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Le Parc national de forêts : des patrimoines en devenir / Pierre Clergeot in Géomètre, n° 2207 (novembre 2022)

Public [article]  inGéomètre > n° 2207 (novembre 2022) . - pp 45 - 48 Titre : Le Parc national de forêts : des patrimoines en devenir Type de document : Article/Communication Auteurs : Pierre Clergeot, Auteur Année de publication : 2022 Article en page(s) : pp 45 - 48 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Environnement [Termes IGN] développement durable [Termes IGN] feuillu [Termes IGN] gestion forestière durable [Termes IGN] parc naturel [Termes IGN] protection du patrimoine Résumé : (Auteur) Le Grenelle de l’environnement a proposé en 2007 la création d’un parc de forêts de feuillus de plaine. Douze ans plus tard, le « Parc national de forêts » a vu le jour. Le but est d’en faire une zone de protection des patrimoines, économiquement vivante, répondant aux objectifs du développement durable, qui puisse faire école en termes d’aménagement. Numéro de notice : A2022-804 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102128 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
063-2022111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Combination of Sentinel-1 and Sentinel-2 data for tree species classification in a Central European biosphere reserve / Michael Lechner in Remote sensing, vol 14 n° 11 (June-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 11 (June-1 2022) . - n° 2687 Titre : Combination of Sentinel-1 and Sentinel-2 data for tree species classification in a Central European biosphere reserve Type de document : Article/Communication Auteurs : Michael Lechner, Auteur ; Alena Dostalova, Auteur ; Markus Hollaus, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2687 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse comparative [Termes IGN] analyse harmonique [Termes IGN] Autriche [Termes IGN] biosphère [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] espèce végétale [Termes IGN] feuillu [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] nébulosité [Termes IGN] phénologie [Termes IGN] Pinophyta [Termes IGN] rapport signal sur bruit [Termes IGN] réserve forestière Résumé : (auteur) Microwave and optical imaging methods react differently to different land surface parameters and, thus, provide highly complementary information. However, the contribution of individual features from these two domains of the electromagnetic spectrum for tree species classification is still unclear. For large-scale forest assessments, it is moreover important to better understand the domain-specific limitations of the two sensor families, such as the impact of cloudiness and low signal-to-noise-ratio, respectively. In this study, seven deciduous and five coniferous tree species of the Austrian Biosphere Reserve Wienerwald (105,000 ha) were classified using Breiman’s random forest classifier, labeled with help of forest enterprise data. In nine test cases, variations of Sentinel-1 and Sentinel-2 imagery were passed to the classifier to evaluate their respective contributions. By solely using a high number of Sentinel-2 scenes well spread over the growing season, an overall accuracy of 83.2% was achieved. With ample Sentinel-2 scenes available, the additional use of Sentinel-1 data improved the results by 0.5 percentage points. This changed when only a single Sentinel-2 scene was supposedly available. In this case, the full set of Sentinel-1-derived features increased the overall accuracy on average by 4.7 percentage points. The same level of accuracy could be obtained using three Sentinel-2 scenes spread over the vegetation period. On the other hand, the sole use of Sentinel-1 including phenological indicators and additional features derived from the time series did not yield satisfactory overall classification accuracies (55.7%), as only coniferous species were well separated. Numéro de notice : A2022-540 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14112687 Date de publication en ligne : 03/06/2022 En ligne : https://doi.org/10.3390/rs14112687 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101103 [article]

Automated inventory of broadleaf tree plantations with UAS imagery / Aishwarya Chandrasekaran in Remote sensing, vol 14 n° 8 (April-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 8 (April-2 2022) . - n° 1931 Titre : Automated inventory of broadleaf tree plantations with UAS imagery Type de document : Article/Communication Auteurs : Aishwarya Chandrasekaran, Auteur ; Guofan Shao, Auteur ; Songlin Fei, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1931 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] feuillu [Termes IGN] hauteur à la base du houppier [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] Indiana (Etats-Unis) [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] orthophotoplan numérique [Termes IGN] plantation forestière [Termes IGN] R (langage) [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) With the increased availability of unmanned aerial systems (UAS) imagery, digitalized forest inventory has gained prominence in recent years. This paper presents a methodology for automated measurement of tree height and crown area in two broadleaf tree plantations of different species and ages using two different UAS platforms. Using structure from motion (SfM), we generated canopy height models (CHMs) for each broadleaf plantation in Indiana, USA. From the CHMs, we calculated individual tree parameters automatically through an open-source web tool developed using the Shiny R package and assessed the accuracy against field measurements. Our analysis shows higher tree measurement accuracy with the datasets derived from multi-rotor platform (M600) than with the fixed wing platform (Bramor). The results show that our automated method could identify individual trees (F-score > 90%) and tree biometrics (root mean square error Numéro de notice : A2022-351 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14081931 Date de publication en ligne : 16/04/2022 En ligne : https://doi.org/10.3390/rs14081931 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100539 [article]

Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 874 Titre : Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests Type de document : Article/Communication Auteurs : Chong Zhang, Auteur ; Jiawei Zhou, Auteur ; Huiwen Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 874 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] échantillonnage de données [Termes IGN] entropie [Termes IGN] estimation quantitative [Termes IGN] feuillu [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] peuplement mélangé [Termes IGN] Pinophyta [Termes IGN] segmentation d'image Résumé : (auteur) High-resolution UAV imagery paired with a convolutional neural network approach offers significant advantages in accurately measuring forestry ecosystems. Despite numerous studies existing for individual tree crown delineation, species classification, and quantity detection, the comprehensive situation in performing the above tasks simultaneously has rarely been explored, especially in mixed forests. In this study, we propose a new method for individual tree segmentation and identification based on the improved Mask R-CNN. For the optimized network, the fusion type in the feature pyramid network is modified from down-top to top-down to shorten the feature acquisition path among the different levels. Meanwhile, a boundary-weighted loss module is introduced to the cross-entropy loss function Lmask to refine the target loss. All geometric parameters (contour, the center of gravity and area) associated with canopies ultimately are extracted from the mask by a boundary segmentation algorithm. The results showed that F1-score and mAP for coniferous species were higher than 90%, and that of broadleaf species were located between 75%–85.44%. The producer’s accuracy of coniferous forests was distributed between 0.8–0.95 and that of broadleaf ranged in 0.87–0.93; user’s accuracy of coniferous was distributed between 0.81–0.84 and that of broadleaf ranged in 0.71–0.76. The total number of trees predicted was 50,041 for the entire study area, with an overall error of 5.11%. The method under study is compared with other networks including U-net and YOLOv3. Results in this study show that the improved Mask R-CNN has more advantages in broadleaf canopy segmentation and number detection. Numéro de notice : A2022-168 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14040874 Date de publication en ligne : 11/02/2022 En ligne : https://doi.org/10.3390/rs14040874 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99793 [article]

Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)  

Public [article]  inGeodetski vestnik > vol 65 n° 2 (June - August 2021) . - pp 234 - 259 Titre : Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops Type de document : Article/Communication Auteurs : Nina Kranjec, Auteur ; Mihaela Triglav Cekada, Auteur ; Milan Kobal, Auteur Année de publication : 2021 Article en page(s) : pp 234 - 259 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Acer pseudoplatanus [Termes IGN] apprentissage automatique [Termes IGN] arbre de décision [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] Fagus sylvatica [Termes IGN] feuillu [Termes IGN] figure géométrique [Termes IGN] Fraxinus excelsior [Termes IGN] houppier [Termes IGN] identification automatique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Larix decidua [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] Picea abies [Termes IGN] Pinophyta [Termes IGN] Pinus sylvestris [Termes IGN] semis de points [Termes IGN] Slovénie Résumé : (auteur) Based on the laser point clouds of 240 individual trees that were also identified in the field, we developed decision trees to distinguish deciduous and coniferous trees and individual tree species: Picea abies, Larix decidua, Pinus sylvestris, Fagus sylvatica, Acer pseudoplatanus, Fraxinus excelsior. The volume of the upper part of the tree crown (height of 3 m) and the average intensity of the laser reflections were used as explanatory variables. There were four aerial laser datasets: May 2012, September 2012, March 2013 and July 2015. We found that the combination of the volume and the average intensity of the first three laser datasets was the most reliable for predicting the selected tree species (60% model performance). A slightly poorer model performance was obtained if only the average intensity of the first three datasets was used (54% model performance). The worst model performance was given by the intensities (31 % model performance) or the volumes (21 % model performance) of dataset 4, which represents the national laser scanning of Slovenia (LSS). The best performing was the deciduous and coniferous separation, which achieved 75% and 95% success based on the test data (combination of volume and average intensity of the first three laser datasets). Using only the LSS intensities, deciduous and coniferous trees could be separated with 81% success. Numéro de notice : A2021-559 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2021.02.234-259 Date de publication en ligne : 27/05/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.02.234-259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98113 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2021021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A meta-analysis of changes in soil organic carbon stocks after afforestation with deciduous broadleaved, sempervirent broadleaved, and conifer tree species / Guolong Hou in Annals of Forest Science, vol 77 n° 4 (December 2020)  

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The crown condition of Norway spruce and occurrence of symptoms caused by Armillaria spp. in mixed stands / Petr Čermák in Journal of forest science, vol 66 n° 12 (December 2020)  

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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, vol 77 n° 3 (September 2020)  

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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)  

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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)  

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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)  

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Réflexions d’une paysagiste sur la progression des boisements spontanés dans les Alpes et les Pyrénées / Françoise Copin in Revue forestière française, vol 71 n° 4-5 (2019)  

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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)  

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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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