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Humanitarian Aids Using Satellite Technology

Abstract

One of the main topics the remote sensing community is interested in regards the monitoring of informal settlements for humanitarian aids, as proved by a number of international projects like the European RESPOND in the framework of GMES (Global Monitoring for Environment and Security) or United Nations’ UNOSAT. This chapter discusses not only the possibility of employing remote sensing imagery to this aim, but above all the capability of semi-automated procedures to analyze such data and to assist the work of Administrations and NGOs. Test areas are located in Darfur region, Sudan, which became in 2003 the scene of one of the worst humanitarian crises of our age. Optical images of those territories were acquired by SPOT-5 and Quickbird satellites between 2003 and 2005, and high resolution radar data by the Japanese PALSAR sensor on board of the ALOS satellite in 2006, after refugee camps were built up for accommodating hundreds of thousands of displaced people. The proposed algorithms intend to provide land-cover/use maps that can be useful to keep changes under control and/or to update existing charts.

Keywords

Satellite remote sensing Radar Optical sensors Data fusion Image processing 

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References

  1. Anselin, L. (1995). Local indicators of spatial association – LISA. Geographical Analysis, 27, 93–115.Google Scholar
  2. Fatone L., Maponi P., and Zirilli F. (2001). Fusion of SAR/Optical images to detect urban areas. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, IEEE, 217–221.Google Scholar
  3. Gamba P., and DellAcqua F. (2003). Increased accuracy multiband urban classification using a neuro-fuzzyclassifier. International Journal of Remote Sensing, 24(4), 827–834.CrossRefGoogle Scholar
  4. Kavanagh, J., and Home, R. (1999). Mapping the refugee camps of Gaza: the surveyor in a political environment, Survey Ireland.Google Scholar
  5. Mason, S.O., and Fraser, C.S. (1998). Image sources for informal settlement management, Photogrammetric Record, 16(92), 313–330.CrossRefGoogle Scholar
  6. Mason, S., and Ruther, H. (1997). Investigation of the Kodak DCS460 digital camera for small-area mapping. Journal of Photogrammetry and Remote Sensing, 52, 202–214.CrossRefGoogle Scholar
  7. Pal S.K., Majumdar T.J., and Amit K. Bhattacharya (2007). ERS-2 SAR and IRS-1C LISS III data fusion: A PCA approach to improve remote sensing based geological interpretation. ISPRS Journal of Photogrammetry and Remote SensingVolume 61, 5, 281–297.Google Scholar
  8. Ruther, H., Martine, H., and Mtalo, E.G. (2002). Application of snakes and dynamic programming optimisation technique in modelling of buildings in informal settlement areas. Journal of Photogrammetry and Remote Sensing, 56, 269–282.CrossRefGoogle Scholar
  9. Haralick R.M., Shanmugam K., and Dinstein I. (1973). Textural features for image classification. IEEE Trans. Syst., Man, Cybern., 3, 610–621.CrossRefGoogle Scholar
  10. Soille P. (2003). Morphology Image Analysis: Principle and Application, Springer-Verlag, (second edition).Google Scholar
  11. Stasolla M., and P. Gamba (2007). Exploiting spatial patterns for informal settlement detection in arid environments using spaceborne optical data. Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXVI, part 3/W49A, 31–36.Google Scholar
  12. UN-HABITAT (2005a), “Financing urban shelters. Global report on human settlements 2005”.Google Scholar
  13. UN-HABITAT (2005b), “International migrants and the city”, M. Balbo, Ed.Google Scholar
  14. Wu, S.-S., Qiu X., and Wang L. (2005). Population estimation methods in gis and remote sensing: A review. GIScience and Remote Sensing, 42(1), 80–96.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Dept. of ElectronicsUniversity of PaviaVia FerrataItaly

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