Descripteur
Termes IGN > sciences naturelles > sciences de la vie > biologie > botanique > phytobiologie > phénologie
phénologie |
Documents disponibles dans cette catégorie (83)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Tris disponibles](./images/orderby_az.gif)
Etendre la recherche sur niveau(x) vers le bas
CNN-based tree species classification using high resolution RGB image data from automated UAV observations / Sebastian Egli in Remote sensing, vol 12 n° 23 (December-2 2020)
![]()
[article]
Titre : CNN-based tree species classification using high resolution RGB image data from automated UAV observations Type de document : Article/Communication Auteurs : Sebastian Egli, Auteur ; Martin Höpke, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre (flore)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'arbres
[Termes IGN] espèce végétale
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] phénologieRésumé : (auteur) Data on the distribution of tree species are often requested by forest managers, inventory agencies, foresters as well as private and municipal forest owners. However, the automated detection of tree species based on passive remote sensing data from aerial surveys is still not sufficiently developed to achieve reliable results independent of the phenological stage, time of day, season, tree vitality and prevailing atmospheric conditions. Here, we introduce a novel tree species classification approach based on high resolution RGB image data gathered during automated UAV flights that overcomes these insufficiencies. For the classification task, a computationally lightweight convolutional neural network (CNN) was designed. We show that with the chosen CNN model architecture, average classification accuracies of 92% can be reached independently of the illumination conditions and the phenological stages of four different tree species. We also show that a minimal ground sampling density of 1.6 cm/px is needed for the classification model to be able to make use of the spatial-structural information in the data. Finally, to demonstrate the applicability of the presented approach to derive spatially explicit tree species information, a gridded product is generated that yields an average classification accuracy of 88%. Numéro de notice : A2020-820 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12233892 Date de publication en ligne : 27/11/2020 En ligne : https://doi.org/10.3390/rs12233892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97239
in Remote sensing > vol 12 n° 23 (December-2 2020)[article]A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)
![]()
[article]
Titre : A novel intelligent classification method for urban green space based on high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhiyu Xu, Auteur ; Yi Zhou, Auteur ; Shixin Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3845 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace vert
[Termes IGN] image à haute résolution
[Termes IGN] image Gaofen
[Termes IGN] milieu urbain
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pékin (Chine)
[Termes IGN] phénologie
[Termes IGN] précision de la classification
[Termes IGN] urbanismeRésumé : (auteur) The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. Numéro de notice : A2020-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223845 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/rs12223845 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96565
in Remote sensing > vol 12 n° 22 (December-1 2020) . - n° 3845[article]Spatio-temporal evolution, future trend and phenology regularity of net primary productivity of forests in Northeast China / Chunli Wang in Remote sensing, vol 12 n° 21 (November 2020)
![]()
[article]
Titre : Spatio-temporal evolution, future trend and phenology regularity of net primary productivity of forests in Northeast China Type de document : Article/Communication Auteurs : Chunli Wang, Auteur ; Qun’Ou Jiang, Auteur ; Xiangzheng Deng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3670 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] analyse spatio-temporelle
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] croissance des arbres
[Termes IGN] développement durable
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] production primaire nette
[Termes IGN] variation saisonnière
