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Proceedings Paper

Determine the optimum spectral reflectance of juniper and pistachio in arid and semi-arid region
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Paper Abstract

Arid and semi-arid areas of northeast Iran cover about 3.4 million ha are populated by two main tree species, the broadleaf Pistacia vera. L (pistachio) and the conifer Juniperus excelsa ssp. polycarpos (Persian juniper). Natural stands of pistachio in Iran are not only environmentally important but genetically essential as seed sources for pistachio production in orchards. In this study, we estimated the optimum spectral reflectance of juniper forests and natural pistachio stands using remote sensing to help in the sustainable management and production of pistachio in Iran. In this research spectral reflectance are able to specify of multispectral from Advanced Land Observing Satellite (ALOS) that provided by JAXA. These data included PRISM is a panchromatic radiometer with a 2.5 m spatial resolution at nadir, has one band with a wavelength of 0.52–0.77 μm and AVNIR-2 is a visible and near infrared radiometer for observing land and coastal zones with a 10 m spatial resolution at nadir, has four multispectral bands: blue (0.42–0.50 μm), green (0.52–0.60 μm), red (0.61–0.69 μm), and near infrared (0.76–0.89 μm). Total ratio vegetation index (TRVI) of optimum spectral reflectance of juniper and pistachio have been evaluated. The result of TRVI for Pistachio and juniper were (R2= 0.71 and 0.55). I hope this research can provide decision of managers to helping sustainable management for arid and semi-arid regions in Iran.

Paper Details

Date Published: 9 November 2012
PDF: 6 pages
Proc. SPIE 8527, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV, 85270H (9 November 2012); doi: 10.1117/12.975116
Show Author Affiliations
Hadi Fadaei, Japan Agency for Marine-Earth Science and Technology (Japan)
Rikie Suzuki, Japan Agency for Marine-Earth Science and Technology (Japan)


Published in SPIE Proceedings Vol. 8527:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications IV
Allen M. Larar; Hyo-Sang Chung; Makoto Suzuki; Jian-yu Wang, Editor(s)

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