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 Low-density 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.
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References
Ackom EK, Adjei KA, Odai SN (2020) Monitoring land-use and land-cover changes due to extensive urbanization in the Odaw River Basin of Accra, Ghana, 1991–2030. Model Earth Syst Environ 6:1131–1143. https://doi.org/10.1007/s40808-020-00746-5
Akın A, Erdoğan MA (2020) Analysing temporal and spatial urban sprawl change of Bursa city using landscape metrics and remote sensing. Model Earth Syst Environ 6:1331–1343. https://doi.org/10.1007/s40808-020-00766-1
Alkheder S, Wang J, Shan J (2006) Change detection—Cellular automata method for urban growth modeling. Paper presented in ISPRS Commission VII Mid-term Symposium “Remote Sensing: From Pixels to Processes”, Enschede, the Netherlands 414–419.
Al-Shaar W, Nehme N, Adjizian Gérard J (2020) The applicability of the extended Markov chain model to the land use dynamics in Lebanon. Arab J Sci Eng. https://doi.org/10.1007/s13369-020-04645-w
Arsanjani JJ, Helbich M, Kainz W, Boloorani AD (2013) Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf 21:265–275. https://doi.org/10.1016/j.jag.2011.12.014
Baker WL (1989) A review of models of landscape change. Landscape Ecol 2:111–133. https://doi.org/10.1007/BF00137155
Chakir R, Parent O (2009) Determinants of land use changes: a spatial multinomial probit approach. Pap Reg Sci 88(2):327–344. https://doi.org/10.1111/j.1435-5957.2009.00239.x
Ching W, Ng MK (2006) Markov chains: models algorithms and applications. Springer, New York
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46. https://doi.org/10.1177/001316446002000104
EarthExplorer (2019) EarthExplorer. EarthExplorer. https://earthexplorer.usgs.gov/ Accessed 30 July 2019
Eastman JR (2012) IDRISI Selva Tutorial. Clark University, Worcester, Massachusetts
Falah N, Karimi A, Harandi AT (2019) Urban growth modeling using cellular automata model and AHP (case study: Qazvin city). Model Earth Syst Environ 6:235–248. https://doi.org/10.1007/s40808-019-00674-z
Fawaz M (2011) Constraints of land use planning in Lebanon. Al Mouhandess Mag 26:16–17
Ford W (2015) Numerical linear algebra with applications using MATLAB. Elsevier, San Diego
Gagniuc PA (2017) Markov chains: from theory to implementation and experimentation. John Wiley & Sons, Hoboken USA
Gharbia SS, Alfatah SA, Gill L, Johnston P, Pilla F (2016) Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms. Model Earth Syst Environ 2(3):20. https://doi.org/10.1007/s40808-016-0210-y
Ghosh P, Mukhopadhyay A, Chanda A, Mondal P, Akhand A, Mukherjee S, Nayak SK, Ghosh S, Mitra D, Ghosh T, Hazra S (2017) Application of cellular automata and Markov-chain model in geospatial environmental modeling—a review. Remote Sens Appl Soc Environ 5:64–77. https://doi.org/10.1016/j.rsase.2017.01.005
Grinstead CM, Snell JL (2006) Grinstead and Snell’s Introduction to Probability. Doyle PG (ed). The American Mathematical Society, Providence, Rhode Island, United States
Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr 63:101–112. https://doi.org/10.1016/j.apgeog.2015.06.015
Hamad R, Balzter H, Kolo K (2018) Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability 10:23. https://doi.org/10.3390/su10103421
Han H, Yang C, Song J (2015) Scenario simulation and the prediction of land use and land cover change in Beijing, China. Sustainability 7:4260–4279. https://doi.org/10.3390/su7044260
He Q, Dai L, Zhang W, Wang H, Liu S, He S (2013) An unsupervised classifier for remote-sensing imagery based on improved cellular automata. Int J Remote Sens 34(21):7821–7837. https://doi.org/10.1080/01431161.2013.822596
Houet T, Hubert-Moy L (2006) Modelling and projecting land-use and land-cover changes with a cellular automaton in considering landscape trajectories: an improvement for simulation of plausible future states. EARSeL eProc Eur Assoc Remote Sens Lab 5(1):63–76
Hua AK (2017) Application of CA-Markov model and land use/land cover changes in Malacca River Watershed, Malaysia. Appl Ecol Environ Res 15(4):605–622. https://doi.org/10.15666/aeer/1504_605622
Iacono M, Levinson D, El-Geneidy A, Wasfi R (2012) A Markov chain model of land use change in the twin cities, 1958–2005. TeMA J Land Use Mob Environ 8(3):263–276. https://doi.org/10.6092/1970-9870/2985
Kabite G, Muleta MK, Gessesse B (2020) Spatiotemporal land cover dynamics and drivers for Dhidhessa River Basin (DRB), Ethiopia. Model Earth Syst Environ 6:1089–1103. https://doi.org/10.1007/s40808-020-00743-8
