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Unexpected negative effect of available water capacity detected on recent conifer forest growth trends across wide environmental gradients / Clémentine Ols in Ecosystems, vol 24 n° inconnu ([01/07/2021])  

Public [article]  inEcosystems > vol 24 n° inconnu [01/07/2021] Titre : Unexpected negative effect of available water capacity detected on recent conifer forest growth trends across wide environmental gradients Type de document : Article/Communication Auteurs : Clémentine Ols , Auteur ; Thomas Gschwantner, Auteur ; Klemens Schadauer, Auteur ; Jean-Daniel Bontemps , Auteur Année de publication : 2021 Projets : ARBRE / AgroParisTech, LUE / Université de Lorraine Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba [Termes IGN] analyse diachronique [Termes IGN] Autriche [Termes IGN] cerne [Termes IGN] changement climatique [Termes IGN] croissance des arbres [Termes IGN] gradient d'altitude [Termes IGN] hétérogénéité environnementale [Termes IGN] inventaire forestier étranger (données) [Termes IGN] inventaire forestier national (données France) [Termes IGN] Larix decidua [Termes IGN] modèle de croissance végétale [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] ressources en eau [Termes IGN] structure d'un peuplement forestier [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) National Forest Inventories (NFIs) perform systematic forest surveys across space and time. They are hence powerful tools to understand climate controls on forest growth at wide geographical scales and account for the effects of local abiotic and biotic interactions. To investigate the effects of climate change upon growth dynamics of four major European conifer species along elevation and continentality gradients, we herein provide an original harmonization of the French and Austrian NFI datasets. The growth of Norway spruce, Scots pine, silver fir and European larch over the 1996–2016 period was studied in pure and even-aged plots across different ecological regions. We derived climate-driven growth trends from > 65, 000 radial increment series filtered out from major biotic and abiotic influences using statistical modeling. We further identified primary environmental drivers of conifer growth by regressing growth trends against regionally aggregated biotic and abiotic forest attributes. Negative growth trends were observed in continental regions undergoing the most rapid warming and thermal amplitude contraction over the study period. Negative trends were also associated with lower forest structural heterogeneity and, surprisingly, with greater available water capacity. Remarkably, we observed these associations both at the inter- and intra-species levels, suggesting the universality of these primary growth determinants. Our study shows that harmonized NFI data at the transnational level provide reliable information on climate–growth interactions. Here, greater forest structural complexity and greater water resource limitation were highlighted as drivers of greater forest resilience to climate change at large-scale. This result forms crucial bases to implementing climate-smart forest management. Numéro de notice : A2021-504 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10021-021-00663-3 En ligne : https://doi.org/10.1007/s10021-021-00663-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98116 [article]

Tree height growth modelling using LiDAR-derived topography information / Milan Kobal in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 6 (June 2021) . - n° 419 Titre : Tree height growth modelling using LiDAR-derived topography information Type de document : Article/Communication Auteurs : Milan Kobal, Auteur ; David Hladnik, Auteur Année de publication : 2021 Article en page(s) : n° 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Abies alba [Termes IGN] croissance des arbres [Termes IGN] données lidar [Termes IGN] données topographiques [Termes IGN] gestion forestière durable [Termes IGN] hauteur des arbres [Termes IGN] hétérogénéité environnementale [Termes IGN] karst [Termes IGN] modèle de croissance végétale [Termes IGN] modèle numérique de surface [Termes IGN] modélisation de la forêt [Termes IGN] semis de points [Termes IGN] Slovénie Résumé : (auteur) The concepts of ecotopes and forest sites are used to describe the correlative complexes defined by landform, vegetation structure, forest stand characteristics and the relationship between soil and physiography. Physically heterogeneous landscapes such as karst, which is characterized by abundant sinkholes and outcrops, exhibit diverse microtopography. Understanding the variation in the growth of trees in a heterogeneous topography is important for sustainable forest management. An R script for detailed stem analysis was used to reconstruct the height growth histories of individual trees (steam analysis). The results of this study reveal that the topographic factors influencing the height growth of silver fir trees can be detected within forest stands. Using topography modelling, we classified silver fir trees into groups with significant differences in height growth. This study provides a sound basis for the comparison of forest site differences and may be useful in the calibration of models for various tree species. Numéro de notice : A2021-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10060419 date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.3390/ijgi10060419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97935 [article]

Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 56 - 73 Titre : Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution Type de document : Article/Communication Auteurs : Vitor Martins, Auteur ; Amy L. Kaleita, Auteur ; Brian K. Gelder, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 56 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données multiéchelles [Termes IGN] étiquetage sémantique [Termes IGN] hétérogénéité environnementale [Termes IGN] image à haute résolution [Termes IGN] occupation du sol [Termes IGN] reconnaissance d'objets [Termes IGN] segmentation d'image [Termes IGN] squelettisation Résumé : (auteur) Convolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km2. Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (“medial axis”) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented better classification accuracy (overall accuracy ~87.2%) compared to traditional patch-based CNN (81.6%) and fixed-input OCNN (82%). In addition, the results showed that this framework is 8.1 and 111.5 times faster than traditional pixel-wise CNN16 or CNN256, respectively. Multiple CNNs and object analysis have proved to be essential for accurate and fast classification. While multi-OCNN produced a high-level of spatial details in the land cover product, misclassification was observed for some classes, such as road versus buildings or shadow versus lake. Despite these minor drawbacks, our results also demonstrated the benefits of IowaNet training dataset in the model performance; overfitting process reduces as the number of samples increases. The limitations of multi-OCNN are partially explained by segmentation quality and limited number of spectral bands in the aerial data. With the advance of deep learning methods, this study supports the claim of multi-OCNN benefits for operational large-scale land cover product at 1-m resolution. Numéro de notice : A2020-634 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.004 date de publication en ligne : 13/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96057 [article]

