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scientifique

Improving tree biomass models through crown ratio patterns and incomplete data sources / Maria Menéndez-Miguélez in European Journal of Forest Research, vol 140 n° 3 (June 2021)  

Public [article]  inEuropean Journal of Forest Research > vol 140 n° 3 (June 2021) . - pppages 675 - 689 Titre : Improving tree biomass models through crown ratio patterns and incomplete data sources Type de document : Article/Communication Auteurs : Maria Menéndez-Miguélez, Auteur ; Ricardo Ruiz-Peinado, Auteur ; Miren del Rio, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pppages 675 - 689 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne [Termes IGN] Espagne [Termes IGN] houppier [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modélisation de la forêt [Termes IGN] puits de carbone [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Aboveground biomass quantification is essential for determining carbon stocks in forests. Multiple tree biomass models are available, but estimations can be biased outside the fitting range. This is due to the lack of data for larger trees, mainly because of the cost and time required. This study proposed a methodology based on tree crown biomass ratio (crown biomass: total aboveground biomass) modelling. The original data used in the existing biomass models in Spain have been notably extended by the inclusion of stem data from First Spanish National Forest Inventory and other databases, covering better tree size variability. The analysis of the crown biomass ratio against tree size (d2h), allowed us to distinguish three different patterns: an increasing pattern, a constant one, and a decreasing pattern. A new system of biomass models was fitted simultaneously by species, including a model for crown biomass ratio according to the identified pattern, a stem biomass model, and a total aboveground biomass model. Using this methodology, models were fitted for the 29 most important species in Spain. The fitted models result in more accurate and unbiased predictions for stem biomass, and realistic estimations for the crown biomass. This methodology means more robust and flexible biomass estimations with the possibility of using different data sources. The absence of crown information is not an obstacle because this component is a percentage of total aboveground biomass. Moreover, determining the crown biomass ratio pattern allows improving the accuracy of tree biomass estimation beyond the range of tree sizes (2–70 cm) for which these models were fitted. Numéro de notice : A2021-430 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01354-3 date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.1007/s10342-021-01354-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97791 [article]

Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change / Jiawei Huang in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 6 (June 2021) . - pp 1155 - 1178 Titre : Walking through the forests of the future: using data-driven virtual reality to visualize forests under climate change Type de document : Article/Communication Auteurs : Jiawei Huang, Auteur ; Melissa S. Lucash, Auteur ; Robert M. Scheller, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1155 - 1178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse forestière [Termes IGN] carte de la végétation [Termes IGN] changement climatique [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] monde virtuel [Termes IGN] paysage forestier [Termes IGN] réalité virtuelle [Termes IGN] visualisation 3D [Termes IGN] Wisconsin (Etats-Unis) [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Communicating and understanding climate induced environmental changes can be challenging, especially using traditional representations such as graphs, maps or photos. Immersive visualizations and experiences offer an intuitive, visceral approach to otherwise rather abstract concepts, but creating them scientifically is challenging. In this paper, we linked ecological modeling, procedural modeling, and virtual reality to provide an immersive experience of a future forest. We mapped current tree species composition in northern Wisconsin using the Forest Inventory and Analysis (FIA) data and then forecast forest change 50 years into the future under two climate scenarios using LANDIS-II, a spatially-explicit, mechanistic simulation model. We converted the model output (e.g., tree biomass) into parameters required for 3D visualizations with analytical modeling. Procedural rules allowed us to efficiently and reproducibly translate the parameters into a simulated forest. Data visualization, environment exploration, and information retrieval were realized using the Unreal Engine. A system evaluation with experts in ecology provided positive feedback and future topics for a comprehensive ecosystem visualization and analysis approach. Our approach to create visceral experiences of forests under climate change can facilitate communication among experts, policy-makers, and the general public. Numéro de notice : A2021-384 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1830997 date de publication en ligne : 10/11/2020 En ligne : https://doi.org/10.1080/13658816.2020.1830997 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97641 [article]

