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Research Article
Anthony Tobore
Department of Soil Science and Land Management, College of Plant Science and Crop Production, Federal University of Agriculture Abeokuta, Ogun State, Nigeria. P.M.B. 2240, Abeokuta, Ogun State, Nigeria.
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3645-2864
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This work is licensed under a CC BY 4.0 License. Read Full License
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- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Forest cover change (FCC) varies from region to region and is thus considered as one of the drivers of climate change. This study identified the pattern of the FCC for the years 2010 and 2020 using spectral vegetation index and Markov chain Techniques. The Markov chain (MC) and Cellular automata (CA) methods were utilized to simulate the forest cover map for the year 2030. The spectral vegetation index of Landsat 7 Enhanced thematic mapper plus (ETM+) and Landsat 8 Operational land images (OLI) were used to assess the forest cover loss for the year 2010 and 2020 focusing on the Normalized difference vegetation index, (NDVI), Green normalized difference vegetation index (GNDVI) and Difference vegetation index (DVI). Based on the validation result, the accuracy of the forest cover simulation model is more than 75 percent (%). The simulation result shows that if the current deforestation and encroachment continues, the forest cover will continue to be endangered and thus leading to a decrease in dense forest, plantation, and sparse vegetation by 20.9%, 16.1%, and 20% respectively. This study will be helpful for planners and decision-makers in ensuring sustainable forest management.
Keywords
Forest cover change, spectral vegetation index, cellular automata, Markov chain
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Anthony Tobore
Department of Soil Science and Land Management, College of Plant Science and Crop Production, Federal University of Agriculture Abeokuta, Ogun State, Nigeria. P.M.B. 2240, Abeokuta, Ogun State, Nigeria.
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3645-2864
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 26 Feb, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Forestry
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Forest cover change (FCC) varies from region to region and is thus considered as one of the drivers of climate change. This study identified the pattern of the FCC for the years 2010 and 2020 using spectral vegetation index and Markov chain Techniques. The Markov chain (MC) and Cellular automata (CA) methods were utilized to simulate the forest cover map for the year 2030. The spectral vegetation index of Landsat 7 Enhanced thematic mapper plus (ETM+) and Landsat 8 Operational land images (OLI) were used to assess the forest cover loss for the year 2010 and 2020 focusing on the Normalized difference vegetation index, (NDVI), Green normalized difference vegetation index (GNDVI) and Difference vegetation index (DVI). Based on the validation result, the accuracy of the forest cover simulation model is more than 75 percent (%). The simulation result shows that if the current deforestation and encroachment continues, the forest cover will continue to be endangered and thus leading to a decrease in dense forest, plantation, and sparse vegetation by 20.9%, 16.1%, and 20% respectively. This study will be helpful for planners and decision-makers in ensuring sustainable forest management.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
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