| Brief Communication |
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| Soil salinity prediction in the Lower Cheliff plain (Algeria) based on remote sensing and topographic feature analysis |
| Ibrahim YAHIAOUI1,2*, Abdelkader DOUAOUI1,3, ZHANG Qiang4, Ahmed ZIANE1 |
1 Laboratory of Crop Production & Sustainable Valorization of Natural Resources, University Djilali Bounaama of Khemis Miliana, Khemis Miliana 44225, Algeria;
2 Laboratory of Plant Ecology & Environment, Biological Sciences Faculty, University of Sciences & Technology Houari Boumediene, Beb Ezzouar, Algiers 16111, Algeria;
3 University Center Morsli Abdellah, Tipaza 42000, Algeria;
4 Institute of Agricultural Environment and Resources, Shanxi Academy of Agricultural Sciences, Taiyuan 030006, China |
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Abstract Soil salinity and ground surface morphology in the Lower Cheliff plain (Algeria) can directly or indirectly impact the stability of environments. Soil salinization in this area is a major pedological problem related to several natural factors, and the topography appears to be important in understanding the spatial distribution of soil salinity. In this study, we analyzed the relationship between topographic parameters and soil salinity, giving their role in understanding and estimating the spatial distribution of soil salinity in the Lower Cheliff plain. Two satellite images of Landsat 7 in winter and summer 2013 with reflectance values and the digital elevation model (DEM) were used. We derived the elevation and slope gradient values from the DEM corresponding to the sampling points in the field. We also calculated the vegetation and soil indices (i.e. NDVI (normalized dif-ference vegetation index), RVI (ratio vegetation index), BI (brightness index) and CI (color index)) and soil salinity indices, and analyzed the correlations of soil salinity with topography parameters and the vegetation and soil indices. The results showed that soil salinity had no correlation with slope gradient, while it was sig-nificantly correlated with elevation when the EC (electrical conductivity) values were less than 8 dS/m. Also, a good relationship between the spectral bands and measured soil EC was found, leading us to define a new salinity index, i.e. soil adjusted salinity index (SASI). SASI showed a significant correlation with elevation and measured soil EC values. Finally, we developed a multiple linear regression for soil salinity prediction based on elevation and SASI. With the prediction power of 45%, this model is the first one developed for the study area for soil salinity prediction by the combination of remote sensing and topographic feature analysis.
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Received: 21 January 2015
Published: 10 December 2015
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Corresponding Authors:
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