Finding robust model for Urban Growth Prediction based on Land Use Land Cover Classification: A comparative study of CA Markov based Models LCM and MOLUSCE

Document Type : Original Research Articles

Authors

1 Student, Department of Remote Sensing and Geoinformatics, Maharshi Dayanand Saraswati University Ajmer, India.

2 Assistant Professor, Department of Geography, School of Earth Sciences, SRT Campus Tehri, HNBGU, India.

Abstract

Background: Scientific information of future LULC is fundamental to make proper land use planning to fulfill the rising human demands and their wellbeing without causing lasting damage to environment.
Objectives: This paper is an attempt to identify the robust model among CA MARKOV based model Land Change Modeler (LCM (IDRISI) and MOLUSCE Model (QGIS Plugin) to predict urban growth for 2020 using LULC dynamics during 2000-2010 of Jaipur city, India, and Austin in America.
Methodology: The predicted maps were compared with observed map and accuracy was checked through mathematical equation as well as Kappa Index Statistic Assessment Tool of MOLUSCE Plugin.
Results: The results obtained through CA Markov method are robust and more accurate compared to MOLUSCE model.  Therefore, it is highly recommended for government and non-government officials to study LULC dynamics and urban growth predictions in order to build urban resilience.
Conclusion: The study also highlights the importance of integrating road network data in improving prediction accuracy. These findings contribute to the development of sustainable urban planning strategies in rapidly growing cities.

Keywords

Main Subjects

References

Aarthi, A. D., & Gnanappazham, L. (2018). Urban growth prediction using neural network coupled agents-based Cellular Automata model for Sriperumbudur Taluk, Tamil Nadu, India. Egyptian Journal of Remote Sensing and Space Science, 21(3), 353–362. https://doi.org/10.1016/j.ejrs.2017.12.004
Achmad, A., Irwansyah, M., & Ramli, I. (2018). Prediction of future urban growth using CA-Markov for urban sustainability planning of Banda Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 126(1). https://doi.org/10.1088/1755-1315/126/1/012166
Aithal, B. H., Vinay, S., Durgappa, S., & Ramachandra, T. V. (2013). Modeling and Simulation of Urbanisation in Greater Bangalore, India. National Spatial Data Infrastructure Conference, 1=7.
Al-Darwish, Y., Ayad, H., Taha, D., & Saadallah, D. (2018). Predicting the future urban growth and its impacts on the surrounding environment using urban simulation models: Case study of Ibb city – Yemen. Alexandria Engineering Journal, 57(4), 2887–2895. https://doi.org/10.1016/j.aej.2017.10.009
Bharath, H. A., Chandan, M. C., Vinay, S., & Ramachandra, T. V. (2018). Modelling urban dynamics in rapidly urbanising Indian cities. Egyptian Journal of Remote Sensing and Space Science, 21(3), 201–210. https://doi.org/10.1016/j.ejrs.2017.08.002
Clarke, K. C., & Gaydos, L. J. (1998). Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12(7), 699–714. https://doi.org/10.1080/136588198241617
Dadras, M., Shafri, H. Z. M., Ahmad, N., Pradhan, B., & Safarpour, S. (2015). Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran. Egyptian Journal of Remote Sensing and Space Science, 18(1), 35–52. https://doi.org/10.1016/j.ejrs.2015.03.005
Dhali, M. K., Chakraborty, M., & Sahana, M. (2019). Assessing spatio-temporal growth of urban sub-centre using Shannon’s entropy model and principal component analysis: A case from North 24 Parganas, lower Ganga River Basin, India. Egyptian Journal of Remote Sensing and Space Science, 22(1), 25–35. https://doi.org/10.1016/j.ejrs.2018.02.002
Ebrahimipour, A., & Farshchin, A. (2021). Prediction of Urban Growth through Cellular Automata-Markov Chain Prediction of Urban Growth through Cellular Automata-Markov Chain Introduction. January 2016.
Gagniuc, P. A. (2017). Markov Chains From Theory to Implementation and Experimentation of Engineering in Foreign Languages. https://lccn.loc.gov/2017011637
Guidigan, M. L. G., Sanou, C. L., Ragatoa, D. S., Fafa, C. O., & Mishra, V. N. (2019). Assessing Land Use/Land Cover Dynamic and Its Impact in Benin Republic Using Land Change Model and CCI-LC Products. Earth Systems and Environment, 3(1), 127–137. https://doi.org/10.1007/s41748-018-0083-5
Jayasinghe, P., Kantakumar, L. N., Raghavan, V., & Yonezawa, G. (2019). Comparative Evaluation of Urban Growth Models. October 2020.
Kar, R., Obi Reddy, G. P., Kumar, N., & Singh, S. K. (2018). Monitoring Spatio-temporal dynamics of urban and peri-urban landscape using remote sensing and GIS – A case study from Central India. Egyptian Journal of Remote Sensing and Space Science, 21(3), 401–411. https://doi.org/10.1016/j.ejrs.2017.12.006
Kumar, S., Radhakrishnan, N., & Mathew, S. (2014). Land-use change modelling using a Markov model and remote sensing. December, 37–41. https://doi.org/10.1080/19475705.2013.795502
Kusuma, S., & Pradesh, A. (2017). Engineering URBAN GROWTH PREDICTION USING GEO- SIMULATION AND MODELING Kusuma . Sundara Kumar Pinnamaneni . Udaya Bhaskar Kollipara . Padma ABSTRACT. December 2016.
Liu, Y., Li, L., Chen, L., Cheng, L., Zhou, X., Cui, Y., Li, H., & Liu, W. (2019). Urban growth simulation in different scenarios using the SLEUTH model: A case study of Hefei, East China. PLoS ONE, 14(11), 1–22. https://doi.org/10.1371/journal.pone.0224998
Maurya, N. K., Rafi, S., & Shamoo, S. (2022). Land use/land cover dynamics study and prediction in jaipur city using CA markov model integrated with road network. GeoJournal, 0123456789. https://doi.org/10.1007/s10708-022-10593-9  
Maurya, R. K., Saxena, M. R., & Akhil, N. (2016). Intelligent Systems Technologies and Applications. Advances in Intelligent Systems and Computing, 384(January 2016), 247–257. https://doi.org/10.1007/978-3-319-23036-8
Mc Cutchan, M., Özdal-Oktay, S., & Giannopoulos, I. (2020). Semantic-based urban growth prediction. Transactions in GIS, 24(6), 1482–1503. https://doi.org/10.1111/tgis.12655
Mishra, V., Rai, P., & Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute Jovan Cvijic, SASA, 64(1), 111–127. https://doi.org/10.2298/ijgi1401111m
Mondal, M. S., Sharma, N., Garg, P. K., & Kappas, M. (2016). Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egyptian Journal of Remote Sensing and Space Science, 19(2), 259–272. https://doi.org/10.1016/j.ejrs.2016.08.001
Mosammam, H. M., Nia, J. T., Khani, H., Teymouri, A., & Kazemi, M. (2017). Monitoring land-use change and measuring urban sprawl based on its spatial forms: The case of Qom city. Egyptian Journal of Remote Sensing and Space Science, 20(1), 103–116. https://doi.org/10.1016/j.ejrs.2016.08.002
Narimah Samat. (2009). Integrating GIS and CA-MARKOV model in evaluating urban spatial growth. Malaysian Journal of Environmental Management, 10(1), 83–100. http://journalarticle.ukm.my/2281/1/Artikel_6_Narimah.pdf
Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA-Markov model. Mathematical and Computer Modelling, 54(3–4), 938–943. https://doi.org/10.1016/j.mcm.2010.11.019
Triantakonstantis, D., & Mountrakis, G. (2012). Urban Growth Prediction: A Review of Computational Models and Human Perceptions. Journal of Geographic Information System, 04(06), 555–587. https://doi.org/10.4236/jgis.2012.46060
Turner, M. G. (1989). Landscape ecology: the effect of pattern on process. Annual Review of Ecology and Systematics. Vol. 20, 171–197. https://doi.org/10.1146/annurev.es.20.110189.001131
 

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