Monitoring Urban Sprawl Dynamics Through Spatiotemporal Analysis Using Normalized Difference Built-up Index in Mardan City, Pakistan

Authors

  • Abdur Raziq Department of Geography, Islamia College University, Peshawar, 25120, Pakistan
  • Ihsan Ullah Department of Geography, University of Peshawar, 25120, Pakistan
  • Muhammad Alamgir Khan Department of Geography, Islamia College University, Peshawar, 25120, Pakistan
  • Tariq Sardar Department of Environmental Science, Kohat University of Science & Technology, Kohat, 26000, KP, Pakistan
  • Izhar Ullah Department of Agricultural and Applied Economics, The University of Agriculture Peshawar, 25120, Pakistan

DOI:

https://doi.org/10.53560/PPASA(63-3)722

Keywords:

Urban Sprawl, NDBI, Landsat, Spatiotemporal Analysis, RS, GIS

Abstract

The assessment of  spatial distrubution and expansion-driven urban sprawl is cruical for effective urban planning and ensuring sustainable resource management. This study presents a spatiotemporal assessemnt of urban expansion dynamics in Mardan City, Pakistan, by employing the Normalized Difference Built-up Index in the ArcGIS 10.7 environment using Landsat satellite images data. For this purpose, the Landsat 4-5 Thematic Mapper for 1998, Landsat 7 Enhanced Thematic Mapper Plus for 2005, and Landsat 8-9 Operational Land Imager for 2017 and 2022 were used to determine spatial changes in the urbanized area of the city. The data indicates a marginal expansion in the built-up area of Mardan City, which rose from 25.12 km2 in 1998 to 25.88 km2 in 2005, reflecting a slow rate of urbanization during this timeframe. However, a considerable growth was observed from 27 km2 to 33 km2, between 2017 and 2022, reflecting rapid growth of urbanization. Prospectively, the proposed study could play a crucial role in supporting decision-making and policy formulation regarding urban growth. Moreover, the present approach can provide valuable guidelines for urban planners, policymakers, and researchers to address the challenges and opportunities associated with urbanization in this region.

References

1. S.L. Huang, C.T. Yeh, and L.F. Chang. The transition to an urbanizing world and the demand for natural resources. Current Opinion in Environmental Sustainability 2(3): 136-143 (2010). https://doi.org/10.1016/j.cosust.2010.06.004

2. B. Cohen. Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society 28(1-2): 63-80 (2006). https://doi.org/10.1016/j.techsoc.2005.10.005

3. E.F. Lambin and H.J. Geist (Eds.). Land-use and land-cover change: local processes and global impacts. Springer Science & Business Media, Berlin, Germany (2008).

4. H. Wang, X. Gong, B. Wang, C. Deng, and Q. Cao. Urban development analysis using built-up area maps based on multiple high-resolution satellite data. International Journal of Applied Earth Observation and Geoinformation 103: 102500 (2021). https://doi.org/10.1016/j.jag.2021.102500

5. V.D. Pham, F. Schug, D. Frantz, and S.V.D. Linden. Mapping urban built-up types from 2000 to 2022 at 10-m resolution using super-resolution of Landsat spectral-temporal metrics and center-patch classification. Remote Sensing of Environment 335: 115251 (2026). https://doi.org/10.1016/j.rse.2026.115251

6. B.R.K. Sinha. Introduction: a broad perspective on the concepts of urban dynamics, environment, and health. In Urban Dynamics, Environment and Health: An International Perspective: 3-79 (2024). https://doi.org/10.1007/978-981-99-5744-6_1

7. A.A. Jenberu and T.G. Admasu. Urbanization and land use pattern in Arba Minch town, Ethiopia: Driving forces and challenges. GeoJournal (2020). https://doi.org/10.1007/s10708-019-09998-w

8. P. Filion. Growth and decline in the Canadian urban system: The impact of emerging economic, policy and demographic trends. GeoJournal 75(6): 517-538 (2010). https://doi.org/10.1007/s10708-009-9275-8

9. A. Rahman, S. Kumar, S. Fazal, and M.A. Siddiqui. Assessments of land use/land cover change in the northwest district of Delhi using remote sensing and GIS techniques. Journal of the Indian Society of Remote Sensing 40(4): 689-697 (2012). https://doi.org/10.1007/s12524-011-0165-4

10. S. Angel, P. Lamson-Hall, A. Blei, S. Shingade, and S. Kumar. Densify and expand: A global analysis of recent urban growth. Sustainability 13(7): 3835 (2021). https://doi.org/10.3390/su13073835

11. Y. Forget, M. Shimoni, M. Gilbert, and C. Linard. Mapping 20 years of urban expansion in 45 urban areas of sub-Saharan Africa. Remote Sensing 13(3): 525 (2021). https://doi.org/10.3390/rs13030525

12. Y. Xi, Y. Liu, T. Li, J. Ding, Y. Zhang, S. Tarkoma, and P. Hui. A satellite imagery dataset for long-term sustainable development in United States cities. Scientific Data 10(1): 866 (2023). https://doi.org/10.1038/s41597-023-02576-3

