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• W2084264462 abstract "The results of a 1990 soil survey of a salinized region in Darab Plain, southern Iran, were combined with soil sampling data taken in 2002 from the same locations and employed as a basis for salinity change detection in the region. New preprocessing of satellite imagery was used, along with statistical analysis of the digital number (DN)−salinity relationship, in order to determine salinization of the area. Removal of outliers on the basis of interfering land uses improved the correlations. Nonlinear regression (NLR) in the form y = a + bx α provided a suitable predictor of salinity (y, dS m−1) for both 1990 and 2002 based on DNs (x). Among the 12 tested methods of salinity classification in this study, the six salinity class method with intervals 0–4, 4–10, 10–32, 32–64, 64–80 and >80 dS m–1 was selected. A series of accuracy assessments through a trial-and-error procedure was the basis of the selection of the best method and led to a final accuracy of 91%. About 42% of the lands located on ‘no saline’ and ‘low salinity’ classes in 1990 had changed to the ‘medium’, ‘very high’ and ‘new agricultural land’ classes in 2002." @default.
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• W2084264462 date "2012-04-26" @default.
• W2084264462 modified "2023-09-26" @default.
• W2084264462 title "Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab Plain, Iran" @default.
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• W2084264462 doi "https://doi.org/10.1080/01431161.2012.676688" @default.
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