Koomen E, Borsboom-van Beurden J (2011) Land-use modelling in planning practice. Springer, Berlin
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174
Food and Agriculture Organization of the United Nations (FAO) (2012) Country study on status of land tenure, planning and management in oriental near east countries: case of Lebanon The United Nations, New York
Levinson D, Chen W (2005) Paving new ground: a markov chain model of the change in transportation networks and land use. In: Levinson DM, Krizek KJ (eds) Access to Destinations. Emerald, Bingley, United Kingdom, pp 243–266
Localiban (2016) Lebanese population density map. Localiban. https://www.localiban.org/lebanese-population-density-map Accessed 13 July 2019
Masri T, Khawlie M, Faour G (2002) Land cover change over the last 40 years in Lebanon. Leban Sci J 3(2):17–28
Mishra VN, Rai PK (2016) A remote sensing aided multi-layer perceptron Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9(4):18. https://doi.org/10.1007/s12517-015-2138-3
Muller MR, Middleton J (1994) A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landsc Ecol 9(2):151–157
Ozturk D (2015) Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and multi-layer perceptron-Markov chain models. Remote Sens 7:5918–5950. https://doi.org/10.3390/rs70505918
Parsa VA, Yavari A, Nejadi A (2016) Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran. Model Earth Syst Environ 2(4):178. https://doi.org/10.1007/s40808-016-0227-2
Rencher AC (2002) Methods of Multivariate Analysis, 2nd edn. Wiley & Sons, New York
Rozario PF, Oduor P, Kotchman L, Kangas M (2017) Transition modeling of land-use dynamics in the Pipestem Creek, North Dakota, USA. J Geosci Environ Protect 5:182–201. https://doi.org/10.4236/gep.2017.53013
Stephan R (2010) Land resources. In: MOE/UNDP/ECODIT (ed) State and trends of the Lebanese Environment. pp 181–210
Subedi P, Subedi K, Thapa B (2013) Application of a hybrid cellular automaton Markov (CA-Markov) Model in land-use change prediction: a case study of saddle creek drainage Basin Florida. Appl Ecol Environ Sci 1(6):126–132. https://doi.org/10.12691/aees-1-6-5
Takada T, Miyamoto A, Hasegawa SF (2010) Derivation of a yearly transition probability matrix for land-use dynamics and its applications. Landsc Ecol 25:561–572. https://doi.org/10.1007/s10980-009-9433-x
Tang W, Hu J, Zhang H, Wu P, He H (2015) Kappa coefficient: a popular measure of rater agreement. Shanghai Arch Psychiatry 27(1):62–67. https://doi.org/10.11919/j.issn.1002-0829.215010
United Nations (2019) World Population Prospects. United Nations DESA/Population Division. https://population.un.org/wpp/ Accessed on 6 Aug 2019
Vázquez-Quintero G, Solís-Moreno R, Pompa-García M, Villarreal-Guerrero F, Pinedo-Alvarez C, Pinedo-Alvarez A (2016) Detection and projection of forest changes by using the Markov chain model and cellular automata. Sustainability 8(3):13. https://doi.org/10.3390/su8030236
Viera AJ, Garrett JM (2005) Understanding interobserver agreement the kappa statistic. Fam Med 37(5):360–363
Wang X, Kockelman KM (2009) Application of the dynamic spatial ordered probit model—patterns of land development change in Austin, Texas. Pap Reg Sci 88(2):345–365. https://doi.org/10.1111/j.1435-5957.2009.00249.x
Weng Q (2002) Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. J Environ Manag 64:273–284. https://doi.org/10.1006/jema.2001.0509
Weng QH (2010) Remote Sensing and GIS integration. McGraw-Hill, New York
Wolfram S (2002) A New Kind of Science. Wolfram Media, Illinois
WorldPopulationReview (2019) Lebanon Population. WorldPopulationReview. https://worldpopulationreview.com/countries/lebanon-population/#popGrowth Accessed 6 Aug 2019
Zurayk R, El Moubayed L (1994) Land degradation and mitigation in the Lebanese mountains: the breakdown of traditional systems. UNDP, DHA Research paper N 9
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All authors contributed to the study: Conceptualization, Methodology, Analysis and Validation. Resources, Data curation, Software, Writing—Original draft preparation were performed by WAS. Supervision, Writing—Reviewing and Editing were performed by JAG, NN, HL, LBB.
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Al-Shaar, W., Adjizian Gérard, J., Nehme, N. et al. Application of modified cellular automata Markov chain model: forecasting land use pattern in Lebanon. Model. Earth Syst. Environ. 7, 1321–1335 (2021). https://doi.org/10.1007/s40808-020-00971-y
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DOI: https://doi.org/10.1007/s40808-020-00971-y