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Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies / Guangqin Song in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 19 - 33 Titre : Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies Type de document : Article/Communication Auteurs : Guangqin Song, Auteur ; Shengbiao Wu, Auteur ; Calvin K.F. Lee, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 19 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] canopée [Termes IGN] classification dirigée [Termes IGN] diagnostic foliaire [Termes IGN] Enhanced vegetation index [Termes IGN] feuille (végétation) [Termes IGN] forêt tropicale [Termes IGN] Panama [Termes IGN] phénologie [Termes IGN] photosynthèse [Termes IGN] segmentation sémantique [Termes IGN] série temporelle [Termes IGN] SLIC algorithm [Termes IGN] superpixel [Termes IGN] variation saisonnière Résumé : (auteur) Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams. Numéro de notice : A2022-009 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.023 Date de publication en ligne : 10/11/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99057 [article]

Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech / Fanny Petibon in Remote sensing of environment, vol 264 (October 2021)  

Public [article]  inRemote sensing of environment > vol 264 (October 2021) . - n° 112601 Titre : Uncertainties in measurements of leaf optical properties are small compared to the biological variation within and between individuals of European beech Type de document : Article/Communication Auteurs : Fanny Petibon, Auteur ; Ewa A. Czyż, Auteur ; Giulia Ghielmetti, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] anisotropie [Termes IGN] diagnostic foliaire [Termes IGN] échantillonnage [Termes IGN] Fagus sylvatica [Termes IGN] feuille (végétation) [Termes IGN] France (administrative) [Termes IGN] incertitude spectrale [Termes IGN] indicateur biologique [Termes IGN] phénologie [Termes IGN] réflectance spectrale [Termes IGN] réflectance végétale [Termes IGN] saison [Termes IGN] spectroradiomètre [Termes IGN] Suisse Résumé : (auteur) The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica. We first use fabrics as standard material to quantify measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over one growing season (2.7 ± 1.7%), or between individuals sampled over one week (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual at the time of the measurement. However, measurements of variation within individuals at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches toward a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance. Numéro de notice : A2021-808 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112601 Date de publication en ligne : 29/07/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98868 [article]

Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 7038 - 7050 Titre : Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners Type de document : Article/Communication Auteurs : Kai Tan, Auteur ; Weiguo Zhang, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7038 - 7050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] bois [Termes IGN] densité du feuillage [Termes IGN] données lidar [Termes IGN] données TLS (télémétrie) [Termes IGN] feuille (végétation) [Termes IGN] réflectance spectrale [Termes IGN] semis de points Résumé : (auteur) Terrestrial laser scanning (TLS) is a highly effective and noninvasive technology for retrieving the structural and biophysical attributes of trees using 3-D high-accuracy and high-density point clouds. The separation of leaf and wood points in TLS data is a prerequisite for the accurate and reliable derivation of these attributes. In this study, a new method is proposed to separate the leaf and wood points of individual trees by combining the TLS radiometric (intensity) and geometric (density) data. The leaf points are separated from the wood ones through three steps. First, the corrected intensity data are used to separate a part of the leaf points preliminarily given the differences in reflectance characteristics. Second, the density data are adopted for the further separation of another part of the leaf points because the density of the remaining leaf points is smaller than that of the wood points. Finally, a connectivity clustering algorithm is conducted to form several clusters with different sizes (points) and the remaining leaf points are separated in accordance with the cluster sizes. Eight different trees are selected to evaluate the performance of the proposed method. The averaged overall accuracy and kappa coefficient of the eight trees are approximately 95% and 0.81, respectively. The results suggest that the combination of TLS intensity and density data can perform a superior separation of leaf and wood points in terms of efficiency and accuracy, and the proposed separation method can be accurately and robustly used for various trees with different species, sizes, and structures. Numéro de notice : A2021-633 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032167 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032167 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98295 [article]

Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 11 (June-1 2021) . - n° 2160 Titre : Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities Type de document : Article/Communication Auteurs : Jingjing Zhou, Auteur ; Ya-Hao Zhang, Auteur ; Ze-Min Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] Chine [Termes IGN] Citrus (genre) [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] feuille (végétation) [Termes IGN] image hyperspectrale [Termes IGN] photosynthèse [Termes IGN] réflectance végétale [Termes IGN] rendement agricole [Termes IGN] stress hydrique [Termes IGN] surveillance de la végétation Résumé : (auteur) Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards. Numéro de notice : A2021-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112160 Date de publication en ligne : 31/05/2021 En ligne : https://doi.org/10.3390/rs13112160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97826 [article]

Search for top‐down and bottom‐up drivers of latitudinal trends in insect herbivory in oak trees in Europe / Elena Valdés-Correcher in Global ecology and biogeography, vol 30 n° 3 (March 2021)  

Public [article]  inGlobal ecology and biogeography > vol 30 n° 3 (March 2021) . - pp 651 - 665 Titre : Search for top‐down and bottom‐up drivers of latitudinal trends in insect herbivory in oak trees in Europe Type de document : Article/Communication Auteurs : Elena Valdés-Correcher, Auteur ; Xoaquín Moreira, Auteur ; Laurent Augusto, Auteur ; Luc Barbaro, Auteur ; Christophe Bouget, Auteur ; Olivier Bouriaud , Auteur ; et al., Auteur Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Article en page(s) : pp 651 - 665 Langues : Anglais (eng) Descripteur : [Termes IGN] Aves [Termes IGN] biochimie [Termes IGN] dommage forestier causé par facteurs naturels [Termes IGN] Europe (géographie politique) [Termes IGN] feuille (végétation) [Termes IGN] Quercus pedunculata [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Aim : The strength of species interactions is traditionally expected to increase toward the Equator. However, recent studies have reported opposite or inconsistent latitudinal trends in the bottom‐up (plant quality) and top‐down (natural enemies) forces driving herbivory. In addition, these forces have rarely been studied together thus limiting previous attempts to understand the effect of large‐scale climatic gradients on herbivory. Location : Europe. Time period : 2018–2019. Major taxa studied : Quercus robur. Methods : We simultaneously tested for latitudinal variation in plant–herbivore–natural enemy interactions. We further investigated the underlying climatic factors associated with variation in herbivory, leaf chemistry and attack rates in Quercus robur across its complete latitudinal range in Europe. We quantified insect leaf damage and the incidence of specialist herbivores as well as leaf chemistry and bird attack rates on dummy caterpillars on 261 oak trees. Results : Climatic factors rather than latitude per se were the best predictors of the large‐scale (geographical) variation in the incidence of gall‐inducers and leaf‐miners as well as in leaf nutritional content. However, leaf damage, plant chemical defences (leaf phenolics) and bird attack rates were not influenced by climatic factors or latitude. The incidence of leaf‐miners increased with increasing concentrations of hydrolysable tannins, whereas the incidence of gall‐inducers increased with increasing leaf soluble sugar concentration and decreased with increasing leaf C : N ratios and lignins. However, leaf traits and bird attack rates did not vary with leaf damage. Main conclusions : These findings help to refine our understanding of the bottom‐up and top‐down mechanisms driving geographical variation in plant–herbivore interactions, and indicate the need for further examination of the drivers of herbivory on trees. Numéro de notice : A2021-178 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/geb.13244 Date de publication en ligne : 31/12/2020 En ligne : https://doi.org/10.1111/geb.13244 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96970 [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)  

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A machine learning framework for estimating leaf biochemical parameters from its spectral reflectance and transmission measurements / Bikram Koirala in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

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Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network / Clara Tallieu in Forest ecology and management, Vol 465 (1st June 2020)  

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Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Exploitation of hyperspectral data for assessing vegetation health under exposure to petroleum hydrocarbons / Guillaume Lassalle (2019)  

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Analyzing the vertical distribution of crown material in mixed stand composed of two temperate tree species / Olivier Martin-Ducup in Forests, vol 9 n° 11 (November 2018)  

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Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model / Ronghai Hu in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)  

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Research on the estimation model of vegetation water content in halophyte leaves based on the newly developed vegetation indices / Zhe Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 9 (September 2018)  

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Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

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