Designing Data-Resolution Dependent Wildlife Corridor Networks for Tigers Using a Tensor-Based Computational Model

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Authors

  • Saurabh Shanu
     University of Petroleum and Energy Studies, Dehradun, Uttarakhand ,IN
  • Alok Agarwal
     University of Petroleum and Energy Studies, Dehradun, Uttarakhand ,IN
  • Nupur Rautela
     Wildlife Institute of India, Dehradun, Uttarakhand ,IN
  • Himanshu Chaudhary
     Manipal University Jaipur, Jaipur ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/33937

Keywords:

Data, Resolution, Set, Tensor, Wildlife Corridor.

Abstract

For many ecological modelling and investigations, data is a key component. As a result, the data resolution employed in the study has a considerable influence on such investigations. The research looks at how data resolution affects the modelling of wildlife corridor networks in a focal landscape. Because wildlife corridors are very species-specific, tigers are the focal species of this study, and the data items used to build tiger corridors are examined in depth.

The current study focuses on applying computational tools to detect and deal with data pieces and data resolution in order to construct wildlife corridor networks. Set theory, membership functions, matrices, and tensor data representation techniques are used in the research. The model presented in this paper shows how data resolution affects our knowledge of tiger mobility in the terrain. The research concluded that the higher is the data resolution, the better will the planning method for wildlife corridor conservation. In addition to GIS and remote sensing, the research supports adequate field studies, which are required for improved modelling. The research will be highly valuable for numerous wildlife stakeholders in terms of strategizing and policy-making in order to protect corridors as part of a bigger objective of species conservation.

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Published

2023-06-01

How to Cite

Shanu, S., Agarwal, A., Rautela, N., & Chaudhary, H. (2023). Designing Data-Resolution Dependent Wildlife Corridor Networks for Tigers Using a Tensor-Based Computational Model. Journal of Mines, Metals and Fuels, 71(4), 552–556. https://doi.org/10.18311/jmmf/2023/33937

Issue

Volume 71, Issue 4, April 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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