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Five decades of ground flora changes in a temperate forest: The good, the bad and the ambiguous in biodiversity terms / K.J. Kirby in Forest ecology and management, vol 505 (February-1 2022)
[article]
Titre : Five decades of ground flora changes in a temperate forest: The good, the bad and the ambiguous in biodiversity terms Type de document : Article/Communication Auteurs : K.J. Kirby, Auteur ; D.R. Bazely, Auteur ; E.A. Goldberg, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 119896 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] biomasse forestière
[Termes IGN] Brachypodium (genre)
[Termes IGN] Cervidae
[Termes IGN] composition floristique
[Termes IGN] dépérissement
[Termes IGN] détection de changement
[Termes IGN] eutrophisation
[Termes IGN] flore forestière
[Termes IGN] forêt tempérée
[Termes IGN] Fraxinus excelsior
[Termes IGN] gestion forestière
[Termes IGN] maladie phytosanitaire
[Termes IGN] richesse floristique
[Termes IGN] Royaume-Uni
[Termes IGN] Tracheophyta
[Vedettes matières IGN] ForesterieRésumé : (auteur) We explore how the ground flora of a temperate woodland (Wytham Woods, southern England) changed in terms of species-richness, cover and biomass over five decades; what the drivers of change were; and possible future change as a consequence of the decline in Fraxinus excelsior as a canopy dominant. Vascular plants were recorded from 164 permanent, 10x10 m plots, distributed as a 141 m grid, in 1974, 1991, 1999, 2012, and 2018. Species presence and frequency/abundance in each plot were estimated and used to model biomass changes. Changes in species-richness, vegetation composition and structure were analysed. Stands opened out by thinning or which became denser through tree growth gained or lost species respectively, particularly non-woodland species. Deer pressure favoured the spread of Brachypodium sylvaticum and reduced Rubus fruticosus. No obvious impacts of climate change, eutrophication or of invasive species were detected in the plot records although other signs suggest these are starting to affect the flora. Just 12 out of 235 species contributed 47% of all species occurrences, 82% of the vegetation cover and 87% of the modelled biomass. We conclude that the ground flora is highly variable over decadal timescales, but the patterns of change observed differ according to the measures used (species richness, cover, biomass, etc). Site level drivers in the short-term swamped effects of slower acting regional/global drivers. Legacy effects were seen in the greater richness of specialists in the older woodland. While some impacts can be mitigated by management, others are largely beyond control at the site level. Numéro de notice : A2022-041 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.foreco.2021.119896 Date de publication en ligne : 02/12/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119896 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99389
in Forest ecology and management > vol 505 (February-1 2022) . - n° 119896[article]A geographically weighted artificial neural network / Julian Haguenauer in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)
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Titre : A geographically weighted artificial neural network Type de document : Article/Communication Auteurs : Julian Haguenauer, Auteur ; Marco Helbich, Auteur Année de publication : 2022 Article en page(s) : pp 215 - 235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse de sensibilité
[Termes IGN] Autriche
[Termes IGN] coût
[Termes IGN] évaluation foncière
[Termes IGN] hétérogénéité spatiale
[Termes IGN] logement
[Termes IGN] régression géographiquement pondérée
[Termes IGN] relation spatiale
[Termes IGN] réseau neuronal artificielRésumé : (auteur) While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear. In practice, however, it is often the case that variables are nonlinearly associated. To address this issue, we propose a geographically weighted artificial neural network (GWANN). GWANN combines geographical weighting with artificial neural networks, which are able to learn complex nonlinear relationships in a data-driven manner without assumptions. Using synthetic data with known spatial characteristics and a real-world case study, we compared GWANN with GWR. While the results for the synthetic data show that GWANN performs better than GWR when the relationships within the data are nonlinear and their spatial variance is high, the results based on the real-world data demonstrate that the performance of GWANN can also be superior in a practical setting. Numéro de notice : A2022-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1871618 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1871618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99785
in International journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 215 - 235[article]A robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)
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Titre : A robust nonrigid point set registration framework based on global and intrinsic topological constraints Type de document : Article/Communication Auteurs : Guiqiang Yang, Auteur ; Rui Li, Auteur ; Yujun Liu, Auteur ; Ji Wang, Auteur Année de publication : 2022 Article en page(s) : pp 603 - 623 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] algorithme espérance-maximisation
[Termes IGN] contrainte géométrique
[Termes IGN] contrainte topologique
[Termes IGN] descripteur local
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] méthode robuste
[Termes IGN] processus gaussien
[Termes IGN] semis de points
