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Sensors 2013, 13(11), 14633-14649;

Hyperspectral Proximal Sensing of Salix Alba Trees in the Sacco River Valley (Latium, Italy)

DICEA-Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy
Author to whom correspondence should be addressed.
Received: 28 August 2013 / Revised: 11 October 2013 / Accepted: 24 October 2013 / Published: 29 October 2013
(This article belongs to the Section Remote Sensors)
PDF [678 KB, uploaded 21 June 2014]


Recent developments in hardware and software have increased the possibilities and reduced the costs of hyperspectral proximal sensing. Through the analysis of high resolution spectroscopic measurements at the laboratory or field scales, this monitoring technique is suitable for quantitative estimates of biochemical and biophysical variables related to the physiological state of vegetation. Two systems for hyperspectral imaging have been designed and developed at DICEA-Sapienza University of Rome, one based on the use of spectrometers, the other on tunable interference filters. Both systems provide a high spectral and spatial resolution with low weight, power consumption and cost. This paper describes the set-up of the tunable filter platform and its application to the investigation of the environmental status of the region crossed by the Sacco river (Latium, Italy). This was achieved by analyzing the spectral response given by tree samples, with roots partly or wholly submerged in the river, located upstream and downstream of an industrial area affected by contamination. Data acquired is represented as reflectance indices as well as reflectance values. Broadband and narrowband indices based on pigment content and carotenoids vs. chlorophyll content suggest tree samples located upstream of the contaminated area are ‘healthier’ than those downstream. View Full-Text
Keywords: hyperspectral imaging; environmental monitoring; proximal sensing hyperspectral imaging; environmental monitoring; proximal sensing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Moroni, M.; Lupo, E.; Cenedese, A. Hyperspectral Proximal Sensing of Salix Alba Trees in the Sacco River Valley (Latium, Italy). Sensors 2013, 13, 14633-14649.

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