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Le mémento inventaire forestier, édition 2021 / Institut national de l'information géographique et forestière (2012 -) (2022)
Titre : Le mémento inventaire forestier, édition 2021 Type de document : Rapport Auteurs : Institut national de l'information géographique et forestière (2012 -), Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2022 Importance : 40 p. Format : 11 x 25 cm Langues : Français (fre) Descripteur : [Termes IGN] bois mort
[Termes IGN] bois sur pied
[Termes IGN] écosystème forestier
[Termes IGN] France (administrative)
[Termes IGN] France d'outre-mer
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] ressources forestières
[Vedettes matières IGN] Inventaire forestierIndex. décimale : 48.20 Inventaire forestier Résumé : (Editeur) Le mémento de l’inventaire forestier – édition 2021 – rassemble dans 40 pages les principaux chiffres, cartes et informations sur la forêt française issus des campagnes d’inventaire 2016 à 2020 de l’IGN. Note de contenu : SURFACES FORESTIERES
La forêt en Outre-Mer
La forêt en France métropolitaine
L'augmentation de la surface forestière
Le taux de boisement
À qui la forêt appartient-elle ?
ECOSYSTEMES FORESTIERS
La santé des forêts
La diversité des peuplements
La composition des peuplements
Le bois mort sur pied
Le bois mort au sol
La répartition de quelques plantes
RESSOURCES FORESTIERES
Le bois vivant sur pied
L'augmentation de la ressource
Informations sur les principales essences
La production biologique annuelle
Les prélèvements de bois
Quelques données régionalesNuméro de notice : 17695 Affiliation des auteurs : IGN (2020- ) Thématique : FORET Nature : Rapport statistique nature-HAL : Rapport DOI : sans En ligne : https://inventaire-forestier.ign.fr/spip.php?article583= Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99508 Voir aussi
- Le mémento inventaire forestier, édition 2018 / Institut national de l'information géographique et forestière (2012 -) (2018)
- Le mémento inventaire forestier, édition 2017 / Institut national de l'information géographique et forestière (2012 -) (2017)
- Le mémento inventaire forestier, édition 2019 / Institut national de l'information géographique et forestière (2012 -) (2019)
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mémento inventaire forestier, édition 2021Adobe Acrobat PDF Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies / Guangqin Song in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)
[article]
Titre : Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies Type de document : Article/Communication Auteurs : Guangqin Song, Auteur ; Shengbiao Wu, Auteur ; Calvin K.F. Lee, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme SLIC
[Termes IGN] apprentissage profond
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] diagnostic foliaire
[Termes IGN] Enhanced vegetation index
[Termes IGN] feuille (végétation)
[Termes IGN] forêt tropicale
[Termes IGN] Panama
[Termes IGN] phénologie
[Termes IGN] photosynthèse
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelle
[Termes IGN] superpixel
[Termes IGN] variation saisonnièreRésumé : (auteur) Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams. Numéro de notice : A2022-009 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.023 Date de publication en ligne : 10/11/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99057
in ISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 19 - 33[article]Réservation
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Titre : Monocular depth estimation in forest environments Type de document : Article/Communication Auteurs : Hristina Hristova, Auteur ; Meinrad Abegg, Auteur ; Christoph Fischer, Auteur ; Nataliia Rehush, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2 Conférence : ISPRS 2022, Commission 2, 24th ISPRS international congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 1017 - 1023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage dirigé
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] image isolée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] jeu de données localisées
[Termes IGN] profondeur
[Termes IGN] vision monoculaireRésumé : (auteur) Depth estimation from a single image is a challenging task, especially inside the highly structured forest environment. In this paper, we propose a supervised deep learning model for monocular depth estimation based on forest imagery. We train our model on a new data set of forest RGB-D images that we collected using a terrestrial laser scanner. Alongside the input RGB image, our model uses a sparse depth channel as input to recover the dense depth information. The prediction accuracy of our model is significantly higher than that of state-of-the-art methods when applied in the context of forest depth estimation. Our model brings the RMSE down to 2.1 m, compared to 4 m and above for reference methods. Numéro de notice : C2022-022 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2022-1017-2022 Date de publication en ligne : 30/05/2022 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLIII-B2-2022-1017-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100848
