Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloStatistical inferential techniques for approaching forest mapping. A review of methods
Anno di pubblicazione2018
FormatoElettronico
Autore/iMaria, Di Biase Rosa; Lorenzo, Fattorini; Maurizio, Marchi
Affiliazioni autoriUniv Tuscia, Dept Innovat Biol Agrofood and Forest Syst, Viterbo, Italy
Autori CNR e affiliazioni
  • MAURIZIO MARCHI
Lingua/e
  • inglese
AbstractThe increasing availability of remote sensing data at no or low costs can be used as ancillary data in order to spatialize and improve the estimation of forest attributes and without increasing the sampling effort and costs. In this review paper, a description of the main statistical inferential techniques for approaching forest mapping is proposed. This article reviews the most used forest mapping methods based on the sole spatial information as well as techniques exploiting auxiliary information from remotely sensed data. The advantages and drawbacks of each method have been described on the basis of several factors, such as the aims of the investigation and the area under examination. Two main groups were here discussed with model-based methods on one side and model-assisted methods on the other, moving the attention from the model used to interpolate surfaces to the sampling scheme. Model-based methods include kriging, locally weighted regression, K-NN, decision trees and neural networks, while the inverse distance weighting interpolator is presented in the model-assisted group.Reliable and up-to-date information on forest characteristics are mandatory tools for any decisional process. The main input data of such systems are wall-to-wall maps depicting the spatial structures of forests and additional elements. Actually, if the original aim of forest inventories was to estimate harvestable timber amounts, a general interest towards multipurpose surveys is mandatory. Such information must deal with increased costs and more time-consuming procedures.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da46
Pagine a58
Pagine totali13
RivistaAnnals of Silvicultural Research
Attiva dal 1932
Editore: CREA - Forestry Research Centre (former ISSAR) - Arezzo
Paese di pubblicazione: Italia
Lingua: inglese
ISSN: 2284-354X
Titolo chiave: Annals of Silvicultural Research
Numero volume della rivista42
Fascicolo della rivista2
DOI10.12899/asr-1738
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:BCI201900184013)
Parole chiaveModelling, Spatial data, Forestry
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IBBR — Istituto di Bioscienze e Biorisorse
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati