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Survival time and mortality rate of regeneration in the deep shade of a primeval beech forest / R. Petrovska in European Journal of Forest Research, vol 141 n° 1 (February 2022)
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[article]
Titre : Survival time and mortality rate of regeneration in the deep shade of a primeval beech forest Type de document : Article/Communication Auteurs : R. Petrovska, Auteur ; Harald Bugmann, Auteur ; Martina Lena Hobi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 43 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer platanoïdes
[Termes IGN] Acer pseudoplatanus
[Termes IGN] analyse de données
[Termes IGN] arbre mort
[Termes IGN] biomasse aérienne
[Termes IGN] croissance des arbres
[Termes IGN] dendrochronologie
[Termes IGN] échantillonnage
[Termes IGN] Fagus sylvatica
[Termes IGN] forêt primaire
[Termes IGN] Leaf Mass per Area
[Termes IGN] mortalité
[Termes IGN] ombre
[Termes IGN] régénération (sylviculture)
[Termes IGN] Ukraine
[Vedettes matières IGN] SylvicultureRésumé : (auteur) Low mortality rates and slow growth differentiate shade-tolerant from shade-intolerant species and define the survival strategy of juvenile trees growing in deep shade. While radial stem growth has been widely used to explain mortality in juvenile trees, the leaf area ratio (LAR), known to be a key component of shade tolerance, has been neglected so far. We assessed the effects of LAR, radial stem growth and tree height on survival time and the age-specific mortality rate of juvenile Fagus sylvatica L. (European beech), Acer pseudoplatanus L. (sycamore maple) and Acer platanoides L. (Norway maple) in a primeval beech forest (Ukraine). Aboveground and belowground biomass and radial stem growth were analysed for 289 living and 179 dead seedlings and saplings. Compared with the other species, F. sylvatica featured higher LAR, slower growth and a lower mortality rate. The average survival time of F. sylvatica juveniles (72 years) allows it to reach the canopy more often than its competitors in forests with low canopy turnover rate. In contrast, a combination of lower LAR, higher growth rate and higher age-specific mortality rate of the two Acer species resulted in their shorter survival times and thus render their presence in the canopy a rare event. Overall, this study suggests that shade tolerance, commonly defined as a relationship between sapling mortality and growth, can alternatively be formulated as a relationship between survival time and the interplay of growth and LAR. Numéro de notice : A2022-199 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01427-3 Date de publication en ligne : 05/11/2021 En ligne : https://doi.org/10.1007/s10342-021-01427-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100000
in European Journal of Forest Research > vol 141 n° 1 (February 2022) . - pp 43 - 58[article]Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography / Somnath Paramanik in Applied Geography, vol 139 (February 2022)
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Titre : Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography Type de document : Article/Communication Auteurs : Somnath Paramanik, Auteur ; Mukunda Dev Behera, Auteur ; J. Dash, Auteur Année de publication : 2022 Article en page(s) : n° 102649 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] allométrie
[Termes IGN] canopée
[Termes IGN] densité de la végétation
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] image hémisphérique
[Termes IGN] Leaf Area Index
[Termes IGN] mangrove
[Termes IGN] régressionRésumé : (auteur) The leaf area index (LAI) serves as a proxy to understand the dynamics of plant productivity, energy balance, and gas exchange. Cost-effective and accurate estimation of LAI is essential for under-assessed carbon-rich tropical forests, e.g., mangroves. Here, we developed allometric equations to estimate LAI using a combination of non-destructive, optical measurements through digital hemispherical photographs (DHP), and genetic programming-based Symbolic Regression (SR). We used three structural variables: diameter at breast height (DBH), tree density (TD), and canopy height (Ht) for a mangrove forest in the BhitarKanika Wildlife Sanctuary (BWS), located along the Eastern coast of India. Triplet combination using SR provided the best equation (R2 = 0.51) than any singlet or duplet combination of the variables, and even it was better than Partial Least Square (PLS) based regression (R2 = 0.42). To the best of our knowledge, the current study is the maiden attempt to develop an allometric model to estimate LAI for a mangrove ecosystem in India. In-situ measurements of structural variables such as DBH, Ht, and TD can be used for LAI estimates, as shown here. LAI estimates using cost-effective methods would greatly enhance our understanding of the spatial and temporal dynamics of mangrove ecosystems. Numéro de notice : A2022-456 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.apgeog.2022.102649 Date de publication en ligne : 29/01/2022 En ligne : https://doi.org/10.1016/j.apgeog.2022.102649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101239
in Applied Geography > vol 139 (February 2022) . - n° 102649[article]Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images / Alireza Hamedianfar in Geocarto international, vol 37 n° 3 ([01/02/2022])
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Titre : Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images Type de document : Article/Communication Auteurs : Alireza Hamedianfar, Auteur ; Mohamed Barakat A. Gibril, Auteur ; Mohammadjavad Hosseinpoor, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 773 - 791 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte d'occupation du sol
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image à très haute résolution
[Termes IGN] image Worldview
[Termes IGN] itération
[Termes IGN] optimisation (mathématiques)
[Termes IGN] optimisation par essaim de particules
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation d'image
[Termes IGN] zone urbaineRésumé : (auteur) Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data. Numéro de notice : A2022-344 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1737974 Date de publication en ligne : 12/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1737974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100525
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 773 - 791[article]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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Code-barres Cote Support Localisation Section Disponibilité 105-2022021 SL Revue Centre de documentation Revues en salle Disponible Using vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)
