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Anirban Mukhopadhyay
Researcher at Jadavpur University
Publications - 73
Citations - 1695
Anirban Mukhopadhyay is an academic researcher from Jadavpur University. The author has contributed to research in topics: Mangrove & Population. The author has an hindex of 17, co-authored 72 publications receiving 1194 citations. Previous affiliations of Anirban Mukhopadhyay include Asian Institute of Technology & Hong Kong University of Science and Technology.
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Journal ArticleDOI
Evaluation of vertical accuracy of open source Digital Elevation Model (DEM)
Sandip Mukherjee,Pawan Kumar Joshi,Samadrita Mukherjee,Aniruddha Ghosh,Rahul Dev Garg,Anirban Mukhopadhyay +5 more
TL;DR: It was found that representation of terrain characteristics is affected in the coarse postings DEM, and the overall vertical accuracy shows RMS error of 12.62 m and 17.76 m for ASTER and SRTM DEM respectively, when compared with Cartosat DEM.
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Application of Cellular automata and Markov-chain model in geospatial environmental modeling- A review
Pramit Ghosh,Anirban Mukhopadhyay,Abhra Chanda,Parimal Mondal,Anirban Akhand,Sandip Mukherjee,S.K. Nayak,Subhajit Ghosh,D. Mitra,Tuhin Ghosh,Sugata Hazra +10 more
TL;DR: The concepts of CA-Markov modeling and their backgrounds are discussed and is followed by a classification of the researches conducted in this domain into two broad groups, one being the development of concepts and the adopted methodologies, while the other discusses the application of these methods in solving and studying real world scenarios.
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Projected changes in area of the Sundarban mangrove forest in Bangladesh due to SLR by 2100
Andres Payo,Anirban Mukhopadhyay,Sugata Hazra,Tuhin Ghosh,Subhajit Ghosh,Sally Brown,Robert J. Nicholls,Lucy Bricheno,Judith Wolf,Susan Kay,Attila N. Lázár,Anisul Haque +11 more
TL;DR: This work illustrates how the Sea Level Affecting Marshes Model (SLAMM) is able to reproduce the observed area losses for the period 2000–2010 and suggests that erosion rather than inundation may remain the dominant loss driver to 2100 under certain scenarios of sea-level rise and net subsidence.
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Spatial soil organic carbon (SOC) prediction by regression kriging using remote sensing data
TL;DR: In this paper, the estimation of the soil organic carbon distribution from point survey data (prepared after laboratory test) by a hybrid interpolation method, viz. regression kriging (RK) in a part of the Narmada river basin in the central India.
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Automatic shoreline detection and future prediction: A case study on Puri Coast, Bay of Bengal, India
Anirban Mukhopadhyay,Sandip Mukherjee,Samadrita Mukherjee,Subhajit Ghosh,Sugata Hazra,D. Mitra +5 more
TL;DR: In this paper, the authors conducted a study on the 142 km-long coastline of Puri district, India, where they used geoinformatics within the prediction models of the models.