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Climate envelope analyses suggests significant rearrangements in the distribution ranges of Central European tree species / Gàbor Illés in Annals of Forest Science [en ligne], vol 79 n° 1 (2022)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 79 n° 1 (2022) . - n° 35 Titre : Climate envelope analyses suggests significant rearrangements in the distribution ranges of Central European tree species Type de document : Article/Communication Auteurs : Gàbor Illés, Auteur ; Norbert Móricz, Auteur Année de publication : 2022 Article en page(s) : n° 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] adaptation (biologie) [Termes IGN] bioclimatologie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Europe centrale [Termes IGN] Fagus sylvatica [Termes IGN] gestion forestière durable [Termes IGN] INSPIRE [Termes IGN] modèle dynamique [Termes IGN] modélisation de la forêt [Termes IGN] Picea abies [Termes IGN] Quercus cerris [Termes IGN] Quercus pubescens [Termes IGN] Quercus sessiliflora [Termes IGN] répartition géographique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message: Climate envelope analysis of nine tree species shows that Fagus sylvatica L. and Picea abies H. Karst could lose 58% and 40% of their current distribution range. Quercus pubescens Willd and Quercus cerris L. may win areas equal with 47% and 43% of their current ranges. The ratio of poorly predictable areas increases by 105% in southern and south-eastern Europe. Context: Climate change requires adaptive forest management implementations. To achieve climate neutrality, we have to maintain and expand forest areas. Impact assessments have great importance. Aims: The study estimates the potential climate envelopes of nine European tree species for a past period (1961–1990) and for three future periods (2011–2040, 2041–2070, 2071–2100) under two emission scenarios (RCP4.5 and RCP8.5) based on the current species distribution. Methods: Climate envelopes were estimated simultaneously using the random forest method. Multi-resolution segmentation was used to determine the climatic characteristics of each species and their combinations. Models were limited to the geographical area within which the climatic conditions correspond to the climatic range of the training areas. Results: Results showed remarkable changes in the extent of geographic areas of all the investigated species’ climate envelopes. Many of the tree species of Central Europe could lose significant portions of their distribution range. Adhering to the shift in climate, these tree species shift further north as well as towards higher altitudes. Conclusion: European forests face remarkable changes, and the results support climate envelope modelling as an important tool that provides guidelines for climate adaptation to identify threatened areas or to select source and destination areas for reproductive material. Numéro de notice : A2022-631 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01154-8 Date de publication en ligne : 09/08/2022 En ligne : https://doi.org/10.1186/s13595-022-01154-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101395 [article]

The role of wood harvest from sustainably managed forests in the carbon cycle / Ernst Detlef Schulze in Annals of Forest Science [en ligne], vol 79 n° 1 (2022)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 79 n° 1 (2022) . - n° 17 Titre : The role of wood harvest from sustainably managed forests in the carbon cycle Type de document : Article/Communication Auteurs : Ernst Detlef Schulze, Auteur ; Olivier Bouriaud , Auteur ; Roland Irslinger, Auteur ; Riccardo Valentini, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bilan du carbone [Termes IGN] coupe (sylviculture) [Termes IGN] dioxyde de carbone [Termes IGN] erreur systématique [Termes IGN] forêt [Termes IGN] lutte contre le changement climatique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) We investigate the flux balance of managed and protected forests and the effects of using wood. Flux parameters of CO2 uptake and respiration do not differ between managed and protected forests. Accounting of harvest as immediate emission by IPCC guidelines results in a bias of forest climate mitigation towards storage and neglects the avoidance of fossil-fuel use by wood use. Numéro de notice : A2022-158 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01127-x Date de publication en ligne : 07/04/2022 En ligne : http://dx.doi.org/10.1186/s13595-022-01127-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100366 [article]

Features predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (1 November 2022)  

Public [article]  inForest ecology and management > vol 523 (1 November 2022) . - n° 120480 Titre : Features predisposing forest to bark beetle outbreaks and their dynamics during drought Type de document : Article/Communication Auteurs : M. Müller, Auteur ; P.O. Olsson, Auteur ; Lars Eklundh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des risques [Termes IGN] canopée [Termes IGN] changement climatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données météorologiques [Termes IGN] humidité du sol [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] Scolytinae [Termes IGN] sécheresse [Termes IGN] Suède [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Climate change is estimated to increase the risk of the bark beetle (Ips typographus L.) mass outbreaks in Norway Spruce (Picea abies (L.) Karst) forests. Habitats that are thermally suitable for bark beetles may expand, and an increase in the frequency and intensity of droughts can promote drought stress on host trees. Drought affects tree vigor and in unison with environmental features it influences the local predisposition risk of forest stands to bark beetle attacks. We aimed to study how various environmental features influence the risk of bark beetle attacks during a drought year and the following years with more normal weather conditions but with higher bark beetle populations. We included features representing local forest stand attributes, topography, soil type and wetness, the proximity of clear-cuts and previous bark beetle attacks, and a machine learning algorithm (random forest) was applied to study the variation of predisposition risk across a 48,600 km2 study area in SE Sweden. Forest stands with increased risk of bark beetle attack were distinguished with high accuracy both during drought and in normal weather conditions. The results show that during both study periods, spruce and mixed coniferous forests had elevated risk of attack, while forests with a mix of deciduous and coniferous trees had a lower risk. Forests with high average canopy height were strongly predisposed to bark beetle attacks. However, during the drought year risk was more similar between stands with lower and higher canopy height, suggesting that during drought periods younger trees can be predisposed to bark beetle attacks. The importance of soil moisture and position within the local landscape were highlighted as important features during the drought year. Identifying areas with increased risk, supported by information on how environmental features control the predisposition risk during drought, could aid adaptation strategies and forest management intervention efforts. We conclude that geospatial data and machine learning have the potential to further support the digitalization of the forest industry, facilitating development of methods capable to quantify importance and dynamics of environmental features controlling the risk in local context. Corresponding methods could help to direct management actions more effectively and offer information for decision-making in changing climate. Numéro de notice : A2022-731 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120480 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120480 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101687 [article]

