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scientifique

Prescribed fire after thinning increased resistance of sub-Mediterranean pine forests to drought events and wildfires / Lena Vilà-Vilardell in Forest ecology and management, vol 527 (January-1 2023)  

Public [article]  inForest ecology and management > vol 527 (January-1 2023) . - n° 120602 Titre : Prescribed fire after thinning increased resistance of sub-Mediterranean pine forests to drought events and wildfires Type de document : Article/Communication Auteurs : Lena Vilà-Vilardell, Auteur ; Miquel De Cáceres, Auteur ; Míriam Piqué, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] brûlis [Termes IGN] éclaircie (sylviculture) [Termes IGN] incendie de forêt [Termes IGN] Pinus nigra [Termes IGN] sécheresse [Termes IGN] sous-étage [Termes IGN] stress hydrique [Termes IGN] structure d'un peuplement forestier [Termes IGN] vulnérabilité [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Vegetation structure affects the vulnerability of a forest to drought events and wildfires. Management decisions, such as thinning intensity and type of understory treatment, influence competition for water resources and amount of fuel available. While heavy thinning effectively reduces tree water stress and intensity of a crown fire, the duration of these benefits may be limited by a fast growth response of the understory. Our aim was to study the effect of forest structure on pine forests vulnerability to extreme drought events and on the potential wildfire behaviour after management, with a special focus on the role of the understory. In three sub-Mediterranean sites of NE Spain dominated by Pinus nigra, two intensities of thinning (light: aiming at 70–75% canopy cover; and heavy: aiming at 50–60% canopy cover) followed by two understory treatments (mechanical only and mechanical plus prescribed burning) were applied, resulting in four differently managed stands plus an untreated control per site. Four to five years after management, we measured forest structure (overstory in one 314 m2 circular plot and understory in 20 quadrats of 1 m2 per treatment unit) and fuel load (in two 10 m transects per treatment unit) and simulated water balance and fire behaviour under extreme weather conditions. Understory contribution was assessed comparing the real structure with a virtual forest stand where understory vegetation equalled the one of the untreated control. Our results suggest that the resulting mid-term structure following treatments effectively reduced water stress and fire behaviour compared with untreated control, and that the most effective treatments were the ones where prescribed burning was applied after light or heavy thinning. While understory clearing contributes to increase the resistance to both disturbances, an additive effect of burning the debris reduced the vulnerability to drought and wildfires after treatments. Our study highlights the importance of managing the understory to further increase forest resistance to both disturbances. Numéro de notice : A2022-858 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120602 Date de publication en ligne : 08/11/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102109 [article]

Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale / Shengwu Qin in Natural Hazards, vol 114 n° 3 (December 2022)  

Public [article]  inNatural Hazards > vol 114 n° 3 (December 2022) . - pp 2709 - 2738 Titre : Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale Type de document : Article/Communication Auteurs : Shengwu Qin, Auteur ; Shuangshuang Qiao, Auteur ; Jingyu Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2709 - 2738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] éboulement [Termes IGN] hétérogénéité spatiale [Termes IGN] prévention des risques [Termes IGN] séparateur à vaste marge [Termes IGN] système d'information géographique Résumé : (auteur) Susceptibility mapping is an effective means of preventing debris flow disasters. However, previous studies have failed to solve spatial heterogeneity well, especially at the regional scale. The main objective of this study is to solve the spatial heterogeneity of regional-scale debris flow susceptibility (DFS) mapping by establishing a geographic information system (GIS)-based processing framework. The framework was realized by integrating the determination factor (DFactor) model with machine learning models. The DFactor model established different combinations of evaluation factors in each local region and clarified the differing contributions of influencing factors to DFS. To test the feasibility of the framework, the support vector machine (SVM) and two-dimensional convolutional neural network (CNN) were integrated with the DFactor model (DFactor-SVM and DFactor-CNN) to evaluate DFS in Jilin Province, China. The individual models (SVM and CNN) were also used to map the DFS for comparison with the integrated models. For debris flow modeling, 868 debris flow samples were collected and randomly divided into two datasets: 70% of the samples were used for training and the result was used for verification. The results of the receiver operating characteristic curve showed that the integrated models performed better. The DFactor-CNN model had the highest predictive accuracy, followed by the DFactor-SVM, CNN and SVM models. In general, the GIS-based processing framework maximizes the contribution of the influencing factors to debris flows and enhances the prediction ability of models. Furthermore, it provides a reliable means to predict debris flows at the regional scale. Numéro de notice : A2022-854 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05487-5 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1007/s11069-022-05487-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102101 [article]

