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Auteur Marco Lavalle |
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Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms / Gustavo H.X. Shiroma in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
[article]
Titre : Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms Type de document : Article/Communication Auteurs : Gustavo H.X. Shiroma, Auteur ; Marco Lavalle, Auteur Année de publication : 2020 Article en page(s) : pp 754 - 3777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bande L
[Termes IGN] décomposition de Gauss
[Termes IGN] Gabon
[Termes IGN] histogramme
[Termes IGN] image captée par drone
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation 3D
[Termes IGN] polarimétrie radar
[Termes IGN] rétrodiffusion
[Termes IGN] structure de la végétationRésumé : (auteur) This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopé National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH−VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements. Numéro de notice : A2020-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2956989 Date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2956989 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95099
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 754 - 3777[article]Extraction of structural and dynamic properties of forests from polarimetric-interferometric SAR data affected by temporal decorrelation / Marco Lavalle in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)
[article]
Titre : Extraction of structural and dynamic properties of forests from polarimetric-interferometric SAR data affected by temporal decorrelation Type de document : Article/Communication Auteurs : Marco Lavalle, Auteur ; S. Hensley, Auteur Année de publication : 2015 Article en page(s) : pp 4752 - 4767 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] cohérence (physique)
[Termes IGN] décorrélation
[Termes IGN] hauteur des arbres
[Termes IGN] Massachusetts (Etats-Unis)
[Termes IGN] Pol-INSAR
[Termes IGN] polarimétrie radarRésumé : (Auteur) This paper addresses the important yet unresolved problem of estimating forest properties from polarimetric-interferometric radar images affected by temporal decorrelation. We approach the problem by formulating a physical model of the polarimetric-interferometric coherence that incorporates both volumetric and temporal decorrelation effects. The model is termed random-motion-over-ground (RMoG) model, as it combines the random-volume-over-ground (RVoG) model with a Gaussian-statistic motion model of the canopy elements. Key features of the RMoG model are: 1) temporal decorrelation depends on the vertical structure of forests; 2) volumetric and temporal coherences are not separable as simple multiplicative factors; and 3) temporal decorrelation is complex-valued and changes with wave polarization. This third feature is particularly important as it allows compensating for unknown levels of temporal decorrelation using multiple polarimetric channels. To estimate model parameters such as tree height and canopy motion, we propose an algorithm that minimizes the least square distance between model predictions and complex coherence observations. The algorithm was applied to L-band NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar data acquired over the Harvard Forest (Massachussetts, USA). We found that the RMS difference at stand level between estimated RMoG-model tree height and NASA's lidar Laser Vegetation and Ice Sensor tree height was within 12% of the lidar-derived height, which improved significantly the RMS difference of 37% obtained using the RVoG model and ignoring temporal decorrelation. This result contributes to our ability of estimating forest biomass using in-orbit and forthcoming polarimetric-interferometric radar missions. Numéro de notice : A2015-524 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2409066 Date de publication en ligne : 13/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2409066 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77535
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 4752 - 4767[article]Réservation
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