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Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment / Maxime Soma in Remote sensing of environment, vol 257 (May 2021)
[article]
Titre : Sensitivity of voxel-based estimations of leaf area density with terrestrial LiDAR to vegetation structure and sampling limitations: A simulation experiment Type de document : Article/Communication Auteurs : Maxime Soma, Auteur ; François Pimont, Auteur ; Jean-Luc Dupuy, Auteur Année de publication : 2021 Article en page(s) : n° 112354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de sensibilité
[Termes IGN] densité du feuillage
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] Leaf Area Index
[Termes IGN] Leaf Mass per Area
[Termes IGN] semis de points
[Termes IGN] structure de la végétation
[Termes IGN] voxelRésumé : (auteur) The need for fine scale description of vegetation structure is increasing as Leaf Area Density (LAD, m2/m3) becomes a critical parameter to understand ecosystem functioning and energy and mass fluxes in heterogeneous ecosystems. Terrestrial Laser Scanning (TLS) has shown great potential for retrieving the foliage area at stand, plant or voxel scales. Several sources of measurement errors have been identified and corrected over the past years. However, measurements remain sensitive to several factors, including, 1) voxel size and vegetation structure within voxels, 2) heterogeneity in sampling from TLS instrument (occlusion and shooting pattern), the consequences of which have been seldom analyzed at the scale of forest plots. In the present paper, we aimed at disentangling biases and errors in plot-scale measurements of LAD with TLS in a simulated vegetation scene. Two negative biases were formerly attributed to (i) the unsampled voxels and to (ii) the subgrid vegetation heterogeneity (i.e. clumping effect), and then quantified, thanks to a the simulation experiment providing known LAD references at voxel scale, vegetation manipulations and unbiased point estimators. We used confidence intervals to evaluate voxel-scale measurement accuracy. Numéro de notice : A2021-278 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112354 Date de publication en ligne : 18/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112354 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97371
in Remote sensing of environment > vol 257 (May 2021) . - n° 112354[article]A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)
[article]
Titre : A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms Type de document : Article/Communication Auteurs : Dimitrios Bellos, Auteur ; Mark Basham, Auteur ; Tony Pridmore, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] acquisition de connaissances
[Termes IGN] apprentissage profond
[Termes IGN] échantillonnage
[Termes IGN] filtrage du bruit
[Termes IGN] rapport signal sur bruit
[Termes IGN] rayon X
[Termes IGN] reconstruction d'image
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelle
[Termes IGN] tomographieRésumé : (auteur) Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available. Numéro de notice : A2021-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01196-4 Date de publication en ligne : 27/04/2021 En ligne : https://doi.org/10.1007/s00138-021-01196-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97902
in Machine Vision and Applications > vol 32 n° 3 (May 2021) . - n° 75[article]Robust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
[article]
Titre : Robust unsupervised small area change detection from SAR imagery using deep learning Type de document : Article/Communication Auteurs : Xinzheng Zhang, Auteur ; Hang Su, Auteur ; Ce Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de changement
[Termes IGN] échantillonnage
[Termes IGN] filtre de déchatoiement
[Termes IGN] image radar moirée
[Termes IGN] ondelette
[Termes IGN] regroupement de données
[Termes IGN] superpixelRésumé : (auteur) Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. Numéro de notice : A2021-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.004 Date de publication en ligne : 17/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96879
in ISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 79 - 94[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021031 SL Revue Centre de documentation Revues en salle Disponible 081-2021033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Variations in temperate forest biomass ratio along three environmental gradients are dominated by interspecific differences in wood density / Baptiste Kerfriden in Plant ecology, vol 222 n° 3 (March 2021)
[article]
Titre : Variations in temperate forest biomass ratio along three environmental gradients are dominated by interspecific differences in wood density Type de document : Article/Communication Auteurs : Baptiste Kerfriden , Auteur ; Jean-Daniel Bontemps , Auteur ; Jean-Michel Leban , Auteur Année de publication : 2021 Projets : XyloDensMap / Leban, Jean-Michel Article en page(s) : 20 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière
[Termes IGN] capacité de rétention d'eau du sol
[Termes IGN] densité du bois
[Termes IGN] échantillonnage
[Termes IGN] forêt tempérée
[Termes IGN] humidité du sol
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Pinophyta
[Termes IGN] puits de carbone
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Background: Biomass ratio (BR) is a forest state variable allowing the conversion of forest volume of growing stock into biomass. Despite huge intraspecific variation in wood density depending on the biotic and abiotic environments of tree growth, this variable is most often considered a tree species constant in C budgets. The aims were i) to identify variations in BR along decorrelated water, soil nutrition and elevation gradients, ii) to test for differences between broadleaved and conifer tree species in BR variations, and iii) to weight the contribution of interspecific and intraspecific diversity in BR variations.
