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Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach

Literature: Journal Articles Available at NO COST

Adhikari S. & Southworth J. 2012. “Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach” Remote Sensing, vol. 4, pp. 3215-3243.

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Department of Geography, Land-Use and Environmental Change Institute, University of Florida, 3141 Turlington Hall, Gainesville, FL 32611, USA


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  • The authors provide use a cellular automata-Markov model using remotely sensed data of Bannerghatta National Park (BNP) of Karnataka State and its surroundings to investigate rapid land cover change between 1973 and 2007 as they relate to policy shifts.
  • Landsat MSS 1973 (path 154, row 51), Landsat TM 1992 and 1999 (path 144, row 51), IRS LISS III 2007 (path 100, row 64) dry season images were processed and classified as native forest, tree plantation, and non-forest using both a supervised (maximum likelihood method) and unsupervised classification using ISODATA.
  • The authors used a cellular automata-Markov model to construct a hypothetical land cover scenario of 109 km2 BNP and a 5km buffer in which there has been no policy intervention and carried it out four times.
  • The models predicted a decline in native forest cover and an increase in non-forest cover after1992. The actual observed landscape experienced an initial decline in forest cover from 1973 to 1992 which then began to recover. Furthermore, the models show higher deforestation and lower reforestation than the observed patterns.
  • The authors maintain that the policy intervention succeeded in protecting the forest cover of BNP evidenced by the decline in deforestation and increase in reforestation inside the park.
  • However, there was also a decrease in non-forest cover outside of the protected area even though there was no formal protection or policy implemented. The authors posit that this is likely due to tree plantations in the buffer zone as opposed to native forest cover increase inside the park.


Geographical Region

  • South Asia
  • Country

  • India
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