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Titre : Visualisation spatio-temporelle de données COVID-19 Type de document : Mémoire Auteurs : Aymeric Dutremble, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2022 Importance : 34 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de projet pluridisciplinaire, cycle ING2Langues : Français (fre) Descripteur : [Termes IGN] application web
[Termes IGN] Cascading Style Sheets CSS
[Termes IGN] données spatiotemporelles
[Termes IGN] épidémie
[Termes IGN] HTML
[Termes IGN] prototype
[Termes IGN] représentation cartographique 3D
[Termes IGN] visualisation 3D
[Vedettes matières IGN] GéovisualisationIndex. décimale : PROJET Mémoires : Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) Suite à l’épidémie que nous venons de vivre, une problématique a été remise en avant. Comment représenter spatialement et temporellement une donnée dans une unique géovisualisation. En d’autres termes, comment sur une carte est-il possible de visualiser des données possédant une position géographique (des coordonnées) et une position dans le temps (une date). Et plus généralement, dans le cadre de la pandémie, comment utiliser la géovisualisation pour analyser les dynamiques d’une telle épidémie. L’équipe GEOVIS du LASTIG a pris en main le sujet en proposant deux prototypes de visualisation spatio-temporelle via deux applications web en utilisant des données COVID-19 simulées. Un premier prototype vient représenter la donnée spatio-temporelle en deux dimensions et un second en trois dimensions. L’objectif est de créer une nouvelle visualisation qui permet de mixer ces deux méthodes. Cette application web en trois dimensions affiche les deux méthodes selon la position de l’utilisateur dans l’espace. Elle affiche aussi une méthode de visualisation hybride entre les deux prototypes. Créer cette application avec ces différents concepts va permettre d’avoir une nouvelle manière d’appréhender les données spatio-temporelles. Elles seront mises en valeur d’une manière inédite qui pourra améliorer l’analyse de ce type de données. Note de contenu : Introduction
1. Contexte et objectif du projet
1.1 Organisation et contexte
1.2 Growth Ring Map
1.3 Space-Time Cube
1.4 Nouvelle visualisation et objectif
2. Prise en main et mise en place
2.1 Langage, structure et bibliothèque
2.2 Maillage hexagonal
2.3 Hexagone 3D
2.4 Transition
3. Légendes et outils additionnels
3.1 Panneau de contrôle
3.2 Interaction
3.3 Fonctionnalités secondaires
3.4 Diagramme temporel
3.5 Légendes
ConclusionNuméro de notice : 26867 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : LASTIG (IGN) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101682 Documents numériques
en open access
Visualisation spatio-temporelle de données COVID-19 - pdf auteurAdobe Acrobat PDF Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models / Arne Nothdurft in Forest ecology and management, vol 502 (December-15 2021)
[article]
Titre : Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models Type de document : Article/Communication Auteurs : Arne Nothdurft, Auteur ; Christoph Gollob, Auteur ; Ralf Krasnitzer, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119714 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Autriche
[Termes IGN] bois sur pied
[Termes IGN] dommage forestier causé par facteurs naturels
[Termes IGN] échantillonnage
[Termes IGN] estimation bayesienne
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] lasergrammétrie
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] modèle de régression
[Termes IGN] modèle mathématique
[Termes IGN] tempête
[Termes IGN] volume en bois
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) A spatial regression model framework is presented to predict growing stock volume loss due to storm Adrian which caused heavy forest damage in the upper Gail valley in Carinthia, Austria, in October 2018. Model parameters were estimated using growing stock volume measured with a terrestrial laser scanner on 62 sample plots distributed across five sub-regions. Predictor variables were derived from high resolution vegetation height measurements collected during an airborne laser scanning campaign. Non-spatial and spatial candidate models were proposed and assessed based on fit to observed data and out-of-sample prediction. Spatial Gaussian processes associated model intercepts and regression coefficients were used to capture spatial dependence. Results show a spatially-varying coefficient model, which allowed the intercept and regression coefficients to vary spatially, yielded the best fit and prediction. Two approaches were considered for prediction over blowdown areas: 1) an areal approach that viewed each blowdown as a single prediction unit