Weak relationships of continuous forest management intensity and remotely sensed stand structural complexity in temperate mountain forests / Thomas Asbeck in European Journal of Forest Research, vol 140 n° 3 (June 2021)  

Public [article]  inEuropean Journal of Forest Research > vol 140 n° 3 (June 2021) . - pp 721 - 731 Titre : Weak relationships of continuous forest management intensity and remotely sensed stand structural complexity in temperate mountain forests Type de document : Article/Communication Auteurs : Thomas Asbeck, Auteur ; Julian Frey, Auteur Année de publication : 2021 Article en page(s) : pp 721 - 731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Abies alba [Termes IGN] composition d'un peuplement forestier [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Forêt-Noire, massif de la [Termes IGN] gestion forestière durable [Termes IGN] modélisation de la forêt [Termes IGN] Picea abies [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) Understanding the relationship of stand structural complexity and forest management is relevant to create desired stand structures by adapting management strategies under changing disturbance scenarios and climatic conditions. To overcome difficulties in differentiating between strict categories of silvicultural practices and to describe the impact of forest management more appropriate, we used a continuous indicator of forest management intensity (ForMI). The ForMI consists of three components including volumes of natural deadwood, non-native tree species and harvested trees. There are a great number of approaches to quantify stand structure; here we used the recently established stand structural complexity index (SSCI) which represents a density-dependent as well as vertical measure of complexity based on the distribution of points in 3D space inventoried by terrestrial laser scanning. The data collection took place in 135 one-hectare plots managed under close-to-nature forest management (CTNFM) located in the Black Forest, Germany. We build generalized additive models to test the relationship of the SSCI with the ForMI. The model results did not prove a significant relationship between the SSCI and the ForMI, but components of the ForMI showed significant relationships to the SSCI. Our results indicate that the relationship between stand structural complexity and forest management intensity is, while plausible, not trivial to demonstrate. We conclude that forest managers have a relatively wide range of choices in CTNFM to adapt forests within a similar range of management intensity as presented here to future challenges, since management intensity does not change the forest structure drastically. Numéro de notice : A2021-431 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s10342-021-01361-4 date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1007/s10342-021-01361-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97793 [article]

Self-thinning tree mortality models that account for vertical stand structure, species mixing and climate / D.I. Forrester in Forest ecology and management, Vol 487 ([01/05/2021])  

Public [article]  inForest ecology and management > Vol 487 [01/05/2021] . - n° 118936 Titre : Self-thinning tree mortality models that account for vertical stand structure, species mixing and climate Type de document : Article/Communication Auteurs : D.I. Forrester, Auteur ; Thomas G. Backer, Auteur ; Stephen R. Elms, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 118936 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba [Termes IGN] Australie [Termes IGN] auto-éclaircie [Termes IGN] changement climatique [Termes IGN] Chine [Termes IGN] dépérissement [Termes IGN] Eucalyptus nitens [Termes IGN] Fagus sylvatica [Termes IGN] modèle de croissance végétale [Termes IGN] mortalité [Termes IGN] peuplement forestier [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Quercus pedunculata [Termes IGN] Quercus sessiliflora [Termes IGN] structure de la végétation [Termes IGN] Suisse [Vedettes matières IGN] Sylviculture Résumé : (auteur) Self-thinning dynamics are often considered when managing stand density in forests and are used to constrain forest growth models. However, self-thinning relationships are often quantified using only data at a conceptualised self-thinning line, even though self-thinning can begin before the stand actually reaches a self-thinning line. Also, few self-thinning relationships account for the effects of species composition in mixed-species forests, and stand structure such as relative height of species (in mixtures), and/or size or age cohorts in uneven-aged forests. Such considerations may be important given the effects of global climate change and interest in mixed-species and uneven-aged forests. The objective of this study was to develop self-thinning relationships based on changes in the tree density relative to mean tree diameter, instead of focusing only on data for state variables (e.g. tree density) at the self-thinning line. This was done while also considering how the change in tree density is influenced by site quality and stand structure (species composition and relative height). The relationships were modelled using data from temperate Australian Eucalyptus plantations (436 plots), subtropical forests in China (88 plots), and temperate forests in Switzerland (1055 plots). Zero-inflated and hurdle generalized linear models with Poisson and negative binomial distributions were fit for several species, as well as for all-species equations. The intercepts and slopes of the self-thinning lines were higher than many published studies which may have resulted from both the less restrictive equation form and data selection. The rates of self-thinning often decreased as the proportion of the object species increased, as relative height increased (species or size cohort became more dominant), and as site (quality) index increased. The effects of aridity varied between species, with self-thinning increasing with aridity index for Abies alba, Pinus sylvestris, Quercus petraea and Quercus robur, but decreasing with aridity index for Eucalyptus nitens, Fagus sylvatica and Picea abies as sites became wetter and cooler. Self-thinning model parameters were not correlated with species traits, including specific leaf area, wood basic density or crown diameter – stem diameter allometry. All-species self-thinning relationships based on all data could be adjusted using a correction factor for rarer species where there were insufficient data to develop species-specific equations. The approach and equations developed could be used in forest growth models to calculate how the tree density declines as mean tree size increases, as height changes relative to other cohorts or species, as species proportions change, and as climatic and edaphic conditions change. Numéro de notice : A2021-355 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.118936 date de publication en ligne : 18/02/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.118936 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97612 [article]

Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest / Seyedeh Kosar Hamidi in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 1 (March 2021) . - n° 4 Titre : Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest Type de document : Article/Communication Auteurs : Seyedeh Kosar Hamidi, Auteur ; Eric K. Zenner, Auteur ; Mahmoud Bayat, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Acer velutinum [Termes IGN] alnus cordata [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] carpinus betulus [Termes IGN] classification barycentrique [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] dynamique de la végétation [Termes IGN] écosystème forestier [Termes IGN] Fagus orientalis [Termes IGN] forêt inéquienne [Termes IGN] inventaire forestier étranger (données) [Termes IGN] Iran [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] peuplement mélangé [Termes IGN] régression linéaire [Termes IGN] volume en bois [Vedettes matières IGN] Sylviculture Résumé : (auteur) Key message: We modeled 10-year net stand volume growth with four machine learning (ML) methods, i.e., artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and nearest neighbor analysis (NN), and with linear regression analysis. Incorporating interactions of multiple variables, the ML methods ANN and SVM predicted nonlinear system behavior and unraveled complex relations with greater accuracy than regression analysis. Context: Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies. Aims: The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models. Methods: In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis. Results: ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%). Conclusion: These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management. Numéro de notice : A2021-071 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-01011-6 date de publication en ligne : 12/01/2021 En ligne : https://doi.org/10.1007/s13595-020-01011-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96794 [article]

Modeling size-density trajectories of even-aged ash (Fraxinus excelsior L.) stands in France. A baseline to assess the impact of Chalara ash dieback / Noël Le Goff in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)  

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Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Stand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties / NIca Huber in Ecological Applications, vol 31 ([01/02/2021])  

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Individual tree diameter growth modeling system for Dalat pine (Pinus dalatensis Ferré) of the upland mixed tropical forests / Bao Huy in Forest ecology and management, vol 480 (15 January 2021)  

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30èmes Journées de la Recherche de l'IGN - ENSG dématérialisées du 25 au 28 mai 2021 / Journées Recherche de l'IGN 2021, 30èmes journées (25 - 28 mai 2021; France) (2021) 

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Turgor – a limiting factor for radial growth in mature conifers along an elevational gradient / Richard L. Peters in New phytologist, vol 229 n° 1 (January 2021)  

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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)  

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Climate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model / Arne Nothdurft in Forest ecology and management, vol 478 ([15/12/2020])  

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Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates / Robert E. Keane in Forest ecology and management, vol 477 ([01/12/2020])  

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Comparison of spatially and nonspatially explicit nonlinear mixed effects models for Norway spruce individual tree growth under single-tree selection / Simone Bianchi in Forests, vol 11 n° 12 (December 2020)  

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