13. C. Small. High spatial resolution spectral mixture analysis of urban reflectance. Remote Sensing of Environment 88(1-2): 170-186 (2003). https://doi.org/10.1016/j.rse.2003.04.008

14. A. Mishra, A.K. Agnihotri, S. Pipil, S. Gaur, A. Ohri. Surveying techniques for urban areas. In Earth Observation in Urban Monitoring, pp. 69-91. Elsevier, (2024). https://doi.org/10.1016/B978-0-323-99164-3.00013-6

15. S. Jiang, W. Jiang, L. Wang. Unmanned Aerial Vehicle-Based Photogrammetric 3D Mapping: A survey of techniques, applications, and challenges. IEEE Geoscience and Remote Sensing Magazine 10(2): 135-171 (2021). https://doi.org/10.1109/MGRS.2021.3122248

16. B. Guindon, Y. Zhang, and C. Dillabaugh. Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sensing of Environment 92(2): 218-232 (2004). https://doi.org/10.1016/j.rse.2004.06.015

17. C. Cleve, M. Kelly, F.R. Kearns, and M. Moritz. Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers, Environment and Urban Systems 32(4): 317-326 (2008). https://doi.org/10.1016/j.compenvurbsys.2007.10.001

18. Y. Zha, J. Gao, and S. Ni. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing 24(3): 583-594 (2003). https://doi.org/10.1080/01431160304987

19. H. Xu. A new index for delineating built-up land features in satellite imagery. International Journal of Remote Sensing 29(14): 4269-4276 (2008). https://doi.org/10.1080/01431160802039957

20. A. Schneider, M.A. Friedl, and D. Potere. Mapping global urban areas using MODIS 500-m data: New methods and datasets based on urban ecoregions. Remote Sensing of Environment 114(8): 1733-1746 (2010). https://doi.org/10.1016/j.rse.2010.03.003

21. R.C. Sharma, R. Tateishi, K. Hara, S. Gharechelou, and K. Iizuka. Global mapping of urban built-up areas of year 2014 by combining MODIS multispectral data with VIIRS nighttime light data. International Journal of Digital Earth 9(10): 1004-1020 (2016). https://doi.org/10.1080/17538947.2016.1168879

22. Y. Su, X. Chen, C. Wang, H. Zhang, J. Liao, Y. Ye, and C. Wang. A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: A case study in the Pearl River Delta, southern China. GIScience & Remote Sensing 52(2): 218-238 (2015). https://doi.org/10.1080/15481603.2015.1007778

23. H. Xu. Extraction of urban built-up land features from Landsat imagery using a thematic oriented index combination technique. Photogrammetric Engineering & Remote Sensing 73(12): 1381-1391 (2007). https://doi.org/10.14358/PERS.73.12.1381

24. S.N. Tin and W. Muttitanon. Analysis of enhanced built-up and bare land index (EBBI) in the urban area of Yangon, Myanmar. International Journal of Geoinformatics 17: 85-96 (2021). https://doi.org/10.52939/ijg.v17i4.1957

25. A.R. As-Syakur, I.W.S. Adnyana, I.W. Arthana, and I.W. Nuarsa. Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sensing 4(10): 2957-2970 (2012). https://doi.org/10.3390/rs4102957

26. A.K. Piyoosh and S.K. Ghosh. Development of a modified bare soil and urban index for Landsat 8 satellite data. Geocarto International 33(4): 423-442 (2018). https://doi.org/10.1080/10106049.2016.1273401

27. M.C. Maloney, S.J. Becker, A.W.H. Griffin, S.L. Lyon, and K. Lasko. Automated built-up infrastructure land cover extraction using index ensembles with machine learning, automated training data, and red band texture layers. Remote Sensing 16(5): 868 (2024). https://doi.org/10.3390/rs16050868

28. S.S. Bhatti and N.K. Tripathi. Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing 51(4): 445-467 (2014). https://doi.org/10.1080/15481603.2014.939539

29. J. Chen, M. Li, Y. Liu, C. Shen, and W. Hu. Extract residential areas automatically by new built-up index. 2010 18th International Conference on Geoinformatics, Beijing, China (2010). https://doi.org/10.1109/GEOINFORMATICS.2010.5567823

30. O.R. Abd El-Kawy, J.K. Rød, H.A. Ismail, and A.S. Suliman. Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography 31(2): 483-494 (2011). https://doi.org/: 10.1016/j.apgeog.2010.10.012

31. Kamran, M.F. Khokhar, J.A. Khan, and I.H. Adil. Spatiotemporal assessment of land use land cover changes and population dynamics using geoinformatics: a case study of Mardan, Khyber Pakhtunkhwa, Pakistan. Engineering Proceedings 56(1): 103 (2023). https://doi.org/10.3390/ASEC2023-15367

32. Z. Sultan, M.A. Khan, Z. Khan, M. Sudais, N. Khan, A. Ullah, and M. Ali. Dynamic of land use land cover and its impact on land surface temperature (LST) using GIS: A study of District Mardan, Pakistan. International Journal of Innovations in Science & Technology 6: 472-488 (2024). https://ideas.repec.org/a/abq/ijist1/v6y2024i6p472-488.html

33. P. Yar, A.U. Rahman, M.A. Khan, Samiullah, and S.A.A. Shah. Spatiotemporal analysis of urban expansion on farmland and its impact on the agricultural land use of Mardan City, Pakistan. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences 53(1): 35-46 (2016). https://ppaspk.org/index.php/PPAS-B/article/view/368

34. S. Bouzekri, A.A. Lasbet, and A. Lachehab. A new spectral index for extraction of built-up area using Landsat-8 data. Journal of the Indian Society of Remote Sensing 43(4): 867-873 (2015). https://doi.org/10.1007/s12524-015-0460-6

35. V.S. Bramhe, S.K. Ghosh, and P.K. Garg. Extraction of built-up area by combining textural features and spectral indices from landsat-8 multispectral image.The International Archives of the Photogrammetry, Remote Sensing

and Spatial Information Sciences 42: 727-733 (2018). https://doi.org/10.5194/isprs-archives-XLII-5-727-2018, 2018

36. W. Zhu, C. Yuan, Y. Tian, Y. Wang, L. Li, and C. Hu. A new high-resolution rural built-up land extraction method based on artificial surface index with short-wave infrared downscaling. Remote Sensing 16(7): 1126 (2024). https://doi.org/10.3390/rs16071126

37. S. Bhaskaran, S. Paramananda, and M. Ramnarayan. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography 30(4): 650-665 (2010). https://doi.org/10.1016/j.apgeog.2010.01.009

38. T. Blaschke, C. Burnett, and A. Pekkarinen. Image segmentation methods for object-based analysis and classification. In: Remote Sensing Image Analysis: Including the Spatial Domain. S.M. De Jong and F.D. Van der Meer (Eds.). Springer, Dordrecht, Netherlands pp. 211-236 (2004). https://doi.org/10.1007/978-1-4020-2560-0_12

39. W. Suparta and H.G. Hartono. Application of the NDBI Method for Determining of Built-Up Land (A Case Study for Surabaya City, Indonesia). International Research Journal of Engineering and Technology 11(7): 1321-1325 (2024).

40. Y. Zheng, L. Tang, and H. Wang. An improved approach for monitoring urban built-up areas by combining NPP-VIIRS nighttime light, NDVI, NDWI, and NDBI. Journal of Cleaner Production 328: 129488 (2021). https://doi.org/10.1016/j.jclepro.2021.129488

41. P. Macarof and F. Statescu. Comparison of NDBI and NDVI as indicators of surface urban heat island effect in Landsat 8 imagery: A case study of Iași. Present Environment and Sustainable Development 11(2): 141-150 (2017). https://doi.org/10.1515/pesd-2017-0032

42. I. Shahid, S. Khan, and L.A.B. Venturi. Spatial analysis of land use/land cover changes in Mardan, Pakistan: A decadal study using GIS and remote sensing. Social Science Review Archives 3(3): 944-954 (2025). https://doi.org/10.70670/sra.v3i3.920

43. R. Khan, H. Li, M. Basir, Y.L. Chen, M.M. Sajjad, I.U. Haq, and W. Hassan. Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan. Environmental Monitoring and Assessment 194(6): 409 (2022). https://doi.org/10.1007/s10661-022-10072-1

44. A. Aziz, A. Asad, M.M. Anwar. Assessing the impacts of urban expansion on sustainability; empirical evidence from Islamabad, Pakistan. GeoJournal 89(2): 82 (2024). https://doi.org/10.1007/s10708-024-11087-6

45. M. Qasim and S. Gul. Spatial and temporal analyses of urbanization of Mardan city, its causes and impacts on environment. International Journal of Environmental Pollution and Environmental Modelling 5(1): 8-18 (2022). https://ijepem.com/doc/ijepem-22-01-02.pdf

46. M. Nasar-u-Minallah, S. Zia, A.U. Rahman, and O. Riaz. Spatiotemporal analysis of urban expansion and future growth patterns of Lahore, Pakistan. Geography, Environment, Sustainability 14(3): 41-53 (2021). https://doi.org/10.24057/2071-9388-2020-215

47. P. Yar. Urban development and its impact on the depletion of groundwater aquifers in Mardan City, Pakistan. Groundwater for Sustainable Development 11: 100426 (2020). https://doi.org/10.1016/j.gsd.2020.100426

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Published

2026-09-18

How to Cite

Raziq, A., Ihsan Ullah, Muhammad Alamgir Khan, Tariq Sardar, & Izhar Ullah. (2026). Monitoring Urban Sprawl Dynamics Through Spatiotemporal Analysis Using Normalized Difference Built-up Index in Mardan City, Pakistan. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 63(3). https://doi.org/10.53560/PPASA(63-3)722

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