indexed by its centroid; and 2) a block approach where each blowdown was partitioned into smaller prediction units to better align with sample plots’ spatial support. Joint prediction was used to acknowledge spatial dependence among block units. Results demonstrated the block approach is preferable as it mitigated change-of-support issues encountered in the areal approach. Despite the small sample size, predictions for 55% of the total 564 blowdown areas, accounting for 93% of the total loss, had a coefficient of variation less than 25%. Key advantages of the proposed regression framework and chosen Bayesian inferential paradigm, were the ability to quantify uncertainty in spatial covariance parameters, propagate parameter uncertainty through to prediction, and provide statistically valid prediction point and interval estimates for individual blowdowns and collections of blowdowns at the sub-region and region scale via posterior predictive distribution summaries. Numéro de notice : A2021-770 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119714 Date de publication en ligne : 07/10/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98822
in Forest ecology and management > vol 502 (December-15 2021) . - n° 119714[article]Mapping temperate forest tree species using dense Sentinel-2 time series / Jan Hemmerling in Remote sensing of environment, vol 267 (December-15 2021)
[article]
Titre : Mapping temperate forest tree species using dense Sentinel-2 time series Type de document : Article/Communication Auteurs : Jan Hemmerling, Auteur ; Dirk Pflugmacher, Auteur ; Patrick Hostert, Auteur Année de publication : 2021 Article en page(s) : n° 112743 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de la végétation
[Termes IGN] espèce végétale
[Termes IGN] Europe centrale
[Termes IGN] filtrage numérique d'image
[Termes IGN] forêt tempérée
[Termes IGN] image Sentinel-MSI
[Termes IGN] série temporelleRésumé : (auteur) Precise information on tree species composition is critical for forest management and conservation, but mapping tree species with satellite data over large areas is still a challenge. Since 2017, Sentinel-2A/B provide multi-spectral time series with global coverage at an unprecedented spatial and temporal resolution. This is a new opportunity for mapping tree species over large areas that has not yet been fully explored. Because of the high spatial and temporal resolution, Sentinel-2 time series improve the characterization of vegetation phenology and canopy structure, parameters that are intrinsically linked to tree species. The objective of this study was to test the utility of a Sentinel-2 time-series based approach for mapping tree species in a temperate forest region in Central Europe. Using stand-wise forest inventory data for single species stands we assess how well main and minor tree species can be mapped, and if the addition of environmental variables and spatial texture metrics improves the classification accuracy. Our time series approach utilizes all available Sentinel-2 observations and an ensemble of radial basis convolution filters to build cloud-free 5-day time series for each spectral band. The time series are then used as input features to classify seventeen tree species. Our results show the potential of Sentinel-2 time-series based classification, but they also show the challenges associated with mapping a diverse portfolio of tree species. Accuracy of the nine main species, with an area proportion greater than 0.5%, ranged between 98.9% and 66.8%, which is promising for a large area. Adding detailed environmental data and texture metrics to the spectral model only marginally increased the accuracy of a few minor tree species. Overall, the eight minor tree species with area proportions less than 0.5% were most strongly affected by classification errors. Although the absolute mapped area of minor species correlated well with the estimated reference area, the small class areas of minor species lead to high classification errors in relative terms. Mapping minor tree species is challenging for statistical reasons (i.e., class imbalance, small sample size and class variance). Using all available Sentinel-2 data allows building dense time series at high spatial resolution that are mandatory for improved tree species mapping. We were able to show that the spectral time series is the prime explanatory information, even when complementing our analyses with texture information and various environmental data. The results suggest that with the applied data harmonization approach precise regional tree species mapping is feasible. Numéro de notice : A2021-939 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112743 Date de publication en ligne : 13/10/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112743 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99748
in Remote sensing of environment > vol 267 (December-15 2021) . - n° 112743[article]Modeling post-logging height growth of black spruce-dominated boreal forests by combining airborne LiDAR and time since harvest maps / Batistin Bour in Forest ecology and management, vol 502 (December-15 2021)
[article]
Titre : Modeling post-logging height growth of black spruce-dominated boreal forests by combining airborne LiDAR and time since harvest maps Type de document : Article/Communication Auteurs : Batistin Bour, Auteur ; Victor Danneyrolles, Auteur ; Yan Boucher, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] carte forestière
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] forêt de production
[Termes IGN] gestion forestière
[Termes IGN] hauteur des arbres
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle de simulation
[Termes IGN] Picea mariana
[Termes IGN] productivité
[Termes IGN] Québec (Canada)
[Termes IGN] récolte de bois
[Termes IGN] semis de pointsRésumé : (auteur) Increase in forest disturbance due to land use as well as climate change has led to an expansion of young forests worldwide, which drives global carbon dynamics and timber allocation. This study presents a method that combines a single airborne LiDAR acquisition and time since harvest maps to model height growth of post-logged black spruce-dominated forests in a 1700 km2 eastern Canadian boreal landscape. We developed a random forest model in which forest height at a 20 m × 20 m pixel resolution is a function of stand age, combined with environmental variables (e.g., slope, site moisture, surface deposit). Our results highlight the model's strong predictive power: least-square regression between predicted and observed height of our validation dataset was very close to the 1:1 relation and strongly supported by validation metrics (R2 = 0.74; relative RMSE = 19%). Environmental variables thus allowed to accurately predict forest productivity with a high spatial resolution (20 m × 20 m pixels) and predicted forest height growth in the first 50 years after logging ranged between 16 and 27 cm·year−1 across the whole study area, with a mean of 20.5 cm·year−1. The spatial patterns of potential height growth were strongly linked to the effect of topographical variables, with better growth rates on mesic slopes compared to poorly drained soils. Such models could have key implications in forest management, for example to maintain forest ecosystem services by adjusting the harvesting rates depending on forest productivity across the landscapes. Numéro de notice : A2021-708 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119697 Date de publication en ligne : 25/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98819
in Forest ecology and management > vol 502 (December-15 2021) . - n° 119697[article]The efficiency of retention measures in continuous-cover forestry for conserving epiphytic cryptogams: A case study on Abies alba / Stefan Kaufmann in Forest ecology and management, vol 502 (December-15 2021)
[article]
Titre : The efficiency of retention measures in continuous-cover forestry for conserving epiphytic cryptogams: A case study on Abies alba Type de document : Article/Communication Auteurs : Stefan Kaufmann, Auteur ; Sarah-Katharina Funck, Auteur ; Franziska Paintner, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119698 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba
[Termes IGN] Allemagne
[Termes IGN] Bryophyte
[Termes IGN] coupe rase (sylviculture)
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] échantillonnage
[Termes IGN] écosystème forestier
[Termes IGN] Fagus sylvatica
[Termes IGN] habitat (nature)
[Termes IGN] lichen
[Termes IGN] Picea abies
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Lacking structural diversity in production forests has been evidenced to decrease epiphytic bryophytes and lichens. One approach to create structurally more diverse forests is retention forestry. Only a small number of studies focused on the effectiveness of retention measures in continuous-cover forestry. Most studies have been conducted in even-aged, clear-cut based management systems and applied different approaches, but they all have in common that the retained trees have been examined for epiphytes only after harvest. Thus, it remains unclear whether these trees or even a certain tree species could take the life-boat function for epiphytes on logged sites. Thus, prior to logging, we assessed epiphytic bryophytes and lichens on potential large living retention trees, here referred to as habitat trees (HT), of Abies alba and compared the diversity pattern