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Estimating Topsoil Salinity from LANDST Data: A Comparison between Classic and Spatial Statistics

    Authors

    • Ali Akbar Noroozi 1
    • Mahdi Homaee 2
    • Abbas Farshad 3

    1 PhD Graduated student, Department of Soil Science, Tarbiat Modares University; Tehran, Iran

    2 Professor, Department of Soil Science, Tarbiat Modares University; Tehran, Iran

    3 Assistant Professor at ITC, Twente University, Enschede, Netherlands

,

Document Type : Research Paper

10.22059/jrwm.2014.50034
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Abstract

Soil salinity is a limiting factor for plant growth and a serious cause of land degradation. Field sampling and statistical analysis for estimating soil salinity is expensive and time consuming. Estimating soil salinity by spatial statistical models and Geographic Information System (GIS) is recommended, because it saves labor and time. This study was conducted to evaluate the performance of spatial statistics with ordinary least square (OLS) incorporation with LANDSAT data to predict soil salinity. The electrical conductivity (EC) of 236 soil samples were collected from Garmsar plain at south east Tehran, Iran and were measured and correlated to 27 variables derived from LANDSAT images, including vegetation indices, salinity indices, bands 1 to 7, principal component analysis and tasseled cap indices. Using factor analysis and similarity index, these variables were divided into three components. Furthermore, two models for soil salinity estimation were derived, using the best correlation correlation coefficient (0.58 and 0.60) method. Simultaneously, soil salinity map was produced in ArcGIS by spatial statistics model ordinary least square (OLS) followed by derivation of the error map, calculated using Moran's index. The error map indicated that the spatial statistics models are superior to classic statistics methods, due to high accuracy in estimation and the fact that it doesn't require exchange information between different software programs

Keywords

  • Ordinary Least Square (OLS)
  • Morans' Index
  • Classic Statistics
  • spatial statistics
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Journal of Range and Watershed Managment
Volume 66, Issue 4 - Serial Number 4
March 2014
Pages 609-620
Files
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  • PDF 1.24 M
History
  • Receive Date: 30 August 2011
  • Revise Date: 11 January 2014
  • Accept Date: 30 January 2013
  • First Publish Date: 20 February 2014
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How to cite
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  • EndNote
  • Mendeley
  • BibTeX
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Statistics
  • Article View: 3,828
  • PDF Download: 1,849

APA

Noroozi, A. A., Homaee, M., & Farshad, A. (2014). Estimating Topsoil Salinity from LANDST Data: A Comparison between Classic and Spatial Statistics. Journal of Range and Watershed Managment, 66(4), 609-620. doi: 10.22059/jrwm.2014.50034

MLA

Ali Akbar Noroozi; Mahdi Homaee; Abbas Farshad. "Estimating Topsoil Salinity from LANDST Data: A Comparison between Classic and Spatial Statistics". Journal of Range and Watershed Managment, 66, 4, 2014, 609-620. doi: 10.22059/jrwm.2014.50034

HARVARD

Noroozi, A. A., Homaee, M., Farshad, A. (2014). 'Estimating Topsoil Salinity from LANDST Data: A Comparison between Classic and Spatial Statistics', Journal of Range and Watershed Managment, 66(4), pp. 609-620. doi: 10.22059/jrwm.2014.50034

VANCOUVER

Noroozi, A. A., Homaee, M., Farshad, A. Estimating Topsoil Salinity from LANDST Data: A Comparison between Classic and Spatial Statistics. Journal of Range and Watershed Managment, 2014; 66(4): 609-620. doi: 10.22059/jrwm.2014.50034

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