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Net Primary Productivity (NPP) is one of the significant indicators to measure environmental changes; thus, the relevant study of NPP in Northeast China, Asia, is essential to climate changes and ecological sustainable development. Based on the Global Production Efficiency (GLO-PEM) model, this study firstly estimated the NPP in Northeast China, from 2001 to 2019, and then analyzed its spatio-temporal evolution, future changing trend and phenology regularity. Over the years, the NPP of different forests type in Northeast China showed a gradual increasing trend. Compared with other different time stages, the high-value NPP (700–1300 gC·m−2·a−1) in Changbai Mountain, from 2017 to 2019, is more widely distributed. For instance, the NPP has an increasing rate of 6.92% compared to the stage of 2011–2015. Additionally, there was a significant advance at the start of the vegetation growth season (SOS), and a lag at the end of the vegetation growth season (EOS), from 2001 to 2019. Thus, the whole growth period of forests in Northeast China became prolonged with the change of phenology. Moreover, analysis on the sustainability of NPP in the future indicates that the reverse direction feature of NPP change will be slightly stronger than the co-directional feature, meaning that about 30.68% of the study area will switch from improvement to degradation. To conclude, these above studies could provide an important reference for the sustainable development of forests in Northeast China. Numéro de notice : A2020-719 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213670 Date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.3390/rs12213670 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96308
in Remote sensing > vol 12 n° 21 (November 2020) . - n° 3670[article]Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers / Jian-Guo Huang in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 34 (August 2020)
![]()
[article]
Titre : Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers Type de document : Article/Communication Auteurs : Jian-Guo Huang, Auteur ; Qianqian Ma, Auteur ; Sergio Rossi, Auteur ; Franco Biondi, Auteur ; Annie Deslauriers, Auteur ; Patrick Fonti, Auteur ; Eryuan Liang, Auteur ; Harri Mäkinen, Auteur ; et al., Auteur ; Henri E. Cuny , Auteur ; et al., Auteur
Année de publication : 2020 Projets : ARBRE / AgroParisTech (2007 -) Article en page(s) : pp 20645 - 20652 Note générale : bibliographie
This work was funded by the National Natural Science Foundation of China (Grants 41861124001, 41661144007, and 31971499), the International Collaborative Key Project of the Chinese Academy of Sciences (CAS) (Grant GJHZ1752), GuangdongNatural Science Foundation (Grant 2019B121202007), and CAS President’s International Fellowship Initiative (Grant 2019VBA0049). Other funding agencies included the Austrian Science Fund (Grant P22280-B16; GrantP25643-B16), Consortium de Recherche sur la Forêt Boréale Commerciale, Fonds de Recherche sur la Nature et les Technologies du Québec, Forêt d’Enseignement et de Recherche Simon couche, Natural Sciences and Engineering Research Council of Canada, Slovenian Research Agency (Young Researchers’ Program, Programs P4-0015 and P4-0107, and Project Z4-7318), Italian Ministry of Education, University and Research–PRIN 2002(Grant 2002075152) and 2005 (Grant 2005072877), Swiss National Science Foundation (Projects INTEGRAL-121859 and LOTFOR-150205), French National Research Agency (ANR) as part of the “Investissements d’Avenir” program (Grant ANR-11-LABX-0002-01, Laboratory of Excellence for Advanced Research on the Biology of Tree and Forest Ecosystems), Academy of Finland (Grants 250299, 257641, and 265504), National Natural Science Foundation of China (Grant 41525001), Grant Agency of Czech Republic (Grant P504/11/P557), and Provincia Autonoma di Trento (Project “SOFIE 2,”3012/2007). F.B. was supported, in part, by the National Science Foundation under Grant AGS-P2C2-1903561. The cooperation among authors was supported by the European Union Cooperation in Science and Technology Action FP1106STReES.Langues : Anglais (eng) Descripteur : [Termes IGN] formation du bois
[Termes IGN] hémisphère Nord
[Termes IGN] phénologie
[Termes IGN] Pinophyta
[Termes IGN] puits de carbone
[Termes IGN] température au sol
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Wood formation consumes around 15% of the anthropogenic CO2 emissions per year and plays a critical role in long-term sequestration of carbon on Earth. However, the exogenous factors driving wood formation onset and the underlying cellular mechanisms are still poorly understood and quantified, and this hampers an effective assessment of terrestrial forest productivity and carbon budget under global warming. Here, we used an extensive collection of unique datasets of weekly xylem tissue formation (wood formation) from 21 coniferous species across the Northern Hemisphere (latitudes 23 to 67°N) to present a quantitative demonstration that the onset of wood formation in Northern Hemisphere conifers is primarily driven by photoperiod and mean annual temperature (MAT), and only secondarily by spring forcing, winter chilling, and moisture availability. Photoperiod interacts with MAT and plays the dominant