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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7393 - 7405 Titre : A machine learning framework for estimating leaf biochemical parameters from its spectral reflectance and transmission measurements Type de document : Article/Communication Auteurs : Bikram Koirala, Auteur ; Zohreh Zahiri, Auteur ; Paul Scheunders, Auteur Année de publication : 2020 Article en page(s) : pp 7393 - 7405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] biochimie [Termes descripteurs IGN] diagnostic foliaire [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs IGN] régression [Termes descripteurs IGN] teneur en chlorophylle des feuilles Résumé : (auteur) Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as the concentrations of chlorophyll a and b, carotenoid, anthocyanin, brown pigment, leaf water content, and leaf mass per area for the quantification of vegetation physiology. The most popular way to estimate these parameters is by using spectral vegetation indices. The use of biochemical models allows us to use the full wavelength range (400–2500 nm) and to physically interpret the result. However, their performance is usually lower than that of supervised machine learning regression techniques. Machine learning regression techniques, on the other hand, have the disadvantage that the relationship between estimated parameters and the reflectance/transmission spectra is unclear. In this article, a hybrid between a supervised learning method and physical modeling for the estimation of leaf parameters is proposed. In this method, a machine learning regression technique is applied to learn a mapping from the true hyperspectral data set to a data set that follows the PROSPECT model. The PROSPECT model then reveals the actual leaf parameters. Two mapping methods, based on Gaussian processes (GPs) and kernel ridge regression (KRR) are proposed. As an alternative, mapping onto the leaf absorption spectra is proposed as well. The proposed methodology not only estimates the leaf parameters with a lower error but also solves the interpretation problem of the parameters estimated by the advanced machine learning regression techniques. This method is validated on the ANGERS and LOPEX data set. Numéro de notice : A2020-589 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2982263 date de publication en ligne : 02/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2982263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95919 [article]

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)  

Public [article]  inForest ecology and management > Vol 465 (1st June 2020) . - 15 p. Titre : Year-to-year crown condition poorly contributes to ring width variations of beech trees in French ICP level I network Type de document : Article/Communication Auteurs : Clara Tallieu, Auteur ; Vincent Badeau, Auteur ; Denis Allard, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] dendrochronologie [Termes descripteurs IGN] Fagus (genre) [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] houppier [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] pollution atmosphérique [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] stress hydrique [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] variation saisonnière [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Since the 1980-90′s episodes of decline in Central European Forests, forest condition has been surveyed thanks to the trans-national network the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). It has been traditionally accepted that leaf loss is directly related to impairment of physiological condition of the tree. A few studies tried to correlate crown condition and growth trends while others concentrated on linking annual growth with crown observation at one date clustered into fertility classes. However, none focussed on the high frequency synchronism between leaf loss from annual network observations and annual radial growth issued from dendrochronology. Therefore, we jointly studied annual leaf loss observations and tree-ring width measurements on 715 common beech (Fagus sylvatica L.) trees distributed in the French part of the ICP monitoring network. Detrended inter-annual variations of leaf loss and tree-ring width index were used as response variables in the machine-learning algorithm Random Forest to investigate a common response to abiotic (current and lagged) and biotic hazards, to test the extent to which leaf loss helped to predict inter-annual variations in radial growth. Using Random Forest was effective to identify a common sensitivity to soil water deficit at different time lags. Previous-year climatic variables tended to control leaf loss while radial growth was more sensitive to current-year soil water deficit. Late frost damages were observed on crown condition in mountainous regions but no impact was detected on radial growth. Few significant biotic damages were observed on growth or leaf loss. Leaf loss series did not show a clear common signal among trees from a plot as did radial growth and captured fewer pointer years. Radial growth index did not fall below normal until a 20% leaf loss was reached. However, this threshold is driven by a few extreme leaf loss events. As shown by our joint analysis of leaf loss and radial growth pointer years, no relationship occurred in cases of slight or moderate defoliation. Crown condition is a poorer descriptor of tree vitality than radial growth. Numéro de notice : A2020-287 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2020.118071 date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118071 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95111 [article]

