Estimating interannual variations in vegetated areas of Sardinia Island using SPOT-VEGETATION NDVI temporal series. (Articolo in rivista)

Type
Label
  • Estimating interannual variations in vegetated areas of Sardinia Island using SPOT-VEGETATION NDVI temporal series. (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/LGRS.2006.877753 (literal)
Alternative label
  • Lasaponara R (2006)
    Estimating interannual variations in vegetated areas of Sardinia Island using SPOT-VEGETATION NDVI temporal series.
    in IEEE geoscience and remote sensing letters (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Lasaponara R (literal)
Pagina inizio
  • 481 (literal)
Pagina fine
  • 483 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1715299&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F8859%2F36100%2F01715299.pdf%3Farnumber%3D1715299 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 3 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 3 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 4 (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-IMAA (literal)
Titolo
  • Estimating interannual variations in vegetated areas of Sardinia Island using SPOT-VEGETATION NDVI temporal series. (literal)
Abstract
  • Principal component analysis (PCA) has been applied to a temporal series 1999-2002 of a yearly maximum value composite of the SPOT/VEGETATION normalized difference vegetation index for the Sardinia Island for extracting interannual variations affecting vegetation covers. Both naturally vegetated areas (forest, shrub-land, and herbaceous cover) and agricultural lands have been investigated in order to obtain information on the most prominent natural and/or man-induced alterations affecting vegetation behavior. Although a correct interpretation of PCA results generally requires additional information, such as geographical knowledge, climatological data, and field surveys, the main finding of the current investigation suggests that PCA can be a feasible tool to separately map areas showing different degrees of interannual variability, providing valuable information for discriminating unidirectional changes (literal)
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