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Termes IGN > sciences naturelles > sciences de la vie > biologie > botanique > formation végétale > forêt
forêt
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Bois (forêts), Boisé, Espace boisé, Espace forestier, Essence forestière, Forêt et sylviculture, Groupement forestier (écologie), Massif forestier, Milieu forestier, Peuplement forestier, Région forestière Ressource forestière, Zone forestière. Campagne, Espace naturel. >> Arbre, Archéologie des forêts, Écologie des forêts, Foresterie, Paysage forestier, Politique forestière, Produit forestier, Sylviculture. Voir aussi aux noms des forêts, par ex. : Fontainebleau, Forêt de (Seine-et-Marne) ; Bayerischer Wald (Allemagne). >>Terme(s) spécifique(s) : Biomasse des forêts, Canopée, Forêt domaniale, Forêt privée, Plante des forêts, Réserve forestière, Sol forestier, Station forestière -- Typologie. Source(s) : Grand Larousse universel . - Terminologie forestière / A. Métro, 1975. Equiv. LCSH : Forests and forestry. Domaine(s) : 577, 580. Synonyme(s)paysage forestierVoir aussi |
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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
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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
Titre : Radar backscatter contribution to tropical forest disturbance monitoring Type de document : Thèse/HDR Auteurs : Bertrand Ygorra, Auteur ; Jean-Pierre Wigneron, Directeur de thèse ; Serge Riazanoff, Directeur de thèse ; Frédéric Frappart, Directeur de thèse Editeur : Bordeaux : Université de Bordeaux Année de publication : 2022 Importance : 253 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de BordeauxLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] couvert forestier
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] forêt tropicale
[Termes IGN] image Sentinel-SAR
[Termes IGN] nébulosité
[Termes IGN] télédétection en hyperfréquenceIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Earth Observations are increasingly used to monitor environmental problems. Its interests lie in the ability of sensors aboard satellites to provide information at global, regional and local scales. Optical remote sensing has shown great potential for the monitoring of forest disturbances. Until recently, deforestation monitoring systems were mainly based on remotely sensed optical images. In the intertropical latitudes, such images often face limitations of frequent cloud cover, leading to late detection or misdetections due to the low temporal availability of new images uncontaminated by clouds. In tropical humid forests, regrowth can close canopy gaps between two non-cloud-contaminated optical images used for detection.New SAR (Synthetic Aperture Radar) systems have opened new perspectives for forest disturbance monitoring in tropical humid forests (Sentinel-1, PALSAR-2). These active sensors penetrate the clouds. The availability of Sentinel-1 C-band images at high spatial and temporal resolutions makes it a potential substitute of optical systems for monitoring disturbances in forest covers.This work is articulated around three parts. The first part consists in the development of a new change detection method for monitoring disturbances in forest cover, based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 Ground-Range Detected (GRD) dual polarization (VV, VH) images obtained in a legal forest concession near Kisangani in the Democratic Republic of the Congo. The results from VV and VH polarization were intersected in VV x VH result map, and a spatial recombination of a high Critical Threshold (Tc) with a low critical threshold was performed. The second part of this work is to develop a multiple-breakpoints version of the CuSum cross-Tc called ReCuSum to further enhance the ability to monitor changes in forest cover. The development was made by applying the CuSum cross-Tc over a time-series in an iterative manner, in the State of Parà, Brazilian Amazon. The third axis of this thesis is to develop a Near-Real-Time (NRT) version of the CuSum cross-Tc and to compare it with the state-of-the-art NRT algorithms (RADD, JJ-FAST GLAD, DETER-B, DETER-R). Note de contenu :
Chapter 1. General introduction
1.1. Introduction
1.2. Thesis objectives and outline
Chapter 2. Radar remote sensing
2.1. The RADAR technique
2.2. Instrumental parameters
2.3. Scattering mechanisms
2.4. Synthetic Aperture Radar
2.5. Sentinel-1
Chapter 3. Methods for monitoring forest cover change using spaceborne SAR sensors
3.1. Introduction
3.2. Publication
3.3. Contribution and perspectives
Chapter 4. Monitoring forest disturbances from Sentinel-1 time-series: a CuSum?based approach
4.1. Introduction
4.2. Publication
4.3. Conference note: IGARSS 2021
4.4. Contribution to this work and perspectives in the PhD course
Chapter 5. Multiple breakpoints Evolution of the cross-Tc CuSum: ReCuSum
5.1. Introduction
5.2. Publication
5.3. Conference note: IGARSS 2022
5.4. Contribution to this work and perspective
Chapter 6. Development of the CuSum cross-Tc as an NRT algorithm
6.1. Introduction
6.2. Publication
6.3. Contribution and perspectives
Chapter 7. Conclusion and perspectives
7.1. Conclusion
7.2. PerspectivesNuméro de notice : 26964 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Physique de l’environnement : Bordeaux : 2022 Organisme de stage : INRAE nature-HAL : Thèse DOI : sans Date de publication en ligne : 16/02/2023 En ligne : https://theses.hal.science/tel-03991973v1/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103001 Regeneration of spruce - fir - beech mixed forests under climate and ungulate pressure / Mithila Unkule (2022)PermalinkPermalinkLa situation des forêts du monde 2022 : Des solutions forestières pour une relance verte et des économies inclusives, résilientes et durables / Organisation des Nations Unies pour l'alimentation et l'agriculture (Rome, Italie) (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)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)PermalinkVegetation changes in the understory of nitrogen-sensitive temperate forests over the past 70 years / Marina Roth in Forest ecology and management, vol 503 (January-1 2022)PermalinkPermalinkMapping temperate forest tree species using dense Sentinel-2 time series / Jan Hemmerling in Remote sensing of environment, vol 267 (December-15 2021)PermalinkAssessing 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)Permalink