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Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 8 (April-2 2021) . - n° 1597 Titre : Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing Type de document : Article/Communication Auteurs : Shangharsha Thapa, Auteur ; Virginia Garcia Millan, Auteur ; Lars Eklundh, Auteur Année de publication : 2021 Article en page(s) : n° 1597 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse multiéchelle [Termes IGN] capteur multibande [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] image Sentinel-MSI [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] réflectance spectrale [Termes IGN] série temporelle [Termes IGN] Suède [Termes IGN] surveillance forestière [Termes IGN] variation saisonnière Résumé : (auteur) The monitoring of forest phenology based on observations from near-surface sensors such as Unmanned Aerial Vehicles (UAVs), PhenoCams, and Spectral Reflectance Sensors (SRS) over satellite sensors has recently gained significant attention in the field of remote sensing and vegetation phenology. However, exploring different aspects of forest phenology based on observations from these sensors and drawing comparatives from the time series of vegetation indices (VIs) still remains a challenge. Accordingly, this research explores the potential of near-surface sensors to track the temporal dynamics of phenology, cross-compare their results against satellite observations (MODIS, Sentinel-2), and validate satellite-derived phenology. A time series of Normalized Difference Vegetation Index (NDVI), Green Chromatic Coordinate (GCC), and Normalized Difference of Green & Red (VIgreen) indices were extracted from both near-surface and satellite sensor platforms. The regression analysis between time series of NDVI data from different sensors shows the high Pearson’s correlation coefficients (r > 0.75). Despite the good correlations, there was a remarkable offset and significant differences in slope during green-up and senescence periods. SRS showed the most distinctive NDVI profile and was different to other sensors. PhenoCamGCC tracked green-up of the canopy better than the other indices, with a well-defined start, end, and peak of the season, and was most closely correlated (r > 0.93) with the satellites, while SRS-based VIgreen accounted for the least correlation (r = 0.58) against Sentinel-2. Phenophase transition dates were estimated and validated against visual inspection of the PhenoCam data. The Start of Spring (SOS) and End of Spring (EOS) could be predicted with an accuracy of Numéro de notice : A2021-382 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081597 date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081597 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97633 [article]

Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations / Shengbiao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 36 - 48 Titre : Monitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations Type de document : Article/Communication Auteurs : Shengbiao Wu, Auteur ; Jing Wang, Auteur ; Zhengbing Yan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Chine [Termes IGN] feuille (végétation) [Termes IGN] forêt tempérée [Termes IGN] houppier [Termes IGN] image Aqua-MODIS [Termes IGN] image captée par drone [Termes IGN] image PlanetScope [Termes IGN] image Terra-MODIS [Termes IGN] phénologie [Termes IGN] photosynthèse [Termes IGN] série temporelle [Termes IGN] surveillance forestière Résumé : (auteur) In temperate forests, autumn leaf phenology signals the end of leaf growing season and shows large variability across tree-crowns, which importantly mediates photosynthetic seasonality, hydrological regulation, and nutrient cycling of forest ecosystems. However, critical challenges remain with the monitoring of autumn leaf phenology at the tree-crown scale due to the lack of spatially explicit information for individual tree-crowns and high (spatial and temporal) resolution observations with nadir view. Recent availability of the PlanetScope constellation with a 3 m spatial resolution and near-daily nadir view coverage might help address these observational challenges, but remains underexplored. Here we developed an integration of PlanetScope with drone observations for improved monitoring of crown-scale autumn leaf phenology in a temperate forest in Northeast China. This integration includes: 1) visual identification of individual tree-crowns (and species) from drone observations; 2) extraction of time series of PlanetScope vegetation indices (VIs) for each identified tree-crown; 3) derivation of three metrics of autumn leaf phenology from the extracted VI time series, including the start of fall (SOF), middle of fall (MOF), and end of fall (EOF); and 4) accuracy assessments of the PlanetScope-derived phenology metrics with reference from local phenocams. Our results show that (1) the PlanetScope-drone integration captures large inter-crown phenological variations, with a range of 28 days, 25 days, and 30 days for SOF, MOF, and EOF, respectively, (2) the extracted crown-level phenology metrics strongly agree with those derived from local phenocams, with a root-mean-square-error (RMSE) of 4.1 days, 3.0 days and 5.4 days for SOF, MOF, and EOF, respectively, and (3) PlanetScope maps large variations in autumn leaf phenology over the entire forest landscape with spatially explicit information. These results demonstrate the ability of our proposed method in monitoring the large spatial heterogeneity of crown-scale autumn leaf phenology in the temperate forest, suggesting the potential of using high-resolution satellites to advance crown-scale phenology studies over large geographical areas. Numéro de notice : A2021-011 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.017 date de publication en ligne : 13/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96305 [article]

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Reply to Elmendorf and Ettinger: Photoperiod plays a dominant and irreplaceable role in triggering secondary growth resumption / Jian-Guo Huang in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 52 (December 2020)  

Public [article]  inProceedings of the National Academy of Sciences of the United States of America PNAS > vol 117 n° 52 (December 2020) . - pp 32865 - 32867 Titre : Reply to Elmendorf and Ettinger: Photoperiod plays a dominant and irreplaceable role in triggering secondary growth resumption 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 Article en page(s) : pp 32865 - 32867 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] croissance végétale [Termes IGN] formation du bois [Termes IGN] modèle de croissance végétale [Termes IGN] phénologie [Vedettes matières IGN] Végétation et changement climatique Numéro de notice : A2020-332 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : sans date de publication en ligne : 15/12/2020 En ligne : https://www.pnas.org/content/117/52/32865 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96886 [article]

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)  

Public [article]  inRemote sensing > vol 12 n° 23 (December-2 2020) 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] phénologie Résumé : (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 [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 2020)  

Public [article]  inRemote sensing > vol 12 n° 22 (December 2020) . - n° 3845 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] urbanisme Résumé : (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 [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)  

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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)  

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Detecting abandoned farmland using harmonic analysis and machine learning / Heeyeun Yoon in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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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)  

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An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)  

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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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Novel 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)  

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Increasing 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 [en ligne], Vol 76 n° 3 (September 2019)  

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Estimating 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)  

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Chilling 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)    

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