Application of modified cellular automata Markov chain model: forecasting land use pattern in Lebanon
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ORIGINAL ARTICLE
Application of modified cellular automata Markov chain model: forecasting land use pattern in Lebanon Walid Al‑Shaar1,2 · Jocelyne Adjizian Gérard1 · Nabil Nehme3 · Hassan Lakiss2 · Liliane Buccianti Barakat1 Received: 18 July 2020 / Accepted: 5 September 2020 © Springer Nature Switzerland AG 2020
Abstract This article modifies the use of the Cellular Automata Markov Chain Model to predict future land use pattern in Lebanon, and compares it to the current developed model. LandSat images of years 2000, 2009 and 2018 are used to generate land use maps within the geographic information system. Current developed model was generated by integrating Population density data with land use classification maps to decompose the built-up development to three sub-classes: High, Medium and Lowdensity built-up land uses. Simulations of future land use pattern over the year 2018 based on these two prediction models reveal that the Modified Cellular Automata Markov Chain Modelling technique is more accurate than the Extended Markov Chain model. Spatial effects of built-up densities are validated in this study. Consequently, the extension of the Cellular Automata Markov Chain Model represents an innovative tool for regional and urban planning to forecast potential locative distribution of old and new urban agglomeration. The sequential shift of the urban areas among different density classes in addition to the interactions of urban agglomerations should be employed as a guiding tool for decision-makers and planners during the phase of developing new population and economic strategies, new urban Masterplan and during the process of enacting/developing new land-use policies. In the final part of the study, a simulation of land use pattern for the year 2036 is generated using TerrSet v.18 software and an analysis of the outcome for the forecasted map is discussed. Keywords Markov chain · Cellular automata · Land use · GIS · Lebanon
Introduction * Walid Al‑Shaar walid.al‑[email protected] Jocelyne Adjizian Gérard [email protected] Nabil Nehme [email protected] Hassan Lakiss [email protected] Liliane Buccianti Barakat [email protected] 1
Doctoral School of Human and Society Sciences, Geography Department, CREEMO (Centre de Recherche en Environnement‑Espace Méditerranée Orientale) Geography Department, Saint Joseph University, Campus des sciences humaines, Rue de Damas, B.P. 17‑5208, Mar Mikhael, Beirut, Lebanon
2
Doctoral School of Science and Technology (Civil Engineering), Lebanese University, Badaro, Museum, P.O. Box 6573/14, Beirut, Lebanon
3
School of Engineering, Lebanese American University, Chouran, P.O. Box 13‑5053, Beirut, Lebanon
Previous applications of Cellular Automata—Markov Chain model (CAMCM) and Markov Chain Model (MCM) in addition to a brief introduction of the models used in the study are presented in this section. Moreover, the selection and the background of the study area are also reported. Relationship of land use dynamics and Cellular Automata—Markov
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