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Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods / João M.B. Carreiras in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 8 (August 2006)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 8 (August 2006) . - pp 897 - 910 Titre : Land-cover mapping in the Brazilian amazon using SPOT-4 Vegetation data and machine learning classification methods Type de document : Article/Communication Auteurs : João M.B. Carreiras, Auteur ; J.M.C. Pereira, Auteur ; Y.E. Shimabukuro, Auteur Année de publication : 2006 Article en page(s) : pp 897 - 910 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] carte d'occupation du sol [Termes IGN] cartographie numérique [Termes IGN] classification ascendante hiérarchique [Termes IGN] image SPOT-Végétation [Termes IGN] Mato Grosso [Termes IGN] occupation du sol Résumé : (Auteur) The main objective of this study is to evaluate the feasibility of deriving a land-cover map of the state of Mato Grosso, Brazil, for the year 2000, using data from the 1 km SPOT-4 VEGETATION (VGT) sensor. For this purpose we used a VGT temporal series of 12 monthly composite images, which were further transformed to physical-meaningful fraction images of vegetation, soil, and shade. Classification of fraction images was implemented using several recent machine learning developments, namely, filtering input training data and probability bagging in a classification tree approach. A 10-fold cross validation accuracy assessment indicates that filtering and probability bagging are effective at increasing overall and class-specific accuracy. Overall accuracy and mean probability of class membership were 0.88 and 0.80, respectively. The map of probability of class membership indicates that the larger errors are associated with cerrado savonna and semi-deciduous forest. Copyright ASPRS Numéro de notice : A2006-313 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.8.897 En ligne : https://doi.org/10.14358/PERS.72.8.897 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28037 [article]

SPOT-4 Vegetation multi-temporal compositing for land cover change studies over tropical regions / João M.B. Carreiras in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 7 (April 2005) . - pp 1323 - 1346 Titre : SPOT-4 Vegetation multi-temporal compositing for land cover change studies over tropical regions Type de document : Article/Communication Auteurs : João M.B. Carreiras, Auteur ; J.M.C. Pereira, Auteur Année de publication : 2005 Article en page(s) : pp 1323 - 1346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] autocorrélation spatiale [Termes IGN] cohérence des données [Termes IGN] détection de changement [Termes IGN] filtrage du bruit [Termes IGN] forêt tropicale [Termes IGN] image multitemporelle [Termes IGN] image optique [Termes IGN] image SPOT-Végétation [Termes IGN] Mato Grosso [Termes IGN] nébulosité [Termes IGN] rapport signal sur bruit [Termes IGN] Soil Adjusted Vegetation Index [Termes IGN] surveillance agricole [Termes IGN] utilisation du sol [Termes IGN] zone intertropicale Résumé : (Auteur) Multi-temporal compositing of SPOT-4 VEGETATION imagery over tropical regions was tested to produce spatially coherent monthly composite images with reduced cloud contamination, for the year 2000. Monthly composite images generated from daily images (S1 product, 1-km) encompassing different land cover. types of the state of Mato Grosso, Brazil, were evaluated in terms of cloud contamination and spatial consistency. A new multi-temporal compositing algorithm was tested which uses different criteria for vegetated and non-vegetated or sparsely vegetated land cover types. Furthermore, a principal components transformation that rescales the noise in the image-Maximum Noise Fraction (MNF)- was applied to a multi-temporal dataset of monthly composite images and tested as a method of additional signal-to-noise ratio improvement. The back-transformed dataset using the first 12 MNF eigenimages yielded an accurate reconstruction of monthly composite images from the dry season (May to September) and enhanced spatial coherence from wet season images (October to April), as evaluated by the Moran's 1 index of spatial autocorrelation. This approach is useful for land cover- change studies in the tropics, where it is difficult to obtain cloud-free optical remote sensing imagery. In Mato Grosso, wet season composite images are important for monitoring agricultural crop cycles. Numéro de notice : A2005-178 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331338005 En ligne : https://doi.org/10.1080/01431160512331338005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27315 [article]

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080-05071	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt