Mapping Land Cover from Sentinel-2A Using Support Vector Classifier and Random Forest Regressor in the Souss Basin Morocco

  • Brahim BouaakkazEmail author
  • Zine El Abidine El Morjani
  • Ahmed Elkouk
  • Lhoussaine Bouchaou
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1104)


The semi-arid to arid area of the Souss basin is vulnerable to flood. This natural phenomenon whose intensity is becoming increasingly alarming. Indeed, the susceptibility of the basin to floods disasters is accentuated by its rapid demographic evolution, uncontrolled land cover, anthropogenic actions and other physical factors. The land cover map represents crucial information for assessing the hydro-meteorological flood hazard as the physical and environmental vulnerabilities to this phenomenon in the Souss region. Therefore, seven optical Sentinel-2A images have been used for this purpose. After a preprocessing operation, different features were extracted from the images, including spectral, morphological, and textural variables to be analyzed using the classification and regression techniques based on the Support Vector Classifier (SVC) and Random Forest Regressor (RFR) algorithms. This operation resulted in the generation of a 10 Land cover classes map and the building density estimation. The two results were merged and enhanced by introducing additional classes from exogenous data to produce a land cover map with 27 classes at 10 m resolution. After field inspection missions, the overall accuracy of this map is 91.6% with a Kappa coefficient of 89% which indicates a very good quality. This map is of great importance for many research projects and for many operational applications. Therefore, the approach developed in this study could be used to understand the current land cover and may update this information over time as an accurate means of monitoring change, which is a vital dimension for land management decision making in the region. This map was also produced under an environmental project on modeling and mapping of flood risk, to develop an integrated management action plan for the Souss basin.


Floods disaster Souss Vulnerabilities Land cover Remote sensing Sentinel-2A Support Vector Classifier Random Forest Regressor Kappa coefficient Risk 



This work has been carried out with in part within charisma project financed by Academy Hassan II. The authors would also like to thank the three anonymous reviewers for their insights and suggestions for improving this manuscript.


