Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/1974
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dc.contributor.authorallBoschetti, M.; Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italyen
dc.contributor.authorallBrivio, P. A.; Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italyen
dc.contributor.authorallCarnesale, D.; Istituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italyen
dc.contributor.authorallDi Guardo, A.; Dipartimento di Scienze Chimiche ed Ambientali, Università degli Studi dell’Insubria, Varese, Italyen
dc.date.accessioned2006-12-07T14:40:51Zen
dc.date.available2006-12-07T14:40:51Zen
dc.date.issued2006-02en
dc.identifier.urihttp://hdl.handle.net/2122/1974en
dc.description.abstractDuring 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.en
dc.format.extent4707384 bytesen
dc.format.mimetypeapplication/pdfen
dc.language.isoEnglishen
dc.relation.ispartofseries1/49 (2006)en
dc.subjecthyperspectral imagingen
dc.subjectspecies distributionen
dc.subjectLAIen
dc.subjectsemi-empirical modelen
dc.subjectPOPsen
dc.titleThe contribution of hyperspectral remote sensing to identify vegetation characteristics necessary to assess the fate of Persistent Organic Pollutants (POPs) in the environmenten
dc.typearticleen
dc.type.QualityControlPeer-revieweden
dc.subject.INGV05. General::05.09. Miscellaneous::05.09.99. General or miscellaneousen
dc.relation.referencesALLEN, R.G., L.S. PEREIRA, D. RAES and M. SMITH (1998): Crop evapotraspiration, FAO Irrigation and Drainage Paper 56. BOSCHETTI, M., R. COLOMBO, M. MERONI, C. PANIGADA, P.A. BRIVIO and J. R. MILLER (2002): Use of semi-empirical and radiation transfer model to estimate biophyisical parameters in a sparse canopy forest, in Remote Sensing for Agriculture, Ecosystems and Hydrology, edited by M. OWE, G. D’URSO and L. TOULIOS, SPIE Proc., 4879, 133-144. BRIVIO, P.A., M. MERONI and C. GIARDINO (2001): Monitoring forest ecosystems using hyperspectral and directional observations, in ESA Workshop on SPECTRA, 12-13 June 2001, Nordwijk (The Netherlands), ESA SP-474. BROWN, L., J.M. CHEN, S.G. LEBLANC and J. CIHLAR (2000): A shortwave infrared modification to the simple ratio for LAI retrieval in Boreal Forests: an image and model analysis, Remote Sensing Environ., 71, 16-25. CALAMARI, D., E. BACCI, S. FOCARDI, C. GAGGI, M. MOROSINI and M. VIGHI (1991): Role of plant biomass in the global environmental partitioning of chlorinated hydrocarbons, Environ. Sci. Technol., 25 (8), 1489-1495. CERIANI, R.M. (2003): Caratteristiche ecologiche, morfofunzionali e riproduttive di specie vegetali delle praterie prealpine lombarde, Tesi di Dottorato (Dottorato di ricerca in «Analisi, protezione e gestione della biodiversità » XV ciclo, Università degli Studi dell’Insubria). CHEN, J.M. and J. CIHLAR (1996): Retrieving leaf area index of boreal conifer forests using Landsat TM images, Remote Sensing Environ., 55 (2), 153-162. CHEN, J.M, P.M. RICH, S.T. GOWER, J.M. NORMAN and S. PLUMMER (1997): Leaf area Index of Boreal Forests: theory, techniques, and measurements, J. Geophys. Res., 102, 29429-29443. COLOMBO, R., M. BOSCHETTI, C. GIARDINO, M. MERONI, C. PANIGADA, L. BUSETTO, P.A. BRIVIO, C.M. MARINO and G.M. SEUFERT (2002): Osservazioni remote iperspettrali e multiangolari per la stima dei parametri biofisici della vegetazione, Parte I. Progettazione dell’esperimento e analisi dei dati, Riv. Ital. Telerilevamento, 24, 5-13. COUSINS, I.T. and D. MACKAY (2001): Strategies for including vegetation compartments in multimedia models, Chemosphere, 44, 643-654. EKLUNDH, L., L. HARRIE and A. KUUSK (2001): Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest, Remote Sensing Environ., 78 (3), 239-251. FUENTES, D., J. GAMON, H. QIU, D. SIMS and D. ROBERTS (2000): Mapping vegetation cover types in the canadian boreal forest using pigment and water absorption features derived from AVIRIS, in