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Hyperion, Ikonos, ALI, and ETM+ sensors in the study of African rainforests / Prasad S. Thenkabail in Remote sensing of environment, vol 90 n° 1 (15/03/2004)  

Public [article]  inRemote sensing of environment > vol 90 n° 1 (15/03/2004) . - pp 23 - 43 Titre : Hyperion, Ikonos, ALI, and ETM+ sensors in the study of African rainforests Type de document : Article/Communication Auteurs : Prasad S. Thenkabail, Auteur ; E.A. Enclona, Auteur ; M.S. Ashton, Auteur ; C. Legg, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 23 - 43 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] Cameroun [Termes IGN] carbone [Termes IGN] carte de la végétation [Termes IGN] classification [Termes IGN] Congo (bassin) [Termes IGN] forêt équatoriale [Termes IGN] image EO1-ALI [Termes IGN] image EO1-Hyperion [Termes IGN] image Ikonos [Termes IGN] image Landsat-ETM+ [Termes IGN] indice de végétation [Termes IGN] masse végétale [Termes IGN] occupation du sol Résumé : (Auteur) The goal of this research was to compare narrowband hyperspectral Hyperion data with broadband hyperspatial IKONOS data and anced multispectral Advanced Land Imager (ALI) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data through modeling and classifying complex rainforest vegetation. For this purpose, Hyperion, ALI, IKONOS, and ETM+ data were acquired for southern Cameroon, a region considered to be a representative area for tropical moist evergreen and semideciduous forests. Field data, collected in near-real time to coincide with satellite sensor overpass, were used to (1) quantify and model the biomass of tree, shrub, and weed species; and (2) characterize forest land use/land cover (LULC) classes. The study established that even the most advanced broadband sensors (i.e., ETM+, IKONOS, and ALI) had serious limitations in modeling biomass and in classifying forest LULC classes. The broadband models explained only 13-60% of the variability in biomass across primary forests, secondary forests, and fallows. The overall accuracies were between 42% and 51% for classifying nine complex rainforest LULC classes using the broadband data of these sensors. Within individual vegetation types (e.g., primary or secondary forest), the overall accuracies increased slightly, but followed a similar trend. Among the broadband sensors, ALI sensor performed better than the IKONOS and ETM+ sensors. When compared to the three broadband sensors, Hyperion narrowband data produced (1) models that explained 36-83% more of the variability in rainforest biomass, and (2) LULC classifications with 45-52% higher overall accuracies. Twenty-three Hyperion narrowbands that were most sensitive in modeling forest biomass and in classifying forest LULC classes were identified and discussed. Numéro de notice : A2004-127 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.11.018 En ligne : https://doi.org/10.1016/j.rse.2003.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26654 [article]

Within-field wheat yield prediction from Ikonos data: a new matrix approach / E.A. Enclona in International Journal of Remote Sensing IJRS, vol 25 n° 2 (January 2004)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 25 n° 2 (January 2004) . - pp 377 - 388 Titre : Within-field wheat yield prediction from Ikonos data: a new matrix approach Type de document : Article/Communication Auteurs : E.A. Enclona, Auteur ; Prasad S. Thenkabail, Auteur ; D. Celis, Auteur ; J. Diekmann, Auteur Année de publication : 2004 Article en page(s) : pp 377 - 388 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agriculture de précision [Termes IGN] blé (céréale) [Termes IGN] carte agricole [Termes IGN] image Ikonos [Termes IGN] pixel [Termes IGN] rendement agricole Résumé : (Auteur) This study demonstrates a unique matrix approach to determine within-field variability in wheat yields using fine spatial resolution 4 m IKONOS data. The matrix approach involves solving a system of simultaneous equations based on IKONOS data and post-harvest yields available at entire field scale. This approach was compared with a regression-based modelling approach involving field-sensor measured yields and the corresponding IKONOS measured indices and wavebands. The IKONOS data explained 74-78% variability in wheat yield. This is a significant result since the finer spatial resolution leads to capturing greater spatial variability and detail in landscape relative to coarser spatial resolution data. A pixel-by-pixel mapping of wheat yield variability highlights the fine spatial detail provided by IKONOS data for precision farming applications. Numéro de notice : A2004-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000102485 En ligne : https://doi.org/10.1080/0143116031000102485 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26584 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-04021	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt