Data: Probabilistic Cellular Automata Modelling and Simulation of Land Use Changes in Okomu National Park

Published: 22-03-2021| Version 1 | DOI: 10.17632/jpy5cmn537.1
Contributors:
Stephen Okonkwo,

Description

This study monitors and models spatio–temporal land use changes in Okomu National Park over two decades (2000 – 2020) with the aim of projecting forest cover changes for the near future. A probabilistic cellular automata (CA) model was created and used to simulate land use changes with the aim of prediction. Landsat 7 ETM+ satellite images for years 2000, 2005, 2010, 2015, and 2020 were classified into Forest and Non–Forest using maximum likelihood supervised classification algorithm. The overall classification accuracy for the years under study was 98.1838%, 97.5169%, 96.3325%, 91.6647%, and 94.6124% with overall kappa coefficients of 0.9654, 0.9557, 0.9524, 0.8563, and 0.9094 respectively. State transition probabilities for 2000–2005, 2005–2010, 2010–2015, and 2015–2020 were calculated from the classified images. A probabilistic cellular automata model using Moore’s neighborhood with a Von Neumann extension was used to simulate land use changes for years 2005, 2010, 2015 and 2020 with year 2000 as the base year. Simulation accuracy was 77.46% for year 2005, 74.1% for year 2010, 70.98% for year 2015, and 78.27% for year 2020. Projections was made for years 2025 and 2030 and it shows a 27.41% decline from the base year by 2025, and a 29.90% decline by 2030. Keywords: Cellular Automata, Markov Chain, Simulation, Supervised Classification

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