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AngiospermeSynonyme(s)plante à fleurs magnoliophyteVoir aussi |
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A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)
[article]
Titre : A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; José Marcato Junior, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 97 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Brésil
[Termes IGN] carte de confiance
[Termes IGN] Citrus (genre)
[Termes IGN] détection d'arbres
[Termes IGN] géolocalisation
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] inventaire de la végétation
[Termes IGN] réseau neuronal convolutif
[Termes IGN] vergerRésumé : (Auteur) Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R2, MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees. Numéro de notice : A2020-045 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.010 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94525
in ISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 97 - 106[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt The effects of different combinations of simulated climate change-related stressors on juveniles of seven forest tree species grown as mono-species and mixed cultures / Alfas Pliüra in Baltic forestry, vol 26 n° 1 ([01/02/2020])
[article]
Titre : The effects of different combinations of simulated climate change-related stressors on juveniles of seven forest tree species grown as mono-species and mixed cultures Type de document : Article/Communication Auteurs : Alfas Pliüra, Auteur ; Gintare Bajerkeviciene, Auteur ; Juozas Labokas, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Alnus glutinosa
[Termes IGN] Betula pendula
[Termes IGN] biomasse
[Termes IGN] croissance des arbres
[Termes IGN] dioxyde de carbone
[Termes IGN] écophysiologie
[Termes IGN] écosystème forestier
[Termes IGN] Fraxinus excelsior
[Termes IGN] Leaf Area Index
[Termes IGN] Lituanie
[Termes IGN] peuplement mélangé
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] Populus tremula
[Termes IGN] Quercus pedunculata
[Termes IGN] sécheresse
[Termes IGN] stress hydrique
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) The aim of the study was to assess changes in performance and competition for light of juveniles of seven forest tree species, Pinus sylvestris, Picea abies, Betula pendula, Alnus glutinosa, Populus tremula, Quercus robur and Fraxinus excelsior, grown in mono-species and mixed cultures with isolated potted roots under the impact of different combinations of climate change-related stressors, simulated in a phytotron under the elevated CO2 concentration during one growing season, as follows: i) heat + elevated humidity (HW); ii) heat + frost +
drought (HFD); iii) heat + elevated humidity + increased UV-B radiation doses + elevated ozone concentration (HWUO); and iv) heat + frost + drought + increased UV-B radiation doses + elevated ozone concentration (HFDUO). For the mixed cultures, three typical species’ mixtures were used: i) P. sylvestris, B. pendula and P. abies, ii) P. abies, B. pendula and Q. robur and iii) F. excelsior, A. glutinosa and P. tremula. For the control, the same material was grown outside the phytotron in ambient conditions. Analysis of variance (ANOVA) revealed that the effects of the complex treatments, species and species by treatment interactions
were highly significant in most of the biomass, growth, physiological and biochemical traits studied. Pattern of species culture had highly significant effect on physiological and biochemical traits (except for H2O2 concentration); meanwhile it was of low significance for biomass and growth traits. Pattern of species culture by treatment interaction was highly significant in all traits, suggesting that the effects of the applied complex treatments vary depending on the pattern of species culture. Under the hot wet conditions the highest stem volume index, tree biomass, and growth were observed in deciduous P. tremula, A. glutinosa and B. pendula with more clearly pronounced differences in performance between different patterns of species cultures than in ambient conditions showing that the enhanced growth conditions facilitate revealing the potential and specific requirements of the fast-growers. P. abies in all treatments had lower stem volume index and tree biomass than in ambient conditions with no significant differences between the patterns of species culture, indicating that it suffered irrespectively of light availability in different cultures. The differences between performances of most tree species in mono- and mixed cultures in HFD treatment were rather small and nonsignificant. A complex HWUO treatment caused further reduction in tree biomass in all species and culture patterns except for mono-species cultures of A. glutinosa and B. pendula. The most complex HFDUO treatment had the strongest negative effect on biomass of almost all tree species compared to that observed in HW treatment, except for Q. robur and P. sylvestris which biomass and height increments remained higher than those in ambient conditions. This was due to relatively high drought tolerance and compensatory effects of the increased CO2 concentration and temperature. Physiological and biochemical responses of species in different patterns of species cultures across treatments were very variable although often did not reflect in the effects on growth and biomass traits. The observed changes in performance of different tree species in different patterns of species cultures under various complex treatments allowed inferring that climate change may condition certain changes in competitiveness of some tree species resulting in atypical ecological successions of species and forest ecosystemsNuméro de notice : A2020-595 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.46490/BF326 Date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.46490/BF326 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95888
