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Enregistrement W2242445149

Insar Applications in Environmental Sciences

2015· article· en· W2242445149 sur OpenAlexaboutno aff
Diana Gheorghe, Iuliana Armaș

Notice bibliographique

RevueGeopolitics History and International Relations · 2015
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueMarine and environmental studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRemote sensingRadarInterferometric synthetic aperture radarExtraterrestrial lifeSynthetic aperture radarRadar imagingVenusSpace-based radarSatelliteSpacecraftGeologyComputer scienceRadar engineering detailsTelecommunicationsAerospace engineeringEngineeringPhysicsAstrobiology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

1. IntroductionSince 1978 a new type of remote sensing has been developed - radar remote sensing, but it was not until 1990 when the applications for this type of remote sensing started to be tested. The present paper tries to present the applications for Interferometric Synthetic Aperture Radar (InSAR) by expounding some cogent papers and their results. Even though there is plenty literature for the methodological approach (Adam et al., 2009, Blanco-Sanchez et al., 2008, Poneos and Dana, 2008, Scheuchl et al., 2009, Simonetto and Follin, 2012, Wegmuller et al., 2010, Zhu et al., 2009, Zhu and Bamler, 2010) in this paper we will focus only on the practical approach for this technique.SAR (Synthetic Aperture Radar) technology involves the existence of a certain type of radar, capable of sending and receiving a long wave signal, based on the move registered by the radar between the antenna mounted on a spacecraft or aircraft, and the object. This type of radar can provide remote images with high resolution. The applications of this technology go beyond the atmosphere into the extraterrestrial environment. An example of extraterrestrial application is the Magellan mission (Campbell, 1995) that studied Venus. The satellite was launched in June 1989 and its mission was to map Venus's surface. The mission lasted for three years and the satellite mapped 98% of the surface with a spatial resolution of the images of about 100 m.The principle behind this technology is to cast a radar signal towards an object, which is reflected and reaches back to the antenna. Depending on the time and the properties of the reflected beam, the signal is used to generate images. A major advantage of radar images, compared to optic ones, is that they can be acquired during the night and also they have a high spatial resolution of about 1 m.SAR images started to be acquired in 1978, when the first SAR satellite (SEASAT) was launched but became popular only after the 1990s as technology improved.After the 1990s many radar satellites were launched: ERS-1 (launched in 1991 by ESA), ERS-2 (launched in 1995), JERS-1 (1992, Japan), RADARSAT (1995, Canada), ALOS PALSAR (2005, Japan). Two of the newest radar satellites are TerraSAR-X (2007) and TanDEM-X (2010), both launched by DLR (German Spatial Agency). TanDEM's mission is to second TerraSAR and to map the entire terrestrial surface to obtain a global digital elevation model with a 1 m resolution.Closely related with the SAR technology, InSAR (Interferometric Synthetic Aperture Radar) uses at least two SAR images (the product obtained is called interferogram) to track the temporal changes of different objects of interest, to map the vertical movements or the texture changes from certain areas or to obtain a very high resolution DEM.The InSAR technique is used frequently in earth sciences because it can measure the finest vertical movements, thus it is being used in many domains, especially in monitoring natural hazards. Also, InSAR reveal better results in urban areas because of scatter elements, so this paper focuses mostly on InSAR applications in urban areas.2. Application of InSARThe applications for this technology ranges from geology - tectonics and neotectonics, earthquakes, volcanology (Perlock et al., 2009) to geomorphology - elevations and subsidence, sometimes under 1 mm, caused by groundwater over exploitation or by old mines, to glaciology (Bamber et al., 1999, Rott et al., 2007) and to land use - forest monitoring and land use changes (Amarsaikhan et al., 2007, Del Frate et al., 2008), but also in urban applications (Dell'Acqua et al., 2011, Dell'Acqua and Gamba, 2003), building extraction and measuring (Bennett and Blacknell, 2003, Dong et al., 2011), and change detection (Schmitt et al., 2011, Thonfeld and Menz, 2011).This technique is successfully applied in risk studies, from prevention to mitigation. In these cases InSAR is useful for monitoring the elements at risk, early response and rapid assessment of damages (Brunner et al. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,484
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,214
Écart entre enseignants0,181 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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