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Pourquoi la forêt française a besoin d’un traitement de fond / Guillaume Decocq in The Conversation France, vol 2022 ([10/02/2022])
[article]
Titre : Pourquoi la forêt française a besoin d’un traitement de fond Type de document : Article/Communication Auteurs : Guillaume Decocq, Auteur ; Serge Muller, Auteur Année de publication : 2022 Langues : Français (fre) Descripteur : [Termes IGN] adaptation (biologie)
[Termes IGN] Chalara fraxinea
[Termes IGN] dépérissement
[Termes IGN] essence indigène
[Termes IGN] France (administrative)
[Termes IGN] gestion forestière durable
[Termes IGN] mortalité
[Termes IGN] productivité
[Termes IGN] puits de carbone
[Termes IGN] sécheresse
[Termes IGN] sylviculture
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Les conclusions des Assises nationales de la forêt et du bois, lancées par le gouvernement en octobre 2021 avec pour objectif de « penser la forêt française de demain », devraient être rendues dans les prochains jours. Un des axes majeurs de cette réflexion concernait le renforcement de la résilience des forêts et la préservation de la biodiversité.
Car la forêt française est aujourd’hui en crise : depuis deux décennies, on assiste en effet à une mortalité croissante des peuplements forestiers et à une baisse globale de leur productivité. Si la surface boisée en France métropolitaine ne cesse de croître depuis le milieu du XIXe siècle, c’est en raison du boisement – spontané ou artificiel – de terres agricoles, car la superficie occupée par des forêts anciennes, elle, ne cesse de diminuer. Ce « dépérissement », est généralement attribué aux modifications climatiques. Les sécheresses estivales récurrentes fragilisent les arbres et la douceur hivernale favorise les pullulations de bioagresseurs, en particulier les scolytes et les hannetons. Le changement climatique en est sans aucun doute une cause essentielle, mais il est aussi le révélateur d’écosystèmes forestiers fragilisés par des décennies de pratiques sylvicoles focalisées sur la production de bois. Non seulement la forêt française fixe moins de carbone par unité de surface, mais l’exploitation des peuplements dépérissants induit des émissions supplémentaires de CO₂ aggravant l’effet de serre et les changements climatiques associés.Numéro de notice : A2022-214 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : sans Date de publication en ligne : 20/02/2022 En ligne : https://theconversation.com/pourquoi-la-foret-francaise-a-besoin-dun-traitement- [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100083
in The Conversation France > vol 2022 [10/02/2022][article]Afforestation with Pinus nigra Arn ssp salzmannii along an elevation gradient: controlling factors and implications for climate change adaptation / Manuel Esteban Lucas-Borja in Trees, vol 36 n° 1 (February 2022)
[article]
Titre : Afforestation with Pinus nigra Arn ssp salzmannii along an elevation gradient: controlling factors and implications for climate change adaptation Type de document : Article/Communication Auteurs : Manuel Esteban Lucas-Borja, Auteur ; Xin Jing, Auteur ; David Candel-Perez, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 93 - 102 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] afforestation
[Termes IGN] bassin méditerranéen
[Termes IGN] Espagne
[Termes IGN] gradient d'altitude
[Termes IGN] mortalité
[Termes IGN] Pinus nigra salzmannii
[Termes IGN] plantation forestière
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Spanish black pine (Pinus nigra Arn ssp. salzmannii) is the most widely distributed pine species in mountain areas of the Mediterranean Basin and is commonly used for afforestation in endangered and degraded areas. Despite its importance, little is known regarding the factors driving seedling survival for this species, which may hamper afforestation success in Mediterranean areas. In this study, we assessed the effects of seed origin and plantation site along a natural gradient with contrasting elevation and climatic conditions in a Mediterranean forest in Central-Eastern Spain. Our results showed: (1) higher seedling survival rates when seed origin differed from plantation site (25.3 ± 5.4%) compared to same origin and plantation site (5.3 ± 2.7%); (2) higher survival probability (~ 20%) for high and medium elevation seeds (colder and wetter locations) compared to the warmer and drier low elevation sites (15%); (3) higher seedling survival (~ 40%) at higher elevation sites compared to low-elevation sites ( Numéro de notice : A2021-975 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s00468-021-02184-x Date de publication en ligne : 22/07/2021 En ligne : http://dx.doi.org/10.1007/s00468-021-02184-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100418
in Trees > vol 36 n° 1 (February 2022) . - pp 93 - 102[article]An open science and open data approach for the statistically robust estimation of forest disturbance areas / Saverio Francini in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)
[article]