Forêt amazonienne : de nouveau sous contrôle ? / Laurent Polidori in Géomètre, n° 2208 (décembre 2022)

Public [article]  inGéomètre > n° 2208 (décembre 2022) . - pp 24 - 24 Titre : Forêt amazonienne : de nouveau sous contrôle ? Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 24 - 24 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amazonie [Termes IGN] déboisement [Termes IGN] forêt [Termes IGN] incendie de forêt [Termes IGN] télédétection spatiale Résumé : (Auteur) La forêt amazonienne s’est encore trouvée au centre de toutes les attentions lors de la COP27 qui vient de se tenir en Egypte, compte tenu de son rôle essentiel dans l’évolution du climat mondial. Mais 2022 était une année atypique. Numéro de notice : A2022-806 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : sans Date de publication en ligne : 01/12/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102191 [article]

Geographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 12 (December 2022) . - n° 598 Titre : Geographic named entity recognition by employing natural language processing and an improved BERT model Type de document : Article/Communication Auteurs : Liufeng Tao, Auteur ; Zhong Xie, Auteur ; Dexin Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] Chine [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données issues des réseaux sociaux [Termes IGN] données publiques [Termes IGN] jeu de données [Termes IGN] reconnaissance de caractères [Termes IGN] reconnaissance de noms [Termes IGN] test de performance [Termes IGN] toponyme [Termes IGN] traitement du langage naturel Résumé : (auteur) Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models. Numéro de notice : A2022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/ijgi11120598 Date de publication en ligne : 28/11/2022 En ligne : https://doi.org/10.3390/ijgi11120598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102178 [article]

Modelling evacuation preparation time prior to floods: A machine learning approach / R. Sreejith in Sustainable Cities and Society, vol 87 (December 2022)  

Public [article]  inSustainable Cities and Society > vol 87 (December 2022) . - n° 104257 Titre : Modelling evacuation preparation time prior to floods: A machine learning approach Type de document : Article/Communication Auteurs : R. Sreejith, Auteur ; K.R. Sinimole, Auteur Année de publication : 2022 Article en page(s) : n° 104257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chronométrie [Termes IGN] données spatiotemporelles [Termes IGN] gestion de crise [Termes IGN] inondation [Termes IGN] Kerala (Inde ; état) [Termes IGN] modèle de simulation [Termes IGN] plan de prévention des risques [Termes IGN] questionnaire [Termes IGN] risque naturel [Termes IGN] secours d'urgence Résumé : (auteur) Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process. Numéro de notice : A2022-819 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104257 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104257 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101986 [article]

«Accompagner le rétablissement spontané de la forêt après un incendie» / Jacques Hazera in Géomètre, n° 2207 (novembre 2022)

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A GIS and hybrid simulation aided environmental impact assessment of city-scale demolition waste management / Zhikun Ding in Sustainable Cities and Society, vol 86 (November 2022)  

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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 (October-15 2022)  

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Deep learning high resolution burned area mapping by transfer learning from Landsat-8 to PlanetScope / V.S. Martins in Remote sensing of environment, vol 280 (October 2022)  

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Investigation of recognition and classification of forest fires based on fusion color and textural features of images / Cong Li in Forests, vol 13 n° 10 (October 2022)  

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Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)  

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Regional climate moderately influences species-mixing effect on tree growth-climate relationships and drought resistance for beech and pine across Europe / Géraud de Streel in Forest ecology and management, vol 520 (September-15 2022)  

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Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)  

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Exploring multi-modal evacuation strategies for a landlocked population using large-scale agent-based simulations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 36 n° 9 (September 2022)  

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Feux de forêt : un drone traque les risques de reprise / Nathalie Da Cruz in Géomètre, n° 2205 (septembre 2022)

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