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Diversity and mean specific leaf area of Mediterranean woody vegetation changes in response to summer drought across a double stress gradient: The role of phenotypic plasticity / Alejandro Carrascosa in Journal of vegetation science, vol 34 n° 2 (April 2023)  

Public [article]  inJournal of vegetation science > vol 34 n° 2 (April 2023) . - n° e13180 Titre : Diversity and mean specific leaf area of Mediterranean woody vegetation changes in response to summer drought across a double stress gradient: The role of phenotypic plasticity Type de document : Article/Communication Auteurs : Alejandro Carrascosa, Auteur ; Mariola Silvestre, Auteur ; Laura Morgado, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° e13180 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbuste [Termes IGN] climat méditerranéen [Termes IGN] diagnostic foliaire [Termes IGN] Espagne [Termes IGN] facteur édaphique [Termes IGN] indice foliaire [Termes IGN] plante ligneuse [Termes IGN] sécheresse [Termes IGN] stress hydrique [Vedettes matières IGN] Végétation et changement climatique Résumé : Aim: Many aspects of vegetation response to increased drought remain uncertain but it is expected that phenotypic plasticity may be key to early adaptation of plants to environmental stress. In this work we observe the response of specific leaf area (SLA) of woody shrub vegetation to the summer drought typical of the Mediterranean climate. In addition, to observe the possible interaction between the impact of drought and the environmental characteristics of the ecosystems, communities from different edaphic and structural contexts distributed along the double stress gradient of the Mediterranean mountains (high temperature and low precipitation at low elevation; low temperature and high irradiation at high elevation) have been analysed. Location: Central Mountain range of the Iberian Peninsula. Methods: Along the entire altitudinal gradient, 33 shrub communities belonging to different habitat typologies (shrublands, rocky areas, hedgerows, understorey) were sampled before and after the passage of summer, both in 2017 and 2019. A total of 1724 individuals and 15,516 leaves were collected and measured to estimate the mean values and diversity of SLA of each community. Results: The community-weighted mean and functional divergence have inverse quadratic relationships with the environmental gradient. Shrub communities at both ends of the gradient have low mean SLA values and high functional divergence of this trait. Summer drought implies a generalised decrease in the mean SLA of the communities throughout the gradient, as well as an alteration in functional richness and uniformity. However, the effect of summer drought on the plant community is mediated by the microenvironmental characteristics of its habitat. Conclusions: Drought acclimatisation of shrub communities through phenotypic plasticity leads to rapid changes in their functional leaf structure. In the long term, our results point to an increase in plant conservative strategies, reduced ecosystem productivity, slower nutrient recycling and the reduction of communities of specific habitats as drought increases. Numéro de notice : A2023-223 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/jvs.13180 Date de publication en ligne : 09/03/2023 En ligne : https://doi.org/10.1111/jvs.13180 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103172 [article]

Improved parametrisation of a physically-based forest reflectance model for retrieval of boreal forest structural properties / Eelis Halme in Silva fennica, vol 57 n° 2 (April 2023)  

Public [article]  inSilva fennica > vol 57 n° 2 (April 2023) . - n° 22028 Titre : Improved parametrisation of a physically-based forest reflectance model for retrieval of boreal forest structural properties Type de document : Article/Communication Auteurs : Eelis Halme, Auteur ; Matti Mõttus, Auteur Année de publication : 2023 Article en page(s) : n° 22028 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Betula pendula [Termes IGN] betula pubescens [Termes IGN] densité du peuplement [Termes IGN] diagnostic foliaire [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] image Sentinel-MSI [Termes IGN] modèle de croissance végétale [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] réflectance végétale [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) Physically-based reflectance models offer a robust and transferable method to assess biophysical characteristics of vegetation in remote sensing. Forests exhibit explicit structure at many scales, from shoots and branches to landscape patches, and hence present a specific challenge to vegetation reflectance modellers. To relate forest reflectance with its structure, the complexity must be parametrised leading to an increase in the number of reflectance model inputs. The parametrisations link reflectance simulations to measurable forest variables, but at the same time rely on abstractions (e.g. a geometric surface forming a tree crown) and physically-based simplifications that are difficult to quantify robustly. As high-quality data on basic forest structure (e.g. tree height and stand density) and optical properties (e.g. leaf and forest floor reflectance) are becoming increasingly available, we used the well-validated forest reflectance and transmittance model FRT to investigate the effect of the values of the “uncertain” input parameters on the accuracy of modelled forest reflectance. With the state-of-the-art structural and spectral forest information, and Sentinel-2 Multispectral Instrument imagery, we identified that the input parameters influencing the most the modelled reflectance, given that the basic forestry variables are set to their true values and leaf mass is determined from reliable allometric models, are the regularity of the tree distribution and the amount of woody elements. When these parameters were set to their new adjusted values, the model performance improved considerably, reaching in the near infrared spectral region (740–950 nm) nearly zero bias, a relative RMSE of 13% and a correlation coefficient of 0.81. In the visible part of the spectrum, the model performance was not as consistent indicating room for improvement. Numéro de notice : A2023-228 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.22028 Date de publication en ligne : 30/05/2023 En ligne : https://doi.org/10.14214/sf.22028 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103260 [article]

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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