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How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps / Marion E. Caduff in Forest ecology and management, vol 514 (15 June 2022)  

Public [article]  inForest ecology and management > vol 514 (15 June 2022) . - n° 120201 Titre : How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps Type de document : Article/Communication Auteurs : Marion E. Caduff, Auteur ; Natalie Brožová, Auteur ; Andrea D. Kupferschmid, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120201 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Alpes [Termes IGN] avalanche [Termes IGN] bois mort [Termes IGN] dépérissement [Termes IGN] image à haute résolution [Termes IGN] modèle de simulation [Termes IGN] orthophotographie [Termes IGN] protection des forêts [Termes IGN] régénération (sylviculture) [Termes IGN] risque naturel [Termes IGN] santé des forêts [Termes IGN] Scolytinae [Termes IGN] Suisse [Termes IGN] xylophage Résumé : (auteur) Large-scale bark beetle outbreaks in spruce dominated mountain forests have increased in recent decades, and this trend is expected to continue in the future. These outbreaks have immediate and major effects on forest structure and ecosystem services. However, it remains unclear how forests recover from bark beetle infestations over the long term, and how different recovery stages fulfil the capacity of forests to protect infrastructures and human lives from natural hazards. The aim of this study was to investigate how a bark beetle infestation (1992–1997) in a spruce dominated forest in the Swiss Alps changed the forest structure and its protective function against snow avalanches. In 2020, i.e. 27 years after the peak of the outbreak, we re-surveyed the composition and height of new trees, as well as the deadwood height and degree of decay in an area that had been surveyed 20 years earlier. With the help of remote sensing data and avalanche simulations, we assessed the protective effect against avalanches before the disturbances (in 1985) and in 1997, 2007, 2014 and 2019 for a frequent (30-year return period) and an extreme (300-year return period) avalanche scenario. Post-disturbance regeneration led to a young forest that was again dominated by spruce 27 years after the outbreak, with median tree heights of 3–4 m and a crown cover of 10–30%. Deadwood covered 20–25% of the forest floor and was mainly in decay stages two and three out of five. Snags had median heights of 1.4 m, leaning logs 0.5 m and lying logs 0.3 m. The protective effect of the forest was high before the bark beetle outbreak and decreased during the first years of infestation (until 1997), mainly in the case of extreme avalanche events. The protective capacity reached an overall minimum in 2007 as a result of many forest openings. It partially recovered by 2014 and further increased by 2019, thanks to forest regeneration. Simulation results and a lack of avalanche releases since the infestation indicate that the protective capacity of post-disturbance forest stands affected by bark beetle may often be underestimated. Numéro de notice : A2022-349 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2022.120201 Date de publication en ligne : 08/04/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100536 [article]

ART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage / M. Moreno in Journal of Cultural Heritage, vol 55 (May - June 2022)  

Public [article]  inJournal of Cultural Heritage > vol 55 (May - June 2022) . - pp 263 - 276 Titre : ART-RISK 3.0, a fuzzy-based platform that combine GIS and expert assessments for conservation strategies in cultural heritage Type de document : Article/Communication Auteurs : M. Moreno, Auteur ; R. Ortiz, Auteur ; D. Cagigas-Muñiz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 263 - 276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse des risques [Termes IGN] conservation du patrimoine [Termes IGN] église [Termes IGN] Espagne [Termes IGN] gelée [Termes IGN] Inférence floue [Termes IGN] inondation [Termes IGN] intelligence artificielle [Termes IGN] logique floue [Termes IGN] monument historique [Termes IGN] patrimoine culturel [Termes IGN] risque naturel [Termes IGN] séisme [Termes IGN] système d'information géographique [Termes IGN] température de l'air Résumé : (auteur) Heritage preservation poses numerous difficulties, especially in emergency situations or during budget cuts. In these contexts, having tools that facilitate efficient and rapid management of hazards-vulnerabilities is a priority for the preventive conservation and triage of cultural assets. This paper presents the first (to the authors' knowledge) free and public availability Artificial Intelligence platform designed for conservation strategies in cultural heritage. Art-Risk 3.0 is a platform designed as a fuzzy-logic inference system that combines information from geographical information system maps with expert assessments, in order to identify the contextual threat level and the degree of vulnerability that heritage buildings present. Thanks to the possibilities that the geographic information system offers, 12 Spanish churches (11th - 16th centuries) were analyzed. The artificial intelligence platform developed makes it possible to analyze the index of hazard, vulnerability and functionality, classify buildings according to the risk in order to do a sustainable use of budgets through the rational management of preventive conservation. The data stored in the system allows identify the danger due to geotechnics, precipitation, torrential downpour, thermal oscillation, frost, earthquake and flooding. Through the use of fuzzy logic, the tool interrelates environmental conditions with 14 other variables related to structural risks and the vulnerability of buildings, which are evaluated through bibliographic search and review of photographic images. The geographic information system has identified torrential rains and thermal oscillations as the environmental threats that mostly impact heritage buildings in Spain. The results obtained highlight the Church of Santiago de Jesús as the most vulnerable building due to a lack of preventive conservation programs. These results, consistent with the inclusion of this monument on the list of heritage at risk defined by Hispania Nostra, corroborate the functionality of the model. Numéro de notice : A2022-472 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.culher.2022.03.012 Date de publication en ligne : 14/04/2022 En ligne : https://doi.org/10.1016/j.culher.2022.03.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100818 [article]

Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 5 (May 2022) . - n° 269 Titre : Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China Type de document : Article/Communication Auteurs : Kezhen Yao, Auteur ; Saini Yang, Auteur ; Shengnan Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] dispersion [Termes IGN] effondrement de terrain [Termes IGN] Extreme Gradient Machine [Termes IGN] modèle de simulation [Termes IGN] régression linéaire [Termes IGN] risque naturel [Termes IGN] vulnérabilité Résumé : (auteur) Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities. Numéro de notice : A2022-371 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11050269 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.3390/ijgi11050269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100613 [article]

Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity / Marie Bouih in Earth and planetary science letters, vol 584 (15 April 2022)  

Public [article]  inEarth and planetary science letters > vol 584 (15 April 2022) . - n° 117465 Titre : Deep mass redistribution prior to the 2010 Mw 8.8 Maule (Chile) Earthquake revealed by GRACE satellite gravity Type de document : Article/Communication Auteurs : Marie Bouih , Auteur ; Isabelle Panet , Auteur ; Dominique Remy, Auteur ; Laurent Longuevergne, Auteur ; Sylvain Bonvalot, Auteur Année de publication : 2022 Projets : Université de Paris / Clerici, Christine Article en page(s) : n° 117465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] champ de pesanteur terrestre [Termes IGN] Chili [Termes IGN] déformation de la croute terrestre [Termes IGN] données GRACE [Termes IGN] gradient de gravitation [Termes IGN] jeu de données [Termes IGN] levé gravimétrique [Termes IGN] prévention des risques [Termes IGN] risque naturel [Termes IGN] séisme [Termes IGN] signal [Termes IGN] subduction [Termes IGN] tectonique des plaques Résumé : (auteur) Subduction zones megathrust faults constitute a considerable hazard as they produce most of the world's largest earthquakes. However, the role in megathrust earthquake generation exerted by deeper subduction processes remains poorly understood. Here, we analyze the 2003 – 2014 space-time variations of the Earth's gravity gradients derived from three datasets of GRACE geoid models over a large region surrounding the rupture zone of the Mw 8.8 Maule earthquake. In all these datasets, our analysis reveals a large-amplitude gravity gradient signal, progressively increasing in the three months before the earthquake, North of the epicentral area. We show that such signals are equivalent to a water storage decrease over 2 months and cannot be explained by hydrological sources nor artefacts, but rather find origin from mass redistributions within the solid Earth on the continental side of the subduction zone. These gravity gradient variations could be explained by an extensional deformation of the slab around 150-km depth along the Nazca Plate subduction direction, associated with large-scale fluid release. Furthermore, the lateral migration of the gravity signal towards the surface from a low coupling segment around North to the high coupling one in the South suggests that the Mw 8.8 earthquake may have originated from the propagation up to the trench of this deeper slab deformation. Our results highlight the importance of observations of the Earth's time-varying gravity field from satellites in order to probe slow mass redistributions in-depth major plate boundaries and provide new information on dynamic processes in the subduction system, essential to better understand the seismic cycle as a whole. Numéro de notice : A2022-280 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.epsl.2022.117465 En ligne : https://doi.org/10.1016/j.epsl.2022.117465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100288 [article]

Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101759 Titre : Determination of building flood risk maps from LiDAR mobile mapping data Type de document : Article/Communication Auteurs : Yu Feng, Auteur ; Qing Xiao, Auteur ; Claus Brenner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101759 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] bâtiment [Termes IGN] cartographie d'urgence [Termes IGN] cartographie des risques [Termes IGN] classification semi-dirigée [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] façade [Termes IGN] infiltration [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] prévention des risques [Termes IGN] risque naturel [Termes IGN] segmentation sémantique Résumé : (auteur) With increasing urbanization, flooding is a major challenge for many cities today. Based on forecast precipitation, topography, and pipe networks, flood simulations can provide early warnings for areas and buildings at risk of flooding. Basement windows, doors, and underground garage entrances are common places where floodwater can flow into a building. Some buildings have been prepared or designed considering the threat of flooding, but others have not. Therefore, knowing the heights of these facade openings helps to identify places that are more susceptible to water ingress. However, such data is not yet readily available in most cities. Traditional surveying of the desired targets may be used, but this is a very time-consuming and laborious process. Instead, mobile mapping using LiDAR (light detection and ranging) is an efficient tool to obtain a large amount of high-density 3D measurement data. To use this method, it is required to extract the desired facade openings from the data in a fully automatic manner. This research presents a new process for the extraction of windows and doors from LiDAR mobile mapping data. Deep learning object detection models are trained to identify these objects. Usually, this requires to provide large amounts of manual annotations. In this paper, we mitigate this problem by leveraging a rule-based method. In a first step, the rule-based method is used to generate pseudo-labels. A semi-supervised learning strategy is then applied with three different levels of supervision. The results show that using only automatically generated pseudo-labels, the learning-based model outperforms the rule-based approach by 14.6% in terms of F1-score. After five hours of human supervision, it is possible to improve the model by another 6.2%. By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels. Thus, our research provides a new geographic information layer for fine-grained urban emergency response. This information can be combined with flood forecasting to provide a more targeted disaster prevention guide for the city's infrastructure and residential buildings. To the best of our knowledge, this work is the first attempt to achieve such a large scale, fine-grained building flood risk mapping. Numéro de notice : A2022-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101759 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99964 [article]

Flood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco / Brahim Benzougagh in Iranian Journal of Science and Technology - Transactions of Civil Engineering, vol 46 n° 2 (April 2022)  

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Natural disturbances risks in European boreal and temperate forests and their links to climate change : A review of modelling approaches / Joyce Machado Nunes Romeiro in Forest ecology and management, vol 509 (1 April 2022)  

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Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)  

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Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers / Jacques Mourey in Natural Hazards and Earth System Sciences, vol 22 n° 2 (February 2022)  

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Atlas of global change risk of population and economic systems / Peijun Shi (2022)  

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Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)  

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Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea / Yong Piao in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

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A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)  

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Investigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations / E-Ping Rau (2022)  

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A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)  

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