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Use of non-destructive test methods on Irish hardwood standing trees and small-diameter round timber for prediction of mechanical properties / Daniel F. Llana in Annals of Forest Science [en ligne], vol 77 n° 3 (September 2020)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 77 n° 3 (September 2020) Titre : Use of non-destructive test methods on Irish hardwood standing trees and small-diameter round timber for prediction of mechanical properties Type de document : Article/Communication Auteurs : Daniel F. Llana, Auteur ; Ian Short, Auteur ; Annette M. Harte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] acer pseudoplatanus [Termes descripteurs IGN] acoustique [Termes descripteurs IGN] Betula pendula [Termes descripteurs IGN] betula pubescens [Termes descripteurs IGN] bois sur pied [Termes descripteurs IGN] densité du bois [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] éclaircie (sylviculture) [Termes descripteurs IGN] élagage (sylviculture) [Termes descripteurs IGN] élasticité [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] fraxinus (genre) [Termes descripteurs IGN] fraxinus excelsior [Termes descripteurs IGN] Irlande [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] résonance [Termes descripteurs IGN] vent [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: Mechanical properties of small-diameter round timber from hardwood thinnings of common alder ( Alnus glutinosa (L.) Gaertn.), European ash ( Fraxinus excelsior L.), European birch ( Betula pendula Roth. and Betula pubescens Ehrh.), and sycamore ( Acer pseudoplatanus L.) can be evaluated by non-destructive testing on either standing trees or green logs without wood density determination. Velocity differences between acoustic and resonance methods are influenced by tree species and age. Tree diameter improves the estimation of bending strength but not of stiffness. Context: There is a need for a reliable, fast, and inexpensive evaluation method to better sort hardwood thinnings according to mechanical properties for use in potential added-value applications. Aims: The estimation by non-destructive testing of mechanical properties of round small-diameter timber of four hardwood species (common alder, European ash, European birch, and sycamore). Methods: Acoustic velocity was measured in 38 standing trees and resonance velocity was recorded in green logs from these trees. The logs were then dried and tested in bending. Estimation models to predict mechanical properties from non-destructive testing measurements were developed. Results: Large differences between velocities from acoustic and resonance techniques were found. Models based on both non-destructive testing velocities together with a species factor are well correlated with bending modulus of elasticity while models including tree diameter are moderately well correlated with bending strength. Inclusion of density in the models does not improve the estimation. Conclusion: Models based on acoustic measurements on standing trees or resonance on green logs together with tree species and diameter provide reliable estimates of mechanical properties of round timber from hardwood thinnings. This methodology can be easily used for pre-sorting material in the forest. Numéro de notice : A2020-403 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00957-x date de publication en ligne : 17/06/2020 En ligne : https://doi.org/10.1007/s13595-020-00957-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95453 [article]

How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n°12 (20 - 30 March 2020) . - pp 4551 - 4569 Titre : How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study Type de document : Article/Communication Auteurs : Enrico Borgogno Mondino, Auteur ; Vanina Fissore, Auteur ; Michael J. Falkowski, Auteur ; Brian Palik, Auteur Année de publication : 2020 Article en page(s) : pp 4551 - 4569 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] auscultation topographique [Termes descripteurs IGN] diamètre des arbres [Termes descripteurs IGN] données dendrométriques [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] Minnesota (Etats-Unis) [Termes descripteurs IGN] modèle d'erreur [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] structure d'un peuplement forestier [Termes descripteurs IGN] surface forestière [Termes descripteurs IGN] télémètre laser aéroporté Résumé : (auteur) Aerial discrete return LiDAR (Light Detection And Ranging) technology (ALS – Aerial Laser Scanner) is now widely used for forest characterization due to its high accuracy in measuring vertical and horizontal forest structure. Random and systematic errors can still occur and these affect the native point cloud, ultimately degrading ALS data accuracy, especially when adopting datasets that were not natively designed for forest applications. A detailed understanding of how uncertainty of ALS data could affect the accuracy of derivable forest metrics (e.g. tree height, stem diameter, basal area) is required, looking for eventual error biases that can be possibly modelled to improve final accuracy. In this work a low-density ALS dataset, originally acquired by the State of Minnesota (USA) for non-forestry related purposes (i.e. topographic mapping), was processed attempting to characterize forest inventory parameters for the Cutfoot Sioux Experimental Forest (north-central Minnesota, USA). Since accuracy of estimates strictly depends on the applied species-specific dendrometric models a first required step was to map tree species over the forest. A rough classification, aiming at separating conifers from broadleaf, was achieved by processing a Landsat 8 OLI (Operational Land Imager) scene. ALS-derived forest metrics initially greatly overestimated those measured at the ground in 230 plots. Conversely, ALS-derived tree density was greatly underestimated. To reduce ALS uncertainty, trees belonging to the dominated plane were removed from the ground dataset, assuming that they could not properly be detected by low-density ALS measures. Consequently, MAE (Mean Absolute Error) values significantly decreased to 4.0 m for tree height and to 0.19 cm for diameter estimates. Remaining discrepancies were related to a bias affecting the native ALS point cloud, which was modelled and removed. Final MAE values were 1.32 m for tree height, 0.08 m for diameter, 8.5 m2 ha−1 for basal area, and 0.06 m for quadratic mean diameter. Specifically focusing on tree height and diameter estimates, the significance of differences between ground and ALS estimates was tested relative to the expected ‘best accuracy’. Results showed that after correction: 94.35% of tree height differences were lower than the corresponding reference value (2.86 m); 70% of tree diameter differences were lower than the corresponding reference value (4.5 cm for conifers and 6.8 cm for broadleaf). Finally, forest parameters were computed for the whole Cutfoot Sioux Experimental Forest. Main findings include: 1) all forest estimates based on a low-density ALS point cloud can be derived at plot level and not at a tree level; 2) tree height estimates obtained by low-density ALS point clouds at the plot level are highly reasonably accurate only after testing and modelling eventual error bias; 3) diameter, basal area, and quadratic mean diameter estimates have large uncertainties, suggesting the need for a higher point density and, probably, a better mapping of tree species (if possible) than achieved with a remote sensing-based approach. Numéro de notice : A2020-450 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723173 date de publication en ligne : 20/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723173 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95535 [article]

This is my spot: What are the characteristics of the trees excavated by the Black Woodpecker? A case study in two managed French forests / Camille Puverel in Forest ecology and management, vol 453 (1 December 2019)  

Public [article]  inForest ecology and management > vol 453 (1 December 2019) . - n° 117621 Titre : This is my spot: What are the characteristics of the trees excavated by the Black Woodpecker? A case study in two managed French forests Type de document : Article/Communication Auteurs : Camille Puverel, Auteur ; Anick Abourachid, Auteur ; Christine Böhmer, Auteur ; Jean-Michel Leban , Auteur ; Miroslav Svoboda, Auteur ; Yoan Paillet, Auteur Année de publication : 2019 Projets : AVINECK / Abourachid, Anick Article en page(s) : n° 117621 Note générale : bibliographie This study was funded by the research project, Avineck: “Bird'sneck, an arm for robots” (Grant number: ANR-16-CE33-0025) Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Ecologie [Termes descripteurs IGN] Fagus (genre) [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] habitat (nature) [Termes descripteurs IGN] habitat forestier [Termes descripteurs IGN] oiseau Résumé : (auteur) The Black Woodpecker (Dryocopus martius L.) is both an ecosystem engineer and an umbrella species: it has the capacity to modify its environment through cavity excavation, which in turn favors a large range of species that depend on cavities but are unable to dig them themselves (secondary cavity nesters). However, the factors driving cavity excavation by the Black woodpecker at the tree scale remain poorly known. We analyzed the characteristics of trees bearing Black Woodpecker cavities to assess the bird's local habitat requirements and their conservation potential as habitat trees. We compared the traits and characteristics of trees bearing Black Woodpecker cavities (n = 60) and control trees (n = 56) in two managed lowland broadleave-dominated forests in France. We hypothesized that: (i) Cavity-trees would have lower wood density and display more conks of fungi than control-trees; (ii) The local environment of cavity-trees would be less crowded than those of the control trees. In particular, the first branch would be higher up, and their first neighboring tree would be further away from cavity-trees compared to control-trees; (iii) Cavity-trees would display a higher number of other woodpecker cavities and more saproxylic microhabitats than the control-trees. We validated most of our hypotheses and showed that cavity trees differed significantly from their control counterparts. Black Woodpeckers excavate trees with softer wood and higher first branches in a less crowded environment, thus minimizing both the energy dedicated to cavity excavation and predation risk. Second, cavity-trees bear more microhabitats and play a complementary umbrella role than what was documented before. They also appear a good candidate for habitat-tree conservation. In terms of biodiversity-friendly management measures, it would be beneficial to favor large isolated standing trees devoid of low branches (notably beech), especially in stands dominated by other tree species. Numéro de notice : A2019-538 Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2019.117621 date de publication en ligne : 09/10/2019 En ligne : https://doi.org/10.1016/j.foreco.2019.117621 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94335 [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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre mort [Termes descripteurs IGN] base de données forestières [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] Québec (Canada) [Termes descripteurs 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 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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Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa Pillai Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)  

Public [article]  inRemote sensing > vol 11 n° 8 (August 2019) . - n° 991 Titre : Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators Type de document : Article/Communication Auteurs : Dinesh Babu Irulappa Pillai Vijayakumar , Auteur ; Jean-Pierre Renaud, Auteur ; François Morneau , Auteur ; Ronald E. McRoberts, Auteur ; Cédric Vega , Auteur Année de publication : 2019 Projets : DIABOLO / Packalen, Tuula Article en page(s) : n° 991 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] Orléans, forêt d' (Loiret) [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] Pinus pinaster [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] quercus pedunculata [Termes descripteurs IGN] quercus sessiliflora [Termes descripteurs IGN] Sologne (France) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter. Numéro de notice : A2019-382 Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11080991 date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.3390/rs11080991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93456 [article]

Comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data / Joris Ravaglia in Forests, vol 10 n° 7 (July 2019)  

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Leaf area density from airborne LiDAR: Comparing sensors and resolutions in a temperate broadleaf forest ecosystem / Aaron G. Kamoske in Forest ecology and management, vol 433 (15 February 2019)  

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Forest conversion from Norway spruce to European beech increases species richness and functional structure of aboveground macrofungal communities / Peggy Heine in Forest ecology and management, vol 432 (15 January 2019)  

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Réévaluation de la ressource et de la disponibilité en bois d’oeuvre des essences feuillues et conifères en France / Philippe Monchaux (2019)

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Detecting the competition between Moso bamboos and broad-leaved trees in mixed forests using a terrestrial laser scanner / Yingjie Yan in Forests, vol 9 n° 9 (September 2018)  

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Factors affecting forest dynamics in the Iberian Peninsula from 1987 to 2012 : The role of topography and drought / Juan José Vidal-Macua in Forest ecology and management, vol 406 (15 December 2017)  

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Total canopy transmittance estimated from small-footprint, full-waveform airborne LiDAR / Milutin Milenković in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)  

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Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany / Helge Walentowski in Annals of forest research, vol 60 n° 1 (January - June 2017)  

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Individual tree basal area increment models for broadleaved forests in Bhutan / Jigme Tenzin in Forestry, an international journal of forest research, vol 90 n° 3 (May 2017)  

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Utilisation d’un modèle numérique de hauteur en stratification des données de l’Inventaire Forestier National / Sophie Georges (2017) 

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