Methods: Analyses were based on massive wood density measurements performed with an X-ray medical scanner on 54,700 tree cores collected in 2016 and 2017 on the spatially systematic plot sampling design of the French national forest inventory (NFI) program.
Results: BR variations along the three gradients were found significant. BR hence decreased by 73 kg.m-3 (conifers) and 126 kg.m-3 (broadleaves) along a 180 mm gradient of soil water holding capacity (SWHC). It also increased by 153 kg.m-3 on average along the full gradient of soil basicity Index (SBI). A negative trend along elevation was also identified, with an average decrease by 155 kg.m-3 from 200 to 2000 m of elevation. Species distribution was found to be the main cause of BR variations along these gradients.
Conclusions: We report dependences of BR on both water (–), nutrient availability (+) and warmth (+) gradients, more acute in broadleaves than in conifers only for water availability. At the scale of the whole French forests, intraspecific variations in wood density do not affect BR estimations along these gradients. BR variations are mainly driven by the tree stand species composition along them.Numéro de notice : A2021-082 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11258-020-01106-0 Date de publication en ligne : 03/01/2021 En ligne : https://doi.org/10.1007/s11258-020-01106-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96826
in Plant ecology > vol 222 n° 3 (March 2021) . - 20 p.[article]Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)
[article]
Titre : Developing a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data Type de document : Article/Communication Auteurs : Juan Guerra-Hernández, Auteur ; Stefano Arellano-Pérez, Auteur ; Eduardo González-Ferreiro, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 118690 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] anomalie de croissance des arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] Galice (Espagne)
[Termes IGN] gestion forestière
[Termes IGN] hauteur des arbres
[Termes IGN] indice de végétation
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] lasergrammétrie
[Termes IGN] Pinus pinaster
[Termes IGN] régression multivariée par spline adaptative
[Termes IGN] série temporelleRésumé : (auteur) Site index (SI) is a common measure of forest site productivity, serving as a valuable baseline for forest management. The main objective of this study was to develop a SI model for Pinus pinaster Ait. in north-west Spain by combining bi–temporal, low–density airborne laser scanning (ALS) data (acquired in the periods 2009–2011 and 2015–2017) with climatic, edaphic and physiographical data. Site productivity, assessed by site quality curves, was modelled using an age-independent difference equation method based on ALS metrics and environmental variables. For the model development process, we used data from 156 sample plots in pure and even-aged P. pinaster stands distributed throughout Galicia (NW Spain) and measured in the Spanish National Forest Inventory (SNFI). The generalized algebraic difference approach (GADA) formulation was tested by using two different base equations for modelling the dominant height growth (ΔH) from ALS variables. The GADA formulation derived from the Bertalanffy’s base model produced the best estimates of dominant height (H) for P. pinaster stands in Galicia. Use of the proposed model to estimate ΔH for a new pine stand requires two ALS data sets for estimating site-specific (local) parameters. To enable use of the model when such information is not available, the relationship between the values of the site-specific parameter and environmental variables was described using Multivariate Adaptive Regression Splines (MARS). Use of the MARS equation enabled us to develop spatially-explicit predictive maps of the site-specific parameter values, which can be used together with the GADA model to derive ΔH curves and SI estimates for P. pinaster stands in the whole study region. Numéro de notice : A2021-225 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2020.118690 Date de publication en ligne : 01/11/2021 En ligne : https://doi.org/10.1016/j.foreco.2020.118690 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97200
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