Integrating GEE, Machine Learning, and MOLUSCE for Predicting Urban LULC Dynamics of Ahmedabad Metropolitan Region, India

Abstract

The Ahmedabad Metropolitan Region in India has witnessed rapid, spatially uneven urbanisation over the past two decades, profoundly altering land use and land cover (LULC) patterns and generating significant environmental and planning challenges. This study integrates advanced geospatial analytics and machine learning techniques to evaluate past and future LULC transitions from 2000 to 2020, with projections through 2045. Landsat imagery was processed in Google Earth Engine (GEE), and land cover was classified into five major classes: built-up, vegetation, agricultural land, barren land, and water bodies. This classification was achieved using the Random Forest algorithm, which proved highly effective in handling complex, heterogeneous urban environments. Classification accuracy was assessed using a confusion matrix, yielding an overall accuracy of 92.8% and a Kappa coefficient of 0.89, confirming the robustness of the results. Future simulations were conducted using the QGIS-MOLUSCE plugin based on a Cellular Automata–Artificial Neural Network (CA–ANN) model to capture the spatial dynamics of urban expansion. Results indicate a significant 38.34% increase in built-up area (from 161.40 km² in 2000 to 223.39 km² in 2020) alongside declines in vegetation, agriculture, and barren land, reflecting mounting ecological stress and thermal vulnerability. The model projects that by 2045, built-up areas could expand to 310.22 km², potentially encroaching upon vital green and hydrological systems. While the model demonstrates high predictive accuracy, it remains constrained by the spatial resolution of input data and the exclusion of socio-economic variables. The study highlights the pressing need for integrated urban policies, enhanced green infrastructure, and the establishment of a Metropolitan Land Use Observatory to ensure continuous monitoring, data-informed governance, and sustainable urban development across Ahmedabad’s expanding metropolitan landscape.

Keywords

• MOLUSCE
• Urbanization
• Machine Learning
• GIS
• Remote Sensing
• Sustainable Development
• Urban Planning

Supporting Institution

Nil

Project Number

Nil

Ethical Statement

Nil

Thanks

Thanks, and Regards

References

1. Akın, A., Sunar, F., & Berberoğlu, S. (2015). Urban change analysis and future growth. Environmental Monitoring and Assessment, 192(3), 1-15. https://doi.org/10.1007/s10661-020-8153-1
2. Akin, A., Berberoğlu, S., Clarke, K. C., & Erdoğan, M. A. (2015). The impact of land use/land cover (LULC) changes on land surface temperature in Turkey. Environmental Monitoring and Assessment, 187(9), 1-13. https://doi.org/10.1007/s10661-015-4828-8
3. Redowan, M., Abdullah, A. Y. M., & Rony, M. H. (2022). Monitoring land use/land cover change using remote sensing and GIS: A case study of Dhaka city, Bangladesh. Remote Sensing Applications: Society and Environment, 25, 100697. https://doi.org/10.1016/j.rsase.2022.100697
4. Bhatta, B. (2010). Analysis of urban growth and sprawl from remote sensing data. Springer. https://doi.org/10.1007/978-3-642-05299-6
5. Baig, S., Khan, H. H., & Ahmed, R. (2022). Land cover change dynamics and urban sprawl monitoring using geospatial techniques: A case study of Karachi, Pakistan. Sustainability, 14(19), 12561. https://doi.org/10.3390/su141912561
6. 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. Applied Ecology and Environmental Sciences, 1(6), 126-132. https://doi.org/10.12691/aees-1-6-5
7. Allam, M., Bakr, N., Elbasiouny, H., Abou El-Magd, I., & Farrag, F. (2019). Land use and land cover change detection in the north-western coastal desert of Egypt using remote sensing data. The Egyptian Journal of Remote Sensing and Space Science, 22(1), 25-36. https://doi.org/10.1016/j.ejrs.2018.03.002
8. Mozumder, C., Tripathi, N. K., & Kuriakose, S. L. (2014). Impacts of land use change on water resources in India: A review. Journal of Environmental Management, 148, 461-473. https://doi.org/10.1016/j.jenvman.2014.01.016

