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Multi-Spatial Resolution Satellite and sUAS Imagery for Precision Agriculture on Smallholder Farms in Malawi / Brad G. Peter in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 2 (February 2020) . - pp 107 - 119 Titre : Multi-Spatial Resolution Satellite and sUAS Imagery for Precision Agriculture on Smallholder Farms in Malawi Type de document : Article/Communication Auteurs : Brad G. Peter, Auteur ; Joseph P. Messina, Auteur ; Jon W. Carroll, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 107 - 119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] agriculture de précision [Termes descripteurs IGN] analyse multirésolution [Termes descripteurs IGN] exploitation agricole [Termes descripteurs IGN] image Pléiades [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] Malawi Résumé : (Auteur) A collection of spectral indices, derived from a range of remote sensing imagery spatial resolutions, are compared to on-farm measurements of maize chlorophyll content and yield at two trial farms in central Malawi to evaluate what spatial resolutions are most effective for relating multispectral images with crop status. Single and multiple linear regressions were tested for spatial resolutions ranging from 7 cm to 20 m using a small unmanned aerial system (sUAS) and satellite imagery from Planet, SPOT 6, Pléiades, and Sentinel-2. Results suggest that imagery with spatial resolutions nearer the maize plant scale (i.e., 14–27 cm) are most effective for relating spectral signals with crop health on smallholder farms in Malawi. Consistent with other studies, green-band indices were more strongly correlated with maize chlorophyll content and yield than conventional red-band indices, and multivariable models often outperformed single variable models. Numéro de notice : A2020-127 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.2.107 date de publication en ligne : 01/02/2020 En ligne : https://doi.org/10.14358/PERS.86.2.107 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94796 [article]

Complex deformation at shallow depth during the 30 October 2016 Mw6.5 Norcia earthquake: interferencebetween tectonic and gravity processes? / Arthur Delorme in Tectonics, Inconnu (2020)    

Public [article]  contenu dans HAL Hyper articles en ligne / Centre pour la Communication Scientifique Directe CCSD (2000)   inTectonics > Inconnu (2020) Titre : Complex deformation at shallow depth during the 30 October 2016 Mw6.5 Norcia earthquake: interferencebetween tectonic and gravity processes? Type de document : Article/Communication Auteurs : Arthur Delorme, Auteur ; Raphaël Grandin, Auteur ; Yann Klinger, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Nathalie Feuillet, Auteur ; Eric Jacques, Auteur ; Ewelina Rupnik , Auteur ; Yu Morishita, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] compensation locale par faisceaux [Termes descripteurs IGN] corrélation d'images [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] géodésie physique [Termes descripteurs IGN] image à résolution submétrique [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] Italie [Termes descripteurs IGN] MicMac [Termes descripteurs IGN] modèle par fonctions rationnelles [Termes descripteurs IGN] séisme Résumé : (Auteur) The relation between slip at the near surface and at depth during earthquakes is still not fully resolved at the moment. This deficiency leads to large uncertainties in the evaluation of the magnitude of past earthquakes based on surfaceobservations, which is the only accessible evidence for such events. A better knowledge of the way slip distributes over distinct rupture strands within the first few kilometers from the surface would contribute greatly to reduce these uncertainties. The 30 October 2016 Mw6.5 Norcia earthquake has been captured by a variety of geodetic techniques, which provide access to the slip distribution both at depth and at the ground surface, with an unprecedented level of detail for a normal-faulting earthquake. Wefirst present coseismic surface offset measurements from correlation of optical satellite imagesof sub-metric resolution, which are compared to field observations made shortly after the earthquake. Based on a joint inversion of optical data together withInSAR and GPS data, we then propose a rupture model that explains the observations both at far-field and near-field scales. Finally we explore different rupture geometriesat shallow depth, in an attempt to better explain the near-field deformation (i.e. within the first hundreds of meters around the fault)observed at the surface. Despite the fact that the solution is not unique, several lines of evidence suggest that gravity processes could be locally involved, which interfere with the dominant tectonic processes. Numéro de notice : A2020-039 Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2019TC005596 date de publication en ligne : 03/01/2020 En ligne : https://dx.doi.org/10.1029/2019TC005596 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94501 [article]

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Matching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 241 - 248 Titre : Matching of TerraSAR-X derived ground control points to optical image patches using deep learning Type de document : Article/Communication Auteurs : Tatjana Bürgmann, Auteur ; Wolfgang Koppe, Auteur ; Michael Schmitt, Auteur Année de publication : 2019 Article en page(s) : pp 241 - 248 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image multicapteur [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Pléiades [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] point d'appui Résumé : (auteur) High resolution synthetic aperture radar (SAR) satellites like TerraSAR-X are capable of acquiring images exhibiting an absolute geolocation accuracy within a few centimeters, mainly because of the availability of precise orbit information and by compensating range delay errors due to atmospheric conditions. In contrast, satellite images from optical missions generally exhibit comparably low geolocation accuracies because of the propagation of errors in angular measurements over large distances. However, a variety of remote sensing applications, such as change detection, surface movement monitoring or ice flow measurements, require precisely geo-referenced and co-registered satellite images. By using Ground Control Points (GCPs) derived from TerraSAR-X, the absolute geolocation accuracy of optical satellite images can be improved. For this purpose, the corresponding matching points in the optical images need to be localized. In this paper, a deep learning based approach is investigated for an automated matching of SAR-derived GCPs to optical image elements. Therefore, a convolutional neural network is pretrained with medium resolution Sentinel-1 and Sentinel-2 imagery and fine-tuned on precisely co-registered TerraSAR-X and Pléiades training image pairs to learn a common descriptor representation. By using these descriptors, the similarity of SAR and optical image patches can be calculated. This similarity metric is then used in a sliding window approach to identify the matching points in the optical reference image. Subsequently, the derived points can be utilized for co-registration of the underlying images. The network is evaluated over nine study areas showing airports and their rural surroundings from several different countries around the world. The results show that based on TerraSAR-X-derived GCPs, corresponding points in the optical image can automatically and reliably be identified with a pixel-level localization accuracy. Numéro de notice : A2019-548 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.010 date de publication en ligne : 05/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94194 [article]

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081-2019122	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest / Angela Blázquez-Casado in Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 76 n° 2 (June 2019) Titre : Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest Type de document : Article/Communication Auteurs : Angela Blázquez-Casado, Auteur ; Rafael Calama, Auteur ; Manuel Valbuena, Auteur ; Marta Vergarechea, Auteur ; Francisco Rodriguez, Auteur Année de publication : 2019 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] forêt méditerranéenne [Termes descripteurs IGN] houppier [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] Pinus pinaster [Termes descripteurs IGN] Pinus pinea Résumé : (Auteur) Context : The discrimination of tree species at individual level in mixed Mediterranean forest based on remote sensing is a field which has gained greater importance. In these stands, the capacity to predict the quality and quantity of non-wood forest products is particularly important due to the very different goods the two species produce. Aims : To assess the potential of using low-density airborne LiDAR data combined with high-resolution Pleiades images to discriminate two different pine species in mixed Mediterranean forest (Pinus pinea L. and Pinus pinaster Ait.) at individual tree level. Methods : A Random Forest model was trained using plots from the pure stand dataset, determining which LiDAR and satellite variables allow us to obtain better discrimination between groups. The model constructed was then validated by classifying individuals in an independent set of pure and mixed stands. Results : The model combining LiDAR and Pleiades data provided greater accuracy (83.3% and 63% in pure and mixed validation stands, respectively) than the models which only use one type of covariables. Conclusion : The automatic crown delineation tool developed allows two very similar species in mixed Mediterranean conifer forest to be discriminated using continuous spatial information at the surface: Pleiades images and open source LiDAR data. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products and aiding forest managers to accurately predict production. Numéro de notice : A2019-180 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-019-0835-x date de publication en ligne : 17/05/2019 En ligne : https://doi.org/10.1007/s13595-019-0835-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92700 [article]

Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth / Sébastien Labarre in Remote sensing of environment, vol 225 (May 2019)  

Public [article]  inRemote sensing of environment > vol 225 (May 2019) . - pp 1 - 15 Titre : Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth Type de document : Article/Communication Auteurs : Sébastien Labarre, Auteur ; Stéphane Jacquemoud, Auteur ; Cécile Ferrari, Auteur ; Arthur Delorme, Auteur ; Allan Derrien, Auteur ; Raphaël Grandin, Auteur ; Mohamed Jalludin, Auteur ; F. LemaÎtre, Auteur ; Marianne Metois, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Ewelina Rupnik , Auteur ; Bernard Tanguy, Auteur Année de publication : 2019 Projets : CAROLInA / Jacquemoud, Stéphane Article en page(s) : pp 1 - 15 Note générale : Bibliographie The PhD thesis of Sébastien Labarre was funded by the Direction générale de l'armement (DGA) and by the Commissariat à l'énergie atomique et aux énergies alternatives (CEA). Field data were acquired in the frame of the CAROLInA (Characterization of Multi-Scale Roughness using OpticaL ImAgery) project funded by CNES. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Djibouti [Termes descripteurs IGN] goniomètre [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] réflectance du sol [Termes descripteurs IGN] rugosité du sol [Termes descripteurs IGN] sol nu Résumé : (Auteur) Surface roughness can be defined as the mean slope angle integrated over all scales from the grain size to the local topography. It controls the energy balance of bare soils, in particular the angular distribution of scattered and emitted radiation. This provides clues to understand the intimate structure and evolution of planetary surfaces over ages. In this article we investigate the capacity of the Hapke photometric model, the most widely used in planetary science, to retrieve surface roughness from multiangular reflectance data. Its performance is still a question at issue and we lack validation experiments comparing model retrievals with ground measurements. To address this issue and to show the potentials and limits of the Hapke model, we compare the mean slope angle determined from very high resolution digital elevation models of volcanic and sedimentary terrains sampled in the Asal-Ghoubbet rift (Republic of Djibouti), to the photometric roughness estimated by model inversion on multiangular reflectance data measured on the ground (Chamelon field goniometer) and from space (Pleiades images). The agreement is good on moderately rough surfaces, in the domain of validity of the Hapke model, and poor on others. Numéro de notice : A2019-154 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.02.014 date de publication en ligne : 02/03/2019 En ligne : https://doi.org/10.1016/j.rse.2019.02.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92492 [article]

Comparison of high-density LiDAR and satellite photogrammetry for forest inventory / Grant D. Pearse in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)  

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3D reconstruction from multi-view VHR-satellite images in MicMac / Ewelina Rupnik in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

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Accuracy assessment of different digital surface models / Ugur Alganci in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)  

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Nouvelle méthode en cascade pour la classification hiérarchique multi-temporelle ou multi-capteur d'images satellitaires haute résolution / Ihsen Hedhli in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)

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Evaluation des performances des modèles numérique d’élévation issus de l’imagerie tri-stéréo Pléiades pour le suivi de l’évolution morphologique des dunes littorales / Mannaïg L'haridon (2018) 

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Mise en évidence de l’activité récente des failles du bassin de Naryn (Kyrgyzstan) à partir de données photogrammétriques Pléiades et drone : un nouvel apport pour l’aléa sismique / Aurélie Médard (2018)

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Pan-sharpening quality investigation of PLÉIADES-1A images / Mustafa Ozendi in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)  

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Refined satellite image orientation in the free open-source photogrammetric tools Apero/MicMac / Ewelina Rupnik in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-1 (July 2016)    

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Supervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

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