21Jun 2017

SPECTRAL INDEXES TO DETECT LAND COVER CHANGES DUE TO THE FLOODS (MOROCCO).

  • Applied Geology, Geomatic and Environment Laboratory, Department of Geology University Hassan II of Casablanca/Faculty of Science Ben M?sik- Casablanca.
  • Geodynamics of Old Chains Laboratory, Department of Geology University Hassan II of Casablanca/Faculty of Science Ben M?sik- Casablanca.
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The objective of this study is to detect land cover changes in fact of flood risks, with using a new method, fast and efficient. This technique needs only a diachronic satellite images in input data. The variability of reflectance spectral indexes is a characteristic of land cover changes. For applying this theory, we were including four spectral indexes those look on the principal indicator for land cover changes which are (Brightness index, Color index, Salinity index and clay index). To applying this method, we choose the Souss Massa basin as a study area. In 2014, this basin especially rounded of Guelmim city was affected by floods which caused a lot of damages in different ecosystems. Another step is used in the laboratory to validate our procedure; we talk about the physic-chemical analysis of land cover taken in two different dates, before and after floods. Finally, the comparison of the spatial results with those of the physic-chemical analysis, show there is a good correlation between of all parameters.


  1. Abd El-Kawy, O. R., R?d, J. K., Ismail, H. a., & Suliman, a. S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied Geography, 31(2), 483?494. doi: 10.1016/j.apgeog.2010.10.012.
  2. (2016). Agence du Bassin Hydraulique de Guelmim.
  3. Bonn, F., Dixon, R. (2005). Monitoring flood extent and forecasting excess runoff risk with RADARSAT-1 data. Natural Hazards, 35(3): 377-393.
  4. (1966). Guide Des techniques du laboratoire de G?ologie Marine de Luminy, 198p.
  5. DIAEA/DRHA/SEEN. (2008). Direction de l?irrigation et de l?am?nagement de l?espace Agricole, Service des Exp?rimentations, des Essais et de la Normalisation- Rabat, 2008.
  6. Disperati, L., & Virdis, S. G. P. (2015). Assessment of land-use and land-cover changes from 1965 to 2014 in Tam Giang-Cau Hai Lagoon, central Vietnam. Applied Geography, 58, 48?64. doi: 10.1016/j.apgeog.2014.12.012.
  7. Drury, S. (1987). Image Interpretation in Geology. London: Allen and Unwin, 243 pp.
  8. Emran, A., Hakdaoui, M, & Chorowics, J. (1996a). Anomalies on Geologic Maps from Multispectral and Textural Classification: The Bleida Mining Disctrict (Morroco). Remote Sens. Environ. 57, pp.13-21.
  9. Emran, A., Hakdaoui, M, & Chorowics, J. (1996b). Contribution des signatures spectrales et des textures ? la cartographie g?ologique par classification dirig?e d'image du HRV-XS de SPOT en r?gime d?sertique : exemple du secteur minier de Zgounder (Anti-Atlas, Maroc). J. Remote Sensing, vol. 17, n05, pp. 863-877
  10. Escadafal, R., Belghit, A., & Ben Moussa, A. (1994). Indices spectraux pour la t?l?d?tection de la d?gradation des milieux naturels en Tunisie aride. P. 253-259, in G. Guyot (r?d.) Actes du 6 eme Symposium international sur les mesures physiques et signatures en t?l?d?tection, ISPRS-CNES, Val d'Is?re, France, 17-24 Janvier.
  11. Hakdaoui, M., & Rahimi, A., (2007). Apport de la classification combin?e supervis?e et non supervis?e d'une image Landsat ETM+ ? la cartographie g?ologique de la boutonni?re de Kerdous, Anti-Atlas, Maroc. Revue photo-interpr?tation Volume 42 pp. 46-52.
  12. Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and
  13. prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101?112.
  14. doi: 10.1016/j.apgeog.2015.06.015.
  15. Hansen, M. C., & Loveland, T. R. (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 122, 66?74. doi: 10.1016/j.rse.2011.08.024.
  16. Hoque, R., Nakayama, D., Matsuyama, H., Matsumoto, J. (2011). Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh. Natural Hazards, 57(2): 525-548.
  17. Li, X., & Yeh, A. G.-O. (2004). Analyzing spatial restructuring of land use patterns in a fast-growing region using remote sensing and GIS. Landscape and Urban Planning, 69(4), 335?354. doi: 10.1016/j.landurbplan.2003.10.033.
  18. Maimouni, S., Bannari, A.,El-Harti, A.,?& El-Ghmari, A.(2011). Potentiels et limites des indices spectraux pour caract?riser la d?gradation des sols en milieu semi-aride. Canadian Journal of Remote Sensing, 37(3), 285-301.doi:10.5589/m11-038.
  19. McLead, E.O. (1983). pH and lime requirements. In: Page, A.L. et al. (Eds.), Methods of Soil Analysis, Part 2, second ed., Agronomy, vol. 9 Soil Society of America, Madison, WI, pp. 199?244.
  20. Mumby, P. J., Green, E. P., Edwards, A. J., & Clark, C. D. (1999). The cost-effectiveness of remote sensing for
  21. tropical coastal resources assessment and management. Environ. Manage, 55, 157e166.
  22. Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science.
  23. Rhoades, J.D and Corwin, D.L. (1984). Monitoring soil salinity. J. Soil and Water Cons., 39(3), 173-175.
  24. Schumann, G. et al. (2007). Deriving distributed roughness values from satellite radar data for flood inundation modelling. Journal of Hydrology, 344(1-2): 96-111.
  25. Shtangeeva (2005). Trace and Ultratrace Elements in Plants and Soil. WIT Press. 348p.
  26. Sohn, H.G., Song, Y.S., Kim, G.H. (2005). Detecting water area during flood event from SAR image. In: Gervasi, O. et al. (Eds.). International Conference on Computational Science and Its Applications - ICCSA 2005, pp. 771-780.
  27. Sparks, D. (2003). Environmental soil chemistry Second Edition. San Diego. 352p.
  28. Sumner, M. (2000). Handbook of Soil Science.CRC Press, 2148p
  29. Vinu Chandran, R., Ramakrishnan, D., Chowdary, V.M., Jeyaram, A., Jha, A.M. (2006). Flood mapping and analysis using air-borne synthetic aperture radar: A case study of July 2004 flood in Baghmati river basin, Bihar. Current Science, 90(2): 249-256.
  30. Walkley, A. and I.A. Black. (1934). An examination of the Degtjareff method for determining organic carbon in soils: Effect of variations in digestion conditions and of inorganic soil constituents. Soil Sci. 63:251-263.
  31. Wulder, M. a., White, J. C., Goward, S. N., Masek, J. G., Irons, J. R., Herold, M., ? Woodcock, C. E. (2008). Landsat continuity: Issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112(3), 955?969.
  32. Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2-3), 317?328. doi: 10.1016/j.rse.2005.08.006.

[I Haidara, M Hakdaoui, S Maimouni, H Mohcine and K Moustarhfer. (2017); SPECTRAL INDEXES TO DETECT LAND COVER CHANGES DUE TO THE FLOODS (MOROCCO). Int. J. of Adv. Res. 5 (Jun). 1141-1152] (ISSN 2320-5407). www.journalijar.com


haidara imane
Geomatic

DOI:


Article DOI: 10.21474/IJAR01/4521      
DOI URL: http://dx.doi.org/10.21474/IJAR01/4521