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Auteur Gregory P. Asner |
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Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing / Gregory P. Asner in Global ecology and conservation, vol 8 (October 2016)
[article]
Titre : Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing Type de document : Article/Communication Auteurs : Gregory P. Asner, Auteur ; Roberta E. Martin, Auteur Année de publication : 2016 Article en page(s) : pp 212 -219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biogéographie
[Termes IGN] canopée
[Termes IGN] couvert forestier
[Termes IGN] couvert végétal
[Termes IGN] politique de conservation (biodiversité)
[Termes IGN] réflectance végétale
[Termes IGN] spectroscopieRésumé : (auteur) With the goal of advancing remote sensing in biodiversity science, Spectranomics represents an emerging approach, and a suite of quantitative methods, intended to link plant canopy phylogeny and functional traits to their spectral-optical properties. The current Spectranomics database contains about one half of known tropical forest canopy tree species worldwide, and has become a forecasting asset for predicting aspects of plant functional and biological diversity to be remotely mapped and monitored with current and future spectral remote sensing technology. To mark ten years of Spectranomics, we review recent scientific outcomes to further stimulate engagement in the use of spectral remote sensing for biodiversity and functional ecology research. In doing so, we highlight three major emerging opportunities for the science and conservation communities based on Spectranomics. Numéro de notice : A2016-715 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article DOI : 10.1016/j.gecco.2016.09.010 En ligne : http://dx.doi.org/10.1016/j.gecco.2016.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82108
in Global ecology and conservation > vol 8 (October 2016) . - pp 212 -219[article]Tree species discrimination in tropical forests using airborne imaging spectroscopy / Jean-Baptiste Féret in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
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Titre : Tree species discrimination in tropical forests using airborne imaging spectroscopy Type de document : Article/Communication Auteurs : Jean-Baptiste Féret, Auteur ; Gregory P. Asner, Auteur Année de publication : 2013 Article en page(s) : pp 73 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse discriminante
[Termes IGN] arbre (flore)
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] distance de Bhattacharyya
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] Hawaii (Etats-Unis)
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation
[Termes IGN] spectroscopieRésumé : (Auteur) We identify canopy species in a Hawaiian tropical forest using supervised classification applied to airborne hyperspectral imagery acquired with the Carnegie Airborne Observatory-Alpha system. Nonparametric methods (linear and radial basis function support vector machine, artificial neural network, and k-nearest neighbor) and parametric methods (linear, quadratic, and regularized discriminant analysis) are compared for a range of species richness values and training sample sizes. We find a clear advantage in using regularized discriminant analysis, linear discriminant analysis, and support vector machines. No unique optimal classifier was found for all conditions tested, but we highlight the possibility of improving support vector machine classification with a better optimization of its free parameters. We also confirm that a combination of spectral and spatial information increases accuracy of species classification: we combine segmentation and species classification from regularized discriminant analysis to produce a map of the 17 discriminated species. Finally, we compare different methods to assess spectral separability and find a better ability of Bhattacharyya distance to assess separability within and among species. The results indicate that species mapping is tractable in tropical forests when using high-fidelity imaging spectroscopy. Numéro de notice : A2013-010 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2199323 Date de publication en ligne : 16/07/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2199323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32148
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 73 - 84[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013011A RAB Revue Centre de documentation En réserve L003 Disponible A robust signal preprocessing chain for small-footprint waveform LiDAR / J. Wu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
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Titre : A robust signal preprocessing chain for small-footprint waveform LiDAR Type de document : Article/Communication Auteurs : J. Wu, Auteur ; Jan Van Aardt, Auteur ; J. Mcglinchy, Auteur ; Gregory P. Asner, Auteur Année de publication : 2012 Article en page(s) : pp 3242 - 3255 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Afrique du sud (état)
[Termes IGN] biomasse
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étalonnage
