Expansion urbaine et recul des surfaces végétalisées à Bouaké et Korhogo (Côte d’Ivoire) : analyse diachronique par télédétection

  • Gaoussou Soro École Doctorale Polytechnique des Sciences et Techniques de l’Ingénieur (EDP-STI), Institut National Polytechnique Houphouët-Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire
  • Noufou Coulibaly École Doctorale Polytechnique des Sciences et Techniques de l’Ingénieur (EDP-STI), Institut National Polytechnique Houphouët-Boigny (INP-HB), Yamoussoukro, Côte d’Ivoire
  • Adja Ferdinand Vanga Université Peleforo Gon Coulibaly (UPGC), Korhogo, Côte d’Ivoire
  • Kouakou Paul-Alfred Kouakou Université Peleforo Gon Coulibaly (UPGC), Korhogo, Côte d’Ivoire
Keywords: Expansion urbaine, surfaces végétalisées, analyse diachronique, télédétection, Random Forest, Bouaké, Korhogo, Côte d’Ivoire

Abstract

Cette étude analyse l’évolution des surfaces bâties et végétalisées dans les départements de Bouaké et de Korhogo entre 2000 et 2025. Elle repose sur une classification supervisée par Random Forest d’images Landsat 7 ETM+ pour 2000 et 2010, et Sentinel-2 L2A pour 2025. Les images ont été harmonisées à une résolution de 30 m. Quatre classes ont été retenues : végétation, zones bâties, sols nus et surfaces en eau. La validation interne, réalisée à partir de pixels extraits des polygones utilisés pour l’apprentissage, a donné des précisions globales comprises entre 99,75 % et 99,95 %. En l’absence d’un échantillon indépendant, ces valeurs mesurent uniquement la cohérence interne des classifications. Entre 2000 et 2025, les surfaces bâties ont augmenté de 56,2 % à Bouaké et de 131,1 % à Korhogo. Dans le même temps, les surfaces végétalisées ont diminué respectivement de 12,6 % et de 39,2 %. À Bouaké, le rythme d’expansion du bâti a nettement ralenti après 2010. À Korhogo, il est resté élevé et le bâti est devenu la classe dominante en 2025. Ces résultats montrent des trajectoires d’expansion différentes. Leur interprétation doit toutefois tenir compte de l’agrégation de la classe « végétation » et des limites du dispositif de validation.

This study examines changes in built-up and vegetated areas in the departments of Bouaké and Korhogo between 2000 and 2025. It uses Landsat 7 ETM+ images for 2000 and 2010 and Sentinel-2 L2A images for 2025. The images were harmonized at a spatial resolution of 30 m and classified using the Random Forest algorithm in R. Four land-cover classes were considered: vegetation, built-up areas, bare land, and water bodies. Internal validation, based on pixels extracted from the training polygons, produced overall accuracy values ranging from 99.75% to 99.95%. These values measure the internal consistency of the classifications and do not represent independent validation. Between 2000 and 2025, built-up areas increased by 56.2% in Bouaké and 131.1% in Korhogo. Vegetated areas decreased by 12.6% and 39.2%, respectively. Urban expansion slowed markedly in Bouaké after 2010 but remained high in Korhogo, where built-up areas became the dominant class in 2025. These results show different urban expansion patterns in the two departments. However, their interpretation must consider the broad definition of the vegetation class and the limits of the validation method.

Downloads

Download data is not yet available.

PlumX Statistics

References

Angel, S., Parent, J., Civco, D. L., Blei, A., & Potere, D. (2011). The dimensions of global urban expansion: Estimates and projections for all countries, 2000–2050. Progress in Planning, 75(2), 53–107. https://doi.org/10.1016/j.progress.2011.04.001 DOI: https://doi.org/10.1016/j.progress.2011.04.001

Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011 DOI: https://doi.org/10.1016/j.isprsjprs.2016.01.011

Bjarnesen, J., & Turner, S. (2013). Intraregional conflict migration in West Africa: Dynamics, trajectories, and local responses. Conflict, Security & Development, 13(4), 349–366. https://doi.org/10.1080/14678802.2013.840620

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324 DOI: https://doi.org/10.1023/A:1010933404324

Cobbinah, P. B., & Amoako, C. (2012). Urban sprawl and the loss of peri-urban land in Kumasi, Ghana. International Journal of Social and Human Sciences, 6, 388–397.

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

Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data: Principles and practices (2nd ed.). CRC Press. https://doi.org/10.1201/9781420055139 DOI: https://doi.org/10.1201/9781420055139

Durand-Lasserve, A., & Royston, L. (Éds.). (2002). Holding their ground: Secure land tenure for the urban poor in developing countries. Earthscan.

Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201. https://doi.org/10.1016/S0034-4257(01)00295-4 DOI: https://doi.org/10.1016/S0034-4257(01)00295-4

Galster, G., Hanson, R., Ratcliffe, M. R., Wolman, H., Coleman, S., & Freihage, J. (2001). Wrestling sprawl to the ground: Defining and measuring an elusive concept. Housing Policy Debate, 12(4), 681–717. https://doi.org/10.1080/10511482.2001.9521426 DOI: https://doi.org/10.1080/10511482.2001.9521426

Herrera Gomez, M., Tetteh, E., & Yemets, B. (2017). Urban land-use change in coastal West African cities. Urban Climate, 22, 95–111. https://doi.org/10.1016/j.uclim.2017.07.004 DOI: https://doi.org/10.1016/j.uclim.2017.07.004

Institut National de la Statistique. (2021). Recensement général de la population et de l’habitat 2021 : Résultats globaux. INS.

Koffi, K. J., Kouame, F. K., & Traore, A. (2017). Dynamique de l’expansion urbaine d’Abidjan entre 1980 et 2014 par télédétection. Revue de Géographie Tropicale et d’Environnement, 1, 44–57.

Korah, P. I., Cobbinah, P. B., & Nunbogu, A. M. (2024). Urban expansion and agricultural land loss in Ghana: Patterns, drivers, and implications. Land Use Policy, 137, 106953. https://doi.org/10.1016/j.landusepol.2023.106953

Kouakou, K. (2017). Impact de la crise sociopolitique ivoirienne sur la ville de Bouaké : Analyse de la restructuration économique et spatiale. Les Cahiers d’Outre-Mer, 70(278), 389–412. https://doi.org/10.4000/com.8498

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. https://doi.org/10.1073/pnas.1100480108 DOI: https://doi.org/10.1073/pnas.1100480108

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. DOI: https://doi.org/10.2307/2529310

Lavigne Delville, P. (1998). Foncier rural, ressources renouvelables et développement en Afrique. Ministère des Affaires étrangères – Coopération et Francophonie.

United Nations, Department of Economic and Social Affairs, Population Division. (2018). World urbanization prospects: The 2018 revision—Highlights. United Nations.

United Nations, Department of Economic and Social Affairs, Population Division. (2019). World urbanization prospects: The 2018 revision (ST/ESA/SER.A/420). United Nations.

Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015 DOI: https://doi.org/10.1016/j.rse.2014.02.015

Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217–222. https://doi.org/10.1080/01431160412331269698 DOI: https://doi.org/10.1080/01431160412331269698

Potere, D., & Schneider, A. (2007). A critical look at representations of urban areas in global maps. GeoJournal, 69(1–2), 55–80. https://doi.org/10.1007/s10708-007-9102-z DOI: https://doi.org/10.1007/s10708-007-9102-z

Roy, D. P., Li, J., Zhang, H. K., & Yan, L. (2016). Best practices for the reprojection and resampling of Sentinel-2 Multi Spectral Instrument Level 1C data. Remote Sensing Letters, 7(11), 1023–1032. https://doi.org/10.1080/2150704X.2016.1212419 DOI: https://doi.org/10.1080/2150704X.2016.1212419

Seto, K. C., Fragkias, M., Güneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. PLoS ONE, 6(8), e23777. https://doi.org/10.1371/journal.pone.0023777 DOI: https://doi.org/10.1371/journal.pone.0023777

Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109 DOI: https://doi.org/10.1073/pnas.1211658109

Simon, D., McGregor, D., & Thompson, D. (2018). Contemporary perspectives on the peri-urban zones of cities in developing areas. Dans D. McGregor, D. Simon, & D. Thompson (Éds.), The peri-urban interface: Approaches to sustainable natural and human resource use (pp. 3–17). Earthscan.

Sylla, G., Coulibaly, T. J.-H., Coulibaly, N., Kouadio, K. C. A., Coulibaly, H. S. J. P., Cissé, S., Sié, K., Camara, I., & N’guessan, K. H. J. (2023). Urban expansion of Korhogo City (Côte d’Ivoire) using GIS and nocturnal remote sensing. Computational Urban Science, 3, Article 23. https://doi.org/10.1007/s43762-023-00099-6 DOI: https://doi.org/10.1007/s43762-023-00099-6

UN-Habitat. (2022). World cities report 2022: Envisioning the future of cities. United Nations Human Settlements Programme.

Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Lattanzio, A., … 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 DOI: https://doi.org/10.1016/j.rse.2022.113195

Yao-Gnabeli, C. Y. (2014). Urbanisation et mutations des espaces agricoles périurbains en Côte d’Ivoire. Annales de l’Université de Bouaké, 11, 203–221.

Yemmafouo, A. (2013). Expansion urbaine et gestion foncière dans les villes moyennes du Cameroun. Les Cahiers d’Outre-Mer, 263, 395–414. https://doi.org/10.4000/com.6842

Published
2026-08-31
How to Cite
Soro, G., Coulibaly, N., Vanga, A. F., & Kouakou, K. P.-A. (2026). Expansion urbaine et recul des surfaces végétalisées à Bouaké et Korhogo (Côte d’Ivoire) : analyse diachronique par télédétection. European Scientific Journal, ESJ, 22(23), 107. https://doi.org/10.19044/esj.2026.v22n23p107
Section
ESJ Humanities