in Baltic forestry > vol 26 n° 1 [01/02/2020] . - 14 p.[article]Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering / Shangpeng Sun in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)
[article]
Titre : Three-dimensional photogrammetric mapping of cotton bolls in situ based on point cloud segmentation and clustering Type de document : Article/Communication Auteurs : Shangpeng Sun, Auteur ; Changying Li, Auteur ; Peng Wah Chee, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 195 - 207 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] cartographie 3D
[Termes IGN] classification basée sur les régions
[Termes IGN] distribution spatiale
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] gestion de production
[Termes IGN] Gossypium (genre)
[Termes IGN] phénologie
[Termes IGN] rendement agricole
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Three-dimensional high throughput plant phenotyping techniques provide an opportunity to measure plant organ-level traits which can be highly useful to plant breeders. The number and locations of cotton bolls, which are the fruit of cotton plants and an important component of fiber yield, are arguably among the most important phenotypic traits but are complex to quantify manually. Hence, there is a need for effective and efficient cotton boll phenotyping solutions to support breeding research and monitor the crop yield leading to better production management systems. We developed a novel methodology for 3D cotton boll mapping within a plot in situ. Point clouds were reconstructed from multi-view images using the structure from motion algorithm. The method used a region-based classification algorithm that successfully accounted for noise due to sunlight. The developed density-based clustering method could estimate boll counts for this situation, in which bolls were in direct contact with other bolls. By applying the method to point clouds from 30 plots of cotton plants, boll counts, boll volume and position data were derived. The average accuracy of boll counting was up to 90% and the R2 values between fiber yield and boll number, as well as fiber yield and boll volume were 0.87 and 0.66, respectively. The 3D boll spatial distribution could also be analyzed using this method. This method, which was low-cost and provided improved site-specific data on cotton bolls, can also be applied to other plant/fruit mapping analysis after some modification. Numéro de notice : A2020-048 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.011 Date de publication en ligne : 25/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94561
in ISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 195 - 207[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey / Alkan Günlü in Geocarto international, Vol 35 n° 1 ([02/01/2020])
[article]
Titre : Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; Ilker Erkanli, Auteur Année de publication : 2020 Article en page(s) : pp 17 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] biomasse aérienne
[Termes IGN] Fagus (genre)
[Termes IGN] image ALOS-PALSAR
[Termes IGN] peuplement forestier
[Termes IGN] puits de carbone
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificiel
[Termes IGN] TurquieRésumé : (auteur) The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values. Numéro de notice : A2020-017 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1499817 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1499817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94410
in Geocarto international > Vol 35 n° 1 [02/01/2020] . - pp 17 - 28[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020011 RAB Livre Centre de documentation En réserve L003 Disponible Application of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)
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 KingdomLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Acer (genre)
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] Castanea (genre)
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] détection de contours
[Termes IGN] diagnostic foliaire
[Termes IGN] image hyperspectrale
[Termes IGN] insecte nuisible
[Termes IGN] modèle de simulation
[Termes IGN] segmentation d'image
[Termes IGN] seuillage
[Termes IGN] surveillance de la végétation
[Termes IGN] taxinomie
[Termes IGN] traitement d'imageRésumé : (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- ConclusionNuméro 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 PermalinkC band radar crops monitoring at high temporal frequency: first results of the MOCTAR campaign / Pierre-Louis Frison (2020)PermalinkClassification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (January 2020)PermalinkDécouverte d'une nouvelle plante vasculaire, Arabis parvula (Brassicaceae) en France continentale / Matthieu Charrier in Journal de botanique, n° 89 (2020)PermalinkDistribution spatiale et dynamique de la population de palmiers rôniers, Borassus aethiopum Mart., par approche de la télédétection et du Système d’Information Géographique (SIG) de la réserve de Lamto (Centre de la Côte d’Ivoire) / Kouakou Guy-Casimir Douffi (2020)PermalinkDynamique spontanée post-tempête de la végétation forestière en contexte de changement climatique / Lucie Dietz (2020)PermalinkEtat des lieux en 2018 du site littoral très dégradé de Capu Laurasu (Propriano, Corse) avant sa réhabilitation par le conservatoire du littoral / Guilhan Paradis in Evaxiana, n° 7 (2020)PermalinkGuide de gestion des crises sanitaires en forêt / Louise Brunier (2020)PermalinkTemporal decorrelation at C- and L-band over olive tree plantations: first insights from the Marocscat campaigns / Ludovic Villard (2020)PermalinkThis 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)Permalink