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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]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2012051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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]

Modelling house unit density from land cover metrics: a Midwestern US example / P. Hardin in Geocarto international, vol 23 n° 5 (October - November 2008)  

Public [article]  inGeocarto international > vol 23 n° 5 (October - November 2008) . - pp 393 - 411 Titre : Modelling house unit density from land cover metrics: a Midwestern US example Type de document : Article/Communication Auteurs : P. Hardin, Auteur ; M. Jackson, Auteur ; R. Jensen, Auteur Année de publication : 2008 Article en page(s) : pp 393 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse discriminante [Termes IGN] densité de population [Termes IGN] densité du bâti [Termes IGN] données socio-économiques [Termes IGN] estimation statistique [Termes IGN] image Landsat-ETM+ [Termes IGN] Indiana (Etats-Unis) [Termes IGN] régression multiple [Termes IGN] spatial metrics Résumé : (Auteur) Geographic applications frequently require the gathering and analysis of socioeconomic data. For many nations, these data are normally collected through a census. However, during the intercensal period (5-10 years), these data lose their currency and must be updated. The objective of this project was to estimate housing unit density from Landsat ETM+ imagery in the Terre Haute, IN, USA, region. Modelling was done for 1945 census blocks in the study area containing 30 972 housing units. Landtype, as represented by six cluster classes, was used as the primary surrogate for housing unit density. The percentage of each landtype within the census blocks was calculated. Other landscape metrics representing landtype patch dominance and diversity were also calculated on a per-block basis. Housing unit density within the census block was then modelled as a function of those percentages and metrics using discriminant analysis and multiple regression. The simple correlation between the observed and modelled housing unit density was 0.79. The mean residual error produced by the model was 0.37 housing units per hectare. Copyright Taylor & Francis Numéro de notice : A2008-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040801950344 Date de publication en ligne : 05/09/2008 En ligne : https://doi.org/10.1080/10106040801950344 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29534 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-08051	RAB	Revue	Centre de documentation	En réserve L003	Disponible