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Article ; Online: Investigation of fire regime dynamics and modeling of burn area over India for the twenty-first century.

Bar, Somnath / Acharya, Prasenjit / Parida, Bikash Ranjan / Sannigrahi, Srikanta / Maiti, Arabinda / Barik, Gunadhar / Kumar, Navneet

Environmental science and pollution research international

2024

Abstract: The characteristics of the vegetation fire (VF) regime are strongly influenced by geographical variables such as regional physiographic settings, location, and climate. Understanding the VF regime is extremely important for managing and mitigating the ... ...

Abstract	The characteristics of the vegetation fire (VF) regime are strongly influenced by geographical variables such as regional physiographic settings, location, and climate. Understanding the VF regime is extremely important for managing and mitigating the impacts of fires on ecosystems, communities, and human activities in forest fire-prone regions. The present study thereby aimed to explore the potential effects of the confounding factors on VF in India to offer actionable and achievable solutions for mitigating this concurring environmental issue sustainably. A global burn area (250 m) data (Fire-CCIv5.1) and fire radiative power (FRP) were used to investigate the dynamics of VF across seven different divisions in India. The study also used the maximum and minimum temperatures, precipitation, population density, and intensity of human modification to model forest burn areas (including grassland). The Coupled Model Intercomparison Project-6 (CMIP6) was used to predict the burn area for 2030 and 2050 future climate scenarios. The present study accounted for a sizable increasing trend of VF during 2001-2019 period. The highest increasing trend was found in central India (513 and 343 km
Language	English
Publishing date	2024-03-19
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-024-32922-w
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Article: Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere Region, India using seven ecosystem models

Sannigrahi, Srikanta

Ecological modelling. 2017 July 24, v. 356

2017

Abstract: The net primary production (NPP) is a key indicator for assessing the terrestrial carbon pools and fluxes from the atmosphere to biosphere in any given ecosystem. Adequate measurement of the sensitivity and uncertainty of regional and global carbon pools ...

Abstract	The net primary production (NPP) is a key indicator for assessing the terrestrial carbon pools and fluxes from the atmosphere to biosphere in any given ecosystem. Adequate measurement of the sensitivity and uncertainty of regional and global carbon pools and fluxes in different climatic and anthropogenic regimes is needed to properly investigate the terrestrial carbon balance. Remote sensing light use efficiency (LUE) approaches can be used to quantify the terrestrial NPP accurately. Using LUE models, NPP was calculated for last two decades of an estuarine ecosystem, the Sundarban Biosphere region, India. Results from seven LUE models: Carnegie-Ames-Stanford Approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), Eddy Covariance-Light Use Efficiency (EC-LUE), MODerate resolution Imaging Spectroradiometer (MOD17), Temperature and Greenness (TG), Greenness and Radiation (GR) models were compared to ascertain model consistency for NPP estimation during the study period 2000–2013. To optimize structural biases in the model accurate parameterization and systematic multi-method assessment were employed. The influence of the input drivers (biophysical, bioclimatic and environmental stress) on model performances was evaluated. Best model performances were observed over cropland, followed by mixed forest and mangrove ecoregion, respectively. Amongst all model, EC-LUE simulated higher NPP at mangrove ecoregion, while the MOD17 model simulated lower NPP in most of the evaluated biomes. In addition, TG and GR models exhibited larger unexplained variances and found statistically significant at mixed forest site. This error was attributed to the absence of environmental stress factors used to drive these model. GLO-PEM and VPM corroborate with NPP prediction among all LUE models. All seven LUE models predict the statistically significant NPP across the biomes and, the poor model performance is attributed to the different parameterization scheme executed for defining the biophysical and stress variables. Biophysical drivers mostly controlled the model performances, followed by environmental stress and bioclimatic drivers, respectively. This analysis is suggesting that the TG and GR model (driven only by the biophysical factors) could be useful in NPP predictions in the regions with no meteorological inputs and real-time eddy covariance flux tower measurement.
Keywords	biosphere ; carbon ; carbon sinks ; cropland ; ecoregions ; eddy covariance ; estuaries ; mixed forests ; model validation ; models ; moderate resolution imaging spectroradiometer ; photosynthesis ; prediction ; primary productivity ; remote sensing ; temperature ; terrestrial ecosystems ; uncertainty ; India
Language	English
Dates of publication	2017-0724
Size	p. 73-90.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	191971-4
ISSN	0304-3800
ISSN	0304-3800
DOI	10.1016/j.ecolmodel.2017.03.003
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Development of automated marine floating plastic detection system using Sentinel-2 imagery and machine learning models.

Sannigrahi, Srikanta / Basu, Bidroha / Basu, Arunima Sarkar / Pilla, Francesco

Marine pollution bulletin

2022 Volume 178, Page(s) 113527

Abstract: The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic ... ...

Abstract	The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%-90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.
MeSH term(s)	Ecosystem ; Environmental Monitoring ; Machine Learning ; Plastics/analysis ; Waste Products/analysis
Chemical Substances	Plastics ; Waste Products
Language	English
Publishing date	2022-04-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2001296-2
ISSN	1879-3363 ; 0025-326X
ISSN (online)	1879-3363
ISSN	0025-326X
DOI	10.1016/j.marpolbul.2022.113527
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Article ; Online: Investigating the Performance of Green Roof for Effective Runoff Reduction Corresponding to Different Weather Patterns

Arunima Sarkar Basu / Bidroha Basu / Francesco Pilla / Srikanta Sannigrahi

Hydrology, Vol 9, Iss 46, p

A Case Study in Dublin, Ireland

2022 Volume 46

Abstract: This article aims to analyse the performance of green roof in runoff reduction. A case study has been conducted through a deployed green roof at the custom house quay building in Dublin, Ireland. Modular green roofs have been deployed which have IoT ... ...

Abstract	This article aims to analyse the performance of green roof in runoff reduction. A case study has been conducted through a deployed green roof at the custom house quay building in Dublin, Ireland. Modular green roofs have been deployed which have IoT scales associated to it for measuring the effective reduction in runoff. Hydro-meteorological variables such as rainfall, temperature, relative humidity and wind speed values were corresponded to the amount of runoff reduction by means of a regression-based relationship. Comparison of the observed runoff reduction from a modular green roof and that estimated based on the developed regression relationship yielded a R 2 value of 0.874. Through this research, a pattern was identified which established that longer records and better weather variables data have the potential to improve the performance of the regression model in predicting the amount of runoff reduction corresponding to different rainfall and weather patterns. In general, performance of green roof was found to be highly positively correlated to the amount of rainfall received; however, low correlation between rainfall and the percentage of runoff reduction indicate that saturated soil in green roofs considerably deteriorates the performance in runoff reduction. Overall, this study can help in identification of locations where installation of green roofs can help mitigate floods at a city scale.
Keywords	green roof ; Dublin CHQ building ; real-world monitoring of green roofs ; multiple linear regression ; Science ; Q
Subject code	333
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development of automated marine floating plastic detection system using Sentinel-2 imagery and machine learning models

Sannigrahi, Srikanta / Basu, Bidroha / Basu, Arunima Sarkar / Pilla, Francesco

Marine Pollution Bulletin. 2022 May, v. 178 p.113527-

2022

Abstract	The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%-90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.
Keywords	Cyprus ; automation ; biodiversity ; cost effectiveness ; marine debris ; marine ecosystems ; marine pollution ; models ; plastics ; remote sensing ; support vector machines ; vegetation index ; Greece ; Italy ; Lebanon ; Marine litter ; Plastic pollution ; Sentinel ; Machine learning ; Open data
Language	English
Dates of publication	2022-05
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	2001296-2
ISSN	1879-3363 ; 0025-326X
ISSN (online)	1879-3363
ISSN	0025-326X
DOI	10.1016/j.marpolbul.2022.113527
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery

Bidroha Basu / Srikanta Sannigrahi / Arunima Sarkar Basu / Francesco Pilla

Remote Sensing, Vol 13, Iss 1598, p

2021 Volume 1598

Abstract: Plastic pollution poses a significant environmental threat to the existence and health of biodiversity and the marine ecosystem. The intrusion of plastic to the food chain is a massive concern for human health. Urbanisation, population growth, and ... ...

Abstract	Plastic pollution poses a significant environmental threat to the existence and health of biodiversity and the marine ecosystem. The intrusion of plastic to the food chain is a massive concern for human health. Urbanisation, population growth, and tourism have been identified as major contributors to the growing rate of plastic debris, particularly in waterbodies such as rivers, lakes, seas, and oceans. Over the past decade, many studies have focused on identifying the waterbodies near the coastal regions where a high level of accumulated plastics have been found. This research focused on using high-resolution Sentinel-2 satellite remote sensing images to detect floating plastic debris in coastal waterbodies. Accurate detection of plastic debris can help in deploying appropriate measures to reduce plastics in oceans. Two unsupervised (K-means and fuzzy c-means (FCM)) and two supervised (support vector regression (SVR) and semi-supervised fuzzy c-means (SFCM)) classification algorithms were developed to identify floating plastics. The unsupervised classification algorithms consider the remote sensing data as the sole input to develop the models, while the supervised classifications require in situ information on the presence/absence of floating plastics in selected Sentinel-2 grids for modelling. Data from Cyprus and Greece were considered to calibrate the supervised models and to estimate model efficiency. Out of available multiple bands of Sentinel-2 data, a combination of 6 bands of reflectance data (blue, green, red, red edge 2, near infrared, and short wave infrared 1) and two indices (NDVI and FDI) were selected to develop the models, as they were found to be most efficient for detecting floating plastics. The SVR-based supervised classification has an accuracy in the range of 96.9–98.4%, while that for SFCM and FCM clustering are between 35.7 and 64.3% and 69.8 and 82.2%, respectively, and for K-means, the range varies from 69.8 to 81.4%. It needs to be noted that the total number of grids with floating ...
Keywords	plastic pollution ; floating plastics in coastal waters ; Sentinel 2 image analysis ; clustering analysis ; support vector regression ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Investigating the Performance of Green Roof for Effective Runoff Reduction Corresponding to Different Weather Patterns: A Case Study in Dublin, Ireland

Basu, Arunima Sarkar / Basu, Bidroha / Pilla, Francesco / Sannigrahi, Srikanta

Hydrology. 2022 Mar. 09, v. 9, no. 3

2022

Abstract	This article aims to analyse the performance of green roof in runoff reduction. A case study has been conducted through a deployed green roof at the custom house quay building in Dublin, Ireland. Modular green roofs have been deployed which have IoT scales associated to it for measuring the effective reduction in runoff. Hydro-meteorological variables such as rainfall, temperature, relative humidity and wind speed values were corresponded to the amount of runoff reduction by means of a regression-based relationship. Comparison of the observed runoff reduction from a modular green roof and that estimated based on the developed regression relationship yielded a R² value of 0.874. Through this research, a pattern was identified which established that longer records and better weather variables data have the potential to improve the performance of the regression model in predicting the amount of runoff reduction corresponding to different rainfall and weather patterns. In general, performance of green roof was found to be highly positively correlated to the amount of rainfall received; however, low correlation between rainfall and the percentage of runoff reduction indicate that saturated soil in green roofs considerably deteriorates the performance in runoff reduction. Overall, this study can help in identification of locations where installation of green roofs can help mitigate floods at a city scale.
Keywords	case studies ; green roofs ; hydrometeorology ; rain ; regression analysis ; relative humidity ; runoff ; soil ; temperature ; wind speed ; Ireland
Language	English
Dates of publication	2022-0309
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2777964-6
ISSN	2306-5338
ISSN	2306-5338
DOI	10.3390/hydrology9030046
Database	NAL-Catalogue (AGRICOLA)

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Article: Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States

Sannigrahi, Srikanta / Pilla, Francesco / Maiti, Arabinda / Bar, Somnath / Bhatt, Sandeep / kaparwan, Ankit / Zhang, Qi / Keesstra, Saskia / Cerda, Artemi

Environmental research. 2022 July, v. 210

2022

Abstract: Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health ... ...

Abstract	Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, would play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM₂.₅ and PM₁₀) and Nitrogen Dioxide (NO₂) were collected from August 1 to October 30 for 2020 (the fire year) and 2019 (the reference year). Both spatial (Multiscale Geographically Weighted Regression) and non-spatial (Negative Binomial Regression) analyses were performed to assess the adverse effects of fire emission on human health. The in-situ data-led measurements showed that the maximum increases in PM₂.₅, PM₁₀, and NO₂ concentrations (μg/m³) were clustered in the West Coastal fire-prone states during August 1 – October 30, 2020. The average concentration (μg/m³) of particulate matter (PM₂.₅ and PM₁₀) and NO₂ was increased in all the fire states severely affected by forest fires. The average PM₂.₅ concentrations (μg/m³) over the period were recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, increasing up to 24.9, 13.4, 25.0, and 17.0 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID-19 incidents. Such association has been demonstrated robust and stable by a total of 30 models developed for analyzing the spatial non-stationary and local association. More in-depth research is needed to better understand the complex relationship between forest fire emission and human health.
Keywords	COVID-19 infection ; Oregon ; air pollution ; air quality ; coasts ; forest fires ; forests ; human health ; nitrogen dioxide ; pandemic ; particulates ; public health ; research ; soil ; California ; Colorado
Language	English
Dates of publication	2022-07
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2022.112818
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Exploring spatiotemporal effects of the driving factors on COVID-19 incidences in the contiguous United States.

Maiti, Arabinda / Zhang, Qi / Sannigrahi, Srikanta / Pramanik, Suvamoy / Chakraborti, Suman / Cerda, Artemi / Pilla, Francesco

Sustainable cities and society

2021 Volume 68, Page(s) 102784

Abstract: Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been ...

Abstract	Since December 2019, the world has witnessed the stringent effect of an unprecedented global pandemic, coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As of January 29,2021, there have been 100,819,363 confirmed cases and 2,176,159 deaths reported. Among the countries affected severely by COVID-19, the United States tops the list. Research has been conducted to discuss the causal associations between explanatory factors and COVID-19 transmission in the contiguous United States. However, most of these studies focus more on spatial associations of the estimated parameters, yet exploring the time-varying dimension in spatial econometric modeling appears to be utmost essential. This research adopts various relevant approaches to explore the potential effects of driving factors on COVID-19 counts in the contiguous United States. A total of three global spatial regression models and two local spatial regression models, the latter including geographically weighted regression (GWR) and multiscale GWR (MGWR), are performed at the county scale to take into account the scale effects. For COVID-19 cases, ethnicity, crime, and income factors are found to be the strongest covariates and explain most of the variance of the modeling estimation. For COVID-19 deaths, migration (domestic and international) and income factors play a critical role in explaining spatial differences of COVID-19 deaths across counties. Such associations also exhibit temporal variations from March to July, as supported by better performance of MGWR than GWR. Both global and local associations among the parameters vary highly over space and change across time. Therefore, time dimension should be paid more attention to in the spatial epidemiological analysis. Among the two local spatial regression models, MGWR performs more accurately, as it has slightly higher Adj. R
Language	English
Publishing date	2021-02-19
Publishing country	Netherlands
Document type	Journal Article
ISSN	2210-6715
ISSN (online)	2210-6715
DOI	10.1016/j.scs.2021.102784
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Article ; Online: Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States.

Sannigrahi, Srikanta / Pilla, Francesco / Maiti, Arabinda / Bar, Somnath / Bhatt, Sandeep / Kaparwan, Ankit / Zhang, Qi / Keesstra, Saskia / Cerda, Artemi

Environmental research

2022 Volume 210, Page(s) 112818

Abstract	Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, would play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM
MeSH term(s)	Air Pollutants/analysis ; Air Pollution/analysis ; COVID-19/epidemiology ; Ecosystem ; Environmental Monitoring ; Humans ; Nitrogen Dioxide/analysis ; Particulate Matter/analysis ; United States/epidemiology ; Wildfires
Chemical Substances	Air Pollutants ; Particulate Matter ; Nitrogen Dioxide (S7G510RUBH)
Language	English
Publishing date	2022-01-29
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2022.112818
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