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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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Titre : Cartographier l'anthropocène 2023 : Altas IGN - L'occupation des sols Type de document : Atlas/Carte Auteurs : IGN, Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2023 Importance : 85 p. Format : 31 x 21,5 cm Langues : Français (fre) Descripteur : [Termes IGN] aménagement du territoire
[Termes IGN] forêt
[Termes IGN] géoportail
[Termes IGN] jumeau numérique
[Termes IGN] parcelle agricole
[Termes IGN] plan d'eau
[Termes IGN] prévention des risquesIndex. décimale : 42.40 Histoire IGN Numéro de notice : 24113 Affiliation des auteurs : IGN (2020- ) Thématique : BIODIVERSITE/FORET/GEOMATIQUE/IMAGERIE Nature : Atlas En ligne : https://www.ign.fr/publications-de-l-ign/institut/kiosque/publications/atlas_ant [...] Format de la ressource électronique : URL Sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103619 Exemplaires(1)
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Antropocène 2023Adobe Acrobat PDF Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])
[article]
Titre : Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data Type de document : Article/Communication Auteurs : Zhuomei Huang, Auteur ; Yichao Tian, Auteur ; et al., Auteur Année de publication : 2023 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] Chine
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] mangrove
[Termes IGN] optimisation par essaim de particulesRésumé : (auteur) Blue carbon ecosystems such as mangroves are natural barriers to resisting and alleviating the impact of storm surges and extreme catastrophic weather. Accurate and efficient determination of the aboveground biomass of mangroves is of great importance for the protection and restoration of blue carbon ecosystems and their response to climate change. This study proposes a light gradient boosting model (LGBM) based on particle swarm optimization (PSO) algorithm for feature selection. We constructed and verified the proposed model using 227 quadrat datasets from a field survey and Sentinel-1 and Sentinel-2 data. The determination coefficient (R2) and root-mean-square error (RMSE) were used to evaluate the performance of the model. Compared with random forest(RF), K-nearest neighbourhood regression(KNNR), extreme gradient boosting(XGBR), LGBM, and other machine learning algorithms, the LGBM-PSO model achieves better results (R2 = 0.7807, RMSE = 24.6864 Mg·ha−1), The predicted range of mangrove biomass is 4.623–206.975 Mg·ha−1. Therefore, the use of multisource remote sensing data combined with the LGBM-PSO model can provide better prediction results of aboveground biomass of mangroves, thereby providing a new method for estimating the aboveground biomass of large-scale mangroves. Numéro de notice : A2022-621 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2102226 Date de publication en ligne : 22/07/2022 En ligne : https://doi.org/10.1080/10106049.2022.2102226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101356
in Geocarto international > vol 38 n° inconnu [01/01/2023][article]Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar / Andrew W. Whelan in Remote sensing of environment, vol 284 (January 2023)
[article]
Titre : Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar Type de document : Article/Communication Auteurs : Andrew W. Whelan, Auteur ; Jeffery B. Cannon, Auteur ; Seth W. Bigelow, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 113362 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] diagnostic foliaire
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Géorgie (Etats-Unis)
[Termes IGN] modélisation de la forêt
[Termes IGN] Pinus palustris
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] surface forestière
[Termes IGN] volume en bois
[Termes IGN] voxelRésumé : (auteur) Modeling forest attributes using lidar data has been a useful tool for forest management but the need to correlate lidar to ground-based measurements creates challenges to modeling in diverse forest landscapes. Many lidar models have been based on metrics derived from summarizations of individual lidar returns over sample plot areas, but more recently, metrics based on summarization by volumetric pixel (voxel) have shown promise to better characterize forest structure and distinguish between diverse forest types. Voxel-based metrics may improve characterization of leaf area distribution and horizontal forest structure, which could help create general models of forest attributes applicable in complex landscapes composed of many distinct forest types. We modeled wood volume in longleaf pine woodlands and associated forests to compare how area- and voxel- based lidar metrics predicted wood volume in forest type specific and general predictive models. We created four area-based and six voxel-based metrics to fit models of wood volume using a multiplicative power function. We selected models and compared metric importance using AIC and evaluated model performance using cross-validated mean prediction error. We found that one area-based metric and four voxel-based metrics consistently improved model predictions We suggest that area-based metrics alone may have limitations for characterizing complex forest structure. Area-based summarizes of lidar returns are more heavily influenced by upper canopy returns because lidar returns attenuate below the canopy. By contrast, summarizing lidar returns into a single value per voxel prior to summarization over plots homogenizes point density, giving added weight to sub-canopy returns. Thus voxel-based metrics may be more sensitive to structural variation that may not be adequately captured by area-based metrics alone. This study highlights the potential of voxel-based metrics for characterizing complex forest structure and model generalization capable of accurate forest attribute prediction across diverse forest types. Numéro de notice : A2023-016 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113362 Date de publication en ligne : 23/11/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102150