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Titre : Using vertices of a triangular irregular network to calculate slope and aspect Type de document : Article/Communication Auteurs : Guanghui Hu, Auteur ; Chun Wang, Auteur ; Sijin Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 382 - 404 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse comparative
[Termes IGN] bassin hydrographique
[Termes IGN] géomorphologie
[Termes IGN] grille
[Termes IGN] loess
[Termes IGN] maillage
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle numérique de surface
[Termes IGN] noeud
[Termes IGN] pente
[Termes IGN] point d'appui
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) Terrain derivative calculations from triangulated irregular network (TIN)-based digital elevation models (DEMs) have been extensively explored in geomorphometry. However, most calculation methods focus on the triangulation facets of TIN-based DEMs and ignore the vertices. In fact, these vertices are the original sampling points from the terrain surface and serve as the basis for triangulation. In this study, we argue that terrain derivative calculations using TIN-based DEMs should focus on the vertices. Employing examples with slope and aspect, we applied the TIN vertex-based method to a mathematical surface and a real topography using TIN-based DEMs with a range of sampling point densities. We performed a comparative analysis of the TIN vertex-based, TIN facet-based, and grid-based methods. Assessments on the mathematical surface showed that the TIN vertex-based method achieved the highest accuracy among the three methods. Error analysis for the real landform case indicated that the TIN vertex-based method performed slightly better than the grid-based method for slope calculation and slightly worse than the grid-based method for aspect calculation. Among the three methods, the TIN facet-based method was most sensitive to error. The TIN vertex-based method can provide a reference for the slope and aspect calculation based on point clouds. Numéro de notice : A2022-165 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1933493 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1933493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99788
in International journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 382 - 404[article]3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)
PermalinkCombined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation / Narissara Nuthammachot in Geocarto international, vol 37 n° 2 ([15/01/2022])
PermalinkIncreasing territorial planning activities through viewshed analysis / Gheorghe-Gavrilă Hognogi in Geocarto international, vol 37 n° 2 ([15/01/2022])
PermalinkSemantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network / Ekrem Saralioglu in Geocarto international, vol 37 n° 2 ([15/01/2022])
PermalinkUse of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])
PermalinkVariable selection for estimating individual tree height using genetic algorithm and random forest / Evandro Nunes Miranda in Forest ecology and management, vol 504 (January-15 2022)
PermalinkPermalinkAbove-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)
PermalinkPermalinkAn assessment of forest loss and its drivers in protected areas on the Copperbelt province of Zambia: 1972–2016 / Darius Phiri in Geomatics, Natural Hazards and Risk, vol 13 (2022)
PermalinkAn extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways / Yimin Chen in Computers, Environment and Urban Systems, vol 91 (January 2022)
PermalinkAnalyse contrastive de la perception de la ville entre fictions climatiques et débats publics / Alexandra Li–Combeau-Longuet (2022)
PermalinkAnalysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)
PermalinkApplication of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image / Efosa Gbenga Adagbasa in Geocarto international, vol 37 n° 1 ([01/01/2022])
PermalinkApport des nouveaux systèmes GNSS de cartographie du niveau marin à l’exploitation des données altimétriques en zone côtière / Clémence Chupin (2022)
PermalinkApprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques / Jean-Yves Franceschi (2022)
PermalinkPermalinkAssessment of the performance of GIS-based analytical hierarchical process (AHP) approach for flood modelling in Uttar Dinajpur district of West Bengal, India / Rajib Mitra in Geomatics, Natural Hazards and Risk, vol 13 (2022)
PermalinkAttributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)
PermalinkAttributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision / Anis Amziane (2022)
PermalinkAutomatic algorithm for georeferencing historical-to-nowadays aerial images acquired in natural environments / Daniela Craciun (2022)
PermalinkA benchmark of named entity recognition approaches in historical documents : application to 19th century French directories / Nathalie Abadie (2022)
PermalinkBest integer equivariant position estimation for multi-GNSS RTK: a multivariate normal and t-distributed performance comparison / Robert Odolinski in Journal of geodesy, vol 96 n° 1 (January 2022)
PermalinkBuyTheDips : PathLoss for improved topology-preserving deep learning-based image segmentation / Minh On Vu Ngoc (2022)
PermalinkClassification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)
PermalinkCombining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)
PermalinkA constraint-based approach for identifying the urban–rural fringe of polycentric cities using multi-sourced data / Jing Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)
PermalinkConstruction d’un plugin QGIS de détection d’îlots de chaleur urbains à partir d’images satellitaires de type optique / Houssayn Meriche (2022)
PermalinkContextual location recommendation for location-based social networks by learning user intentions and contextual triggers / Seyyed Mohammadreza Rahimi in Geoinformatica, vol 26 n° 1 (January 2022)
PermalinkContraintes observationnelles historiques sur la sensibilité climatique : implications pour les projections de la hausse du niveau de la mer / Jonathan Chenal (2022)
PermalinkContribution to object extraction in cartography : A novel deep learning-based solution to recognise, segment and post-process the road transport network as a continuous geospatial element in high-resolution aerial orthoimagery / Calimanut-Ionut Cira (2022)
PermalinkConventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)
PermalinkPermalinkCultivating historical heritage area vitality using urban morphology approach based on big data and machine learning / Jiayu Wu in Computers, Environment and Urban Systems, vol 91 (January 2022)
PermalinkDART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications / Yingjie Wang (2022)
PermalinkDeep image translation with an affinity-based change prior for unsupervised multimodal change detection / Luigi Tommaso Luppino in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)
PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkDetecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)
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