Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab Plain, Iran

Mojtaba Pakparvar, Donald Gabriels, Kazem Aarabi, Masoud Edraki, Dirk Raes, Wim Cornelis

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)

    Abstract

    The results of a 1990 soil survey of a salinized region in Darab Plain, southernIran, were combined with soil sampling data taken in 2002 from the same locationsand employed as a basis for salinity change detection in the region. New preprocessingof satellite imagery was used, along with statistical analysis of the digitalnumber (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' provided a suitable predictorof salinity (y, dS m'1) for both 1990 and 2002 based on DNs (x). Among the12 tested methods of salinity classification in this study, the six salinity class methodwith intervals 0'4, 4'10, 10'32, 32'64, 64'80 and >80 dSm'1 was selected. A seriesof accuracy assessments through a trial-and-error procedure was the basis of theselection of the best method and led to a final accuracy of 91%. About 42% of thelands located on 'no saline' and 'low salinity' classes in 1990 had changed to the'medium', 'very high' and 'new agricultural land' classes in 2002.
    Original languageEnglish
    Pages (from-to)6215-6238
    Number of pages24
    JournalInternational Joural of Remote Sensing
    Volume33
    Issue number19
    DOIs
    Publication statusPublished - Oct 2012

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