Titre : Multi-layer modeling of dense vegetation from aerial LiDAR scans Type de document : Article/Communication Auteurs : Ekaterina Kalinicheva , Auteur ; Loïc Landrieu , Auteur ; Clément Mallet , Auteur ; Nesrine Chehata , Auteur Editeur : Computer vision foundation CVF Année de publication : 2022 Projets : 1-Pas de projet / Conférence : EarthVision 2022, Large Scale Computer Vision for Remote Sensing Imagery, workshop joint to CVPR 2022 19/06/2022 24/06/2022 New Orleans Louisiane - Etats-Unis OA Proceedings Importance : pp 1341 - 1350 Format : 21 x 30 cm Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] canopée
[Termes IGN] carte d'occupation du sol
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] étage de végétation
[Termes IGN] foresterie
[Termes IGN] maillage
[Termes IGN] parcelle forestière
[Termes IGN] reconstruction d'objet
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (auteur) The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep net- work architecture predicting for the first time both 3D point- wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://github.com/ ekalinicheva/multi_layer_vegetation. Numéro de notice : C2022-007 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers CVF Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/CVPRW56347.2022.00140 Date de publication en ligne : 25/04/2022 En ligne : https://arxiv.org/abs/2204.11620 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100509 New insights in the modeling and simulation of tree and stand level variables in Mediterranean mixed forests in the present context of climate change / Diego Rodríguez de Prado (2022)
Titre : New insights in the modeling and simulation of tree and stand level variables in Mediterranean mixed forests in the present context of climate change Type de document : Thèse/HDR Auteurs : Diego Rodríguez de Prado, Auteur ; Celia Herrero de Aza, Directeur de thèse ; Felipe Bravo Oviedo, Directeur de thèse Editeur : Valladolid [Espagne] : Université de Valladolid Année de publication : 2022 Importance : 168 p. Format : 21 x 30 cm Note générale : bibliographie
Doctoral dissertation, Valladolid UniversityLangues : Anglais (eng) Descripteur : [Termes IGN] allométrie
[Termes IGN] climat aride
[Termes IGN] croissance des arbres
[Termes IGN] Espagne
[Termes IGN] Fagus sylvatica
[Termes IGN] forêt méditerranéenne
[Termes IGN] gestion forestière adaptative
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modélisation de la forêt
[Termes IGN] peuplement mélangé
[Termes IGN] Pinus nigra
[Termes IGN] Pinus pinaster
[Termes IGN] Pinus sylvestris
[Termes IGN] puits de carbone
[Termes IGN] Quercus pyrenaica
[Vedettes matières IGN] Végétation et changement climatiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) An increase of droughts intensity and frequency episodes combined with new extreme climate events are predicted to appear in the Mediterranean Basin due to global warming. In this context, mixed forests have become a sustainable opportunity to mitigate the effects of climate change. Species mixing may lead to the provision of a greater variety of ecosystem services and products while increasing temporal stability compared to pure forests. The development of new models that explain different tree and stand level variables may be vital to better understand the structure, composition and dynamics of this type of forests. In addition, it is essential to analyze how climate may influence these variables in order to design adaptive and sustainable management guidelines for mixed forests under future climate change scenarios. In this study, we sought to advance in the modelization and simulation of different tree and stand level variables along a range of different forest and aridity conditions in Spain. To achieve that, climate-dependent models were fitted using data from the Spanish National Forest Inventory and the WorldClim databases. We focused our study on fifteen Mediterranean tree species from the Pinus, Quercus, and Fagus genus. In our first study, we analyzed how climate may potentially influence the maximum stand carrying capacity, by terms of the maximum stand carrying capacity (SDImax), for the species under study in pure stands. This variable was chosen because its importance in (1) managing density and (2) defining species mixing proportions in mixed forest stands. To do that, climate-dependent MSDR models were fitted for each species under study. 