Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloThe potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas
Anno di pubblicazione2017
Autore/iSanti E.; Paloscia S.; Pettinato S.; Fontanelli G.; Mura M.; Zolli C.; Maselli F.; Chiesi M.; Bottai L.; Chirici G.
Affiliazioni autoriInstitute of Applied Physics, National Research Council of Italy (IFAC - CNR), Via Madonna del Piano 10, Florence, 50019, Italy; Department of Agricultural, Food and Forestry Systems, Università degli Studi di Firenze, Via San Bonaventura 13, Florence, 50145, Italy; Italian Academy of Forest Sciences, P.zza Edison 11, Florence, 50133, Italy; Institute of Biometeorology, National Research Council of Italy (IBIMET - CNR), via Madonna del Piano 10, Florence, 50019, Italy; LaMMA Consortium, Via Madonna del Piano 10, Florence, 50019, Italy
Autori CNR e affiliazioni
  • inglese
AbstractThe extraction of forest information from SAR images is particularly complex in Mediterranean areas, since they are characterized by high spatial fragmentation and heterogeneity. We have investigated the use of multi-frequency SAR data from different sensors (ALOS/PALSAR and ENVISAT/ASAR) for estimating forest biomass in two test areas in Central Italy (San Rossore and Molise), where detailed in-situ measurements and Airborne Laser Scanning (ALS) data were available. The study focused on the estimation of growing stock volume (GS, in m(3)/ha) by using an inversion algorithm based on artificial neural networks (ANN). The ANN algorithm was first appropriately trained using the available GS estimates obtained from ALS data. The potential of this algorithm was then improved through the innovative use of a simulated dataset, generated by a forward electromagnetic model based on the Radiative Transfer Theory (RTT). The algorithm is able to merge SAR data at L and C bands for predicting GS in diversified Mediterranean environments. The performed analyses indicated that GS was correctly estimated by integrating information from L and C bands on both test areas, with the following statistics: R > 0.97 and RMSE = 28.5 m(3)/ha for the independent test, and R = 0.86 and RMSE approximate to 77 m(3)/ha for the final independent validation, the latter performed on the forest stands of both areas not included in the ALS acquisitions and where conventional measurements were available. The research then illustrates the potential of using the obtained GS estimates from SAR data to drive the simulations of forest net primary production (NPP). This experiment produced spatially explicit estimates of GS current annual increments that are slightly less accurate than those obtained from ground observations (R = 0.75 and RMSE approximate to 1.5 m(3)/ha/year).
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da63
Pagine a73
Pagine totali11
RivistaRemote sensing of environment
Attiva dal 1969
Editore: American Elsevier Pub. Co., - New York,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0034-4257
Titolo chiave: Remote sensing of environment
Titolo proprio: Remote sensing of environment.
Titolo abbreviato: Remote sens. environ.
Numero volume della rivista200
Fascicolo della rivista-
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000412607600005)
  • Scopus (Codice:2-s2.0-85026898300)
Parole chiaveSAR, Growing stock volume, ANN, Inversion algorithms, Net primary production (NPP)
Link (URL, URI)
Titolo parallelo-
Scadenza embargo-
Data di accettazione31/07/2017
Note/Altre informazioni-
Strutture CNR
  • IBIMET — Istituto di biometeorologia
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR
  • DTA.AD004.103.001 : Programma IFAC-DTA
Progetti Europei-
The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas (documento privato )
Descrizione: PDF dell'articolo
Tipo documento: application/pdf