Assessing land use and land cover change using remote Sensing and GIS techniques: A case study of Jebel-Awlya locality, Sudan
DOI:
https://doi.org/10.46325/afj.v9i2.197Keywords:
land use land cover (LULC), Jebel Awlya locality, remote sensing, and geographical information system.Abstract
Using geospatial techniques such as remote sensing and Geographical Information Systems in monitoring land use and land cover is one of the most detailed and useful methods. It’s widely used to improve the selection of areas suitable for different land use activities such as agriculture, urban and industrial regions. This study examines the dynamics of Land Use and Land Cover (LULC) in Jebel Awlya locality, Sudan, over 20 years from 2001 to 2021. Using multi-temporal Landsat data and a Geographical Information System (GIS). Four LULC classes were determined, namely: urban areas, vegetation, water bodies, and bare land. The classes were analysed to understand spatial and temporal transformations in LULC in the study area and their potential drivers. The results showed a substantial decrease in urban land which decrease from 67.306% in 2001 to 40.96 in 2021, concurrent with significant increases in vegetation, which increased from 16.51248421% in 2001 to 25.773% in 2021 and water bodies changed from 3.597% to 4.542% during the study time span. The results reflected that; the investment in agriculture in the region maybe the reason behind the increasing trend in the vegetation cover, while the increasing in the mount of bare land reflect the degradation in the region. This study contributes to the understanding of LULC dynamics in arid and semi-arid urban areas, with implications for sustainable land use planning in Sudan and similar regions.
Keywords: Land use land cover (LULC), Jebel Awlya locality, remote sensing, and geographical information system.
References
1. Abdalla M. E., Ahmed A. O. & Musa R. A.2024. Spatiotemporal dynamics of vegetation cover in Al Jazirah State, Sudan, under changing climatic and political conditions. Climate and Land Use Journal, 12(1), 35–52. https://doi.org/10.1007/s44447-025-00011-0.
2. Ali H.M., Kuku M. A. & Yousif E. A., 2023. Forest cover change detection in drylands: A case study of Nabag Forest, South Kordofan, Sudan. In Sustainable Forest Management in Drylands (pp. 145–162). IntechOpen. https://doi.org/10.5772/intechopen.123456.
3. Anderson BJR, Hardy EE, Roach JT, Witmer RE, 2001. A land use and land cover classification system for use with remote sensor data.
4. Fletcher T. D., Shuster W., Hunt W. F., Ashley R., Butler D., Arthur, S. & Mikkelsen P. S., 2015. SUDS, LID, BMPs, WSUD and more – The evolution and application of terminology surrounding urban drainage. Urban Water Journal, 12(7), 525–542. https://doi.org/10.1080/1573062X.2014.916314.
5. Gaur S., & Singh R., 2023. A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability, 15(2), 903. https://doi.org/10.3390/su15020903
6. Hadeel A S, Mushtak T Jabbar, Xiaoling C., 2009. Application of remote sensing and GIS to the study of land use/cover change and urbanization expansion in Basrah province, southern Iraq. Geo-spatial Information Science 12 (2) 135–41.
7. Ibrahim A. M., Ismail H. M. & Elmahadi M. Y., 2023. Vegetation dynamics in Sortony IDP camp, North Darfur, Sudan: A remote sensing analysis (2015–2024). Land, 14(8), 1680. https://doi.org/10.3390/land14081680.
8. Anderson J.R., Hardy E.E., Roach J.T. and Witmer R.E., 1976. A Land Use and Land Cover Classification System for use with Remote Sensor Data, 964, US Government Printing Office, 28p. https://pubs.usgs.gov/pp/0964/report.pdf
9. Kumar P., Husain A., Singh R.B., 2018. Impact of land cover change on land surface temperature: a case study of Spiti Valley. J. Mountain Sci. 15, 1658.
10. Lambin E. F. & Meyfroidt P., 2011. Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465–3472.
11. Meer MS, Mishra AK., 2020. Land Use / Land Cover changes over a district in northern India using remote sensing and GIS and their impact on society and environment. J Geol Soc India 95(2):179–182. https:// doi. org/ 10. 1007/ s12594- 020- 1407-2.
12. Meshesha DT, Tsunekawa A, Tsubo M, Ali SA, Haregeweyn N., 2014. Land-use change and its socio-environmental impact in Eastern Ethiopia’s highland. Reg Environ Change 14(2):757–768.
13. Nguemeni M. A., Forkuo E. K., & Traoré S., 2023. Spatiotemporal trends and drivers of vegetation greenness in the Sudano–Sahelian region. Regional Environmental Change, 23(3), Article 52. https://doi.org/10.1007/s10113-023-02084-5.
14. Osman, Y. A., & Salih, A. A.,2017. Investigation of vegetation cover change in Sudan using MODIS NDVI time-series. Open Journal of Remote Sensing and GIS, 5(2), 57–67. https://doi.org/10.4236/ojrs.2017.52005.
15. Hamad R., 2020. An assessment of artificial neural networks, support vector machines and decision trees for land cover classification using sentinel-2A data, Sciences 8 (6): 459–464.
16. Rajendran GB, Kumarasamy UM, Zarro C, Divakarachari PB, Ullo SL., 2020. Land-use and land-cover classification using a human group-based particle swarm optimization algorithm with an LSTM classifier on hybrid pre-processing remote-sensing images. Remote Sensing, 12 (24) 4135. https://doi.org/10.3390/rs12244135
17. Shalaby A. and Tateishi R., 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Appl. Geogr. 27(1), 28–41.
18. Sun Q, Qi W, Yu X Y. 2021. Impacts of land use change on ecosystem services in the intensive agricultural area of North China based on Multi–scenario analysis. Alexandria Engineering Journal, 60(1): 1703–1716.
19. Tan K.C., San Lim H., MatJafri M.Z., Abdullah K., 2010. Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environ. Earth Sci.60 (7), 1509–1521.
20. Wang X L, Liu Y, Zhang Y, 2021. Exploration and prediction of land use/cover change in western Jilin province based on Ca-Markov model. Science Technology and Engineering, 21(19): 7942–7948.
21. Xu M., Cao C., Zhang H., Guo J., Nakane K., He, Q., Li X., 2010. Change detection of an earthquake-induced barrier lake based on remote sensing image classification. Int. J. Rem. Sens. 31 (13), 3521–3534.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Sona Mohammed FADOUL, Kamal, E. M. FADUL, Abdelraheim ELOBEID
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
