ESTIMATING TOPSOIL SALINITY FROM LANDST DATA: A COMPARISON BETWEEN CLASSIC AND SPATIAL STATISTIC

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

NOROOZI ALI AKBAR; HOMAEE MEHDI; ABBASI FARSHAD

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Range and Watershed Managment Year:2014 | Volume:66 | Issue:4 Page(s): 609-620

Download Full-Text

Persian Verion

View:

1,326

Download:

Cites:

Information Journal Paper

Title

ESTIMATING TOPSOIL SALINITY FROM LANDST DATA: A COMPARISON BETWEEN CLASSIC AND SPATIAL STATISTIC

Author(s)

NOROOZI ALI AKBAR | HOMAEE MEHDI | ABBASI FARSHAD | Issue Writer Certificate

Keywords

CLASSIC STATISTICSQ2

MORANS' INDEXQ2

ORDINARY LEAST SQUARE (OLS)Q2

SPATIAL STATISTICSQ2

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 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.

Cites

No record.

References

No record.

Cite

APA: Copy

NOROOZI, ALI AKBAR, HOMAEE, MEHDI, & ABBASI, FARSHAD. (2014). ESTIMATING TOPSOIL SALINITY FROM LANDST DATA: A COMPARISON BETWEEN CLASSIC AND SPATIAL STATISTIC. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), 66(4), 609-620. SID. https://sid.ir/paper/162405/en

Vancouver: Copy

NOROOZI ALI AKBAR, HOMAEE MEHDI, ABBASI FARSHAD. ESTIMATING TOPSOIL SALINITY FROM LANDST DATA: A COMPARISON BETWEEN CLASSIC AND SPATIAL STATISTIC. JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES)[Internet]. 2014;66(4):609-620. Available from: https://sid.ir/paper/162405/en

IEEE: Copy

ALI AKBAR NOROOZI, MEHDI HOMAEE, and FARSHAD ABBASI, “ESTIMATING TOPSOIL SALINITY FROM LANDST DATA: A COMPARISON BETWEEN CLASSIC AND SPATIAL STATISTIC,” JOURNAL OF RANGE AND WATERSHED MANAGEMENT (IRANIAN JOURNAL OF NATURAL RESOURCES), vol. 66, no. 4, pp. 609–620, 2014, [Online]. Available: https://sid.ir/paper/162405/en

Related Journal Papers