A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation / Fang Gao in Computers & geosciences, vol 168 (November 2022)  

Public [article]  inComputers & geosciences > vol 168 (November 2022) . - n° 105219 Titre : A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation Type de document : Article/Communication Auteurs : Fang Gao, Auteur ; Yihui Li, Auteur ; Peng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bâtiment [Termes IGN] filtre de Gauss [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image Jilin [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] image satellite [Termes IGN] lissage de données Résumé : (auteur) The main difficulty of panchromatic-multispectral image fusion is to balance the quality of spatial information and the spectral fidelity. Most of the practical fusion methods determine the optimal parameters based on the spatial and spectral characteristics of all original panchromatic and multispectral bands. However, for built-up and non-built-up areas (like cropland, forest) in one image, there may be large differences in their spatial and spectral characteristics, so their fused results are not optimal respectively with same parameters. To address above issues, this paper presents a high-resolution satellite image fusion method assisted with building segmentation. First, the proposed approach computes the average gradient and Gaussian filtering parameters of built-up and non-built-up areas separately according to the building segmentation results, on the basis of smoothing filter-based intensity modulation (SFIM). Then the intermediate data of two types of areas are computed in parallel and they are composited to obtain the final fused image, weighted by the pixel-wise “building factors” derived from the building segmentation results. Moreover, to better simulate the spatial characteristics of the multispectral image, we perform the “gradient simulation” operation to extract the gradient values in the multispectral image. Experimental results on Jilin-1 satellite images show that the proposed method provides competitive performance in spatial resolution, multispectral fidelity and quantity of information, as compared to the state-of-the-art methods in mainstream commercial software. Numéro de notice : A2022-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105219 Date de publication en ligne : 11/09/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105219 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101657 [article]

Machine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)  

Public [article]  inJournal of structural engineering > Vol 148 n° 11 (November 2022) Titre : Machine learning models applied to a GNSS sensor network for automated bridge anomaly detection Type de document : Article/Communication Auteurs : Nicolas Manzini, Auteur ; André Orcesi, Auteur ; Christian Thom , Auteur ; Marc-Antoine Brossault, Auteur ; Serge Botton , Auteur ; Miguel Ortiz, Auteur ; John Dumoulin, Auteur Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Note générale : bibliographie EN ATTENTE DU DOCUMENT Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] détection d'anomalie [Termes IGN] ouvrage d'art [Termes IGN] pont [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance d'ouvrage [Termes IGN] topométrie de précision Résumé : (auteur) Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set. Numéro de notice : A2022-672 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1061/(ASCE)ST.1943-541X.0003469 En ligne : https://doi.org/10.1061/(ASCE)ST.1943-541X.0003469 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101615 [article]

A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe / Bingbin Wen in Forest ecology and management, vol 522 (15 October 2022)  

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Analysis of the spatial range of service and accessibility of hospitals designated for coronavirus disease 2019 in Yunnan Province, China / Liangting Zheng in Geocarto international, vol 37 n° 22 ([10/10/2022])  

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Land use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])  

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Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model / Santanu Dinda in Geocarto international, vol 37 n° 22 ([10/10/2022])  

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Raster-based method for building selection in the multi-scale representation of two-dimensional maps / Yilang Shen in Geocarto international, vol 37 n° 22 ([10/10/2022])  

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Canopy self-replacement in Pinus sylvestris rear-edge populations following drought-induced die-off and mortality / Jordi Margalef- Marrase in Forest ecology and management, vol 521 (1 October 2022)  

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Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review / Sahar S. Matin in Geocarto international, Vol 37 n° 21 ([01/10/2022])  

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Challenging the link between functional and spectral diversity with radiative transfer modeling and data / Javier Pacheco-Labradora in Remote sensing of environment, vol 280 (October 2022)  

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Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

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Estimation of ionospheric total electron content using GNSS observations derived from a smartphone / Li Xu in GPS solutions, vol 26 n° 4 (October 2022)  

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