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A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data / Haiyan Tao in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 3 (March 2020) . - pp 604 - 624 Titre : A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data Type de document : Article/Communication Auteurs : Haiyan Tao, Auteur ; Keli Wang, Auteur ; Li Zhuo, Auteur Année de publication : 2020 Article en page(s) : pp 604 - 624 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] Chine [Termes IGN] diffusion spatiale [Termes IGN] distribution de Poisson [Termes IGN] données socio-économiques [Termes IGN] hétérogénéité environnementale [Termes IGN] hétérogénéité spatiale [Termes IGN] maladie infectieuse [Termes IGN] migration humaine [Termes IGN] mobilité territoriale [Termes IGN] modèle de régression [Termes IGN] modèle mathématique [Termes IGN] origine - destination [Termes IGN] point d'intérêt [Termes IGN] risque sanitaire [Termes IGN] urbanisation Résumé : (auteur) International communication and global cooperation have greatly accelerated the worldwide spread of dengue fever, increasing the impact of imported cases on dengue outbreaks in non-naturally endemic areas. Existing studies mostly focus on describing the quantitative relationship between imported cases and local transmission but ignore the space-time diffusion mode of imported cases under the influence of individual mobility. In this paper, we propose a comprehensive framework at a fine scale to establish the disease transmission network and a mathematical model, which constructs ‘source-sink’ links between the imported and indigenous cases on a regular grid with a spatial resolution of 1 km to explore the diffusion pattern and spatiotemporal heterogeneity of imported cases. An application to Guangzhou, China, reveals the main flow and transmission path of imported cases under the influence of human movement and identifies the spatiotemporal distribution of transmission speed according to the time lag of each source-sink link. In addition, we demonstrate that using individual-based movement data and socio-economic factors to study human mobility and imported cases can help to understand the driving forces of dengue spread. Our research provides a comprehensive framework for the analysis of early dengue transmission patterns with benefits to similar urban applications. Numéro de notice : A2020-107 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1684497 date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1080/13658816.2019.1684497 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94707 [article]

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Patterns of tree diameter distributions in managed and unmanaged Abies alba Mill. and Fagus sylvatica L. forest patches / Rafał Podlaski in Forest ecology and management, vol 435 (1 March 2019)  

Public [article]  inForest ecology and management > vol 435 (1 March 2019) . - pp 7 - 105 Titre : Patterns of tree diameter distributions in managed and unmanaged Abies alba Mill. and Fagus sylvatica L. forest patches Type de document : Article/Communication Auteurs : Rafał Podlaski, Auteur ; Tomasz Sobala, Auteur ; Maciej Kocurek, Auteur Année de publication : 2019 Article en page(s) : pp 7 - 105 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes IGN] Abies alba [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] diamètre des arbres [Termes IGN] distribution spatiale [Termes IGN] Europe centrale [Termes IGN] Fagus sylvatica [Termes IGN] forêt tempérée [Termes IGN] hétérogénéité environnementale [Termes IGN] phytogéographie Résumé : (Auteur) Temperate forests with shade-tolerant canopy tree species can develop vertical structures of varying complexity. Forests with Abies alba Mill. and Fagus sylvatica L. can be composed of one-, two-, and multi-storied patches and selection patches. A dominant view in forest ecology is that unmanaged forests tend to have greater structural heterogeneity than managed stands. Structural integrity, however, may differ among forest developmental stages. The main objective of this study was to compare the tree diameter complexity in managed and unmanaged patches during the early developmental stage. Data were collected between 2016 and 2018 in the Świętokrzyskie Mountains in Central Europe. The investigated tree communities were dominated by A. alba and F. sylvatica. Sample plots representing the growing-up developmental stage were randomly selected; of these, 30 plots were in managed stands, and 30 plots were in unmanaged forests. The diameter at breast height (DBH) distribution patterns were determined using hierarchical cluster analysis (HCA), clustering indices, and finite mixture models. Three main DBH distribution patterns were identified for the managed stands (K-A, K-B, and K-C). These patterns consisted of three or two sub-populations. The patterns represented structurally diversified patches composed of trees of all ages with multi-, three- or two-layered canopies and with intensive natural processes of regeneration. Two main DBH distribution patterns were identified for the unmanaged forests (S-A, and S-B). These patterns consisted of two clearly separated sub-populations. They are typical in patches with two-layered canopies, and the trees from the upper layer had a large share (40–60%). The distinguished DBH distribution patterns indicated there was greater tree size diversity in the managed stands than in the unmanaged forests. When comparing managed versus unmanaged patches, it is important to consider the developmental stage. Numéro de notice : A2019-185 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2018.12.046 date de publication en ligne : 04/01/2019 En ligne : https://doi.org/10.1016/j.foreco.2018.12.046 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92719 [article]

Effect of forest structure on stand productivity in Central European forests depends on developmental stage and tree species diversity / Laura Zeller in Forest ecology and management, vol 434 (28 February 2019)  

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A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)  

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Remote Sensing of Evapotranspiration / Prasanna Gowda (2019)  

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Within- and between-tree variation of wood density components in Pinus nigra at six sites in Portugal / Alexandra Dias in Annals of Forest Science [en ligne], vol 75 n° 2 (June 2018)  

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Plant species coexistence at local scale in temperate swamp forest: test of habitat heterogeneity hypothesis / Jan Douda in Oecologia, vol 169 n° 2 (June 2012)

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