Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/1974
Authors: Boschetti, M.* 
Brivio, P. A.* 
Carnesale, D.* 
Di Guardo, A.* 
Title: The contribution of hyperspectral remote sensing to identify vegetation characteristics necessary to assess the fate of Persistent Organic Pollutants (POPs) in the environment
Issue Date: Feb-2006
Series/Report no.: 1/49 (2006)
URI: http://hdl.handle.net/2122/1974
Keywords: hyperspectral imaging
species distribution
LAI
semi-empirical model
POPs
Subject Classification05. General::05.09. Miscellaneous::05.09.99. General or miscellaneous 
Abstract: During recent years hyperspectral remote sensing data were successfully used to characterise the state and properties of vegetation. The information on vegetation cover and status is useful for a range of environmental modelling studies. Recent works devoted to the understanding of the fate of Persistent Organic Pollutants (POPs) in the environment showed that forests and vegetation in general act as a «sponge» for chemicals present in air and the intensity of this «capture» effect depends on some vegetation parameters such as surface area, leaf composition, turnover etc. In the framework of the DARFEM experiment conducted in late June 2001, different airborne hyperspectral images were acquired and analysed to derive some vegetation parameters of relevance for multimedia models, such as the spatial distribution of plant species and their relative foliage biomass. The study area, south west of Milan, encompasses a range of land cover types typical of Northern Italy, including intensive poplar plantations and natural broad-leaf forest. An intensive field campaign was accomplished during the aerial survey to collect vegetation parameters and radiometric measurements. Results obtained from the analysis of hyperspectral images, map of vegetation species, Leaf Area Index (LAI) and foliage biomass are presented and discussed.
Appears in Collections:Annals of Geophysics

Files in This Item:
File Description SizeFormat
20 Boschetti.pdf4.6 MBAdobe PDFView/Open
Show full item record

Page view(s)

120
checked on Apr 20, 2024

Download(s) 20

459
checked on Apr 20, 2024

Google ScholarTM

Check