Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria
This study uses satellite acquired vegetation index data to monitor changes in Akure forest reserve. Enhanced Vegetation Index (EVI) time series datasets were extracted from Landsat images; extraction was performed on the Google Earth Engine (GEE) platform. The datasets were analyzed using Bayesian...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)
2021-11-01
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| Series: | Journal of Applied Sciences and Environmental Management |
| Subjects: | |
| Online Access: | https://www.ajol.info/index.php/jasem/article/view/218038 |
| _version_ | 1797228203112136704 |
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| author | P.A. Ukoha S.J. Okonkwo A.R. Adewoye |
| author_facet | P.A. Ukoha S.J. Okonkwo A.R. Adewoye |
| author_sort | P.A. Ukoha |
| collection | DOAJ |
| description |
This study uses satellite acquired vegetation index data to monitor changes in Akure forest reserve. Enhanced Vegetation Index (EVI) time series datasets were extracted from Landsat images; extraction was performed on the Google Earth Engine (GEE) platform. The datasets were analyzed using Bayesian Change Point (BCP) to monitor the abrupt changes in vegetation dynamics associated with deforestation. The BCP shows the magnitude of changes over the years, from the posterior data obtained. BCP focuses on changes in the long‐range using Markov Chain Monte Carlo (MCMC) methods, this returns posterior probability at > 0.5% of a change point occurring at each time index in the time series. Three decades of Landsat data were classified using the random forest algorithm to assess the rate of deforestation within the study area. The results shows forest in 2000 (97.7%), 2010 (89.4%), 2020 (84.7%) and non-forest increase 2000 (2.0%), 2010 (10.6%), 2020 (15.3%). Kappa coefficient was also used to determine the accuracy of the classification.
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| first_indexed | 2024-04-24T14:52:58Z |
| format | Article |
| id | doaj.art-e7a9d9585d414dafb9a3dc2b70808389 |
| institution | Directory Open Access Journal |
| issn | 2659-1502 2659-1499 |
| language | English |
| last_indexed | 2024-04-24T14:52:58Z |
| publishDate | 2021-11-01 |
| publisher | Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP) |
| record_format | Article |
| series | Journal of Applied Sciences and Environmental Management |
| spelling | doaj.art-e7a9d9585d414dafb9a3dc2b708083892024-04-02T19:47:39ZengJoint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)Journal of Applied Sciences and Environmental Management2659-15022659-14992021-11-0125810.4314/jasem.v25i8.25Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, NigeriaP.A. UkohaS.J. OkonkwoA.R. Adewoye This study uses satellite acquired vegetation index data to monitor changes in Akure forest reserve. Enhanced Vegetation Index (EVI) time series datasets were extracted from Landsat images; extraction was performed on the Google Earth Engine (GEE) platform. The datasets were analyzed using Bayesian Change Point (BCP) to monitor the abrupt changes in vegetation dynamics associated with deforestation. The BCP shows the magnitude of changes over the years, from the posterior data obtained. BCP focuses on changes in the long‐range using Markov Chain Monte Carlo (MCMC) methods, this returns posterior probability at > 0.5% of a change point occurring at each time index in the time series. Three decades of Landsat data were classified using the random forest algorithm to assess the rate of deforestation within the study area. The results shows forest in 2000 (97.7%), 2010 (89.4%), 2020 (84.7%) and non-forest increase 2000 (2.0%), 2010 (10.6%), 2020 (15.3%). Kappa coefficient was also used to determine the accuracy of the classification. https://www.ajol.info/index.php/jasem/article/view/218038Bayesian Change Point, EVI, MCMC, Random Fores |
| spellingShingle | P.A. Ukoha S.J. Okonkwo A.R. Adewoye Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria Journal of Applied Sciences and Environmental Management Bayesian Change Point, EVI, MCMC, Random Fores |
| title | Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria |
| title_full | Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria |
| title_fullStr | Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria |
| title_full_unstemmed | Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria |
| title_short | Bayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria |
| title_sort | bayesian change point detection of vegetation cover dynamics of akure forest reserve ondo state in southwestern nigeria |
| topic | Bayesian Change Point, EVI, MCMC, Random Fores |
| url | https://www.ajol.info/index.php/jasem/article/view/218038 |
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