ديناميكيات النمو العمراني ومحاكاة تغيراته في مدينة الدمام باستخدام نموذج تقييم تغيير استخدام الأرض (MOLUSCE)

Maliha Hamed Al-Abdali

doi:10.33193/JALHSS.131.2026.1701

Maliha Hamed Al-Abdali Associate Professor, Department of Geography, Faculty of Social Sciences, Umm Al-Qura University, Makkah, Saudi Arabia

DOI: https://doi.org/10.33193/JALHSS.131.2026.1701

Keywords: Urban growth, Artificial Neural Network (ANN), Modules for Land Use Change Evaluation (MOLUSCE), Dammam City, Sentinel-2 satellite

Abstract

This research analyzes land use/land cover (LULC) dynamics in Dammam (2016–2026) and projects urban growth spatial transformations until 2056. The methodology applied supervised classification to Sentinel-2 imagery for baseline maps, utilizing the MOLUSCE model in QGIS, supported by Artificial Neural Networks (ANN) and Cellular Automata. Spatial forecasting integrated historical layers with environmental determinants (elevation, wadis) and accessibility/development criteria (buildings, services, road network and quality).

Results revealed that in 2016, sand deposits dominated at 48.36% (162.92 km²), followed by buildings (23.92%), roads (14.63%), water (9.3%), and vegetation (3.99%). By 2026, a radical shift occurred; buildings climbed to the first rank at 40.6% (136.8 km²), sand deposits dropped to 29.25%, while roads and vegetation grew to 18.66% and 6.18%, and water contracted to 5.31%. Modeling outputs (2036–2056) confirm continuous upward urban expansion at the expense of natural covers. The 2056 projection indicates the dominance of built-up areas at 60.33% (203.27 km²) and roads at 23.1%, whereas sand deposits, vegetation, and water surfaces will recede to 10.8%, 3.1%, and 2.67%, respectively, of the city's total area.

References

1. Ahmad, M.N., Shao, Z., Javed, A. (2022). Modelling Land Use/Land Cover (LULC) Change Dynamics, Future Prospects, and Its Environmental Impacts Based on Geospatial Data Models and Remote Sensing Data. Environ. Sci. Pollut. Res, 30, 32985–33001.
2. Baghel, S., Kothari, M.K., Tripathi, M.P., Singh, P.K.,Jain, S.K. (2024). Spatiotemporal LULC Change Detection and Future Prediction for the Mand Catchment Using MOLUSCE Tool. Environ. Earth Sci, 83, 1–15.
3. Baig, M. F., Mustafa, M. R. U., Baig, I., Takaijudin, H. B., & Zeshan, M. T. (2022). Assessment of Land Use Land Cover Changes and Future. Water, 1–17. https://doi.org/ 10.3390/w14030402.
4. Beroho, M., Briak, H., Cherif, E. K., Boulahfa, I., Ouallali, A., Mrabet, R., Kebede, F., et al. (2023). Future Scenarios of Land Use/Land Cover (LULC) Based on A CA-Markov Simulation Model: Case of a Mediterranean Watershed in Morocco. Remote Sensing 15(4): 1162. DOI: 10.3390/rs15041162.
5. Boakye, E., Anyemedu, F. O. K., Quaye-Ballard, J. A., and Donkor, E. A. (2020). Spatio-Temporal Analysis of Land Use/Cover Changes in the Pra River Basin, Ghana. Applied Geomatics 12: 83–93. DOI: 10.1007/s12518-019-00278-3.
6. Eman A., Gawali, B. W. (2022). Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE. Hindawi Journal of Sensors, 2022, 1-15, https://doi.org/10.1155/2022/741903.
7. Gündüz, H. H. (2025). Land-Use Land-Cover Dynamics and Future Projections Using GEE, ML, and QGIS-MOLUSCE: A Case Study in Manisa. Sustainability, 17(4), 1363; https://doi.org/10.3390/su17041363.
8. Iskandar, B., Kurnia. A. A., Jauhari, A., Zannah, F. (2024). Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah. Jurnal Sylva Lestari, 12(2), 242-257, May 2024 DOI: https://doi.org/10.23960/jsl.v12i2.865.
9. Khwarahm, Nabaz R., Najmaddin, Peshawa M., Ararat, Korsh and Qader, Sarchil (2021) Past and future prediction of land cover land use change based on earth observation data by the CA–Markov model: a case study from Duhok governorate, Iraq. Arabian Journal of Geosciences, 14 (15), [1544]. (doi:10.1007/s12517-021-07984-6).
10. Kumari, S. and Roy, A.. (2024). Simulation of Land Use and Land Cover Using the MOLUSCE Plugin Integrated with QGIS for the Western Himalayan Region of India, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-5, 81–85, https://doi.org/10.5194/isprs-archives-XLVIII-M-5-2024-81-2025, 2025.
11. Mandal, B., Mondal, S., & Mandal, S. (2023). GISbased landslide susceptibility zonation (LSZ) mapping of Darjeeling Himalaya, India using weights of evidence (WoE) model. Arabian Journal of Geosciences, 16(7). https://doi.org/10.1007/s12517-023-11523-w.
12. Osman, M. A. A., Abdel-Rahman, E. M., Onono, J. O., Olaka, L. A., et al. (2023). Mapping, Intensities and Future Prediction of Land Use/Land Cover Dynamics Using Google Earth Engine and CA-Artificial Neural Network Model. PLOS ONE 18(7): e0288694. DOI: 10.1371/journal.pone.0288694.
13. Ramadan, G. F., and Hidayati, I. N. (2022). Prediction and Simulation of Land Use and Land Cover Changes Using Open Source QGIS. A Case Study of Purwokerto, Central Java, Indonesia. The Indonesian Journal of Geography, 54(3), 344–351. DOI: 10.22146/ijg.68702.
14. Tewabe, D., and Fentahun, T. (2020). Assessing Land Use and Land Cover Change Detection Using Remote Sensing in the Lake Tana Basin, Northwest Ethiopia. Cogent Environmental Science, 6, https://doi.org/10.1080/23311843.2020.1778998.
15. Wang, Q., Guan, Q., Sun, Y., Du, Q., Xiao, X. et al. (2023). Simulation of Future Land Use/Cover Change (LUCC) in Typical Watersheds of Arid Regions under Multiple Scenarios. J. Environ. Manag, 335, 117543.

Urban Growth Dynamics and Simulation of Changes in Dammam City Using the Land Use Change Assessment Model (MOLUSCE)

Abstract

References

Journal publishing fields

Address