Probabilistic Cellular Automata modelling and simulation of land-use changes in Okomu National Park

Abstract

Land use, land cover (LULC) change technique is essential for measuring ecological quality, environmental sustainability, and uncontrolled development at various spatiotemporal scales. To construct effective land use management plans, the probable future scenario of LULC changes can be easily detected utilizing a simulation technique.This study monitors and models spatiotemporal land-use changes in Okomu National Park over two decades (2000 – 2020) to project 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 predicting future land-use scenarios. Landsat7 ETM+ satellite images for years 2000, 2005, 2010, 2015, and 2020 were classified into Forest and Non–Forest using a maximum likelihood supervised classification algorithm. 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 the year 2000 as the base year.The overall classification accuracy for the years under study was 98.18%, 97.52%, 96.33%, 91.67%, and 94.61%with overall kappa coefficients of 0.97, 0.96, 0.95, 0.86, and 0.91 respectively. State transition probabilities for 2000–2005, 2005–2010, 2010–2015, and 2015–2020 were calculated from the classified images. Simulation accuracywas 77.46%, 74.1%, 70.98%, and 78.27% for the year 2005, 2010, 2015, and 2020 respectively. Projections were made for years 2025 and 2030 and it shows a 27.41% decline from the base year by 2025 anda 29.90% decline by 2030.The amount of forest cover in the actual and simulated land-use changes shows a gradual drop from 185.15 km2 in the base year 2000 to 136.07 and 135.30 km2 in the year 2020, respectively.Spatial simulation models, which provide a scientific basis for supporting sustainable forest management based on different simulation scenarios alsocontribute significantly to the implementation framework for the United Nations' Reducing Emissions from Deforestation and Forest Degradation (REDD/REDD+) program, as well as reference scenarios for REDD/REDD+ incentive payments.