[Termes IGN] forme d'onde
[Termes IGN] lasergrammétrie
[Termes IGN] prétraitement du signal
[Termes IGN] savane
[Termes IGN] signal lidarRésumé : (Auteur) The extraction of structural object metrics from a next-generation remote sensing modality, namely waveform Light Detection and Ranging (LiDAR), has garnered increasing interest from the remote sensing research community. However, the raw incoming (received) LiDAR waveform typically exhibits a stretched, misaligned, and relatively distorted character. In other words, the LiDAR signal is smeared and the effective temporal (vertical) resolution decreases, which is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, off-nadir scanning, the receiver impulse response impacts, and system noise. Theoretically, such a loss of resolution and increased data ambiguity can be remediated by using proven signal preprocessing approaches. In this paper, we present a robust signal preprocessing chain for waveform LiDAR calibration, which includes noise reduction, deconvolution, waveform registration, and angular rectification. This preprocessing chain was initially validated using simulated waveform data, which were derived via the digital imaging and remote sensing image generation modeling environment. We also verified the approach using real small-footprint waveform LiDAR data collected by the Carnegie Airborne Observatory in a savanna region of South Africa and specifically in terms of modeling woody biomass in this region. Metrics, including the spectral angle for cross-section recovery assessment and goodness-of-fit (R2) statistics, along with the root-mean-squared error for woody biomass estimation, were used to provide a comprehensive quantitative evaluation of the performance of this preprocessing chain. Results showed that our approach significantly increased our ability to recover the temporal signal resolution, improved geometric rectification of raw waveform LiDAR, and resulted in improved waveform-based woody biomass estimation. This preprocessing chain has the potential to be applied across the board for h- gh fidelity processing of small-footprint waveform LiDAR data, thereby facilitating the extraction of valid and useful structural metrics from ground objects. Numéro de notice : A2012-389 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2178420 Date de publication en ligne : 04/01/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2178420 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31835
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 8 (August 2012) . - pp 3242 - 3255[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012081 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of Earth observing-1 ALI and Landsat ETM+ for crop identification and yield prediction in Mexico / D.B. Lobell in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)
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Titre : Comparison of Earth observing-1 ALI and Landsat ETM+ for crop identification and yield prediction in Mexico Type de document : Article/Communication Auteurs : D.B. Lobell, Auteur ; Gregory P. Asner, Auteur Année de publication : 2003 Article en page(s) : pp 1277 - 1282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] agriculture de précision
[Termes IGN] analyse comparative
[Termes IGN] bande spectrale
[Termes IGN] blé (céréale)
[Termes IGN] étalonnage des données
[Termes IGN] image EO1-ALI
[Termes IGN] image Landsat-ETM+
[Termes IGN] maïs (céréale)
[Termes IGN] Mexique
[Termes IGN] photo-interprétation
[Termes IGN] réflectance végétale
[Termes IGN] rendement agricoleRésumé : (Auteur) This paper presents a comparison of Earth Observing 1 (EO-1) Advanced Land Imager (ALI) and Landsat-7. Enhanced Thematic Mapper Plus (ETM+) images collected over agricultural region in northwest Mexico. Across 115 fields a range of cover types, radiance measurements collected by ALI were within 3% of ETM+ for all five common bands. Crop discrimination was significantly improved with ALI compared ETM+, with an increase from 85% to 95% accuracy for distinguishing maize from wheat fields. This improvement was attributed to the greater SNR in ALI, as well as the unique information content of ALI band 4p (0.84-0.89 um), which may be to sensitivity to canopy water content. Yield predictions from reflectance-calibrated data did not reveal significant differences between the sensors. The greatest distinction between ALI and ETM+ was observed in the panchromatic band, with ALI providing more detailed information on inter and intrafield radiance differences, which show promise for precision agriculture applications. We conclude that ALI meets or exceeds ETM+ performance for agricultural applications evaluated here, thus providing a plausible option for continuity of the valuable Landsat record. Numéro de notice : A2003-215 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.812909 En ligne : https://doi.org/10.1109/TGRS.2003.812909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22511
in IEEE Transactions on geoscience and remote sensing > vol 41 n° 6 (June 2003) . - pp 1277 - 1282[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-03061 RAB Revue Centre de documentation En réserve L003 Disponible