Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem

Sannigrahi, Srikanta; Zhang, Qi; Pilla, Francesco; Joshi, P. K.; Basu, Bidroha; Keesstra, Saskia; Roy, P. S.; Wang, Ying; Sutton, Paul; Chakraborti, Suman; Paul, Saikat Kumar; Sen, Shashwati

doi:10.1016/j.scitotenv.2020.137004

Cited by 135 publications

(68 citation statements)

References 78 publications

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“…The spatial regression models (SRM) have been used extensively for evaluating demographic pattern analysis ( Chi & Zhu, 2008 ), estimating land surface temperature ( Chakraborti et al, 2019 ; Jain et al, 2019 ), urban air quality monitoring ( Fang, Liu, Li, Sun, & Miao, 2015 ), ecosystem service valuation ( Sannigrahi et al, 2020 ; Sannigrahi, Zhang, Pilla et al, 2020 ). The specific application of spatial regression models is to understand the spatial effects such as spatial autocorrelation, spatial stationarity, and heterogeneity of feature distribution.…”

Section: Methodsmentioning

confidence: 99%

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach

Sannigrahi

Pilla

Basu

et al. 2020

Sustainable Cities and Society

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273

215

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Highlights The spatial association between socio-demographic composition and COVID-19 deaths and cases were evaluated. Four spatial regression models were implemented for spatial regression modelling. Socio-demographic composition significantly impacting the overall casualties caused by COVID-19. The spatially predicted COVID-19 cases and deaths were found highly consistent with actual estimates.

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Section: Methodsmentioning

confidence: 99%

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach

Sannigrahi

Pilla

Basu

et al. 2020

Sustainable Cities and Society

Self Cite

273

215

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“…Our spatially explicit CPZs are, therefore, perfectly matched with the existing literature and highlight the administrative zones and landscape that should be protected. Earlier studies (Sannigrahi, Chakraborti et al 2019; Sannigrahi, Joshi et al 2019; Sannigrahi, Zhang, Joshi et al 2020; Sannigrahi, Zhang, Pilla et al 2020) in this region have revealed that the mangrove and water bodies (coastal estuary and inland wetland) are the most sensitive ecosystems among the major ecosystem types of the Indian Sundarbans. To preserve the ecological stability of this ecosystem, several conservation and protection initiatives have been adopted by local stakeholders (forest protection committees [FPCs] and forest directories [FDs]).…”

Section: Resultsmentioning

confidence: 87%

“…However, the valuation approaches adopted in the present study considered only tangible and realized benefits of the ecosystems but did not include intangible benefits provided by the ecosystems; if it had been so, the real values of such ESs would have changed significantly (Kubiszewski et al 2013). Additionally, the Sundarbans is one of the most dynamic ecosystems in the world, where several climatic and anthropogenic extremities, including floods, cyclones, coastal erosion, destruction of mangroves, and depletion of coastal and maritime resources, are posing severe environmental and socioeconomic threats to coastal communities of the Sundarbans (Giri et al 2011; Sannigrahi, Zhang, Pilla et al 2020).…”

Section: Resultsmentioning

confidence: 99%

Identification of Conservation Priority Zones Using Spatially Explicit Valued Ecosystem Services: A Case from the Indian Sundarbans

Sannigrahi

Pilla

Basu

et al. 2020

Integr Envir Assess & Manag

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Demarcation of conservation priority zones (CPZs) using spatially explicit models is the new challenge in ecosystem services (ESs) research. This study identifies the CPZs of the Indian Sundarbans by integrating 2 different approaches, that is, ESs and ecosystem health (EH). Five successive steps were followed to conduct the analysis: First, the ESs were estimated using biophysical and economic methods and a hybrid method (that combines biophysical and economic methods); second, the vigor–organization–resilience (VOR) model was used for estimating EH; third, the risk characterization value (RCV) of ESs was measured using the function of EH and ESs; fourth, Pearson correlation test was performed to analyze the interaction between ESs and EH components; and fifth, the CPZs were defined by considering 7 relevant components: ecosystem vigor, ecosystem organization, ecosystem resilience, RCV, EH, ESs, and the correlation between EH and ESs. Among the major ecoregions of the Sundarbans, the highest ESs value in economic terms is provided by the mangrove ecosystem (US$19 144.9 million per year). The highest conservation priority score was projected for the Gosaba block, which is dominated by dense mangrove forests. The estimated CPZs were found to be highly consistent with the existing biodiversity zonations. The outcome of this study could be a reference for environmentalists, land administrators, researchers, and decision makers to design relevant policies to protect the high values of the Sundarbans ecosystem. Integr Environ Assess Manag 2020;16:773–787. © 2020 SETAC

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“…Identifying the important factors which have the highest explanatory power and less auto-collinearity is one of the most challenging parts in regression modelling ( Sannigrahi et al, 2020c ; Sannigrahi et al, 2020d ). The current study has employed several dimensionality reductions approaches, including stepwise forward regression, Confidence Interval (CI) approximation, parametric and non-parametric correlations estimation, linear regression modelling, Variance Inflation Factor (VIF) approximation, etc.…”

Section: Methodsmentioning

confidence: 99%

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis

Chakraborti

Maiti

Pramanik

et al. 2021

Science of The Total Environment

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Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.

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Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem

Cited by 135 publications

References 78 publications

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach

Identification of Conservation Priority Zones Using Spatially Explicit Valued Ecosystem Services: A Case from the Indian Sundarbans

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis

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