Titre : An open science and open data approach for the statistically robust estimation of forest disturbance areas Type de document : Article/Communication Auteurs : Saverio Francini, Auteur ; Ronald E. McRoberts, Auteur ; Giovanni d' Amico, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102663 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] coupe rase (sylviculture)
[Termes IGN] détection de changement
[Termes IGN] estimation statistique
[Termes IGN] Fagus sylvatica
[Termes IGN] Google Earth Engine
[Termes IGN] image Sentinel-MSI
[Termes IGN] Italie
[Termes IGN] méthode robuste
[Termes IGN] perturbation écologique
[Termes IGN] Quercus cerris
[Termes IGN] Quercus pedunculata
[Termes IGN] Quercus pubescens
[Termes IGN] Quercus sessiliflora
[Termes IGN] surveillance forestièreRésumé : (auteur) Forest disturbance monitoring is critical for understanding forest-related greenhouse gas emissions and for determining the role of forest management in mitigating climate change. Multiple algorithms for the automated mapping of forest disturbance using remotely sensed imagery have been developed and applied; however, variability in natural and anthropogenic disturbance phenomena, as well as image acquisition conditions, can result in maps that may be incomplete or that contain inaccuracies that prevent their use for directly estimating areas of disturbance. To reduce errors in reporting disturbance areas, stratified estimators can be applied to obtain statistically robust area estimates, while simultaneously circumventing the need to conduct a complete census or in situations where such a census may not be possible. We present a semi-automated procedure for implementation in Google Earth Engine, 3I3D-GEE, for regional to global mapping of forest disturbance (including clear-cut harvesting, fire, and wind damage) and sample-based estimation of related areas using data from the processing capacity of Google Earth Engine. Documentation for the application is also provided in Appendix A. Using Sentinel-2 (S2) imagery, our procedure was applied and tested for 2018 in Italy for which the approximately 11 million ha of forests (mostly Q. pubescens, Q. robur, Q. cerris, Q. petraea, and Fagus sylvatica) serve as an appropriate case study because national statistics on forest disturbance areas are not available. To decrease the overall standard errors of the area estimates, the sampling intensities in areas where greater variability in the form of greater commission and omission errors are expected can be increased. To this end, we augmented the predicted forest disturbance map with a buffer class consisting of a two-pixel buffer (20 m) on each side of the disturbance class boundary. We selected a reference sample of 19,300 points: a simple random sample of 9,300 points from the buffer and simple random samples of 5000 from each of the undisturbed and disturbed classes. The reference sample was photointerpreted using fine resolution orthophotos (30 cm) and S2 imagery. While the estimate of the disturbed area obtained by adding the areas of pixels classified as disturbed was 41,732 ha, the estimate obtained using the unbiased stratified estimator was 27% greater at 57,717716 ha. Regarding map accuracy, we found several omission errors in the buffer (53.4%) but none (0%) in the undisturbed map class. Similarly, among the 1035 commission errors, the majority (7 4 4) were in the buffer class. The methods presented herein provide a useful tool that can be used to estimate areas of forest disturbance, which many nations must report as part of their commitment to international conventions and treaties. In addition, the information generated can support forest management, enabling the forest sector to monitor stand-replacing forest harvesting over space and time. Numéro de notice : A2022-072 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102663 En ligne : https://doi.org/10.1016/j.jag.2021.102663 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99437
in International journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102663[article]A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)
[article]
Titre : A combination of convolutional and graph neural networks for regularized road surface extraction Type de document : Article/Communication Auteurs : Jingjing Yan, Auteur ; Shunping Ji, Auteur ; Yao Wei, Auteur Année de publication : 2022 Article en page(s) : n° 4409113 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] image aérienne
[Termes IGN] jeu de données
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression
[Termes IGN] réseau neuronal de graphes