35 different climatic annual and seasonal variables (temperature, precipitation, evapotranspiration, aridity indexes) were simultaneously included into the models. In this study, climate was found to have significant influence on MSDR, and therefore on the maximum stand carrying capacity (SDImax). The best climate-dependent MSDR models indicated that climatic variables related to temperature better explained the influence of climate on MSDR. Specifically, seasonal (MXTi) and annual (MXT) maximum temperatures were the most representative climatic variables explaining changes in MSDR. Based on the selected seasonal variables, spring and summer were consistently appeared as key periods. A common trend in SDImax variation for coniferous and broadleaf species was found, with higher SDImax values negatively linked to temperature and positively linked to precipitation. This trend suggested that aridity may play a key role reducing the maximum stand 12 carrying capacity of the main Mediterranean tree species. In addition, the impact of climate on maximum stand carrying capacity was evaluated by the creation of the Q index. In general, broadleaved species presented higher values of Q indexes than coniferous species, suggesting that the maximum stand carrying capacity of the first ones would suffer more the influence of potential climate changes. Our findings highlight the importance of using specific climatic variables to better characterize how they affect MSDR. Since we saw that aridity could play a key role influencing stand level variables such as SDImax, we aimed to analyze how it may influence tree growth and tree allometry. Moreover, we aimed to analyze how species mixing effects may influence these variables on mixed forests. Thus, two more studies focused on 29 two-species Mediterranean mixtures were developed. To study the influence of aridity and species mixing on tree growth, the basal area increment within a span of five years (BAI5), was modelled based on individual tree size, stand development and other variables of site and competition. Two distance independent competition indexes were considered: total stand basal area (BA) representing size-symmetric competition, and the basal area of trees larger than the subject tree (BAL) representing size-asymmetric competition. To uncover the complex mixing effects on basal area increment at tree level, competition indexes were splitting into intraspecific and interspecific components. All possible combinations of competition structures were included and tested in the BAI models. Positive, negative or neutral mixing effects were determined by comparing the intraspecific and interspecific component of the selected models. Then, the biological interactions taking place between species were determined based on size-symmetric and sizeasymmetric competition. Finally, the influence of aridity on basal area increment was studied including the De Martonne Index into the BAI models. A common trend among mixtures was found with higher productivity in mixed than pure stands, suggesting that BAI values may increase with the increment of species diversity. Based on model parameters, a novel approach to determine potential biological interactions between species in mixed forests was also presented in this study. Competition seemed to be the most representative biological interaction in coniferconifer mixtures, since neutralism and facilitation may occur more frequently in conifer-broadleaved and broadleaved-broadleaved mixtures. Our findings also suggested that tree productivity may be significantly limited by arid conditions, excepting for Pinus halepensis and Pinus pinea [...] Note de contenu : 1- Introduction
2- Objectives
3- Data
4- Methods
5- Results
6- Discussion
ConclusionNuméro de notice : 24064 Affiliation des auteurs : non IGN Thématique : FORET Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Systemes Forestiers Durables : Valladolid : 2022 Organisme de stage : Sustainable Forest Management Research Institute (Université de Valladolid) DOI : sans En ligne : https://uvadoc.uva.es/handle/10324/55195 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102046 Planning coastal Mediterranean stone pine (Pinus pinea L.) reforestations as a green infrastructure: combining GIS techniques and statistical analysis to identify management options / Luigi Portoghesi in Annals of forest research, vol 65 n° 1 (January - June 2022)PermalinkPermalinkRegeneration of spruce - fir - beech mixed forests under climate and ungulate pressure / Mithila Unkule (2022)PermalinkA square-grid sampling support to reconcile systematicity and adaptivity in the periodic spatial survey of natural resources / Olivier Bouriaud (2022)PermalinkPermalinkThe long-term development of temperate woodland creation sites: from tree saplings to mature woodlands / Elisa Fuentes-Montemayor in Forestry, an international journal of forest research, vol 95 n° 1 (January 2022)PermalinkTowards sustainable forestry: Using a spatial Bayesian belief network to quantify trade-offs among forest-related ecosystem services / Catherine Frizzle in Journal of Environmental Management, vol 301 ([01/01/2022])PermalinkUnderstory plant community responses to widespread spruce mortality in a subalpine forest / Trevor A. Carter in Journal of vegetation science, vol 33 n° 1 (January 2022)PermalinkEstimating 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)PermalinkModeling 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)Permalink