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Proceedings Paper

Remote mapping of susceptible areas to soil salinity, based on hyperspectral data and geochemical, in the southern part of Tunisia
Author(s): Moncef Bouaziz; Richard Gloaguen; Bouaziz Samir
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Paper Abstract

We conducted a remote sensing analysis to discern features and patterns of areas affected by salt. Maximum likelihood classification (MLC), Support Vector Machine (SVM), and Minimum Distance (MD) with four classes (slightly, moderately, high and extreme saline soil) are applied to classify the salt affected areas. 102 samples, collected from the investigated region, are used as input data set . Soil properties, land use and ground water table are selected as the main parameters affecting soil salinity. These parameters are used to understand the spatial distribution of the different classes of soil salinity. Our approach was applied on hyperspectral data from the EO-1 Mission. The present study highlighted that gypsum soil is obviously fitting with class of extreme and high saline soil. Thus, high content of gypsum in soil is the most important parameter controlling the soil salinity in this region. Moreover, water logging is lightly affecting the soil salinity through the rising of the water table level by sea water seeping; especially in the irrigation areas located no more than 5 km from the coast line. Computed accuracy from the classification gave different but encouraging accuracy results varying between 46% and 75%. SVM is showing the best performance in extracting patterns and features of soil salinity classes (kappa coefficient of 63% and overall accuracy of 75%). Furthermore, this work reveals the high potential of hyperspectral data in discerning areas that are highly and extremely affected by salinity.

Paper Details

Date Published: 7 October 2011
PDF: 7 pages
Proc. SPIE 8174, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII, 81740Z (7 October 2011); doi: 10.1117/12.898199
Show Author Affiliations
Moncef Bouaziz, Technische Univ. Bergakademie Freiberg (Germany)
National School of Engineers of Sfax (Tunisia)
Richard Gloaguen, Technische Univ. Bergakademie Freiberg (Germany)
Bouaziz Samir, National School of Engineers of Sfax (Tunisia)


Published in SPIE Proceedings Vol. 8174:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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