[Termes IGN] superposition de donnéesRésumé : (auteur) The problem of registering nonrigid point sets, with the aim of estimating the correspondences and learning the transformation between two given sets of points, often arises in computer vision tasks. This paper proposes a novel method for performing nonrigid point set registration on data with various types of degradation, in which the registration problem is formulated as a Gaussian mixture model (GMM)-based density estimation problem. Specifically, two complementary constraints are jointly considered for optimization in a GMM probabilistic framework. The first is a thin-plate spline-based regularization constraint that maintains global spatial motion consistency, and the second is a spectral graph-based regularization constraint that preserves the intrinsic structure of a point set. Moreover, the correspondences and the transformation are alternately optimized using the expectation maximization algorithm to obtain a closed-form solution. We first utilize local descriptors to construct the initial correspondences and then estimate the underlying transformation under the GMM-based framework. Experimental results on contour images and real images show the effectiveness and robustness of the proposed method. Numéro de notice : A2022-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02037-7 Date de publication en ligne : 21/02/2022 En ligne : https://doi.org/10.1007/s00371-020-02037-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100040
in The Visual Computer > vol 38 n° 2 (February 2022) . - pp 603 - 623[article]Spatiotemporal temperature fusion based on a deep convolutional network / Xuehan Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)
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Titre : Spatiotemporal temperature fusion based on a deep convolutional network Type de document : Article/Communication Auteurs : Xuehan Wang, Auteur ; Zhenfeng Shao, Auteur ; Xiao Huang, Auteur ; Deren Li, Auteur Année de publication : 2022 Article en page(s) : pp 93 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] données spatiotemporelles
[Termes IGN] fusion de données multisource
[Termes IGN] image Landsat
[Termes IGN] image Terra-MODIS
[Termes IGN] réseau neuronal convolutif
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] température de surfaceRésumé : (Auteur) High-spatiotemporal-resolution land surface temperature (LST) images are essential in various fields of study. However, due to technical constraints, sensing systems have difficulty in providing LSTs with both high spatial and high temporal resolution. In this study, we propose a multi-scale spatiotemporal temperature-image fusion network (MSTTIFN) to generate high-spatial-resolution LST products. The MSTTIFN builds nonlinear mappings between the input Moderate Resolution Imaging Spectroradiometer (MODIS) LSTs and the out- put Landsat LSTs at the target date with two pairs of references and therefore enhances the resolution of time-series LSTs. We conduct experiments on the actual Landsat and MODIS data in two study areas (Beijing and Shandong) and compare our proposed MSTTIFN with four competing methods: the Spatial and Temporal Adaptive Reflectance Fusion Model, the Flexible Spatiotemporal Data Fusion Model, a two-stream convolutional neural network (StfNet), and a deep learning-based spatiotemporal temperature-fusion network. Results reveal that the MSTTIFN achieves the best and most stable performance. Numéro de notice : A2022-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00023R2 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00023R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99724
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 93 - 101[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022021 SL Revue Centre de documentation Revues en salle Disponible Three-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)
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Titre : Three-Dimensional point cloud analysis for building seismic damage information Type de document : Article/Communication Auteurs : Fan Yang, Auteur ; Zhiwei Fan, Auteur ; Chao Wen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 103 - 111 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] analyse en composantes principales
[Termes IGN] densité des points
[Termes IGN] détection du bâti
[Termes IGN] dommage matériel
[Termes IGN] données localisées 3D
[Termes IGN] extraction de données
[Termes IGN] filtrage de points
[Termes IGN] mur
[Termes IGN] séisme
[Termes IGN] semis de pointsRésumé : (Auteur) Postearthquake building damage assessment requires professional judgment; however, there are factors such as high workload and human error. Making use of Terrestrial Laser Scanning data, this paper presents a method for seismic damage information extraction. This new method is based on principal component analysis calculating the local surface curvature of each point in the point cloud. Then use the nearest point angle algorithm, combined with the data features of the actual measured value to identify point cloud seismic information, and filter the points that tend to the plane by setting the threshold value. Based on the statistical analysis of the normal vector, the raw point cloud data are deplanarized to obtain the preliminary results of seismic damage information. The density clustering algorithm is used to denoise the initially extracted seismic damage information. Ultimately, we can obtain the distribution patterns and characteristics of cracks in the walls of the building. The extraction result of the seismic damage information point cloud data is compared with the photos collected at the site, showing that the algorithm steps successfully identify the crack and shed wall skin information recorded in the site photos (identification rate: 95%). Point cloud distribution maps of cracked and shed siding areas determine quantitative information on seismic damage, providing a higher level of performance and detail than direct contact measurements. Numéro de notice : A2022-065 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00019R3 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.14358/PERS.21-00019R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99727
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 2 (February 2022) . - pp 103 - 111[article]Réservation
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