Journal of Spectral Imaging,   Volume 9   Article ID a11   (2020)

Peer reviewed Paper

Spatial analysis of Hyperion hyperspectral indices to map the vegetation state in the coastal oases of Tunisia

  • Rim Katlane
  • Jean-Claude Berges
  • Gérard Beltrando
Université Diderot, Sorbonne Paris-Cité, (PRODIG), 75205 Paris Cedex 13, France
[email protected]
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Université Diderot, Sorbonne Paris-Cité, (PRODIG), 75205 Paris Cedex 13, France. Gérard Beltrando died on 28 January 2016

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 Corresponding Author
GEOMAG (LR19ES07)/PRODIG (UMR 8586), University of Mannouba-Tunis, Campus universitaire B.P.95 2010 Mannouba, Tunisia
[email protected]
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An elevated human presence due to the involvement of the coastal oases of Tunisia in the global petrochemical industry and population pressure in the 1970s has resulted in major changes in the oases’ agro–ecosystem environment. The consequences of this have been urbanisation and rural exodus, priority to the industrial sectors and services at the expense of agriculture, high mobility and rise of trade. The coastal oases of Gabes located in the South-East of Tunisia are considered in this study. This has been affected by sharp degradation, mainly of anthropogenic origins such as demographic growth, extension of the urban areas and creation of a highly contaminating chemical zone amplifying their environmental vulnerability. Satellite data is an essential tool in the study and mapping of these types of environment and for that, we started with the mapping of the vegetative land use using the vegetation indices derived from the hyperspectral scene of the Hyperion sensor (25 April 2010) and field data. This has allowed us to better characterise the most vulnerable areas and to identify the socio–environmental risks. The analysis of the radiometric indices leads to the definition of the spatial extension of vegetation cover in the oases. This study has permitted us to outline the oases’ typologies in Gabes and to discuss their dynamics in the short term.

Keywords: Gabes, coastal oases, state of vegetation cover, Hyperion sensor, radiometric indices




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