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Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series / Gherardo Chirici in Annals of Forest Science [en ligne], Vol 77 n° 2 (June 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > Vol 77 n° 2 (June 2020) Titre : Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series Type de document : Article/Communication Auteurs : Gherardo Chirici, Auteur ; Francesca Giannetti, Auteur ; Erica Mazza, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] brûlis [Termes descripteurs IGN] canopée [Termes descripteurs IGN] coupe rase (sylviculture) [Termes descripteurs IGN] dégradation du signal [Termes descripteurs IGN] forêt méditerranéenne [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] reconstruction du signal [Termes descripteurs IGN] régénération (sylviculture) [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taillis [Termes descripteurs IGN] télémètre laser aéroporté Résumé : (auteur) Key message: This work analyses the rate of recovery of the spectral signal from clearcut areas of coppice Mediterranean forests using Landsat Time Series (LTS). The analysis revealed a more rapid rate of spectral signal recovery than what was found in previous investigations in boreal and temperate forests. Context: The rate of post-disturbance vegetation recovery is an important component of forest dynamics. Aims: In this study, we analyze the recovery of the spectral signal from forest clearcut areas in Mediterranean conditions when the coppice system of forest management is applied. Methods: We used LTS surface reflectance data (1999–2015). We generated an annual reference database of clearcuts using visual interpretation and local forest inventory data, and then derived the Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) spectral trajectories for these clearcuts. From these spectral trajectories, we calculated the Years to Recovery or Y2R, the number of years it takes for a pixel to return to within a specified threshold (i.e., 70%, 80%, 90%, 100%) of its pre-disturbance value. Spectral recovery rates were then corroborated using measures of canopy height derived from airborne laser scanning (ALS) data. Results: The coppice system is associated with rapid recovery rates when compared to rates of recovery from seeds or seedlings in temperate and boreal forest conditions. We found that the Y2R derived from the spectral trajectories of post-clearcut NBR and NDVI provided similar characterizations of rapid recovery for the coppice system of forest management applied in our study area. The ALS measures of canopy height indicated that the Y2R metric accurately captured the rapid regeneration of coppice systems. Conclusion: The rapid rate of spectral recovery associated with the coppice system is 2–4 years, which contrasts with values reported in boreal and temperate forest environments, where spectral recovery was attained in approximately 10 years. NBR is an effective index for assessing rapid recovery in this forest system. Numéro de notice : A2020-293 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00936-2 date de publication en ligne : 15/04/2020 En ligne : https://doi.org/10.1007/s13595-020-00936-2 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95123 [article]

Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 650 - 665 Titre : Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network Type de document : Article/Communication Auteurs : Yuxing Zhao, Auteur ; Yue Li, Auteur ; Baojun Yang, Auteur Année de publication : 2020 Article en page(s) : pp 650 - 665 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] algorithme de filtrage [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] approche géométrique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] désert [Termes descripteurs IGN] enregistrement de données [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] filtre passe-bande [Termes descripteurs IGN] interruption du signal [Termes descripteurs IGN] lutte contre le bruit [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] reconstruction du signal [Termes descripteurs IGN] séisme Résumé : (auteur) Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). For the desert noise that shares the same frequency band with the effective signal and has complex characteristics (nonlinear, nonstationary, and non-Gaussian), it is difficult to find a universally applicable method. In response to this problem, a multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed in this article. One of the most important features of the CNN is that it can extract data-rich intrinsic information from the training set without relying on a priori assumption. By introducing the CNN into the MGA, a new kind of denoising method can be created, which can achieve good results even under a low SNR. This article takes the nonsubsampled contourlet transform as an example to create a denoising network named NC-CNN for high-efficiency and intelligent denoising of desert seismic data. The processing results of synthetic seismic records and field seismic records prove that NC-CNN can effectively suppress the low-frequency noise (random noise and surface wave), and the effective signal almost has no energy loss. In addition, the reconstruction ability of the missing signals is also an advantage of this method. Numéro de notice : A2020-076 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2938836 date de publication en ligne : 24/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2938836 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94608 [article]