to nearby average trees (AT; A. alba, Fagus sylvatica or Picea abies) of smaller sizes in selectively harvested continuous-cover forests. Selection of AT was based on the average stem diameter of all trees within the stand. We found that species richness and Simpson diversity of lichens were significantly higher on HT. For bryophytes, F. sylvatica AT showed significantly higher Simpson diversity. Mixed models revealed positive effects of F. sylvatica on bryophytes, whereas large stem diameters and elevation were the driving forces for lichens. Additionally, ordinations revealed clear patterns in species composition separating between conifers and broadleaved trees, and along increasing altitude and stem diameter. Concerning HT selection, we suggest to focus rather on the tree species diversity than on stem diameter, when aiming to protect epiphytic bryophytes and lichens. Numéro de notice : A2021-769 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119698 Date de publication en ligne : 30/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119698 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98821
in Forest ecology and management > vol 502 (December-15 2021) . - n° 119698[article]Assessing the agreement of ICESat-2 terrain and canopy height with airborne lidar over US ecozones / Lonesome Malambo in Remote sensing of environment, vol 266 (December 2021)PermalinkClimate warming-induced replacement of mesic beech by thermophilic oak forests will reduce the carbon storage potential in aboveground biomass and soil / Jan Kasper in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkDrought in the forest breaks plant–fungi interactions / Andrzej Boczoń in European Journal of Forest Research, vol 140 n° 6 (December 2021)PermalinkEarly detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)PermalinkEstimation of individual tree stem biomass in an uneven-aged structured coniferous forest using multispectral LiDAR data / Nikos Georgopoulos in Remote sensing, vol 13 n° 23 (December-1 2021)PermalinkExtensification and afforestation of cultivated mineral soil for climate change mitigation in Finland / Boris Tupek in Forest ecology and management, vol 501 (December-1 2021)PermalinkA generic information framework for decision-making in a forest-based bio-economy / Jean-Baptiste Pichancourt in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkGenetic diversity of seeds from four German Douglas fir (Pseudotsuga menziesii) seed orchards / Birte Pakull in European Journal of Forest Research, vol 140 n° 6 (December 2021)PermalinkHow geographic and climatic factors affect the adaptation of Douglas-fir provenances to the temperate continental climate zone in Europe / Marzena Niemczyk in European Journal of Forest Research, vol 140 n° 6 (December 2021)PermalinkImproving the Fagacées growth model with an expanded common beech (Fagus sylvatica L.) data series from France and Germany / Gilles Le Moguédec in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkMapping tropical forest trees across large areas with lightweight cost-effective terrestrial laser scanning / Shengli Tao in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkModelling bark volume for six commercially important tree species in France: assessment of models and application at regional scale / Rodolphe Bauer in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkModelling the impact of climate change on the occurrence of frost damage in Sitka spruce (Picea sitchensis) in Great Britain / A.A. Atucha-Zamkova in Forestry, an international journal of forest research, vol 94 n° 5 (December 2021)PermalinkMulti-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)PermalinkNational scale mapping of larch plantations for Wales using the Sentinel-2 data archive / Suvarna M. Punalekar in Forest ecology and management, vol 501 (December-1 2021)PermalinkPrescribed burning as a cost-effective way to address climate change and forest management in Mediterranean countries / Renata Martins Pacheco in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkProgress on incorporating biodiversity monitoring in REDD+ through national forest inventories / Loïc Gillerot in Global ecology and conservation, vol 32 (December 2021)PermalinkRadiative transfer modeling in structurally complex stands: towards a better understanding of parametrization / Frédéric André in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkShifting precipitation patterns drive growth variability and drought resilience of European Atlas cedar plantations / J. Julio Camarero in Forests, vol 12 n° 12 (December 2021)PermalinkUnderstanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models / Tong Zhang in Transactions in GIS, vol 25 n° 6 (December 2021)Permalink