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Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar / Andrew W. Whelan in Remote sensing of environment, vol 284 (January 2023)  

Public [article]  inRemote sensing of environment > vol 284 (January 2023) . - n° 113362 Titre : Improving generalized models of forest structure in complex forest types using area- and voxel-based approaches from lidar Type de document : Article/Communication Auteurs : Andrew W. Whelan, Auteur ; Jeffery B. Cannon, Auteur ; Seth W. Bigelow, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 113362 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] canopée [Termes IGN] classification par maximum de vraisemblance [Termes IGN] diagnostic foliaire [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Géorgie (Etats-Unis) [Termes IGN] modélisation de la forêt [Termes IGN] Pinus palustris [Termes IGN] structure d'un peuplement forestier [Termes IGN] surface forestière [Termes IGN] volume en bois [Termes IGN] voxel Résumé : (auteur) Modeling forest attributes using lidar data has been a useful tool for forest management but the need to correlate lidar to ground-based measurements creates challenges to modeling in diverse forest landscapes. Many lidar models have been based on metrics derived from summarizations of individual lidar returns over sample plot areas, but more recently, metrics based on summarization by volumetric pixel (voxel) have shown promise to better characterize forest structure and distinguish between diverse forest types. Voxel-based metrics may improve characterization of leaf area distribution and horizontal forest structure, which could help create general models of forest attributes applicable in complex landscapes composed of many distinct forest types. We modeled wood volume in longleaf pine woodlands and associated forests to compare how area- and voxel- based lidar metrics predicted wood volume in forest type specific and general predictive models. We created four area-based and six voxel-based metrics to fit models of wood volume using a multiplicative power function. We selected models and compared metric importance using AIC and evaluated model performance using cross-validated mean prediction error. We found that one area-based metric and four voxel-based metrics consistently improved model predictions We suggest that area-based metrics alone may have limitations for characterizing complex forest structure. Area-based summarizes of lidar returns are more heavily influenced by upper canopy returns because lidar returns attenuate below the canopy. By contrast, summarizing lidar returns into a single value per voxel prior to summarization over plots homogenizes point density, giving added weight to sub-canopy returns. Thus voxel-based metrics may be more sensitive to structural variation that may not be adequately captured by area-based metrics alone. This study highlights the potential of voxel-based metrics for characterizing complex forest structure and model generalization capable of accurate forest attribute prediction across diverse forest types. Numéro de notice : A2023-016 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113362 Date de publication en ligne : 23/11/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102150 [article]

Interactive effects of abiotic factors and biotic agents on Scots pine dieback: A multivariate modeling approach in southeast France / Jean Lemaire in Forest ecology and management, vol 526 (December-15 2022)  

Public [article]  inForest ecology and management > vol 526 (December-15 2022) . - n° 120543 Titre : Interactive effects of abiotic factors and biotic agents on Scots pine dieback: A multivariate modeling approach in southeast France Type de document : Article/Communication Auteurs : Jean Lemaire, Auteur ; Michel Vennetier, Auteur ; Bernard Prévosto, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120543 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bilan hydrique [Termes IGN] climat méditerranéen [Termes IGN] croissance des arbres [Termes IGN] dépérissement [Termes IGN] diagnostic foliaire [Termes IGN] facteur édaphique [Termes IGN] France (administrative) [Termes IGN] indice foliaire [Termes IGN] insecte nuisible [Termes IGN] Pinus sylvestris [Termes IGN] régression des moindres carrés partiels [Termes IGN] Viscum album [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forest dieback is a high risk factor for the sustainability of these ecosystems in the climate change context. Productivity losses and increased defoliation and mortality rates have already been recorded for many tree species worldwide. However, dieback is a process that depends on complex interactions between many biotic and environmental factors acting at different scales, and is thus difficult to address and predict. Our aim was to build tree- and stand-level foliar deficit models integrating biotic and abiotic factors for Scots pine (Pinus sylvestris), a species particularly threatened in Europe, and especially in the southeastern part of France. To this end, we quantified foliar deficit in 1740 trees from 87 plots distributed along an environmental gradient. We also measured tree annual radial growth and the abundance of two parasites: the pine processionary moth (Thaumetopoea pityocampa Den. & Schiff.) and mistletoe (Viscum album L.). Topographic, soil, climate and water balance indices were assessed for each plot, together with the stand dendrometric characteristics. Given the large number of environmental factors and the strong correlations between many of them, models were developed using a partial least squares (PLS) regression approach. All the models pointed to a preponderance of the biotic factors (processionary moth and mistletoe) in explaining the intensity of foliar deficit at both tree- and stand- levels. We also show that strong interactions between climate, soil, water balance and biotic factors help to explain the intensity of dieback. Dieback was thus greater in the driest topoedaphic and climatic conditions where the mistletoe and processionary moth were present. This study highlights the need to account for a wide range of biotic and abiotic factors to explain the complex process of forest dieback, and especially the environmental variables that contribute to the water balance on the local scale. The phenomenological modeling approach presented here can be used in other regions and for other species, after a re-calibration and some adaptations to local constraints considering the limited distribution area of some biotic agents. Numéro de notice : A2022-825 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120543 Date de publication en ligne : 20/10/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120543 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102003 [article]

