• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Editorial Staff
    • Publication Ethics
    • Indexing and Abstracting
    • Related Links
    • FAQ
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter
Desert
Articles in Press
Current Issue
Journal Archive
Volume Volume 24 (2019)
Volume Volume 23 (2018)
Volume Volume 22 (2017)
Volume Volume 21 (2016)
Volume Volume 20 (2015)
Volume Volume 19 (2014)
Volume Volume 18 (2013)
Volume Volume 17 (2012)
Issue Issue 2
Summer and Autumn 2012, Page 111-209
Issue Issue 3
Summer and Autumn 2012, Page 211-307
Issue Issue 1
Winter and Spring 2012, Page 1-109
Volume Volume 16 (2011)
Volume Volume 15 (2010)
Volume Volume 14 (2009)
Volume Volume 13 (2008)
Volume Volume 12 (2007)
Volume Volume 11 (2006)
Volume Volume 10 (2005)
Shirazi, M., Zehtabian, G., Matinfar, H., Alavipanah, S. (2012). Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for Detection Saline Soils (Case Study: Najmabad Region). Desert, 17(3), 277-289. doi: 10.22059/jdesert.2013.35260
M. Shirazi; Gh.R. Zehtabian; H.R. Matinfar; S.K. Alavipanah. "Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for Detection Saline Soils (Case Study: Najmabad Region)". Desert, 17, 3, 2012, 277-289. doi: 10.22059/jdesert.2013.35260
Shirazi, M., Zehtabian, G., Matinfar, H., Alavipanah, S. (2012). 'Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for Detection Saline Soils (Case Study: Najmabad Region)', Desert, 17(3), pp. 277-289. doi: 10.22059/jdesert.2013.35260
Shirazi, M., Zehtabian, G., Matinfar, H., Alavipanah, S. Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for Detection Saline Soils (Case Study: Najmabad Region). Desert, 2012; 17(3): 277-289. doi: 10.22059/jdesert.2013.35260

Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for Detection Saline Soils (Case Study: Najmabad Region)

Article 9, Volume 17, Issue 3, Summer and Autumn 2012, Page 277-289  XML PDF (688.04 K)
Document Type: Research Paper
DOI: 10.22059/jdesert.2013.35260
Authors
M. Shirazi* 1; Gh.R. Zehtabian2; H.R. Matinfar3; S.K. Alavipanah4
1M.Sc Graduate, University of Tehran, Karaj, Iran
2Professor, University of Tehran, Karaj, Iran
3Assistant Professor, University of Lorestan, Khoram abad, Iran
4Professor, University of Tehran, Tehran, Iran
Abstract
Soil Salinity has been a large problem in arid and semi arid regions. Preparation of such maps is useful for Natural resource managers. Old methods of preparing such maps require a lot of time and cost. Multi-spectral remotely sensed dates due to the broad vision and repeating of these imageries is suitable for provide saline soil maps. This investigation is conducted to provide saline soil maps with sensor LISS-III of IRS-P6 satellite data, in Najmabad of Savojbolagh. Satellite images belonging to 25 June 2006. For enhancement of images, salt Indices, Digital Elevation Model (DEM), False Color Composite imageries (FCC) and Principal Component Analysis (PCA), were used. Supervised classification method includes Box classifier, Minimum Distance, Minimum Mahalanobis Distance and Maximum Likelihood classifier, DEM, PCA1, PCA4 and Saline Indices (SI) were used. After classification, the class map salinity S0, S1, S2, S3 S4, were prepared. The results shows highest overall accuracy and kappa coefficient for the maximum Likelihood classifier estimate, respectively 99% and 97% and the lowest overall accuracy and kappa coefficient for PCA1 estimate, respectively 1% and 0% were obtained. Using Digital Elevation Model (DEM) also due to the difference in height position to the separation of saline lands is usefully. Most spectral interference related
to non-saline soils and low saline soil. From among indices INT2 and PVI greatest ability to segregate is salty soils(especially classes S0 and S1).
Keywords
LISS-III Sensor; Saline soil maps; Classification; Salt indices; DEM; PCA
Statistics
Article View: 2,711
PDF Download: 1,988
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

Journal Management System. Designed by sinaweb.