role in regulating the onset of secondary meristem growth, contrary to its as-yet-unquantified role in affecting the springtime phenology of primary meristems. The unique relationships between exogenous factors and wood formation could help to predict how forest ecosystems respond and adapt to climate warming and could provide a better understanding of the feedback occurring between vegetation and climate that is mediated by phenology. Our study quantifies the role of major environmental drivers for incorporation into state-of-the-art Earth system models (ESMs), thereby providing an improved assessment of long-term and high-resolution observations of biogeochemical cycles across terrestrial biomes. Numéro de notice : A2020-329 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1073/pnas.2007058117 En ligne : https://doi.org/10.1073/pnas.2007058117 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96865
in Proceedings of the National Academy of Sciences of the United States of America PNAS > vol 117 n° 34 (August 2020) . - pp 20645 - 20652[article]Detecting abandoned farmland using harmonic analysis and machine learning / Heeyeun Yoon in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
![]()
[article]
Titre : Detecting abandoned farmland using harmonic analysis and machine learning Type de document : Article/Communication Auteurs : Heeyeun Yoon, Auteur ; Soyoun Kim, Auteur Année de publication : 2020 Article en page(s) : pp 201 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse harmonique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Corée du sud
[Termes IGN] gestion des ressources
[Termes IGN] inventaire
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Normalized Difference Water Index
[Termes IGN] phénologie
[Termes IGN] production agricole
[Termes IGN] Soil Adjusted Vegetation Index
[Termes IGN] surface cultivéeRésumé : (auteur) It is critical to inventory abandoned farmland soon after it is generated, to better manage agricultural resources and to prevent negative consequences that would otherwise follow. This study aims to distinguish abandoned farmlands from active croplands—rice paddy and agricultural fields—by discerning the phenological trajectories over a short-term period of three years (Jan. 2016 to Dec. 2018) in Gwanyang City in South Korea. For Support Vector Machine (SVM) classification, we fully utilized parameters derived from harmonic analyses of the three vegetation indices (VIs: NDVI, NDWI, and SAVI) extracted from Sentinel-2A imagery. The harmonic analyses proved that higher-order sinusoid components produced better fitting to explain the trajectory of the VIs—the maximum adjusted was 95.23%—and the multiple VIs diversified the attributes for the classifications. Consequently, the higher-order harmonic components and the additional VIs increased the accuracy when used in SVM classification. The best performing classification was achieved with a composite of harmonic terms derived from the three VIs, yielding overall accuracy of 90.72%, Kappa index of 0.858, and user’s accuracy for abandoned farmland of 93.40%. The proposed method here would greatly improve the process of detecting abandoned farmland, despite a relatively short observation period, and enable a rapid response to the occurrence of abandonment. Numéro de notice : A2020-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.021 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95243
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 201 - 212[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Modeling strawberry biomass and leaf area using object-based analysis of high-resolution images / Zhen Guan in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)
PermalinkAn original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)
PermalinkThree-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)
PermalinkCartographie des essences forestières à partir de séries temporelles d’images satellitaires à hautes résolutions : stabilité des prédictions, autocorrélation spatiale et cohérence avec la phénologie observée in situ / Nicolas Karasiak (2020)
PermalinkNovel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images / Zhi Yong Lv in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
PermalinkIncreasing temperatures over an 18-year period shortens growing season length in a beech (Fagus sylvatica L.)-dominated forest / Quentin Hurdebise in Annals of Forest Science, Vol 76 n° 3 (September 2019)
PermalinkEstimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
PermalinkChilling and forcing temperatures interact to predict the onset of wood formation in Northern Hemisphere conifers / Nicolas Delpierre in Global change biology, vol 25 n° 3 (March 2019)
![]()
PermalinkStem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
PermalinkPermalink