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Recent increase in European forest harvests as based on area estimates (Ceccherini et al. 2020a) not confirmed in the French case / Nicolas Picard in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 1 (March 2021) . - n° 9 Titre : Recent increase in European forest harvests as based on area estimates (Ceccherini et al. 2020a) not confirmed in the French case Type de document : Article/Communication Auteurs : Nicolas Picard, Auteur ; Jean-Michel Leban , Auteur ; Jean-Marc Guehl, Auteur ; Erwin Dreyer, Auteur ; Olivier Bouriaud , Auteur ; Jean-Daniel Bontemps , Auteur ; Guy Landmann, Auteur ; Antoine Colin , Auteur ; Jean-Luc Peyron, Auteur ; Pascal Marty, Auteur Année de publication : 2021 Projets : 1-Pas de projet / Article en page(s) : n° 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique [Termes IGN] base de données forestières [Termes IGN] exploitation forestière [Termes IGN] France (administrative) [Termes IGN] inventaire forestier national (données France) [Termes IGN] récolte de bois [Termes IGN] série temporelle [Termes IGN] superficie [Termes IGN] tempête Klaus de 2009 [Termes IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) A recent paper by Ceccherini et al.( 2020a ) reported an abrupt increase of 30% in the French harvested forest area in 2016–2018 compared to 2004–2015. A re-analysis of their data rather led us to conclude that, when accounting for the singular effect of storm Klaus, the rate of change in harvested area depended on the change year used to separate the two periods to compare. Moreover, the comparison with data on harvested volumes from different sources brought contrasted results depending on the source. Therefore, it cannot be concluded that wood harvest increased in France in 2016–2018 compared to 2004–2015. The discrepancy between Ceccherini et al.’s data and other data on harvested volumes points out the difficulty of reconciling different approaches to estimate wood harvest at a country level. Numéro de notice : A2021-130 Affiliation des auteurs : LIF+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01030-x date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1007/s13595-021-01030-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96964 [article]

From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100 Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] base de données forestières [Termes IGN] carte de la végétation [Termes IGN] classification par forêts aléatoires [Termes IGN] couvert forestier [Termes IGN] échantillonnage [Termes IGN] France (administrative) [Termes IGN] image Sentinel-MSI [Termes IGN] mise à jour de base de données [Termes IGN] Populus (genre) [Termes IGN] série temporelle Résumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417 [article]

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Improving aboveground biomass estimates by taking into account density variations between tree components / Antoine Billard in Annals of Forest Science [en ligne], vol 77 n° 4 (December 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 4 (December 2020) . - n° 103 Titre : Improving aboveground biomass estimates by taking into account density variations between tree components Type de document : Article/Communication Auteurs : Antoine Billard, Auteur ; Rodolphe Bauer, Auteur ; Frédéric Mothe, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] allométrie [Termes IGN] base de données forestières [Termes IGN] biomasse aérienne [Termes IGN] bois de chauffage [Termes IGN] branche (arbre) [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] écorce [Termes IGN] hauteur des arbres [Termes IGN] résineux [Termes IGN] tomographie radar [Termes IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Key message: Strong density differences were observed between stem wood at 1.30 m and other tree components (stem wood, stem bark, knots, branch stumps and branches). The difference, up to 40% depending on the component, should be taken into account when estimating the biomass available for industrial uses, mainly fuelwood and wood for chemistry. Context: Basic density is a major variable in the calculation of tree biomass. However, it is usually measured on stem wood only and at breast height. Aims: The objectives of this study were to compare basic density of stem wood at 1.30 m with other tree components and assess the impact of differences on biomass. Methods: Three softwood species were studied: Abies alba Mill., Picea abies (L.) H. Karst., Pseudotsuga menziesii (Mirb.) Franco. X-Ray computed tomography was used to measure density. Results: Large differences were observed between components. Basic density of components was little influenced by tree size and stand density. Overall, bark, knot and branch biomasses were highly underestimated by using basic density measured at 1.30 m. Conclusion: Using available wood density databases mainly based on breast height measurements would lead to important biases (up to more than 40%) on biomass estimates for some tree components. Further work is necessary to complete available databases. Numéro de notice : A2020-714 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-020-00999-1 date de publication en ligne : 26/10/2020 En ligne : https://doi.org/10.1007/s13595-020-00999-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96282 [article]

Mapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 14 - 26 Titre : Mapping dead forest cover using a deep convolutional neural network and digital aerial photography Type de document : Article/Communication Auteurs : Jean-Daniel Sylvain, Auteur ; Guillaume Drolet, Auteur ; Nicolas Brown, Auteur Année de publication : 2019 Article en page(s) : pp 14 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] arbre mort [Termes IGN] base de données forestières [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couvert forestier [Termes IGN] feuillu [Termes IGN] forêt boréale [Termes IGN] image aérienne [Termes IGN] orthoimage [Termes IGN] peuplement mélangé [Termes IGN] Pinophyta [Termes IGN] Québec (Canada) [Termes IGN] santé des forêts Résumé : (Auteur) Tree mortality is an important forest ecosystem variable having uses in many applications such as forest health assessment, modelling stand dynamics and productivity, or planning wood harvesting operations. Because tree mortality is a spatially and temporally erratic process, rates and spatial patterns of tree mortality are difficult to estimate with traditional inventory methods. Remote sensing imagery has the potential to detect tree mortality at spatial scales required for accurately characterizing this process (e.g., landscape, region). Many efforts have been made in this sense, mostly using pixel- or object-based methods. In this study, we explored the potential of deep Convolutional Neural Networks (CNNs) to detect and map tree health status and functional type over entire regions. To do this, we built a database of around 290,000 photo-interpreted trees that served to extract and label image windows from 20 cm-resolution digital aerial images, for use in CNN training and evaluation. In this process, we also evaluated the effect of window size and spectral channel selection on classification accuracy, and we assessed if multiple realizations of a CNN, generated using different weight initializations, can be aggregated to provide more robust predictions. Finally, we extended our model with 5 additional classes to account for the diversity of landcovers found in our study area. When predicting tree health status only (live or dead), we obtained test accuracies of up to 94%, and up to 86% when predicting functional type only (broadleaf or needleleaf). Channel selection had a limited impact on overall classification accuracy, while window size increased the ability of the CNNs to predict plant functional type. The aggregation of multiple realizations of a CNN allowed us to avoid the selection of suboptimal models and help to remove much of the speckle effect when predicting on new aerial images. Test accuracies of plant functional type and health status were not affected in the extended model and were all above 95% for the 5 extra classes. Our results demonstrate the robustness of the CNN for between-scene variations in aerial photography and also suggest that this approach can be applied at operational level to map tree mortality across extensive territories. Numéro de notice : A2019-316 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.07.010 date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93353 [article]

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GIS Coop: networks of silvicultural trials for supporting forest management under changing environment / Ingrid Seynave in Annals of Forest Science [en ligne], vol 75 n° 2 (June 2018)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 75 n° 2 (June 2018) . - n° 48 Titre : GIS Coop: networks of silvicultural trials for supporting forest management under changing environment Titre original : Review Paper Type de document : Article/Communication Auteurs : Ingrid Seynave, Auteur ; Alain Bailly, Auteur ; Philippe Balandier, Auteur ; Jean-Daniel Bontemps , Auteur ; Priscilla Cailly, Auteur ; Thomas Cordonnier, Auteur ; Christine Deleuze, Auteur ; Jean-François Dhôte, Auteur ; Christian Ginisty, Auteur ; François Lebourgeois, Auteur ; Dominique Merzeau, Auteur ; Eric Paillassa, Auteur ; Sandrine Perret, Auteur ; Claudine Richter, Auteur ; Céline Meredieu, Auteur Année de publication : 2018 Projets : ARBRE / AgroParisTech Article en page(s) : n° 48 Note générale : Bibliographie The GIS Coop networks benefits from the financial support of the French Ministry of Agriculture and Forest since 1994. As partner of GIS Coop, AgroParisTech (formerly ENGREF), CPFA, IDF, FCBA (formerly Afocel), INRA, Irstea (formerly Cemagref), and ONF support the GIS Coop and have made available more than 175 people since 1994. The projects below also contributed to GIS Coop networks: ADAREEX (2017): RMT AFORCE, Ministère en charge des Forêts, France Bois Forêt, Labex ARBRE CoopEco (2012–2017): Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt, Office National des Forêts; E16/2012,E31/2012, E22/2015 Dolar (2014–2018): Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt (DGAL-DSF); 2014-331 et 2015-339 GPMF (2009): Conseil régional d’Aquitaine, Ministère en charge de la forêt FORBOIS2 (2015–2020): Etat, Conseil régional de Lorraine, FEDER Fortius (2010–2014): Conseil régional d’Aquitaine (convention n°14007648), DRAAF Aquitaine (ADV14R072000016, AE OSIRIS150004147365) Imprebio (2011–2013): Ministère de l’Ecologie, du Développement Durable et de l’Energie, Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt; 10-MBGD-BGF-3-CVS-081 INSENSE (2014–2016): ADEME, 1360C0088OBUP (2012): Labex ARBRE Pinaster (2015–2019): Conseil régional d’Aquitaine (16004034), DRAAF Aquitaine (ADV15R072000012), FEDER (FEDER-FSE-2014-2020 Axe 1) Sylvogène (2005–2008) : convention 06 2 90 6259): Fonds unique interministériel FUI, Conseil régional d’Aquitaine, FEDER XPSilv (2017–2018): Labex ARBRE Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes IGN] Abies alba [Termes IGN] auto-éclaircie [Termes IGN] base de données forestières [Termes IGN] données écologiques [Termes IGN] essai [Termes IGN] facteur édaphique [Termes IGN] France (administrative) [Termes IGN] gestion forestière [Termes IGN] modélisation de la forêt [Termes IGN] Pinus nigra [Termes IGN] Pinus pinaster [Termes IGN] Pseudotsuga menziesii [Termes IGN] Quercus pedunculata [Termes IGN] Quercus sessiliflora [Termes IGN] SIG participatif Résumé : (Auteur) Key message: The diversity of forest management systems and the contrasted competition level treatments applied make the experimental networks of the GIS Coop, a nationwide testing program in the field of emerging forestry topics within the framework of the ongoing global changes. Context: To understand the dynamics of forest management systems and build adapted growth models for new forestry practices, long-term experiment networks remain more crucial than ever. Aims: Two principles are at the basis of the experimental design of the networks of the Scientific Interest Group Cooperative for data on forest tree and stand growth (GIS Coop): contrasted and extreme silvicultural treatments in diverse pedoclimatic contexts. Methods: Various forest management systems are under study: regular and even-aged stands of Douglas fir, sessile and pedunculate oaks, Maritime and Laricio pines, mixed stands of sessile oak, European silver fir, and Douglas fir combined with other species. Highly contrasted stand density regimes, from open growth to self-thinning, are formalized quantitatively. Results: One hundred and eighty-five sites representing a total of 1206 plots have been set up in the last 20 years, where trees are measured regularly (every 3 to 10 years). The major outputs of these networks for research and management are the calibration/validation of growth and yield models and the drawing up of forest management guides Conclusion: The GIS Coop adapts its networks so that they can contribute to develop growth models that explicitly integrate pedoclimatic factors and thus also contribute to research on the sustainability of ecosystems under environmental and socio-economic changes. Numéro de notice : A2018-325 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0692-z date de publication en ligne : 09/04/2018 En ligne : https://doi.org/10.1007/s13595-018-0692-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90468 [article]

Within- and between-tree variation of wood density components in Pinus nigra at six sites in Portugal / Alexandra Dias in Annals of Forest Science [en ligne], vol 75 n° 2 (June 2018)  

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Contribuer à l'observation des écosystèmes forestiers [diaporama] / Fabienne Benest (2018)  

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Enjeux pour le monitoring forestier à l'échelle européenne [diaporama] / Annemarie Bastrup-Birk (2018)  

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How to enrich forest information by the analysis of the hardwood selling prices from public forests? : Case study, hardwood in Bourgogne Franche-Comte, France [diaporama] / Jean-Michel Leban (2018)  

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Interaction avec les inventaires européens : ENFIN/DIABOLO [diaporama] / Claude Vidal (2018)  

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Outil d'aide à la décision pour les territoires forestiers (exemple du Plan d’Approvisionnement Territorial du PNR des Boucles de la Seine Normande) [diaporama] / Silvère Gabet (2018)  

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Progresser dans la quantification de la biomasse forestière française (les premiers résultats du projet XyloDensMap, un projet INRA/IGN) [diaporama] / Jean-Michel Leban (2018)  

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Optimizing the bioindication of forest soil acidity, nitrogen and mineral nutrition using plant species / Paulina E. Pinto in Ecological indicators, vol 71 (December 2016)  

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La force de la mise en commun des données des partenaires : inventaire national, gestion et recherche / Christine Deleuze in Rendez-vous techniques, n° 39-40 (Hiver-printemps 2013) 

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Corylus : influence de la composition et de la structure des masses forestières sur la biodiversité / Jean-Luc Dupouey (2010)

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