Journal of
Medicinal Plants Research

  • Abbreviation: J. Med. Plants Res.
  • Language: English
  • ISSN: 1996-0875
  • DOI: 10.5897/JMPR
  • Start Year: 2007
  • Published Articles: 3834

Full Length Research Paper

Proposing a methodology in preparation of olive orchards map as an important medicinal plant in Iran by remote sensing (RS) and geographical information system (GIS)

Ali Mohammadi Torkashvand* and Alireza Eslami
Rasht Branch, Islamic Azad University, Rasht, Iran.
Email: [email protected], [email protected]

  •  Accepted: 07 December 2011
  •  Published: 09 February 2012

Abstract

Present study focuses on identification and mapping of olive in the part of Roodbar region, Guilan, Iran by IRS images and GIS. Two methods were evaluated to separate olive orchards spectrum reflex from the other surface covers. At the first method, upper and lower limit of digital number of olive orchards were determined by the addition and subtraction of standard deviation from the mean in each band and initial classified map of olive orchards was prepared in every band. The final map of olive orchards was prepared from crossing three initial maps of olive orchards. At the second method, olive orchards map was prepared by four models include: Box classifier, maximum likelihood, minimum distance and minimum Mahalanobis distance. Methods accuracy was evaluated from crossing the map of training points (pixel) with olive orchards map. The results indicated that in classification of less-condensed olive orchards, because of spectrum wave interference of olive green canopy and the soil zone between the canopy cover, the interference of digital number of low-condensed olive is observed with the other vegetation cover and bare lands. There was this issue even for wave interference of low-condensed olive with urban and residential regions. There is the interference of spectrum reflexes between the agriculture land and olive. This issue also exists for different methods of supervised classification.

 

Key words: Spectrum reflex, olive, training points, map.