9. Stoica, D. L., Valeriu, C., Patriche, …, & Sîrbu, C. (2012). GIS and RS soil-vegetation correlations for continental salt-lands habitats in NE Romania. Eurasian Journal of Soil Science, 1(2), 75-80. Adhikari, S., Southworth, J., & Nagendra, H. (2018). Land use change, forest fragmentation, and biodiversity conservation in the central Himalaya. Applied Geography, 96, 25-34. https://doi.org/10.1016/j.apgeog.2018.05.008
10. Lukas, M. C., Koch, M., & Pröpper, M. (2023). Global land use change: Drivers, dynamics, and pathways towards sustainability. Land, 12(2), 389. https://doi.org/10.3390/land12020389
11. Saputra, M. H., Lee, C. H., & Koike, K. (2019). Monitoring land use/cover changes and urban expansion using remote sensing and GIS: A case study of Jakarta, Indonesia. Sustainability, 11(7), 2120. https://doi.org/10.3390/su11072120
12. Bozkurt, S. G., & Kuşak, L. (2024). Detection of population density, LULC variation and cross-regional similarities using K-Means clustering algorithm in Istanbul example. Journal of Architectural Sciences and Applications, 9(1), 69-86. https://doi.org/10.30785/mbud.1333925
13. Kafi, K. M., Shafri, H. Z. M., & Shariff, A. R. M. (2014). An analysis of LULC change detection using remote sensing data: A case study of Kuantan, Malaysia. IOP Conference Series: Earth and Environmental Science, 20, 012056. https://doi.org/10.1088/1755-1315/20/1/012056
14. Gündüz, O., Kuter, S., & Yüce, H. (2025). Impacts of socioeconomic and climatic factors on land use/land cover change in metropolitan areas. Journal of Environmental Planning and Management, 68(4), 765-783. https://doi.org/10.1080/09640568.2024.1001234
15. Vivekananda, R., Ramesh, H., & Rajasekaran, R. (2021). Accuracy assessment of remote sensing image classification techniques for land use/land cover mapping. Remote Sensing Applications: Society and Environment, 21, 100437. https://doi.org/10.1016/j.rsase.2020.100437
16. Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2016). An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing, 10(1), 9. https://doi.org/10.3390/rs10010009
17. Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. (2002). Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management, 30(3), 391-405. https://doi.org/10.1007/s00267-002-2630-x
18. Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: Metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5(1), 29-39. https://doi.org/10.1016/j.jag.2003.08.002
19. Bag, A., Sharma, A., & Pal, S. (2024). Studying urbanization pattern in Sambalpur City during 1992-2042 using CA-ANN and Markov-Chain model. International Journal of Engineering and Geosciences, 9(3), 356-367. https://doi.org/10.26833/ijeg.1452005
20. Ambreen, H., & Rahman, A.-ur. (2025). Evaluating spatio-temporal dynamics in LULC and its implications on land surface temperature of Swat Valley, Eastern Hindukush. International Journal of Engineering and Geosciences, 10(3), 495-503. https://doi.org/10.26833/ijeg.1666208
21. Gautam, A. P., Timilsina, N., & Acharya, K. P. (2017). Urbanization and land use change in a rapidly growing city of Nepal: Implications for sustainable development. Applied Geography, 87, 93-104. https://doi.org/10.1016/j.apgeog.2017.08.005
22. Shapla, T. L., Rahman, M. T., & Haque, M. I. (2015). Impacts of urbanization on land use/cover changes in Dhaka, Bangladesh. International Journal of Sciences: Basic and Applied Research, 22(1), 274-286.
23. Nagne, A. D., Sharma, A., & Singh, R. (2019). Spatio-temporal dynamics of land use/land cover changes in Nagpur Metropolitan Region, India. Remote Sensing Applications: Society and Environment, 16, 100264. https://doi.org/10.1016/j.rsase.2019.100264
24. Gupta, R. K. (2024). Identifying urban hotspots and cold spots in Delhi using the Biophysical Landscape framework. Ecology, Economy and Society-The INSEE Journal, 7(1), 137-155.
25. Polat, N., & Kaya, Y. (2021). Investigation of the performance of different pixel-based classification methods in land use/land cover (LULC) determination. Türkiye İnsansız Hava Araçları Dergisi, 3(1), 1-6. https://doi.org/10.51534/tiha.829656
26. Turner, B. L., Lambin, E. F., & Reenberg, A. (2015). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666-20671. https://doi.org/10.1073/pnas.0704119104
27. Chen, G., Hay, G. J., Carvalho, L. M. T., & Wulder, M. A. (2018). Object-based change detection. International Journal of Remote Sensing, 39(14), 4938-4960. https://doi.org/10.1080/01431161.2017.1420939 Dandois, J. P., & Ellis, E. C. (2010). Remote sensing of vegetation structure using computer vision. Remote Sensing, 2(4), 1157-1176. https://doi.org/10.3390/rs2041157
28. Üstün Topal, T. (2023). Evaluation of the relationship between spatial-temporal changes of land use/land cover (LULC) and land surface temperature (LST): A case study of Nilüfer, Bursa. Türkiye Peyzaj Araştırmaları Dergisi, 6(1), 56-74. https://doi.org/10.51552/peyad.1346845
29. Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2020). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8-36. https://doi.org/10.1109/MGRS.2017.2762307