Proceedings of the Ninth JPL Airborne Earth Science Workshop NASA. KRUSE, F.A., A.B. LEFKOFF, J.B. BOARDMAN, K.B. HEIDEBRECHT, A.T. SHAPIRO, P.J. BARLOON and A.F.H. GOETZ (1993): The Spectral Image Processing System (SIPS) – Interactive visualization and analysis of imaging spectrometer data, Remote Sensing Environ., 44, 145-163. MACKAY, D., A. DI GUARDO, S. PATERSON, G. KICSI and C. E. COWAN (1996): Assessing the fate of new and existing chemicals: a five-stage process, Environ. Toxicol. Chem., 15 (9), 1618-1626. MARAZZATO, M. (2001): Variazioni stagionali di alcuni parametri fogliari nelle principali specie legnose delle vegetazioni delle Alpi, Tesi di Laurea (Facoltà di Scienze Biologiche, Università degli Studi dell’Insubria). MARTIN, M.E., S.D. NEWMAN, J.D. ABER and R.G. CONGALTON (1998): Determining forest species composition using high spectral resolution remote sensing data, Remote Sensing Environ., 65, 249-254. MCLACHLAN, M.S. (1999): Framework for the interpretation of measurements of SOCs in plants, Environ. Sci. Technol., 33, 1799-1804. MCLACHLAN, M.S. and M. HORSTMANN (1998): Forests as filters of airborne organic pollutants; a model, Environ. Sci. Technol., 32, 413-420. MERONI, M., R. COLOMBO, M. BOSCHETTI, C. PANIGADA, M. ROSSINI, P.A. BRIVIO and J.R. MILLER (2002): LAI retrieval from multi-angle and hyperspectral observations in an intensively-managed poplar plantation, in 1st Int. Symp. «Recent Advances in Quantitative Remote Sensing», Valencia (Spain), 900-903. SPANNER, L., L. PIERCE, D.L PETERSON and S.W. RUNNING (1990): Remote sensing of temperate coniferous forest leaf area index. the influence of canopy closure, understorey vegetation and background reflectance, Int. J. Remote Sensing, 11, 95-111. THOMAS, V., P. TREITZ, D. JELINSKI, J.R. MILLER, P. LAFLEUR and J.H. MCCAUGHEY (2002): Image classification of a northern peatland complex using spectral and plant community data, Remote Sensing Environ., 84, 83-99. TURNER, D.P.,W.B. COHEN, R.E. KENNEDY, K.S. FASSNACHT and J.M. BRIGGS (1999): Relationship between Leaf Area Index and Landsat TM Spectral Vegetation Indices across three temperate zone sites, Remote Sensing Environ., 70, 52-68. VAN GENDEREN, J.L., B.F. LOCK and P.A. VASS (1978): Remote Sensing: Statistical testing of thematic map accuracy, Remote Sensing Environ., 7, 3-14. WANIA, F. and M.S. MCLACHLAN (2001): Estimating the influence of forests on the overall fate of semivolatile organic compounds using a multimedia fate model, Environ. Sci. Technol., 35, 582-590. WELLES, J.M. and J.M. NORMAN (1991): Instrument for Indirect Measurements of Canopy Architecture, Agron. J., 83 (5), 818-825.en
dc.description.journalTypeJCR Journalen
dc.description.fulltextopenen
dc.contributor.authorBoschetti, M.en
dc.contributor.authorBrivio, P. A.en
dc.contributor.authorCarnesale, D.en
dc.contributor.authorDi Guardo, A.en
dc.contributor.departmentIstituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italyen
dc.contributor.departmentIstituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italyen
dc.contributor.departmentIstituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italyen
dc.contributor.departmentDipartimento di Scienze Chimiche ed Ambientali, Università degli Studi dell’Insubria, Varese, Italyen
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.openairetypearticle-
item.grantfulltextopen-
crisitem.author.deptIstituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italy-
crisitem.author.deptIstituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italy-
crisitem.author.deptIstituto per il Rilevamento Elettromagnetico dell’Ambiente (IREA), CNR, Milano, Italy-
crisitem.author.deptDipartimento di Scienze Chimiche ed Ambientali, Università degli Studi dell’Insubria, Varese, Italy-
crisitem.classification.parent05. General-
Appears in Collections:Annals of Geophysics
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