Main Article Content

Abstract

The objective of this research is to analyze land use changes in the municipality of Constantine over a thirty year period (1993 – 2023) using Landsat satellite data provided through the Google Earth Engine (GEE) platform and a supervised classification approach. Five land cover classes were mapped at six key dates: water bodies, green spaces, built-up areas, bare soil and agricultural land. The quantified changes highlight a considerable reduction in bare soil in favor of an increase in cultivated land, as well as an expansion of artificial surfaces during certain periods (2008 to 2023). These dynamics reflect the processes of urban sprawl and peri-urbanization at work in this territory. The results obtained demonstrate the potential of remote sensing through GEE for detailed spatio-temporal monitoring of land use. Coupled with other data, this approach contributes to a better understanding of territorial evolutions and provides valuable information for sustainable land use planning.

Article Details

How to Cite
Benoumeldjadj, M., Rached-Kanouni, M., Bouchareb, A., & Ababsa, L. (2024). Quantifying LULC Changes in Constantine, Algeria Using Google Earth Engine. Indonesian Journal of Social Science Research, 5(1), 1-8. https://doi.org/10.11594/ijssr.05.01.01

References

1. Aksoy, S., Yildirim, A., Gorji, T., Ham-zehpour, N., Tanik, A., & Sertel, E. (2022). Assessing the performance of machine learning algorithms for soil salinity
mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data. Advances in Space Research, 69(2), 1072–1086. https://ui.adsabs.harvard.edu/abs/2022AdSpR..69.1072A/abstract
2. Bellanger, L., Coulon, A., Husi, P., Bel-langer, L., Coulon, A., & Husi, P. (2021). Une méthode de classification ascendante hiérarchique par compromis : hclustcom-pro Conférence Internationale Franco-phone sur la Science des Données. https://hal.science/hal-03280918v1/document
3. Benoumeldjadj, M., Bouarroudj, N., & Bou-chareb, A. (2023). The effect of vegetation cover on dust concentration: Case study (Constantine, Algeria). Indonesian Journal of Geography, 55(2), 311. https://doi.org/10.22146/ijg.82364
4. Benoumeldjadj, M., Kanouni, M. R., Chouiter, N., Ben, L., & Bouaghi, O. El. (2023). Exploring the association between vegetation cover and land surface temper-ature in constantine; a comparative analy-sis. 13. https://journals.univ-ouar-gla.dz/index.php/RBR/article/view/2154
5. Benoumeldjadj, M., Rached-kanouni, M., & Bouchareb, A. (2024). Assessing Agricul-tural Drought Vulnerability Using The Veg-etation Health Index : A Case Study In Con-stantine ABSTRACT : 93(1), 32–49. https://www.researchgate.net/publication/377535572_Assessing_Agricultural_Drought_
6. Benoumeldjadj Maya. (2022). doctoral the-sis in science,option: urban project. 2022 .240p. https://fau.univ-constantine3.dz/2023/01/29
7. da Silva, M. V., Pandorfi, H., de Oliveira-Júnior, J. F., da Silva, J. L. B., de Almeida, G. L. P., de Assunção Montenegro, A. A., Mes-quita, M., Ferreira, M. B., Santana, T. C., & Marinho, G. T. B. (2022). Remote sensing techniques via Google Earth Engine for land degradation assessment in the Brazili-an semiarid region, Brazil. Journal of South American Earth Sciences, 120, 104061. https://www.mdpi.com/2072-4292/15/10/2550
8. Diédhiou, I., Mering, C., Sy, O., & Sané, T. (2020a). Cartographier par télédétection l’occupation du sol et ses changements. Application à l’analyse de la dynamique des paysages forestiers sénégambiens en-tre 1972 et 2016. EchoGéo, 54. https://journals.openedition.org/echogeo/20510
9. Diédhiou, I., Mering, C., Sy, O., & Sané, T. (2020b). Cartographier par télédétection l’occupation du sol et ses changements. EchoGéo, 54, 0–41. https://doi.org/10.4000/echogeo.20510
10. El Garouani, A., & Aharik, K. (2021). Ap-port des images LANDSAT à l’étude de l’évolution de l’occupation du sol dans la plaine de SAÏSS au MAROC, pour la période 1987-2018. Revue Française de Photo-grammétrie et de Télédétection, 223, 173–188. https://rfpt.sfpt.fr/index.php/RFPT/article/view/490
11. El Garouani, A., Aharik, K., & El Garouani, S. (2020). Water balance assessment using remote sensing, Wet-Spass model, CN-SCS, and GIS for water resources management in Saïss Plain (Morocco). Arabian Journal of Geosciences, 13, 1–9. https://www.researchgate.net/publication/343242033
12. El Garouani, A., & Nabunya, V. (n.d.). Anal-ysis of Climate Trend and Effect of Land Cover Change on Streamflow in Oued Fez Basin, Morocco. https://www.mdpi.com/1424-2818/15/12/1220
13. Interior, D. of the, & Survey, U. S. G. (2020). Landsat Cloud Optimized GeoTIFF (COG) Data Format Control Book (DFCB). https://www.usgs.gov/media/files/landsat-cloud-optimized-geotiff-data-format-control-book
14. Jabbar, & Yusoff, M. M. (2022). Assessing the Spatiotemporal Urban Green Cover Changes and Their Impact on Land Surface Temperature and Urban Heat Island in La-hore (Pakistan) Research Paper. Geogra-phy, Environment, Sustainability, 15(1), 122–140. https://doi.org/10.24057/2071-9388-2021-005
15. Seeberg, G., Hostlowsky, A., Huber, J., Kamm, J., Lincke, L., & Schwingshackl, C. (2022). Evaluating the Potential of Landsat Satellite Data to Monitor the Effectiveness of Measures to Mitigate Urban Heat Is-lands: A Case Study for Stuttgart (Germa-ny). Urban Science, 6(4), 82.
16. https://www.mdpi.com/2413-8851/6/4/82
17. Ujaval Gandhi. (2022). Cloud-Based Re-mote Sensing with Google Earth Engine. In Cloud-Based Remote Sensing with Google Earth Engine. https://doi.org/10.1007/978-3-031-26588-4