Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloApplication of neural networks for the retrieval of forest woody volume from SAR multifrequency data at l and C bands
Anno di pubblicazione2015
FormatoElettronico
Autore/iSanti E.; Paloscia S.; Pettinato S.; Chirici G.; Mura M.; Maselli F.
Affiliazioni autoriInstitute of Applied Physics, National Research Council (IFAC - CNR), via Madonna del Piano 10, Florence, 50019, Italy; Department of Agricultural, Food and Forestry Systems, Università di Firenze, Via San Bonaventura 13, Florence, 50145, Italy; Dipartimento di Bioscienze e Territorio, Università del Molise, Contrada Fonte Lappone snc, Pesche, Isernia, 86090, Italy; IBIMET- National Research Council of Italy (IBIMET-CNR), via Madonna del Piano 10, Florence, 50019, Italy
Autori CNR e affiliazioni
  • EMANUELE SANTI
  • SIMONE PETTINATO
  • SIMONETTA PALOSCIA
Lingua/e
  • inglese
AbstractThis work aims at investigating the potential of L (ALOS/PALSAR) and C (ENVISAT/ASAR) band SAR images in forest biomass monitoring and setting up a retrieval algorithm, based on Artificial Neural Networks (ANN), for estimating the Woody Volume (WV, in m3/ha) from combined satellite acquisitions. The investigation was carried out on two test areas in central Italy, where ground WV measurements were available. An innovative retrieval algorithm based on ANN was developed for estimating WV from L and C bands SAR data. The novelty consists of an accurate training of the ANN with several thousands of data, which allowed the implementation of a very robust algorithm. The RMSE values found on San Rossore area were ?40 m3/ha (L band data only), and 25-30 m3/ha (L with C band). On Molise, by using combined data at L and C bands, RMSE<30m3/ha was obtained. Keywords: ANN; backscattering; Woody Volume; LiDAR; ALOS/PALSAR; ENVISAT/ASAR.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da673
Pagine a687
Pagine totali-
RivistaEuropean Journal of Remote Sensing
Editore: Italian Society for Remote Sensing - Firenze
Paese di pubblicazione: Italia
Lingua: inglese
ISSN: 2279-7254
Titolo chiave: European Journal of Remote Sensing
Numero volume della rivista48
Fascicolo della rivista-
DOI10.5721/EuJRS20154837
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84952666983)
Parole chiaveALOS/PALSAR, ANN, Backscattering, ENVISAT/ASAR, LiDAR, Woody Volume
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-84952666983&partnerID=q2rCbXpz
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Strutture CNR
  • IBIMET — Istituto di biometeorologia
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
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