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3D reconstruction of internal wood decay using photogrammetry and sonic tomography / Junjie Zhang in Photogrammetric record, vol 35 n° 171 (September 2020)  

Public [article]  inPhotogrammetric record > vol 35 n° 171 (September 2020) . - pp 357-374 Titre : 3D reconstruction of internal wood decay using photogrammetry and sonic tomography Type de document : Article/Communication Auteurs : Junjie Zhang, Auteur ; Kourosh Khoshelham, Auteur Année de publication : 2020 Article en page(s) : pp 357-374 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] dépérissement [Termes descripteurs IGN] hauteur des arbres [Termes descripteurs IGN] interpolation spatiale [Termes descripteurs IGN] onde acoustique [Termes descripteurs IGN] qualité du bois [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] temps de vol [Termes descripteurs IGN] tomographie [Termes descripteurs IGN] tronc [Termes descripteurs IGN] visualisation 3D Résumé : (Auteur) Knowledge of deteriorations within tree trunks is critical for arborists to conduct individual tree health assessments. Sonic tree tomography, a non‐destructive technique using sound waves, has been widely used to estimate the size and shape of internal decay based on sound wave velocity variations. However, it has commonly been applied to 2D horizontal or vertical cross sections and its accuracy is questionable due to the poor approximation of the shape of the cross section. This paper proposes an integration of close‐range photogrammetry and sonic tomography to enable accurate reconstruction of the exterior and interior of the tree trunk in 3D. The internal wood quality is represented by the spatially interpolated sound wave velocities, using the time of flight of the sound waves and the coordinates of the acoustic sensors obtained from the photogrammetric model. Experimental results show that the proposed approach provides a realistic 3D visualisation of the size, shape and location of the internal deteriorations. Numéro de notice : A2020-436 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12328 date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1111/phor.12328 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95842 [article]

Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis / T. Poblete in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 27 - 40 Titre : Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis Type de document : Article/Communication Auteurs : T. Poblete, Auteur ; C. Camino, Auteur ; P.S.A. Beck, Auteur Année de publication : 2020 Article en page(s) : pp 27 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] fluorescence [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] Italie [Termes descripteurs IGN] maladie bactérienne [Termes descripteurs IGN] maladie phytosanitaire [Termes descripteurs IGN] Olea europaea [Termes descripteurs IGN] stress hydrique [Termes descripteurs IGN] surveillance de la végétation [Termes descripteurs IGN] télédétection aérienne [Termes descripteurs IGN] traitement d'image Résumé : (auteur) Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with large-scale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar–induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from ∼80% (kappa, κ = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to ∼74% (κ = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing κ to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive. Numéro de notice : A2020-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.010 date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94745 [article]

Individual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle / Takeshi Hoshikawa in Journal of The Remote Sensing Society of Japan, vol 40 n° 1 (2020)  

Public [article]  inJournal of The Remote Sensing Society of Japan > vol 40 n° 1 (2020) . - pp 13 - 19 Titre : Individual tree detection and classification for mapping pine wilt disease using multispectral and visible color imagery acquired from unmanned aerial vehicle Type de document : Article/Communication Auteurs : Takeshi Hoshikawa, Auteur ; Kazukiyo Yamamoto, Auteur Année de publication : 2020 Article en page(s) : pp 13 - 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] maladie phytosanitaire [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Pinus (genre) [Termes descripteurs IGN] protection des forêts [Termes descripteurs IGN] régression logistique Résumé : (auteur) Pine wilt disease is one of the most destructive disease of pine forests. It is important to detect and exterminate infected trees for preservation of the forest. We demonstrated a novel method combining individual tree detection (ITD) and classification by logistic regression using unmanned aerial vehicle (UAV) images for the mapping of infected trees. In the ITD phase, 50 % and 84 % of damaged trees were automatically detected from the 3D point cloud generated from the UAV images using the local maximum filter. These rates of detection were comparable to previous studies that used UAV imagery. Subsequently, five vegetation indices calculated from multispectral and visible color (RGB) images were used. Among the vegetation indices, normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and vegetation atmospherically resistant index (VARI) were preferable explanatory variable in the logistic regression to divide damaged and undamaged trees. The accuracy of these models ranged from 98 % to 100 % and the F-measure ranged from 94 % to 100 %. The best model, the logistic regression model using VARI as the explanatory variable, was then tested using five datasets to evaluate general performance. Each model showed explicitly high accuracy ranging from 95 % to 100 %. The best accuracy when considering the ITD and classification was 84 %. To map pine wilt disease, the proposed method is suitable for practical use due to its high-efficient and low-cost. Numéro de notice : A2020-405 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.11440/rssj.40.13 date de publication en ligne : 31/01/2020 En ligne : https://doi.org/10.11440/rssj.40.13 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96090 [article]

Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)  

Public [article]  inISPRS International journal of geo-information > Vol 9 n° 1 (January 2020) Titre : Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods Type de document : Article/Communication Auteurs : Wolfgang B. Hamer, Auteur ; Tim Birr, Auteur ; Joseph-Alexander Verreet, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] diffusion spatiale [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] maladie phytosanitaire [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] temps réel Résumé : (auteur) Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted Numéro de notice : A2020-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9010044 date de publication en ligne : 15/01/2020 En ligne : https://doi.org/10.3390/ijgi9010044 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94723 [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 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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