Remote sensing as a tool for monitoring plasticulture in agricultural landscapes

Levin, N., Lugass, R., Ramon, U., Braun, O. and Ben-Dor, E. (2007) Remote sensing as a tool for monitoring plasticulture in agricultural landscapes. International Journal of Remote Sensing, 28 1: 183-202. doi:10.1080/01431160600658156

Author Levin, N.
Lugass, R.
Ramon, U.
Braun, O.
Ben-Dor, E.
Title Remote sensing as a tool for monitoring plasticulture in agricultural landscapes
Journal name International Journal of Remote Sensing   Check publisher's open access policy
ISSN 0143-1161
Publication date 2007-01-10
Sub-type Article (original research)
DOI 10.1080/01431160600658156
Open Access Status Not yet assessed
Volume 28
Issue 1
Start page 183
End page 202
Total pages 20
Place of publication Abingdon, Oxfordshire, United Kingdom
Publisher Taylor & Francis
Language eng
Formatted abstract
Agricultural landscapes are changing their appearance with the increasing use of man-made plastic materials in covered agriculture (plasticulture) all around the world. As these affect the landscape visually, increase pollution and decrease local biodiversity, better monitoring and planning of their uses and areas are needed. Using a field spectrometer we studied the spectral properties of a sample of polyethylene sheets and various nets used in Israel. We detected three major absorption features around 1218 nm, 1732 nm and 2313 nm. These were not affected by settling dust, whitewashing or by the underlying surface, but were not apparent in black coloured polyethylene sheets or nets. A hyperspectral AISA-ES image with a spatial resolution of 1m achieved a detection accuracy of above 90% for bright sheets and nets but of only 70% for the black nets. The best spectral feature for plastic mapping was found to be that around 1732 nm as it does not coincide with spectral features of other minerals, soils, vegetation or atmospheric attenuation. As most of the greenhouses patches in Israel are smaller than 3200 m2, the optimal spatial resolution of a sensor for mapping them should be equal or better than 8-16 m. As a result of their low spectral and spatial resolution, Landsat images proved inadequate for mapping greenhouses, and strengthen the need of hyperspectral technology for that end.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Geography, Planning and Environmental Management Publications
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
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