in Remote sensing of environment > vol 284 (January 2023) . - n° 113362[article]Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography / Ihor Kozak in Urban Forestry & Urban Greening, vol 79 (January 2023)
[article]
Titre : Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography Type de document : Article/Communication Auteurs : Ihor Kozak, Auteur ; Mikhail Popov, Auteur ; Igor Semko, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 127793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] biomasse aérienne
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] forêt urbaine
[Termes IGN] houppier
[Termes IGN] image hémisphérique
[Termes IGN] Leaf Area Index
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de régression
[Termes IGN] modèle numérique de terrain
[Termes IGN] photographie numérique
[Termes IGN] Pinus sylvestris
[Termes IGN] Pologne
[Termes IGN] semis de points
[Termes IGN] surface terrièreRésumé : (auteur) The article proposes methods for combining Airborne Laser Scanning (ALS) with Digital Hemispherical Photography (DHP) data required by the Urban Forest Biomass (UFB) model to predict the aboveground biomass (AGB) of Scotch pine (Pinus sylvestris L.) in urban forests of Lublin (Poland). The article also demonstrates the potential of ALS and DHP data in urban AGB estimation. ALS and Leaf Area Index (LAI) data were calculated using a voxels-vector approach based on the measurements taken at eight permanent sample plots (PSPs). The research was conducted in 2014 and the prediction was made until 2030. It was found that the determination coefficients (R2) for the Basal Area (BA) of the trees are 0.97, and the BA modeling parameters have a high correlation with those observed in the field (model efficiency (ME) 0.94). 83 % growth trajectory based on the measured BA was appropriately modeled using the UFB model (P > 0.9). The results for AGB show that the degree of fitting and accuracy are greatest for the Monte Carlo (MC) simulation technique based on ALS and DHP data (UBF with ALS and DHP) where R2 = 0.98, RMSE = 2.97 t/ha, MAE = 2.35 t/ha, rRMSE = 1.28 %, which performed better than MC simulation technique without ALS and DHP (UBF without ALS and DHP) where R2 = 0.94, RMSE = 4.58 t/ha, MAE = 3.64 t/ha, rRMSE = 3.29 %. The results indicate that the proposed method based on combining the UFB model, LiDAR and DHP allows us to improve the accuracy of the AGB prediction. Numéro de notice : A2023-023 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ufug.2022.127793 Date de publication en ligne : 23/11/2022 En ligne : https://doi.org/10.1016/j.ufug.2022.127793 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102246
in Urban Forestry & Urban Greening > vol 79 (January 2023) . - n° 127793[article]
Titre : Metsätilastollinen vuosikirja 2022 Titre original : Finnish statistical yearbook of forestry 2022 Type de document : Rapport Auteurs : Eeva Vaahtera, Éditeur scientifique Editeur : Natural Resources Institute Finland Luke Année de publication : 2023 Importance : 198 p. Langues : Finnois (fin) Anglais (eng) Descripteur : [Termes IGN] Finlande
[Termes IGN] forêt
[Termes IGN] ressources forestières
[Vedettes matières IGN] ForesterieRésumé : (éditeur) The Statistical Yearbook of Forestry compiles key annual statistics on Finland's forests, forestry and forest industry. The book also covers forest biodiversity and conservation. The final chapter presents international forest statistics. The book forms part of Finland's forest statistics system, which, by international standards, is among the best in the world. The most recent statistics in the book mainly cover the year 2021.
The Forest Statistics Yearbook has a long tradition. It has been produced in seven different decades, since the late 1960s. The content of the book has been continuously developed to meet the growing demand for up-to-date information.
The editor-in-chief is Eeva Vaahtera. The book was prepared by an expert editorial team familiar with forest statistics. The editorial team included Irma Kulju, Tuomas Niinistö, Aarre Peltola, Minna Räty, Tiina Sauvula-Seppälä, Jukka Torvelainen and Esa Uotila.Numéro de notice : 17755 Affiliation des auteurs : non IGN Autre URL associée : http://urn.fi/URN:ISBN:978-952-380-584-2 Thématique : FORET Nature : Rapport statistique DOI : sans En ligne : https://jukuri.luke.fi/bitstream/handle/10024/553167/Metsatilastollinen_vuosikir [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103170 Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)PermalinkPrescribed fire after thinning increased resistance of sub-Mediterranean pine forests to drought events and wildfires / Lena Vilà-Vilardell in Forest ecology and management, vol 527 (January-1 2023)PermalinkA simple approach to enhance the TROPOMI solar-induced chlorophyll fluorescence product by combining with canopy reflected radiation at near-infrared band / Xinjie Liu in Remote sensing of environment, vol 284 (January 2023)PermalinkTree diversity and identity modulate the growth response of thermophilous deciduous forests to climate warming / Giovanni Jacopetti in Oikos, vol 2023 n° inconnu (2023)PermalinkTree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkUsing Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study / W.D.K.V. Nandasena in Geocarto international, vol 38 n° inconnu ([01/01/2023])PermalinkAssessment of camera focal length influence on canopy reconstruction quality / Martin Denter in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)PermalinkDiscriminating pure Tamarix species and their putative hybrids using field spectrometer / Solomon G. Tesfamichael in Geocarto international, vol 37 n° 25 ([01/12/2022])PermalinkForêt amazonienne : de nouveau sous contrôle ? / Laurent Polidori in Géomètre, n° 2208 (décembre 2022)PermalinkIdentification and spatial extent of understory plant species requiring vegetation control to ensure tree regeneration in French forests / Noé Dumas in Annals of Forest Science, vol 79 n° 1 (2022)Permalink