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070 Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] atmosphère terrestre [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] faisceau laser [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] foresterie [Termes descripteurs IGN] forêt feuillue [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] transfert radiatif Résumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 208 - 220 Titre : Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Tiejun Wang, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 208 - 220 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] angle [Termes descripteurs IGN] Bavière (Allemagne) [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] modèle numérique de surface de la canopée Mots-clés libres : inclinaison longitudinale  Leaf inclination angle  leaf angle distribution Résumé : (Auteur) Leaf inclination angle and leaf angle distribution (LAD) are important plant structural traits, influencing the flux of radiation, carbon and water. Although leaf angle distribution may vary spatially and temporally, its variation is often neglected in ecological models, due to difficulty in quantification. In this study, terrestrial LiDAR (TLS) was used to quantify the LAD variation in natural European beech (Fagus Sylvatica) forests. After extracting leaf points and reconstructing leaf surface, leaf inclination angle was calculated automatically. The mapping accuracy when discriminating between leaves and woody material was very high across all beech stands (overall accuracy = 87.59%). The calculation accuracy of leaf angles was evaluated using simulated point cloud and proved accurate generally (R2 = 0.88, p  Numéro de notice : A2019-075 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.005 date de publication en ligne : 15/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92162 [article]

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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)  

Public [article]  inForests > vol 9 n° 11 (November 2018) Titre : Analyzing the vertical distribution of crown material in mixed stand composed of two temperate tree species Type de document : Article/Communication Auteurs : Olivier Martin-Ducup, Auteur ; Robert Schneider, Auteur ; Richard A. Fournier, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Abies balsamea [Termes descripteurs IGN] acer saccharum [Termes descripteurs IGN] densité du feuillage [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] écologie forestière [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] houppier [Termes descripteurs IGN] voxel Résumé : (Auteur) The material distribution inside tree crowns is difficult to quantify even though it is an important variable in forest management and ecology. The vertical distribution of a relative density index (i.e., vertical profile) of the total, woody, and leafy material at the crown scale were estimated from terrestrial laser scanner (TLS) data on two species, sugar maple (Acer saccharum Marsh.) and balsam fir (Abies Balsamea Mill.). An algorithm based on a geometrical approach readily available in the Computree open source platform was used. Beta distributions were then fitted to the vertical profiles and compared to each other. Total and leafy profiles had similar shapes, while woody profiles were different. Thus, the total vertical distribution could be a good proxy for the leaf distribution in the crown. Sugar maple and balsam fir had top heavy and bottom heavy distributions respectively, which can be explained by their respective architectural development. Moreover, the foliage distribution of sugar maples shifted towards the crown base when it was found in mixed stands, when compared to pure stands. The opposite behavior was observed for balsam firs, but less pronounced. According to the shape of the foliage distribution, sugar maple takes advantages from mixture contrarily to balsam fir. From a methodological point of view, we proposed an original approach to separate wood from leaf returns in TLS data while taking into account occlusion. Wood and leaf separation and occlusion problems are two challenging issues for most TLS-based studies in forest ecology. Numéro de notice : A2018-487 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9110673 date de publication en ligne : 26/10/2018 En ligne : https://doi.org/10.3390/f9110673 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91223 [article]

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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Mapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data / Abel Chemura in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Evaluation of seasonal variations of remotely sensed leaf area index over five evergreen coniferous forests / Rong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Characterizing vegetation canopy structure using airborne remote sensing data / Debsunder Dutta in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

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Examining view angle effects on leaf N estimation in wheat using field reflectance spectroscopy / Xiao Song in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)  

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Multisensor and multispectral Lidar characterization and classification of a forest environment / Christopher Hopkinson in Canadian journal of remote sensing, vol 42 n° 5 ([01/05/2016])  

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Laser scanning based growth analysis of plants as a new challenge for deformation monitoring / Jan Dupuis in Journal of applied geodesy, vol 10 n° 1 (March 2016)  

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Changes in thermal infrared spectra of plants caused by temperature and water stress / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 111 (January 2016)  

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