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Titolo:
AVHRR-based forest proportion map of the Pan-European area
Autore:
Hame, T; Stenberg, P; Andersson, K; Rauste, Y; Kennedy, P; Folving, S; Sarkeala, J;
Indirizzi:
VTT Automat, FIN-02044 VTT, Finland VTT Automat VTT Finland FIN-02044VTT Automat, FIN-02044 VTT, Finland Univ Helsinki, Helsinki, Finland Univ Helsinki Helsinki FinlandUniv Helsinki, Helsinki, Finland Joint Res Ctr, Sci Applicat Inst, Ispra, Italy Joint Res Ctr Ispra Italy oint Res Ctr, Sci Applicat Inst, Ispra, Italy
Titolo Testata:
REMOTE SENSING OF ENVIRONMENT
fascicolo: 1, volume: 77, anno: 2001,
pagine: 76 - 91
SICI:
0034-4257(200107)77:1<76:AFPMOT>2.0.ZU;2-I
Fonte:
ISI
Lingua:
ENG
Soggetto:
MAXIMUM-LIKELIHOOD CLASSIFICATION; DIFFERENCE VEGETATION INDEX; HIGH-RESOLUTION RADIOMETER; LAND-COVER; SURFACE-TEMPERATURE; CONTINENTAL-SCALE; UNITED-STATES; DATA SETS; DEFORESTATION; REGIONS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
52
Recensione:
Indirizzi per estratti:
Indirizzo: Hame, T VTT Automat, Box 1304, FIN-02044 VTT, Finland VTT Automat Box 1304VTT Finland FIN-02044 FIN-02044 VTT, Finland
Citazione:
T. Hame et al., "AVHRR-based forest proportion map of the Pan-European area", REMOT SEN E, 77(1), 2001, pp. 76-91

Abstract

A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors (reflectance values) of the cluster members. Values of the target variable - the proportion of forested area - were determined for the spectral classes using sampling from CORINE (Coordination ofInformation on the Environment) Land Cover database. Each pixel was assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution Radiometer (AVHRR) images acquired fromthe National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated forest areas were compared with those extracted from the full-coverage CORINE data and with official Forest statistics reported to the European Commission's Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12 countries of the European Union was underestimated by 1.8% compared to the CORINE data. It was underestimated by 4.2% when compared with EUROSTAT's statistics and 6.0% when compared to United Nations Economic Commission for Europe/Food and Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to CORINE) was in France (5.9%). The largest overestimation was found in Ireland. 15.6%. (C) 2001 Elsevier Science Inc. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/09/20 alle ore 23:52:12