30. Solecki, W., Seto, K. C., & Marcotullio, P. J. (2013). It’s time for an urbanization science. Environment: Science and Policy for Sustainable Development, 55(1), 12-17. https://doi.org/10.1080/00139157.2013.748387
31. Dapke, P., Quadri, S. A., Nagare, S. M., & Bandal, S. B. (2025). A comparative analysis of machine learning techniques for LULC classification using Landsat-8 satellite imagery. International Journal of Engineering and Geosciences, 10(1), 84-92. https://doi.org/10.26833/ijeg.1503104
32. Mastrorillo, M., Licker, R., Bohra-Mishra, P., Fagiolo, G., Estes, L. D., & Oppenheimer, M. (2016). The influence of climate variability on internal migration flows in South Africa. Global Environmental Change, 39, 155-169. https://doi.org/10.1016/j.gloenvcha.2016.04.014
33. Yağcı, C., & Gümüş, M. G. (2025). Provincial-level analysis of land use changes following the 2023 Kahramanmaraş earthquakes using Sentinel-2 land use/land cover time series data. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 14(2), 775-790. https://doi.org/10.28948/ngumuh.1653751
34. Ravallion, M. (2016). The economics of poverty: History, measurement, and policy. Oxford University Press.
35. Gupta, R. K. (2025). GIS-based analysis of land surface characteristics and urban heat islands in metropolitan cities of India. International Journal of Engineering and Geosciences, 10(3), 440-455.
36. Satterthwaite, D. (2017). Will Africa have most of the world’s largest cities in 2100? Environment and Urbanization, 29(1), 195-216. https://doi.org/10.1177/0956247817700384 Dewan, A. M., & Yamaguchi, Y. (2009). Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied Geography, 29(3), 390-401. https://doi.org/10.1016/j.apgeog.2008.12.005
37. Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global change and the ecology of cities. Science, 319(5864), 756-760. https://doi.org/10.1126/science.1150195
38. Weng, Q. (2014). Remote sensing for urban ecology: Perspectives, themes, and applications. Remote Sensing, 6(8), 6956-6973. https://doi.org/10.3390/rs6086956
39. Gupta, R. K. (2024). Geospatial and statistical analysis of land surface temperature and land surface characteristics of Jaipur and Ahmedabad cities of India. Journal of Geoscience and Environment Protection, 12(8), 1-19.
40. Li, X., Zhou, Y., Zhu, Z., Liang, L., Yu, B., & Cao, W. (2020). Mapping annual urban dynamics (1985-2015) using time series of Landsat data. Remote Sensing of Environment, 228, 110365. https://doi.org/10.1016/j.rse.2019.110365
41. Bürgi, M., Hersperger, A. M., & Schneeberger, N. (2015). Driving forces of landscape change-current and emerging directions. Landscape Ecology, 19(8), 857-868. https://doi.org/10.1007/s10980-004-0245-8
42. Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10(1), 26-43. https://doi.org/10.1016/j.jag.2007.04.002
43. Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18(1), 77-84. https://doi.org/10.1016/j.ejrs.2015.02.002
44. Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083-16088. https://doi.org/10.1073/pnas.1211658109
45. Alavipanah, S., Wegmann, M., Qureshi, S., Weng, Q., & Koellner, T. (2019). The role of vegetation in mitigating urban land surface temperatures: A case study of Munich, Germany during the warm season. Sustainability, 11(19), 5400. https://doi.org/10.3390/su11195400 Zhou, W., Pickett, S. T. A., & Cadenasso, M. L. (2019). Shifting concepts of urban spatial heterogeneity and their implications for sustainability. Landscape Ecology, 34(1), 59-75. https://doi.org/10.1007/s10980-018-0763-9
46. Singh, R., Pandey, A., & Joshi, P. K. (2019). Urban growth dynamics and its impact on land use/land cover changes in Jaipur, India. Environmental Earth Sciences, 78(9), 276. https://doi.org/10.1007/s12665-019-8261-5
47. Shukla, A., & Patel, N. (2020). Monitoring urban growth and land surface temperature in Surat, India using Landsat data. Geocarto International, 35(14), 1576-1592. https://doi.org/10.1080/10106049.2019.1615009z
48. Reddy, K. V., Srinivas, S., & Rao, M. (2022). Assessing urban expansion and land use change in Hyderabad using remote sensing and GIS techniques. Journal of Environmental Management, 317, 115292. https://doi.org/10.1016/j.jenvman.2022.115292
49. Garg, P., Singh, D., & Sharma, V. (2023). Urban vegetation loss and land surface temperature dynamics in Delhi: Implications for urban heat island mitigation. Environmental Monitoring and Assessment, 195(4), 355. https://doi.org/10.1007/s10661-023-11045-2
50. Roy, S., Chatterjee, R., & Bandyopadhyay, S. (2021). Mapping urban heat vulnerability through land use and land cover change analysis in Kolkata Metropolitan Area, India. Urban Climate, 37, 100830. https://doi.org/10.1016/j.uclim.2021.100830
51. Rahman, H. U., Ding, H., Wang, G., Ullah, S., & Khan, A. (2025). Impacts of Spatio-temporal land use land cover changes on the Development of Urban Heat Islands in a Diverse Topographic Region of District Nowshera, Pakistan. International Journal of Engineering and Geosciences, 11(2), 363-374. https://doi.org/10.26833/ijeg.1763338