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Road surface extraction from high-resolution remote sensing images has many engineering applications; however, extracting regularized and smooth road surface maps that reach the human delineation level is a very challenging task, and substantial and time-consuming manual work is usually unavoidable. In this article, to solve this problem, we propose a novel regularized road surface extraction framework by introducing a graph neural network (GNN) for processing the road graph that is preconstructed from the easily accessible road centerlines. The proposed framework formulates the road surface extraction problem as two-sided width inference of the road graph and consists of a convolutional neural network (CNN)-based feature extractor and a GNN model for vertex attribute adjustment. The CNN extracts the high-level abstract features of each vertex in the graph as the input of the GNN and also the road boundary features that allow us to distinguish roads from the background. The GNN propagates and aggregates the features of the vertices in the graph to achieve global optimization of the regression of the regularized widths of the vertices. At the same time, a biased centerline map can also be corrected based on the width prediction result. To the best of the authors’ knowledge, this is the first study to have introduced a GNN to regularized human-level road surface extraction. The proposed method was evaluated on four diverse datasets, and the results show that the proposed method comprehensively outperforms the recent CNN-based segmentation methods and other regularization methods in the intersection over union (IoU) and smoothness score, and a visual check shows that a majority of the prediction results of the proposed method approach the human delineation level. Numéro de notice : A2022-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3151688 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3151688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100355
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 2 (February 2022) . - n° 4409113[article]Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method / Yijiang Zhao in Transactions in GIS, vol 26 n° 1 (February 2022)
[article]
Titre : Discovering transition patterns among OpenStreetMap feature classes based on the Louvain method Type de document : Article/Communication Auteurs : Yijiang Zhao, Auteur ; Wentao Yang, Auteur ; Yizhi Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 236 - 258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] Açores, archipel des
[Termes IGN] algorithme glouton
[Termes IGN] données localisées des bénévoles
[Termes IGN] étiquette
[Termes IGN] géobalise
[Termes IGN] Indiana (Etats-Unis)
[Termes IGN] OpenStreetMap
[Termes IGN] réseau routierRésumé : (auteur) Numerous studies have shown that OpenStreetMap (OSM) data can achieve high positional quality. However, the thematic attributes of OSM objects can be modified several times, which has a large impact on semantic heterogeneity. Identifying transition patterns within OSM feature classes is an important preliminary step for the tag recommendation algorithm, which can reduce the number of modifications and enhance the efficiency of OSM data updates. In this article, we propose an approach for discovering transition patterns among OSM feature classes. We first produced the transition matrix of feature classes and then developed a graph. Next, the Louvain method for community detection was utilized to cluster the feature classes. OSM data from Indiana, USA, and the Azores, Portugal, were used for our experiments. Some transition patterns were discovered: (1) many feature classes with the most transitions are the same in both datasets and most transitions occur in road-related feature classes; (2) people tend to tag general classes if they are unsure of the specific classes of tagged objects; and (3) most class transitions occurred as a result of volunteers improving the specificity and precision of feature classes. Moreover, consistently confusing concept pairs were identified. Numéro de notice : A2022-178 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12843 Date de publication en ligne : 08/10/2021 En ligne : https://doi.org/10.1111/tgis.12843 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99835
in Transactions in GIS > vol 26 n° 1 (February 2022) . - pp 236 - 258[article]European-wide forest monitoring substantiate the neccessity for a joint conservation strategy to rescue European ash species (Fraxinus spp.) / Jan-Peter George in Scientific reports, vol 12 (2022)PermalinkFive 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)PermalinkA geographically weighted artificial neural network / Julian Haguenauer in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)PermalinkGrowing stock monitoring by European National Forest Inventories: Historical origins, current methods and harmonisation / Thomas Gschwantner in Forest ecology and management, vol 505 (February-1 2022)PermalinkHow much does it take to be old? Modelling the time since the last harvesting to infer the distribution of overmature forests in France / Lucie Thompson in Diversity and distributions, vol 28 n° 2 (February 2022)PermalinkA limited number of species is sufficient to assign a vegetation plot to a forest vegetation unit / Lise Maciejewski in Applied Vegetation Science, vol 25 n° 1 (January/March 2022)PermalinkMapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data / Sébastien Dujardin in Landscape and Urban Planning, vol 218 (February 2022)PermalinkMapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)PermalinkNational implementation of the forest Europe indicators for sustainable forest management / Stefanie Linser in Forests, vol 13 n° 2 (February 2022)PermalinkPlanning of commercial thinnings using machine learning and airborne Lidar data / Tauri Arumäe in Forests, vol 13 n° 2 (February 2022)Permalink