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Scenarios of the Tropical Dry Forest of Purulia District West Bengal: A CA-MARKOV Model Approach

Scenarios of the Tropical Dry Forest of Purulia District West Bengal: A CA-MARKOV Model Approach

Masuma Begum, Niloy Pramanick, Anirban Mukhopadhyay, Sayani Datta Majumdar
Copyright: © 2020 |Pages: 14
ISBN13: 9781799800149|ISBN10: 1799800148|EISBN13: 9781799800163
DOI: 10.4018/978-1-7998-0014-9.ch013
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MLA

Begum, Masuma, et al. "Scenarios of the Tropical Dry Forest of Purulia District West Bengal: A CA-MARKOV Model Approach." Handbook of Research on the Conservation and Restoration of Tropical Dry Forests, edited by Rahul Bhadouria, et al., IGI Global, 2020, pp. 254-267. https://doi.org/10.4018/978-1-7998-0014-9.ch013

APA

Begum, M., Pramanick, N., Mukhopadhyay, A., & Majumdar, S. D. (2020). Scenarios of the Tropical Dry Forest of Purulia District West Bengal: A CA-MARKOV Model Approach. In R. Bhadouria, S. Tripathi, P. Srivastava, & P. Singh (Eds.), Handbook of Research on the Conservation and Restoration of Tropical Dry Forests (pp. 254-267). IGI Global. https://doi.org/10.4018/978-1-7998-0014-9.ch013

Chicago

Begum, Masuma, et al. "Scenarios of the Tropical Dry Forest of Purulia District West Bengal: A CA-MARKOV Model Approach." In Handbook of Research on the Conservation and Restoration of Tropical Dry Forests, edited by Rahul Bhadouria, et al., 254-267. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0014-9.ch013

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Abstract

In this chapter, satellite images of the years 1995, 2005, and 2015 of LANDSAT have been used. After pre-processing (geometric correction and atmospheric correction using FLAASH, LULC change dynamics have been assessed to estimate the changes in total forest cover in Purulia district through an unsupervised K-means classification scheme. To evaluate the health status, vegetation indices, namely NDVI, SAVI, and CVI, have been used. The increase in NDVI, SAVI, and CVI values was inferred as no significant degradation of Purulia forest cover. Moreover, future scenarios have been predicted by implementing a CA-MARKOV model. Using the land cover map of 1995 as the base map, and from 1995 to 2005 as training data, a land cover map of 2015 has been generated which in turn validated by the actual land cover of 2015. After validation, prediction of land cover was possible for the years 2035 and 2050. The prediction suggested that forest area will increase by approximately 4% from 2015 to 2035 and by 3% from 2035 to 2050.

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