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An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data / Van-Tho Nguyen in Annals of Forest Science, vol 78 n° 2 (June 2021)
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Titre : An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data Type de document : Article/Communication Auteurs : Van-Tho Nguyen, Auteur ; Thiéry Constant, Auteur ; Francis Colin, Auteur Année de publication : 2021 Article en page(s) : Article 32 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] détection d'anomalie
[Termes IGN] données de terrain
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] écorce
[Termes IGN] Fagus sylvatica
[Termes IGN] qualité du bois
[Termes IGN] Quercus sessiliflora
[Termes IGN] segmentation d'image
[Termes IGN] télémétrie laser terrestre
[Termes IGN] troncRésumé : (Auteur) We designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark.
Context : Ground-based light detection and ranging (LiDAR) technology describes standing trees with a high level of detail. This provides an opportunity to assess standing tree quality and to use this information in forest inventory. Assuming the availability of a very high level of detail, we could extract information about the surface defects, mainly inherited from past ramification and having a strong impact on wood quality.
Aims : Within the general framework of the development of a computing method able to detect, identify, and quantify the defects on the trunk surface described from 3D data produced by a terrestrial LiDAR, this study focuses on the relevance of the whole process for two tree species with contrasted bark roughness (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) in terms of detection, identification of the defects, and comparison with measurements performed manually on the bark surface.
Methods : First, a segmentation algorithm detected singularities on the trunk surface. Next, a Random Forests machine learning algorithm identified the most probable defect type and allowed the elimination of false detections. Finally, we estimated the position, horizontal, and vertical dimensions of each defect from 3D data, and we compared them to those observed directly on the trunk by an operator.
Results : The defects were detected and classified with a high accuracy with an average F1
score (harmonic mean of precision and recall) of 0.74. There were differences in computed and observed defect areas, but a much closer agreement for the number of defects.
Conclusion : The information about the defects present on the trunk surface measured from terrestrial LiDAR data can be used in an automated procedure for grading standing trees or roundwoods.Numéro de notice : A2021-326 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01022-3 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1007/s13595-020-01022-3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97484
in Annals of Forest Science > vol 78 n° 2 (June 2021) . - Article 32[article]Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])
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Titre : Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery Type de document : Article/Communication Auteurs : Sikdar M. M. Rasel, Auteur ; Hsing-Chung Chang, Auteur ; Timothy J. Ralph, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1075-1099 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] biomasse
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Worldview
[Termes IGN] marais salé
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] variableRésumé : (Auteur) Assessing large scale plant productivity of coastal marshes is essential to understand the resilience of these systems to climate change. Two machine learning approaches, random forest (RF) and support vector machine (SVM) regression were tested to estimate biomass of a common saltmarshes species, salt couch grass (Sporobolus virginicus). Reflectance and vegetation indices derived from 8 bands of Worldview-2 multispectral data were used for four experiments to develop the biomass model. These four experiments were, Experiment-1: 8 bands of Worldview-2 image, Experiment-2: Possible combination of all bands of Worldview-2 for Normalized Difference Vegetation Index (NDVI) type vegetation indices, Experiment-3: Combination of bands and vegetation indices, Experiment-4: Selected variables derived from experiment-3 using variable selection methods. The main objectives of this study are (i) to recommend an affordable low cost data source to predict biomass of a common saltmarshes species, (ii) to suggest a variable selection method suitable for multispectral data, (iii) to assess the performance of RF and SVM for the biomass prediction model. Cross-validation of parameter optimizations for SVM showed that optimized parameter of ɛ-SVR failed to provide a reliable prediction. Hence, ν-SVR was used for the SVM model. Among the different variable selection methods, recursive feature elimination (RFE) selected a minimum number of variables (only 4) with an RMSE of 0.211 (kg/m2). Experiment-4 (only selected bands) provided the best results for both of the machine learning regression methods, RF (R2= 0.72, RMSE= 0.166 kg/m2) and SVR (R2= 0.66, RMSE = 0.200 kg/m2) to predict biomass. When a 10-fold cross validation of the RF model was compared with a 10-fold cross validation of SVR, a significant difference (p = Numéro de notice : A2021-367 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1624988 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1624988 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97729
in Geocarto international > vol 36 n° 10 [01/06/2021] . - pp 1075-1099[article]Characterization of mixed and monospecific stands of Scots pine and Maritime pine: soil profile, physiography, climate and vegetation cover data / Daphne Lopez-Marcos in Annals of Forest Science, vol 78 n° 2 (June 2021)
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Titre : Characterization of mixed and monospecific stands of Scots pine and Maritime pine: soil profile, physiography, climate and vegetation cover data Type de document : Article/Communication Auteurs : Daphne Lopez-Marcos, Auteur ; Maria-Belen Turrion, Auteur ; Felipe Bravo, Auteur ; Carolina Martinez-Ruiz, Auteur Année de publication : 2021 Article en page(s) : Article 28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] composition d'un peuplement forestier
