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Classification of urban tree species using hyperspectral imagery / R. Jensen in Geocarto international, vol 27 n° 5 (August 2012)  

Public [article]  inGeocarto international > vol 27 n° 5 (August 2012) . - pp 443 - 458 Titre : Classification of urban tree species using hyperspectral imagery Type de document : Article/Communication Auteurs : R. Jensen, Auteur ; P. Hardin, Auteur ; A. Hardin, Auteur Année de publication : 2012 Article en page(s) : pp 443 - 458 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] analyse en composantes principales [Termes IGN] arbre (flore) [Termes IGN] arbre urbain [Termes IGN] espèce végétale [Termes IGN] flore urbaine [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] indice de végétation [Termes IGN] Utah (Etas-Unis) Résumé : (Auteur) Urban areas serve as humanity's principal habitat. Because of this, it is important to understand the biophysical components of the urban environment – including the urban forest. The goal of this study was to determine the potential to classify individual urban trees as a function of spectral features derived from airborne hyperspectral data. To determine this, 500 urban trees were identified (through fieldwork) in the built-up zone of Provo-Orem, Utah, USA. Visible and near infrared airborne hyperspectral imagery was collected over the same area. The 500 trees were identified on the images, and spectral features of each tree were extracted. Principal components, vegetation indices, band means, and band ratios were all used as features to discriminate between different tree species. The tree classification was 82% accurate when just the six principal components were used. Classification accuracy increased to 91.4% after combining vegetation indices, band mean values and band ratios. Numéro de notice : A2012-373 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.687400 Date de publication en ligne : 24/05/2012 En ligne : https://doi.org/10.1080/10106049.2012.687400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31819 [article]

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Estimating urban leaf area index (LAI) of individual trees with hyperspectral data / R. Jensen in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 5 (May 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 5 (May 2012) . - pp 495 - 504 Titre : Estimating urban leaf area index (LAI) of individual trees with hyperspectral data Type de document : Article/Communication Auteurs : R. Jensen, Auteur ; P. Hardin, Auteur ; A. Hardin, Auteur Année de publication : 2012 Article en page(s) : pp 495 - 504 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse spectrale [Termes IGN] arbre (flore) [Termes IGN] feuillu [Termes IGN] image hyperspectrale [Termes IGN] Leaf Area Index [Termes IGN] réflectance végétale [Termes IGN] Utah (Etas-Unis) [Termes IGN] zone urbaine Résumé : (Auteur) This study estimated leaf area index (LAI) of individual urban trees as a function of spectral features derived from airborne hyperspectral data. Candidate features included spectral indexes, principal components, and calibrated reflectance values. Hyperspectral images were acquired over Provo, Utah area, and LAI of 204 deciduous trees was measured in the field. These tree canopies were identified on the images, and spectral features were extracted using both whole canopy and mean-lit spectra techniques. Multiple regression and artificial neural networks were used to model leaf area and determine which spectral features were most strongly related to it. Results established that simple hyperspectral vegetation indexes explained more variation in urban tree LAI than either principal component scores or simple band reflectance values. The neural network model trained with a subset of those indexes explained more variation in LAI (R2 = 64.8 percent) than any of the multiple regression models tested. Numéro de notice : A2012-234 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.78.5.495 En ligne : https://doi.org/10.14358/PERS.78.5.495 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31680 [article]