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 : 2022 Article en page(s) : pp 19 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme SLIC [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] 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]

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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]

Terrestrial laser scanning intensity captures diurnal variation in leaf water potential / S. Junttila in Remote sensing of environment, Vol 255 (March 2021)  

Public [article]  inRemote sensing of environment > Vol 255 (March 2021) . - n° 112274 Titre : Terrestrial laser scanning intensity captures diurnal variation in leaf water potential Type de document : Article/Communication Auteurs : S. Junttila, Auteur ; T. Hölttä, Auteur ; Eetu Puttonen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Betula (genre) [Termes IGN] diagnostic foliaire [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] dynamique de la végétation [Termes IGN] Pinus sylvestris [Termes IGN] sécheresse [Termes IGN] semis de points [Termes IGN] stress hydrique [Termes IGN] teneur en eau de la végétation [Termes IGN] variation diurne Résumé : (auteur) During the past decades, extreme events have become more prevalent and last longer, and as a result drought-induced plant mortality has increased globally. Timely information on plant water dynamics is essential for understanding and anticipating drought-induced plant mortality. Leaf water potential (ΨL), which is usually measured destructively, is the most common metric that has been used for decades for measuring water stress. Remote sensing methods have been developed to obtain information on water dynamics from trees and forested landscapes. However, the spatial and temporal resolutions of the existing methods have limited our understanding of the water dynamics and diurnal variation of ΨL within single trees. Thus, we investigated the capability of terrestrial laser scanning (TLS) intensity in observing diurnal variation in ΨL during a 50-h monitoring period. We aimed to improve the understanding on how large a part of the diurnal variation in ΨL can be captured using TLS intensity observations. We found that TLS intensity at the 905 nm wavelength measured from a static position was able to explain 77% of the variation in ΨL for three trees of two tree species with a root mean square error of 0.141 MPa. Based on our experiment with three trees, a time series of TLS intensity measurements can be used in detecting changes in ΨL, and thus it is worthwhile to expand the investigations to cover a wider range of tree species and forests and further increase our understanding of plant water dynamics at wider spatial and temporal scales. Numéro de notice : A2021-192 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2020.112274 Date de publication en ligne : 14/01/2021 En ligne : https://doi.org/10.1016/j.rse.2020.112274 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97113 [article]

Secondary metabolites in leaves of hybrid aspen are affected by the competitive status and early thinning in dense coppices / Linda Rusalepp in Annals of Forest Science, vol 78 n° 1 (March 2021)  

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Apport de la modélisation physique pour la cartographie de la biodiversité végétale en forêts tropicales par télédétection optique / Dav Ebengo Mwampongo (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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Warming effects on morphological and physiological performances of four subtropical montane tree species / Yiyong Li in Annals of Forest Science, Vol 77 n° 1 (March 2020)  

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Application of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)  

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État sanitaire et croissance radiale des arbres / Clara Tallieu (2020)  

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Effects of stand density on ecosystem properties of subalpine forests in the southern Rocky Mountains, USA / Sharon J. Hall in Annals of Forest Science, vol 67 n° 1 (January-February 2010)  

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Estimation of chlorophyll in Quercus leaves using a portable chlorophyll meter: effects of species and leaf age / Fernando Silla in Annals of Forest Science, vol 67 n° 1 (January-February 2010)  

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Temporal trends in the foliar nutritional status of the French, Walloon and Luxembourg broad-leaved plots of forest monitoring / Mathieu Jonard in Annals of Forest Science, Vol 66 n° 4 (June 2009)  

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Interactive responses of Quercus suber L. seedlings to light and mild water stress: effects on morphology and gas exchange traits / Jaime Puértolas in Annals of Forest Science, Vol 65 n° 6 (September 2008)  

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