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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 [en ligne], vol 78 n° 2 (June 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 31 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 descripteurs IGN] abattage (sylviculture) [Termes descripteurs IGN] Afzelia (genre) [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] apprentissage non-dirigé [Termes descripteurs IGN] bois [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] identification de plantes [Termes descripteurs IGN] signature spectrale [Termes descripteurs IGN] spectrométrie [Termes descripteurs IGN] taxinomie [Termes descripteurs IGN] temps réel Résumé : (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 [article]

An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1822 - 1848 Titre : An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Changxiu Cheng, Auteur ; Raul Zurita-Milla, Auteur Année de publication : 2020 Article en page(s) : pp 1822 - 1848 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par nuées dynamiques [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taxinomie Résumé : (auteur) Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns. Numéro de notice : A2020-477 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1726922 date de publication en ligne : 16/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1726922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95624 [article]

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Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)  

Public [article]  inRemote sensing of environment > Vol 237 (February 2020) . - 13 p. Titre : Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Maylis Lopes, Auteur ; Titouan Dubo, Auteur ; Justine Rivers-Moore, Auteur ; Pierre-Louis Frison , Auteur ; Nicolas Gross, Auteur ; Annie Ouin, Auteur Année de publication : 2020 Projets : SEBIOREF / Ouin, Annie Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] biodiversité végétale [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Haute-Garonne (31) [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prairie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taxinomie Résumé : (auteur) The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. Numéro de notice : A2020-004 Affiliation des auteurs : UPEM-LaSTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.111536 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.rse.2019.111536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94296 [article]

Application of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)  

Public Titre : Application of digital image processing in automated analysis of insect leaf mines Type de document : Thèse/HDR Auteurs : Yee Man Theodora Cho, Auteur Editeur : York [Royaume-Uni] : University of York Année de publication : 2020 Importance : 202 p. Format : 21 x 30 cm Note générale : bibliographie PhD thesis, Electronic Engineering, University of York, United Kingdom Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Acer (genre) [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Castanea (genre) [Termes descripteurs IGN] classification par Perceptron multicouche [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] diagnostic foliaire [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] insecte nuisible [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] seuillage [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] taxinomie [Termes descripteurs IGN] traitement d'image Résumé : (auteur) Automated species identificationhas become a popular alternative to manual classification in the past few decades, as a result of advancement in digital image processing techniques and machine learning algorithms. This project aims to devise a new approach for the detection of leaf mines and fungal spots from digital images, and to investigate the possibility of monitoring the growth of leaf mines. Leaf-mining insects primarily belong to the orders of moths (Lepidoptera), flies (Diptera) and beetles (Coleoptera); or the suborders of sawflies (Symphyta) and wasps (Apocrita). Every spring and summer the larvae of leaf-mining insects feed on leaf tissues until maturity and vacate the mines as adults. As most species of leaf miners attack garden plants or crops, they are generally regarded as pests, despiterarely causing severe long-term detrimental effect on their host plants. Increase in human activities has led to the spread of these invasive species globally in recent years, and the demand for an effective classification system to monitor their distribution is rising consistently. Samples from three species of leaf-mining insects were included in this project: horse chestnut leaf miner (Cameraria ohridella), apple leaf miner (Lyonetia clerkella), and holly leaf miner (Phytomyza ilicis). Leaves with tar spots (Rhytisma acerinum)were also introduced as variations.The proposed method uses image processing techniques such as thresholding, conversion between colour spaces, edge detection, image segmentation,and morphological operations. This project also explores the use of machine learning algorithmsas analytical monitoring and predictive tools, using the growth of C. ohridellaleaf mines as an example. Note de contenu : 1- Introduction 2- Background 3- Digital image processing 4- Automated classification 5- Implementation 6- Data analysis 7- Conclusion Numéro de notice : 28552 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Electronic Engineering : University of York : 2020 En ligne : https://etheses.whiterose.ac.uk/27749/1/Cho_105036528_Thesis.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97414

A learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)    

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 12 (December 2019) . - pp 865 - 878 Titre : A learning approach to evaluate the quality of 3D city models Type de document : Article/Communication Auteurs : Oussama Ennafii , Auteur ; Arnaud Le Bris , Auteur ; Florent Lafarge, Auteur ; Clément Mallet , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Article en page(s) : pp 865 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Bâti-3D [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] détection d'erreur [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] modèle 3D de l'espace urbain [Termes descripteurs IGN] modèle d'erreur [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] taxinomie Résumé : (Auteur) The automatic generation of three-dimensional (3D) building models from geospatial data is now a standard procedure. An abundance of literature covers the last two decades, and several solutions are now available. However, urban areas are very complex environments. Inevitably, practitioners still have to visually assess, at a city-scale, the correctness of these models and detect frequent reconstruction errors. Such a process relies on experts and is highly time-consuming, with approximately two hours/km 2 per expert. This work proposes an approach for automatically evaluating the quality of 3D building models. Potential errors are compiled in a novel hierarchical and versatile taxonomy. This allows, for the first time, to disentangle fidelity and modeling errors, whatever the level of details of the modeled buildings. The quality of models is predicted using the geometric properties of buildings and, when available, Very High Resolution images and Digital Surface Models. A baseline of handcrafted, yet generic, features is fed into a Random Forest classifier. Both multiclass and multilabel cases are considered: due to the interdependence between classes of errors, it is possible to retrieve all errors at the same time while simply predicting correct and erroneous buildings. The proposed framework was tested on three distinct urban areas in France with more than 3000 buildings. 80%–99% F-score values are attained for the most frequent errors. For scalability purposes, the impact of the urban area composition on the error prediction was also studied, in terms of transferability, generalization, and representativeness of the classifiers. It showed the necessity of multimodal remote sensing data and mixing training samples from various cities to ensure a stability of the detection ratios, even with very limited training set sizes. Numéro de notice : A2019-569 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.12.865 date de publication en ligne : 01/12/2019 En ligne : https://doi.org/10.14358/PERS.85.12.865 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94440 [article]

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