Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments. International Journal of Remote (Articolo in rivista)

Type
Label
  • Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments. International Journal of Remote (Articolo in rivista) (literal)
Anno
  • 2002-01-01T00:00:00+01:00 (literal)
Alternative label
  • Maselli F., Rembold F. (2002)
    Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments. International Journal of Remote
    in International journal of remote sensing (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Maselli F., Rembold F. (literal)
Pagina inizio
  • 2475 (literal)
Pagina fine
  • 2488 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 23 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
Titolo
  • Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments. International Journal of Remote (literal)
Abstract
  • The need for utilizing long, consistent time series of NOAA-AVHRR NDVI data is particularly pressing in semi-arid countries where, however, the scarce availability of high resolution LAC data often leads to their substitution with GAC images, with consequent loss of spatial detail. The efficient integration of the few available LAC images with the more abundant GAC data is therefore a topic of high practical importance. On the basis of previous investigations about the integration of data with different spatial and temporal properties, a new approach is currently proposed to generate a NDVI data set with enhanced features. The approach merges the temporally stable, high spatial resolution land cover variations derived from LAC images with the temporally variable NDVI information brought by GAC data. After its presentation, the approach is applied to a case study using LAC and GAC images taken over two semi-arid Mediterranean African countries, Algeria and Tunisia. The result is an integrated data set with the pixel size of the LAC images (about 1 km) and the temporal coverage of the GAC data. The evaluation of this product by comparison to independent LAC images indicates its good quality in terms of both radiometric and geometric features. (literal)
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