Home⇛DAVAO RESEARCH JOURNAL⇛vol. 16 no. 1 (2025)

Urban Sprawl Mapping Based on Land Use and Land Cover (LULC) Time-Series in District II of Davao City, Philippines

Fillmore D Masancay | Arrin E. Arizobal | Vincent T. Pascua | Izza Grace D. Petalcorin

Discipline: engineering (non-specific)

 

Abstract:

Urban sprawl, driven by rapid industrial and residential growth, significantly impacts land use and the environment. This study analyzes its effects on land use and land cover (LULC) in Davao City’s District II from 2013 to 2023 using Geographic Information System (GIS) technology and remote sensing data. Vegetation areas, which accounted for 80.32% of the land in 2013, declined to 68.49% by 2023, Four key categories were assessed: built-up areas, vegetation, water bodies, and barren land. Landsat 8 images from 2013, 2017, 2020, and 2023 were processed using the maximum likelihood classification method, with accuracy assessments conducted to ensure reliable results. Additionally, the Normalized Difference Vegetation Index (NDVI) was used to monitor changes in vegetation and track urban expansion. The findings reveal a significant reduction in vegetated land and a corresponding increase in built-up areas, underscoring the negative impact of urban development on the local environment. This study highlights the need for continued monitoring of LULC changes to support sustainable urban growth and protect valuable natural resources.



References:

  1. Almeida, C. (2020). Urban Planning and COVID-19: Lessons From the Coronavirus Pandemic.
  2. Barnes, K. B., Morgan III, J. M., Roberge, M. C., & Lowe, S. (2001). Sprawl development: its patterns, consequences, and measurement. Towson University, Towson, 1, 24.
  3. Behnisch, M., Krüger, T., & Jaeger, J. A. (2022). Rapid rise in urban sprawl: Global hotspots and trends since 1990. PLOS Sustainability and Transformation, 1(11), e0000034. https://doi.org/10.1371/journal.pstr.0000034
  4. Bhatta, B. (2010). Analysis of urban growth and sprawl from remote sensing data. Springer Science & Business Media.
  5. Burchell, R. W., Lowenstein, G., Dolphin, W. R., Galley, C. C., Downs, A., Seskin, S., Gray Still, K., & Moore, T. (2002). Costs of Sprawl-2000. Transportation Cooperative Research Program, Report 74 – National Academic Press, Washington, DC.
  6. Chen, D., & Stow, D. (2002). The effect of training strategies on supervised classification at different spatial resolutions. Photogrammetric Engineering and Remote Sensing, 68(11), 1155-1162.
  7. Choi, A. (2021). Cost Inflation: Construction Costs and the COVID-19 Pandemic.
  8. Cohen, B. (2006). Urbanization in developing countries: current trends, future projections, and key challenges for sustainability. Technology in Society, 28, 63–80. https://doi.org/10.1016/j.techsoc.2005.10.005
  9. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B
  10. Congalton, R. G. (2001). Accuracy assessment and validation of remotely sensed and other spatial information. International Journal of Wildland Fire, 10(4), 321–328. https://doi.org/10.1071/WF01031
  11. Das, S., & Angadi, D. P. (2022). Land use land cover change detection and urban growth monitoring using remote sensing and GIS techniques: a micro-level study. GeoJournal, 87(3), 2101–2123.
  12. Debolini, M., Valette, E., François, M., & Chéry, J. P. (2015). Mapping land use competition in the rural–urban fringe and future perspectives on land policies: A case study of Meknès (Morocco). Land Use Policy, 47, 373–381. https://doi.org/10.1016/j.landusepol.2015.01.035
  13. Dhanaraj, K., & Angadi, D. P. (2022). Land use land cover mapping and monitoring urban growth using remote sensing and GIS techniques in Mangaluru, India. GeoJournal, 87(2), 1133–1159.
  14. Douglas, I. (2005). The Urban Environment in Southeast Asia. In The Physical Geography of Southeast Asia (Oxford Academic).
  15. Dumayas, A. D. R. (2015). City Development in Emerging Economies: The Case of Davao City in the Philippines. Firms’ Location Selections and Regional Policy in the Global Economy, 267–280.
  16. Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., & Bargellini, P. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026
  17. Epstein, J., Payne, K., & Kramer, E. (2002). Techniques for mapping suburban sprawl. Photogrammetric Engineering and Remote Sensing, 63(9), 913–918.
  18. Fulton, W. (2021). 6 post-pandemic predictions about how cities will be different going forward.
  19. 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, 756–760. https://doi.org/10.1126/science.1150195
  20. Habibi, S., & Asadi, N. (2011). Causes, results, and methods of controlling urban sprawl. Procedia Engineering, 21, 133–141. https://doi.org/10.1016/j.proeng.2011.11.1996
  21. Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate, Egypt. International Journal of Sustainable Built Environment, 4(1), 117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005
  22. Ismail, R. (2014). Southeast Asian urbanization and the challenge to sustainability: implications for the environment and health. Environmental Policy & Law, 44, 55.
  23. Liu, Z., He, C., Zhou, Y., & Wu, J. (2014). How much of the world’s land has been urbanized? A hierarchical framework for avoiding confusion. Landscape Ecology, 29(5), 763–771. https://doi.org/10.1007/s10980-014-0034-y
  24. Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2401. https://doi.org/10.1080/0143116031000139863
  25. Masancay, F. D., Comendador, Y. J. F., Dizon, J. A. L., & Jallores, L. P. L. (2024). Geographic information system-based prioritization mapping for urban search and rescue in Poblacion, Davao City. Davao Research Journal, 15(3), 111–121. https://doi.org/10.59120/drjv15i3.254
  26. Masancay, F. D., & Jimenez, L. A. (2024). Predicting the unseen: A shoreline shift analysis and prediction along Mayo Bay, Davao Oriental. Davao Research Journal, 15(4), 19–45. https://doi.org/10.59120/drj.v15i4.269
  27. Monash University. (2024). Urban expansion in Southeast Asia: Do Private Cities Contribute to Inequality? Land Use Policy, 47, 373–381. https://doi.org/10.1016/j.landusepol.2015.01.035
  28. Mundhe, N. N., & Jaybhaye, R. G. (2014). Impact of urbanization on land use/land cover change using Geo-spatial techniques. International Journal of Geomatics and Geosciences, 5(1), 50–60.
  29. Owojori, A., & Xie, H. (2005, March). Landsat image-based LULC changes of San Antonio, Texas, using advanced atmospheric correction and object-oriented image analysis approaches. In 5th International Symposium on Remote Sensing of Urban Areas, Tempe, AZ Satellite.
  30. Pettorelli, N., Laurance, W. F., O’Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2014). Remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology, 51(4), 839–848. https://doi.org/10.1111/1365-2664.12261
  31. Philippine Statistics Authority. (2021). Highlights of the Population Density of the Philippines 2020 Census of Population and Housing (2020 CPH).
  32. Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., & Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
  33. Seto, K. C., Sánchez-Rodríguez, R., & Fragkias, M. (2010). The new geography of contemporary urbanization and the environment. Annual Review of Environment and Resources, 35(1), 167–194. https://doi.org/10.1146/annurev-environ-100809-125336
  34. Shirazi, S. A., & Kazmi, S. J. H. (2020). Analysis of population growth and urban development in Lahore-Pakistan using geospatial techniques: Suggesting some future options. South Asian Studies, 29(1).
  35. Tinoy, M. M., Novero, A. U., Landicho, K. P., Baloloy, A. B., & Blanco, A. C. (2019). Urban effects on land surface temperature in Davao City, Philippines. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 433–440. https://doi.org/10.5194/isprs-archives-XLII-4-W19-433-2019
  36. Van Niel, T. G., McVicar, T. R., & Datt, B. (2005). On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification. Remote Sensing of Environment, 98(4), 468–480. https://doi.org/10.1016/j.rse.2005.08.011
  37. Weng, Q. H. (2010). Remote Sensing and GIS Integration. McGraw-Hill, New York.
  38. Wilder-Smith, A., & Freedman, D. O. (2020). Isolation, quarantine, social distancing, and community containment: a pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. Journal of Travel Medicine, 27(2), taaa020. https://doi.org/10.1093/jtm/taaa020
  39. Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., & Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. https://doi.org/10.1016/j.rse.2022.113195
  40. Yasin, M. Y., Yusof, M. M., & Noor, N. M. (2019). Urban sprawl assessment using time-series LULC and NDVI variation: A case study of Sepang, Malaysia. Applied Ecology & Environmental Research, 17(3). https://doi.org/10.15666/aeer/1703_55835602
  41. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594. https://doi.org/10.1080/01431160304987 

Tools


Copyright © 2025  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic