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Abdellatif Dehni, Mourad Lounis
Procedia Engineering, Volume 33, 2012
Abstract
Satellite remote sensing of land affected by salinity, is a useful tool for decision support system through digital image processing for materials delineation (crystallography, detection of rocks, mineralogy, etc.). Today, with the advent of technology integration, merger, with optical data radar (InSAR and Signal Processing) actively contributed to the modelling of radar backscattering coefficient for the quantitative and qualitative salinity (modelled coefficients for models in relation to soil moisture, surface roughness). Thus, our approach has been to exploit the multi-spectral optical data from the LANDSAT ETM + (Enhanced Thematic Mapper) to map surface states, including indices of salinity and sodicity as: (BI: Brightness Index), NDSI: Normalized Difference Salinity Index, SI: Salinity Index, ASI: Aster Salinity Index (Agriculture), Index of Salinity (using GIS Geographic Information System and remote sensing), and finally the SSSI “Soil Salinity and Sodicity Index”. These indicators of salinity were tested for the Oran region in accordance with the spectral sensor ALI (Advanced Land Imager) satellite EO-1 (NASA from 2002 to 2006). Remote sensing helps identify salts are highly reflective and improved mapping of saline soil surface. Reports of More frequently used is the combined near infrared and visible (4 / 1 ETM), or bands in the infrared (7 / 4 or 7 / 5 ETM). Consequently, the spectral curves of the satellite ALI EO-1 show a match for saline soils and two test plots were chosen (Aquifer of Es-Sénia) to study corresponding with the measured data in-situ (electrical conductivity and pH) for the classification of saline soils [2]. The confusions that arise between the effects of salt stress and water stress are removed followed by seasonal applying the Geo-statistical analysis with the Geo-modelling approach in GIS techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt affected soil and segmentation accuracy model.
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