Open AccessBayesian Change Point Detection of Vegetation Cover Dynamics of Akure Forest Reserve, Ondo State in Southwestern, Nigeria
Author(s) -
P.A. Ukoha,
S.J. Okonkwo,
A.R. Adewoye
Publication year - 2021
Publication title -
journal of applied science and environmental management
Language(s) - English
Resource type - Journals
eISSN - 2659-1499
pISSN - 2659-1502
DOI - 10.4314/jasem.v25i8.25
Subject(s) - deforestation (computer science) , enhanced vegetation index , random forest , normalized difference vegetation index , markov chain monte carlo , land cover , vegetation (pathology) , bayesian probability , environmental science , cohen's kappa , physical geography , geography , remote sensing , statistics , vegetation index , climate change , land use , computer science , mathematics , ecology , machine learning , medicine , pathology , biology , programming language
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.