[Termes IGN] couvert forestier
[Termes IGN] données environnementales
[Termes IGN] échantillonnage de données
[Termes IGN] Espagne
[Termes IGN] état du sol
[Termes IGN] humidité du sol
[Termes IGN] peuplement mélangé
[Termes IGN] Pinus pinaster
[Termes IGN] Pinus sylvestris
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) This document provides valuable environmental information about a triplets’ essay of Scots pine and Maritime pine in Spain. The dataset characterizes the soil profile (physicochemical parameters of organic and mineral horizons), climate, physiography, understory and overstory. Numéro de notice : A2021-328 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01042-7 Date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.1007/s13595-021-01042-7 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97489
in Annals of Forest Science > vol 78 n° 2 (June 2021) . - Article 28[article]Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia / Peter Kitin in Annals of Forest Science, vol 78 n° 2 (June 2021)
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Titre : Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) of wood reveals distinct chemical signatures of two species of Afzelia Type de document : Article/Communication Auteurs : Peter Kitin, Auteur ; Edgard Espinoza, Auteur ; Hans Beeckman, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : Article 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] abattage (sylviculture)
[Termes IGN] Afzelia (genre)
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage non-dirigé
[Termes IGN] bois
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] identification de plantes
[Termes IGN] signature spectrale
[Termes IGN] spectrométrie
[Termes IGN] taxinomie
[Termes IGN] temps réelRésumé : (Auteur) Distinct chemical fingerprints of the wood of Afzelia pachyloba and A. bipindensis demonstrated an effective method for identifying these two commercially important species. Direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) allowed high-throughput examination of chemotypes with vast potential in taxonomic, ecological, and forensic research of wood.
Context : Afzelia is a genus of valuable tropical timber trees. Accurate identification of wood is required for the prevention of illicit timber trade as well as for certification purposes in the forest and wood products industry. For many years, particular interest has been focused on attempts to distinguish the wood of A. bipindensis Harms from A. pachyloba Harms due to substantial differences in the commercial values of these two species.
Aims : We investigated if wood chemical signatures and microscopy could identify the wood of A. bipindensis and A. pachyloba.
Methods : We used two approaches, namely metabolome profiling by direct analysis in real-time (DART) time-of-flight mass spectrometry (TOFMS) and wood microstructure by light microscopy and SEM. In all, we analyzed samples from 89 trees of A. bipindensis, and A. pachyloba.
Results : The two species could not be separated by the IAWA standard microscopic wood features. SEM analysis showed considerable variation in the morphology of vestured pits; however, this variation was not species-specific. In contrast, DART-TOFMS followed by unsupervised statistics (Discriminant Analysis of Principal Components) showed distinct metabolome signatures of the two species.
Conclusion : DART-TOFMS provides a rapid method for wood identification that can be easily applied to small heartwood samples. Time- and cost-effective classification of wood chemotypes by DART-TOFMS can have potential applications in various research questions in forestry, wood science, tree-ecophysiology, and forensics.Numéro de notice : A2021-327 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01024-1 Date de publication en ligne : 31/03/2021 En ligne : https://doi.org/10.1007/s13595-020-01024-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97488
in Annals of Forest Science > vol 78 n° 2 (June 2021) . - Article 31[article]Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)
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Titre : Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities Type de document : Article/Communication Auteurs : Jingjing Zhou, Auteur ; Ya-Hao Zhang, Auteur ; Ze-Min Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] Citrus (genre)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] feuille (végétation)
[Termes IGN] image hyperspectrale
[Termes IGN] photosynthèse
[Termes IGN] réflectance végétale
[Termes IGN] rendement agricole
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétationRésumé : (auteur) Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards. Numéro de notice : A2021-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112160 Date de publication en ligne : 31/05/2021 En ligne : https://doi.org/10.3390/rs13112160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97826
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2160[article]Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkIdentifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing / Elliott White Jr in Remote sensing of environment, vol 258 (June 2021)
PermalinkImproving tree biomass models through crown ratio patterns and incomplete data sources / Maria Menéndez-Miguélez in European Journal of Forest Research, vol 140 n° 3 (June 2021)
PermalinkIndividual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data / Haijian Liu in Remote sensing of environment, vol 258 (June 2021)
PermalinkModel-based estimation of forest canopy height and biomass in the Canadian boreal forest using radar, LiDAR, and optical remote sensing / Michael L. Benson in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
PermalinkPredicting 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)
PermalinkProvisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science, vol 78 n° 2 (June 2021)
PermalinkRapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)
PermalinkThe social drift of trees. Consequence for growth trend detection, stand dynamics, and silviculture / Hans Pretzsch in European Journal of Forest Research, vol 140 n° 3 (June 2021)
PermalinkTree height growth modelling using LiDAR-derived topography information / Milan Kobal in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)
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