  1. 1.
    Abdollahian, N., Ratliff, J.L., Wood, N.J.: Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts, in Open-File Report. Reston, VA (2016)Google Scholar
  2. 2.
    Etkin, D.: 4 - Hazard, Vulnerability, and Resilience, in Disaster Theory, Butterworth-Heinemann: Boston, pp. 103–150 (2016)Google Scholar
  3. 3.
    El Morjani, Z.E.A.: Conception d’un système d’information à référence spatiale pour la gestion environnementale: application à la sélection de sites potentiels de stockage de déchets ménagers et industriels en région semi-aride (Souss, Maroc), University of Geneva (2002)Google Scholar
  4. 4.
    John, R.J.: Introductory Digital Image Processing: A Remote Sensing Perspective, vol. 07458, 2nd edn. Prentice Hall, New Jersy (1996)Google Scholar
  5. 5.
    Thomas, M.L., Ralph, K.W., Jonathan, C.W.: Remote Sensing and Image Interpretation, 5th edn. Wiley, Hoboken (2004)Google Scholar
  6. 6.
    Alexandre, F.: Détection et classification de changements sur des scènes urbaines en télédétection, Institut Supérieur de l’Aéronautique et de l’Espace (2008)Google Scholar
  7. 7.
    Peña-Barragán, J.M., et al.: Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Environ. 115(2011), 1301–1316 (2011)CrossRefGoogle Scholar
  8. 8.
    Peña-Barragán, J.M., et al.: Object-based image classification of summer crops with machine learning methods. Remote Sens. 6(6), 5019–5041 (2014)CrossRefGoogle Scholar
  9. 9.
    Markus, I., Francesco, V., Clement, A.: First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens. 8(3), 166 (2016)CrossRefGoogle Scholar
  10. 10.
    Monica, H., et al.: USGS-NPS National Vegetation Mapping Program: Sunset Crater Volcano National Monument, Arizona, Vegetation Classification and Distribution, U.S. Geological Survey, Southwest Biological Science Center (2004)Google Scholar
  11. 11.
    Huang, X., et al.: Multiple morphological profiles from multicomponent-base images for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(12), 4653–4669 (2014)CrossRefGoogle Scholar
  12. 12.
    Zhang, T., et al.: Urban building density estimation from high-resolution imagery using multiple features and support vector regression. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 10(7), 3265–3280 (2017)CrossRefGoogle Scholar
  13. 13.
    Huang, X., Zhang, L.: Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 5(1), 161–172 (2012)CrossRefGoogle Scholar
  14. 14.
    Bhaskaran, S., Paramananda, S., Ramnarayan, M.: Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Appl. Geogr. 30(4), 650–665 (2010)CrossRefGoogle Scholar
  15. 15.
    Myint, S.W., et al.: Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115(5), 1145–1161 (2011)CrossRefGoogle Scholar
  16. 16.
    Yan, G., et al.: Comparison of pixel-based and object-oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China. Int. J. Remote Sens. 27(18), 4039–4055 (2006)CrossRefGoogle Scholar
  17. 17.
    Susaki, J., Kajimoto, M., Kishimoto, M.: Urban density mapping of global megacities from polarimetric SAR images. Remote Sens. Environ. 155, 334–348 (2014)CrossRefGoogle Scholar
  18. 18.
    Krehl, A., et al.: A comprehensive view on urban spatial structure: urban density patterns of German city regions. ISPRS Int. J. Geo-Inf. 5(6), 76 (2016)CrossRefGoogle Scholar
  19. 19.
    Kajimoto, M., Susaki, J.: Urban density estimation from polarimetric SAR images based on a POA correction method. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 6(3), 1418–1429 (2013)CrossRefGoogle Scholar
  20. 20.
    Pesaresi, M., Halkia, M., Ouzounis, G.K.: Quantitative estimation of settlement density and limits based on textural measurements. In: 2011 Joint Urban Remote Sensing Event (JURSE). IEEE (2011)Google Scholar
  21. 21.
    CRTS, Elaboration de la carte d’occupation du sol, suivi de l’extension des zones irriguées et estimation de l’évapotranspiration dans la plaine de Souss-Massa, Centre royal de télédétection spatiale et Gesellschaft für internationale zusammenarbeit, p. 38 (2015)Google Scholar
  22. 22.
    Intergraph, ERDAS Field Guide™, Intergraph Corporation (2013)Google Scholar
  23. 23.
    Lafarge, F., Descombes, X., Zerubia, J.: Noyaux texturaux pour les problèmes de classification par SVM en télédétection, p. 39. INRIA (2004)Google Scholar
  24. 24.
    OTBTeam, OTB CookBook Documentation Release 6.4.0, in (2018)Google Scholar
  25. 25.
    El Kharki, O., et al.: Panorama sur les méthodes de classification des images satellites et techniques d’amélioration de la precision de classification. Revue Française de Photogrammétrie et de Télédétection (2015)Google Scholar
  26. 26.
    Taubenbock, H., et al.: Pattern-based accuracy assessment of an urban footprint classification using TerraSAR-X data. IEEE Geosci. Remote Sens. Lett. 8(2), 278–282 (2011)CrossRefGoogle Scholar
  27. 27.
    Taubenböck, H., et al.: Delineation of central business districts in mega city regions using remotely sensed data. Remote Sens. Environ. 136, 386–401 (2013)CrossRefGoogle Scholar
  28. 28.
    Ho, H.C., et al.: Mapping maximum urban air temperature on hot summer days. Remote Sens. Environ. 154, 38–45 (2014)CrossRefGoogle Scholar
  29. 29.
    Abdel-Rahman, E.M., Ahmed, F.B., Ismail, R.: Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data. Int. J. Remote Sens. 34(2), 712–728 (2013)CrossRefGoogle Scholar
  30. 30.
    Brokamp, C., et al.: Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmos. Environ. 151, 1–11 (2017)CrossRefGoogle Scholar
  31. 31.
    Hu, X., et al.: Estimating PM2 5 concentrations in the conterminous united states using the random forest approach. Environ. Sci. Technol. 51(12), 6936–6944 (2017)CrossRefGoogle Scholar
  32. 32.
    Mutanga, O., Adam, E., Cho, M.A.: High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Observation Geoinf. 18, 399–406 (2012)CrossRefGoogle Scholar
  33. 33.
    Prasad, A.M., Iverson, L.R., Liaw, A.: Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9(2), 181–199 (2006)CrossRefGoogle Scholar
  34. 34.
    Svetnik, V., et al.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947–1958 (2003)CrossRefGoogle Scholar
  35. 35.
    Hong, H., Xiaoling, G., Hua, Y.: Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest. In: 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) (2016)Google Scholar
  36. 36.
    Archer, K.J., Kimes, R.V.: Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52(4), 2249–2260 (2008)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Bouaakkaz, B., et al. Flood risk management in the Souss watershed. EDP Sciences (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Brahim Bouaakkaz
    • 1
    Email author
  • Zine El Abidine El Morjani
    • 1
  • Ahmed Elkouk
    • 1
  • Lhoussaine Bouchaou
    • 2
  1. 1.Polydisciplinary Faculty Taroudant, Exploration and Management of Natural and Environmental Resources Team (EGERNE) TaroudantIbn Zohr UniversityAgadirMorocco
  2. 2.Faculty of Science, Applied Geology and Geo-Environment LaboratoryIbn Zohr UniversityAgadirMorocco

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