studied intricately using wind rose, Conditional Bivariate Probability Function (CBPF), and Concentration Weighted Trajectory (CWT) analysis. Case studies of high BCbb specifically in the months of November, January, and May precisely segregated the dominant effect of meteorology and transport phenomena in different seasons. Primarily during the months of November and May, the long-range transport of aerosols from regions dominated by crop residue burning and forest fires, respectively, increased the BCbb concentration over the study site. While during January, emissions generated from local burning activities for space heating and cooking, aided by lower temperatures, increased the BCbb, indicating the dominant role of meteorology. These results were further substantiated through the aerosol subtypes acquired from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite.

ACS Style

Vignesh Prabhu; Ashish Soni; Sandeep Madhwal; Amitesh Gupta; Sangeeta Sundriyal; Vijay Shridhar; V. Sreekanth; Parth Sarathi Mahapatra. Black carbon and biomass burning associated high pollution episodes observed at Doon valley in the foothills of the Himalayas. Atmospheric Research 2020, 243, 105001 .

AMA Style

Vignesh Prabhu, Ashish Soni, Sandeep Madhwal, Amitesh Gupta, Sangeeta Sundriyal, Vijay Shridhar, V. Sreekanth, Parth Sarathi Mahapatra. Black carbon and biomass burning associated high pollution episodes observed at Doon valley in the foothills of the Himalayas. Atmospheric Research. 2020; 243 ():105001.

Chicago/Turabian Style

Vignesh Prabhu; Ashish Soni; Sandeep Madhwal; Amitesh Gupta; Sangeeta Sundriyal; Vijay Shridhar; V. Sreekanth; Parth Sarathi Mahapatra. 2020. "Black carbon and biomass burning associated high pollution episodes observed at Doon valley in the foothills of the Himalayas." Atmospheric Research 243, no. : 105001.

Conference paper
Published: 29 July 2019 in Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition
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LiDAR is one of the fast growing remote sensing techniques. Terrestrial laser Scanner (TLS) provides precise information about forest inventory in the form of 3D point cloud. An approach to extract tree parameters: diameter at breast height (dbh) tree height and stem length followed by volume estimation has been discussed here. The area selected for the study is Barkot Forest Range in Dehradun district of Uttarakhand. The main tree species in this region are Shorea robusta (Sal), Tectona grandis (Teak), Mallotus philippensis (Indian Red Wood) and Terminalia alata (Saj). Scans were collected using Riegl vz- 400 laser scanner. Ground measurements were also recorded which included tree height and diameter at breast height (dbh). Both field measurements and TLS based computations showed excellent correlation. Forest Survey of India (FSI) in 1996 published site and species specific volumetric equations using dbh. However, three parameters: dbh, stem length and form factor have been considered in the present study. The comparative study shows excellent match between ground based measurements and TLS derived height and dbh parameters with R2 value of 0.96 and 0.98 respectively. Moreover, in respect to volume estimation, excellent correlation of 0.98 and 0.97 was achieved between the two approaches for Sal and Teak respectively. However, for Indian Red Wood, one of the estimations using FSI equation showed negative value but the present approach produced no such anomalous outcome. The reason may be attributed to the fact that FSI based volumetric equation valid only for Assam was used due to the unavailability of Barkot specific equation. Finally, volumetric equation was developed for Terminalia alata (Saj) as there is no equation available for this particular species. The study also advocates an inverse relationship between Form Factor and dbh. This new approach may prove to be indispensable for species and site specific volume estimation in near future.

ACS Style

Rajarshi Bhattacharjee; Amitesh Gupta; Subrata Nandy; Triparna Sett. Tree Parameters retrieval and volume estimation using Terrestrial Laser Scanner: A case Study on Barkot Forest. Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition 2019, 1 .

AMA Style

Rajarshi Bhattacharjee, Amitesh Gupta, Subrata Nandy, Triparna Sett. Tree Parameters retrieval and volume estimation using Terrestrial Laser Scanner: A case Study on Barkot Forest. Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition. 2019; ():1.

Chicago/Turabian Style

Rajarshi Bhattacharjee; Amitesh Gupta; Subrata Nandy; Triparna Sett. 2019. "Tree Parameters retrieval and volume estimation using Terrestrial Laser Scanner: A case Study on Barkot Forest." Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition , no. : 1.

Conference paper
Published: 14 June 2019 in Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition
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Seismic risk assessment in the high mountains of Himalaya is necessary toaccommodate safe and suitable sites for homing with also to direct the pathway of plan and policyfor development sustainably. The continuous orogeny results often earthquakes, mostly, the areaaround fault lines as have been documented by USGS. Hence, to prepare the vulnerability andsusceptible zonation in East and South Sikkim districts Analytical Hierarchy Process (AHP)technique has been adopted. LANDSAT 8 onboard OLI multispectral data is used to prepare theLand-use Land-cover map of study area using supervised classification techniques, whileCartoSAT-1 version 2 DEM is used to look into the physiographical aspects of this region. Withalso, Geological Survey of India prepared soil and geological map is used to prepare the soil andlineament map. Not only that, ground motion data of four different parameters, have also beenacquired form USGS on about of an earthquake event on 18th September 2011, which hadepicenter at 27.730°N, 88.155°E. Certainly, it has been achieved that, area with soil type ofudorthents entisol in the 10 km buffer zone from the major faults victimized of seismic hazardmostly. By the AHP comparison matrix, proximity of any area to the fault lines found to be mostinfluential followed by the ground motion vectors while the LULC categories are the leastinfluential. Using the weighted overlay analysis, area along the western boundary of East districtand north-west of South district in Sikkim found to be under high seismic risk zone. Risk zones hasbeen verified with the help of archive earthquake data from USGS and approximately 22% area inthese two districts comes under high risk zone.

ACS Style

Amitesh Gupta; Rajarshi Bhattacharjee; Rachna Kachhap; Triparna Sett. Seismic risk zonation using the geospatial tool: A case study over East and South district of Sikkim. Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition 2019, 1 .

AMA Style

Amitesh Gupta, Rajarshi Bhattacharjee, Rachna Kachhap, Triparna Sett. Seismic risk zonation using the geospatial tool: A case study over East and South district of Sikkim. Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition. 2019; ():1.

Chicago/Turabian Style

Amitesh Gupta; Rajarshi Bhattacharjee; Rachna Kachhap; Triparna Sett. 2019. "Seismic risk zonation using the geospatial tool: A case study over East and South district of Sikkim." Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition , no. : 1.

Proceedings
Published: 01 January 2019 in Proceedings
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DInSAR is a renowned method for estimating land subsidence based on the principles of interferometric synthetic aperture radar using different series of the temporal dataset. The present study has been performed using GMTSAR software with Sentinel 1 SAR data of C band for the duration of 2017–2019 (January to April) and focused particularly over the area of Jagadhri city which is situated 100 km away from Chandigarh, which has been identified under the potential threat of land subsidence. The DInSAR method has been applied in this study that came up with an outcome of three interferograms and yearly displacement that broadcast an update on the diagnosis of subsidence activity in the area. A total of six Single Look Complex (SLC) datasets were selectively chosen with a minimum temporal and spatial baseline so that the problem of decorrelation would be minimal. Goldstein filtering has been applied to the deburst interferograms which reduced the noise and, in turn, improved the quality of output. The city is located on the western bank of river Yamuna and about 55 km on the east of Ambala. Due to the presence of unconsolidated sediments in the aquifer system and over-exploitation of groundwater to meet the domestic needs has led to surface deformation in and around the city area. The outcome of this study identifies the area of depression quite distinctly while the accuracy has been assessed by ground survey. The rate of subsidence estimated approximately 4.98 cm/year which can prove to be disastrous over the course of time.

ACS Style

Amitesh Gupta; Udit Asopa; Rajarshi Bhattacharjee. Land Subsidence Monitoring in Jagadhri City Using Sentinel 1 Data and DInSAR Processing. Proceedings 2019, 24, 25 .

AMA Style

Amitesh Gupta, Udit Asopa, Rajarshi Bhattacharjee. Land Subsidence Monitoring in Jagadhri City Using Sentinel 1 Data and DInSAR Processing. Proceedings. 2019; 24 (1):25.

Chicago/Turabian Style

Amitesh Gupta; Udit Asopa; Rajarshi Bhattacharjee. 2019. "Land Subsidence Monitoring in Jagadhri City Using Sentinel 1 Data and DInSAR Processing." Proceedings 24, no. 1: 25.

Journal article
Published: 19 November 2018 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Urbanization has given a massive pace in Land Use Land Cover (LULC) changes in rapidly growing cities like Khulna, i.e. the third largest city of Bangladesh. Such impacting changes have taken place in over-decadal scale. It is important because detailed analysis with regularly monitoring will be fruitful to drag the attention of decision maker and urban planner for sustainable development and to overcome the problem of urban sprawl. In this present study, changes in LULC as an impact of urbanization, have been investigated for years 1997, 2002, 2007, 2012 and 2017; using three generation of Landsat data in geographic information system (GIS) domain which has the height competence in recent time. Initially, LULC have categorised into Built-up, Vegetation, Vacant Land, and Waterbody with the help of supervised classification technique. Field work had been carried out for acquiring training dataset and validation. The accuracy has been achieved more than 85% for the changes assessed. Analysis has an outlet with increase in built-up area by 27.92% in year 1997 to 2017 and continued respectively in each successive interval of half a decade at the given years. On the other side waterbody and vacant land decreased correspondingly. Bound to mention, instead to having largest temporal durability, the moderate spatial resolution of Landsat data has a limitation for such urban studies. These changes are responsible by both of natural or anthropogenic factors. Such study will provide a better way out of optimization of land-use to prepare detail area plan (DAP) of Khulna City Corporation (KCC) and Khulna development authority (KDA).

ACS Style

M. Moniruzzam; A. Roy; C. M. Bhatt; A. Gupta; N. T. T. An; M. R. Hassan. IMPACT ANALYSIS OF URBANIZATION ON LAND USE LAND COVER CHANGE FOR KHULNA CITY, BANGLADESH USING TEMPORAL LANDSAT IMAGERY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, XLII-5, 757 -760.

AMA Style

M. Moniruzzam, A. Roy, C. M. Bhatt, A. Gupta, N. T. T. An, M. R. Hassan. IMPACT ANALYSIS OF URBANIZATION ON LAND USE LAND COVER CHANGE FOR KHULNA CITY, BANGLADESH USING TEMPORAL LANDSAT IMAGERY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; XLII-5 ():757-760.

Chicago/Turabian Style

M. Moniruzzam; A. Roy; C. M. Bhatt; A. Gupta; N. T. T. An; M. R. Hassan. 2018. "IMPACT ANALYSIS OF URBANIZATION ON LAND USE LAND COVER CHANGE FOR KHULNA CITY, BANGLADESH USING TEMPORAL LANDSAT IMAGERY." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5, no. : 757-760.

Journal article
Published: 15 November 2018 in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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People effected due to air pollution in India rose by almost 150% during 1990 to 2015. Diwali event is one of the major anthropogenic source contributing to the air pollution. The study focuses on spatial and temporal distribution of trace gases emitted during pre, on and post diwali days and identify areas with high concentration using station measured and satellite derived data during 2008-2017. The ground measured data shows that during diwali days, NO2, SO2, CO & O3 concentration is almost 1.5 to 7 times the NAAQ safety limits over major cities particularly in northern, western and eastern India. Central and southern India experience low to moderate increase in pollution concentration. Spatial distribution over diwali days using satellite data reveal that NO2 values over India are mostly below NAAQ standards, however high range are observed (27–48 μg/m3) over Delhi, Punjab, Haryana region (Northern zones), Western, central and Eastern Indo-Gangetic plain and this concentration is seen denser on diwali days compared to pre and post diwali. The observation reveal that SO2 concentration is below safety levels over almost entire country except few cities like Delhi region, part of Gujarat, Tamil Nadu and Kolkata region. CO concentration is at higher level than NAAQ standards over Western, central and Eastern Indo-gangetic plain. The regression shows that the satellite derived values are in close agreement with the ground measured over the diwali days. The analysis conclude that the peak of the pollutants during diwali may not be increasing quite drastically over many parts of the cities but the overall spatial distribution of the pollutants is increasing from ‘moderate’ to ‘moderately high’ range.

ACS Style

C. Nanda; Y. Kant; A. Gupta; D. Mitra. SPATIO-TEMPORAL DISTRIBUTION OF POLLUTANT TRACE GASES DURING DIWALI OVER INDIA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2018, IV-5, 339 -350.

AMA Style

C. Nanda, Y. Kant, A. Gupta, D. Mitra. SPATIO-TEMPORAL DISTRIBUTION OF POLLUTANT TRACE GASES DURING DIWALI OVER INDIA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018; IV-5 ():339-350.

Chicago/Turabian Style

C. Nanda; Y. Kant; A. Gupta; D. Mitra. 2018. "SPATIO-TEMPORAL DISTRIBUTION OF POLLUTANT TRACE GASES DURING DIWALI OVER INDIA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5, no. : 339-350.

Article
Published: 14 October 2017 in Spatial Information Research
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In this paper, an attempt has been made to delineate groundwater potential zones in Hingoli district, Maharashtra, India. Remote sensing and traditional data were collected from different sources and analysed in GIS software to prepare thematic maps of different geo-environmental factors such as lithology, drainage, lineaments, slope etc. as these factors having an impact on groundwater availability of an area, directly or indirectly. All these factors have been integrated into GIS software and multi influence factor method was applied to delineate groundwater potential zones. The result has shown about 50% area is having the good potentiality of groundwater, whereas about 6% region falls under very low potential area. The Central part of the study area is having very low groundwater potential, mainly due to the steep slope and rocky outcrop. The southern part of the Hingoli district has shown good groundwater potential, because of the gentle slope which influences water to infiltrate. All the major towns and villages are located in good groundwater prospect areas. Therefore, these locations do not experience extreme drought conditions. The present work is valuable for future planning and management of groundwater resource in Hingoli district.

ACS Style

Sumit Das; Amitesh Gupta; Sasanka Ghosh. Exploring groundwater potential zones using MIF technique in semi-arid region: a case study of Hingoli district, Maharashtra. Spatial Information Research 2017, 25, 749 -756.

AMA Style

Sumit Das, Amitesh Gupta, Sasanka Ghosh. Exploring groundwater potential zones using MIF technique in semi-arid region: a case study of Hingoli district, Maharashtra. Spatial Information Research. 2017; 25 (6):749-756.

Chicago/Turabian Style

Sumit Das; Amitesh Gupta; Sasanka Ghosh. 2017. "Exploring groundwater potential zones using MIF technique in semi-arid region: a case study of Hingoli district, Maharashtra." Spatial Information Research 25, no. 6: 749-756.