Band-limited signal reconstruction from irregular samples with variable apertures / David G. Long in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 2424 - 2436 Titre : Band-limited signal reconstruction from irregular samples with variable apertures Type de document : Article/Communication Auteurs : David G. Long, Auteur ; Reinhard O. W. Franz, Auteur Année de publication : 2016 Article en page(s) : pp 2424 - 2436 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] échantillonnage de signal [Termes descripteurs IGN] largeur de bande [Termes descripteurs IGN] reconstruction d'image [Termes descripteurs IGN] reconstruction du signal [Termes descripteurs IGN] télédétection Résumé : (Auteur) Sampling plays a critical role in remote sensing and signal analysis. In conventional sampling theory, the signal is sampled at a uniform rate at a minimum of twice the signal bandwidth. Sampling with an aperture function requires a fixed-aperture function, which can be removed by deconvolution after signal reconstruction. However, in some cases, the signal samples are available only at irregular positions, and different samples use different aperture functions. In this paper, the theory of finite-length signal reconstruction with irregular samples and variable apertures in one and two dimensions is considered. In the 1-D case, a band-limited discrete signal can be exactly reconstructed from a finite number of arbitrarily spaced samples with few restrictions on the aperture functions. Exact reconstruction in the 2-D case requires the sampling matrix be invertable, and is not always possible. Variable aperture functions, while complicating the process, can enable reconstruction for a broader range of sample locations. Practical issues are discussed, and numerical examples are provided. Variable aperture reconstruction has application in a variety of remote sensing problems. In this paper, reconstruction from 2-D irregular sampling with variable apertures is illustrated using Special Sensor Microwave/Imager radiometer observations. Numéro de notice : A2016-842 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2501366 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82886 [article]

Retrieval of phase history parameters from distributed scatterers in urban areas using very high resolution SAR data / Y. Wang in ISPRS Journal of photogrammetry and remote sensing, vol 73 (September 2012)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 73 (September 2012) . - pp 89 - 99 Titre : Retrieval of phase history parameters from distributed scatterers in urban areas using very high resolution SAR data Type de document : Article/Communication Auteurs : Y. Wang, Auteur ; X. Zhu, Auteur ; R. Balmer, Auteur Année de publication : 2012 Article en page(s) : pp 89 - 99 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] coin réflecteur [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image TerraSAR-X [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] phase [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] reconstruction du signal Résumé : (Auteur) In a recent contribution Ferretti and co-workers (Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., Rucci, A., 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR IEEE Transactions on Geoscience and Remote Sensing 49(9), pp. 3460–3470) have proposed the SqueeSAR method, a way to exploit temporally coherent distributed scatterers in coherent SAR data stacks. Elevation and deformation or subsidence estimates are obtained with accuracy similar as in the well known persistent scatterer interferometry (PSI). In this paper, we propose an alternative approach and provide a first demonstration of the optimal estimation of distributed scatterers’ phase histories in urban areas. Different to SqueeSAR, we derive phase histories for each distributed scatterer pixel rather than for groups of pixels. We use the Anderson–Darling statistical test to identify neighboring samples of the same distribution. Prior to covariance matrix estimation required for maximum likelihood estimation we apply a multi-resolution defringe technique. By using TerraSAR-X high resolution spotlight data, it is demonstrated that we are able to retrieve reliable phase histories and motion parameter estimates from distributed scatterers with signal-to-noise-ratio far below the common range. Numéro de notice : A2012-547 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31993 [article]

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A stochastic approach for modelling airborne lidar waveforms / Clément Mallet (01/12/2009) 

Public contenu dans 3-W8. Workshop Laserscanning'09 / Frédéric Bretar (2009)   Titre : A stochastic approach for modelling airborne lidar waveforms Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; Florent Lafarge, Auteur ; Michel Roux, Auteur ; Uwe Soergel, Auteur ; et al., Auteur Congrès : ISPRS Workshop Laser Scanning 2009 (1 - 2 septembre 2009; Paris, France), Auteur Année de publication : 01/12/2009 Importance : pp 201 - 206 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] forme d'onde pleine [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] reconstruction du signal [Termes descripteurs IGN] signal laser [Termes descripteurs IGN] signal lidar Résumé : (Auteur) In contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signals of each laser pulse. Instead of clouds of individual 3D points, FW devices provide ID profiles of the 3D scene, which allows gaining additional and more detailed observations of the illuminated surfaces. Indeed, lidar waveforms are signals consisting of a train of echoes where each of them corresponds to a scattering target of the Earth surface or a group of close objects leading to superimposed signals. Modelling these echoes with the appropriate parametric function is necessary to retrieve physical information about these objects and characterize their properties. Henceforth, the extracted parameters can be useful for subsequent object segmentation and/or classification. This paper presents a stochastic based model to reconstruct lidar waveforms in terms of a set of parametric functions. The model takes into account both a data term which measures the coherence between the proposed configurations and the waveforms, and a regularizing term which introduces physical knowledge on the reconstructed signal. We search for the best configuration of functions by performing a Reversible Jump Markov Chain Monte Carlo sampler coupled with a stochastic relaxation. Finally, the algorithm is validated on waveforms from several airborne lidar sensors, showing the suitability of the approach even when the traditional assumption of Gaussian decomposition of waveforms is invalid. Numéro de notice : C2009-004 Thématique : IMAGERIE/POSITIONNEMENT Nature : Communication Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=65044

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Lidar waveform modeling using a marked point process / Clément Mallet (07/11/2009) 

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