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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])
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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)
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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 Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks / Angel Bueno in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)
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Titre : Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks Type de document : Article/Communication Auteurs : Angel Bueno, Auteur ; Carmen Benitez, Auteur ; Silvio De Angelis, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] apprentissage profond
[Termes IGN] classification bayesienne
[Termes IGN] classification par réseau neuronal
[Termes IGN] forme d'onde
[Termes IGN] incertitude des données
[Termes IGN] réseau bayesien
[Termes IGN] réseau neuronal profond
[Termes IGN] Russie
[Termes IGN] séisme
[Termes IGN] sismologie
[Termes IGN] surveillance géologique
[Termes IGN] volcanologie
[Termes IGN] Washington (Etats-Unis ; état)Résumé : (auteur) Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios. Numéro de notice : A2020-094 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 Date de publication en ligne : 07/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94657
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp[article]Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)
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Titre : Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods Type de document : Article/Communication Auteurs : Liheng Peng, Auteur ; Kai Liu, Auteur ; Jingjing Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 813 - 838 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] boosting adapté
[Termes IGN] Chine, mer de
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] écosystème
[Termes IGN] extraction de la végétation
[Termes IGN] île
[Termes IGN] image Gaofen
[Termes IGN] image RapidEye
[Termes IGN] image satellite
[Termes IGN] mangrove
[Termes IGN] modèle numérique de surface
[Termes IGN] précision de la classification
[Termes IGN] Rotation Forest classificationRésumé : (auteur) Mangrove forests are important constitutions for sustainable development of coastal ecosystems, and they are often mapped and monitored with remote sensing approaches. Satellite images allow detailed studies of the distribution and composition of mangrove forests, and therefore facilitate the management and conservation of the ecosystems. The combination of multiple types of satellite images with different spatial and spectral resolutions is helpful in mangrove forests extraction and mangrove species discrimination as it reduces sampling workload and increases classification accuracies. In this study, the 1.0-m-resolution Gaofen-2 (GF-2) and the 5.0-m-resolution RapidEye-4 (RE-4) satellite images, acquired in February 2017 and November 2016 respectively, were used with ensemble machine-learning and object-oriented methods for mangroves mapping at both the community and species levels of the Qi’ao Island, Zhuhai, China. First, the mangroves on the island were segmented from the GF-2 image on a large scale, and then they were extracted combining with their digital elevation model (DEM) data. Second, the GF-2 image was further processed on a fine scale, in which object-oriented features from both the GF-2 and RE-4 images were extracted for each mangrove species. Third, it is followed by the mangrove species classification process which involves three ensemble machine-learning methods: the adaptive boosting (AdaBoost), the random forest (RF) and the rotation forest (RoF). These three methods employed a classification and regression tree (CART) as the base classifier. The results show that the overall accuracy (OA) of mangrove area extraction on the Qi’ao Island with the auxiliary data, DEM, achieves 98.76% (Kappa coefficient (κ) = 0.9289). The features extracted by the GF-2 and RE-4 images were shown to be beneficial for mangrove species discrimination. A maximum improvement in the OA of approximately 8% and a κκ of approximately 0.10 were achieved when employing RoF (OA = 92.01%, κ = 0.9016). Ensemble-learning methods can significantly improve the classification accuracy of CART, and the use of a bagging scheme (RF and RoF) is shown as a better way to map mangrove species than adaptive boosting (AdaBoost). In addition, RoF performed well in mangrove species classification but it was not as robust as the RF, whose average OA and κκ were 80.59% and 0.7608, respectively, while the RoF’s were 77.45% and 0.7214, respectively, in the 10-fold cross-validation. Numéro de notice : A2020-212 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1648907 Date de publication en ligne : 30/07/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648907 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94897
in International Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 813 - 838[article]Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model / Xiaoping Wang in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)
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Titre : Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model Type de document : Article/Communication Auteurs : Xiaoping Wang, Auteur ; Fei Zhang, Auteur ; Hsiang-Te Kung, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 953 - 973 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme de filtrage
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] état du sol
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Sentinel-MSI
[Termes IGN] sel
[Termes IGN] sol salin
[Termes IGN] zone sècheRésumé : (auteur) The remote sensing information on the extraction method is of great importance to improve the accuracy and efficiency of soil salinization information. The objective of this study is to develop remote sensing extraction techniques to improve soil salinization maps. The following procedures were used in this study: (1) developed a fractional-order algorithm-based methodology of filter from high-resolution remote sensing imagery (Sentinel-2 MSI); (2) investigated the changing trend of image under different order filters; and (3) used a grid-search algorithm-support vector machines (GS-SVM) classification to employ extraction information of soil salinization. The results showed that the Fractional-order filter method outperformed the integer derivative in extracted information of soil salinization. In comparison of the classification accuracy between fractional-order processing algorithm and integer-order image processing algorithm, the fractional order has improved remarkably. The optimal classification model was 0.6 order, 0.8 order, 1.4 order, 1.6 order, and 1.8 order models. The overall accuracy and kappa coefficient (κ) of these models are 91.90% and 0.90, respectively. Analysing and comparing between soil salt index and filtering algorithm (1.2 order), the researchers found that the classification results of the two methods are similar. In general, this method can successfully extract soil salinization information in dry regions. Numéro de notice : A2020-213 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1654142 Date de publication en ligne : 14/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1654142 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94898
in International Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 953 - 973[article]A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images / Xiaohui Ding in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)
PermalinkSpatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland / Paweł Cybulski in Journal of maps, vol 16 n° 1 ([02/01/2020])
Permalink3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)
PermalinkAdvanced GNSS tropospheric products for monitoring severe weather events and climate, ch. 5. Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3) / Olivier Bock (2020)
PermalinkPermalinkAn indoor spatial accessible area generation approach considering distance constraints / Lina Yang in Annals of GIS, Vol 26 n° 1 (January 2020)
PermalinkPermalinkApplying iterative method to solving high-order terms of seafloor topography / Diao Fan in Marine geodesy, Vol 43 n° 1 (January 2020)
PermalinkArctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products / Mengmeng Li in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)
PermalinkAssessing the quality of ionospheric models through GNSS positioning error: methodology and results / Adria Rovira-Garcia in GPS solutions, vol 24 n° 1 (January 2020)
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