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Mapping territorial vulnerability to wildfires: A participative multi-criteria analysis / Miguel Rivière in Forest ecology and management, vol 539 (July-1 2023)  

Public [article]  inForest ecology and management > vol 539 (July-1 2023) Titre : Mapping territorial vulnerability to wildfires: A participative multi-criteria analysis Type de document : Article/Communication Auteurs : Miguel Rivière, Auteur ; Jonathan Lenglet, Auteur ; Adrien Noirault, Auteur ; et al., Auteur Année de publication : 2023 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse de sensibilité [Termes IGN] analyse multicritère [Termes IGN] cartographie des risques [Termes IGN] incendie de forêt [Termes IGN] processus de hiérarchisation analytique [Termes IGN] Rhône-Méditerranée-Corse [Termes IGN] système d'information géographique [Termes IGN] utilisation du sol [Termes IGN] vulnérabilité Numéro de notice : A2023-216 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1016/j.foreco.2023.121014 Date de publication en ligne : 22/04/2023 En ligne : https://doi.org/10.1016/j.foreco.2023.121014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103146 [article]

Prévenir et limiter l’impact des feux de forêts / Martine Kis in Géomètre, n° 2215 (juillet - août 2023)

Public [article]  inGéomètre > n° 2215 (juillet - août 2023) . - pp 20 - 21 Titre : Prévenir et limiter l’impact des feux de forêts Type de document : Article/Communication Auteurs : Martine Kis, Auteur Année de publication : 2023 Article en page(s) : pp 20 - 21 Langues : Français (fre) Descripteur : [Termes IGN] incendie de forêt [Termes IGN] prévention des risques Résumé : (auteur) Réchauffement climatique, sécheresse, urbanisation se traduisent par une augmentation du nombre d’incendies de forêt sur une plus longue période. Lutter contre ce fléau implique l’Etat, les collectivités et les citoyens. Numéro de notice : A2023-232 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103536 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
063-2023071	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images / Ziyao Xing in Sustainable Cities and Society, vol 92 (May 2023)  

Public [article]  inSustainable Cities and Society > vol 92 (May 2023) . - n° 104467 Titre : Flood vulnerability assessment of urban buildings based on integrating high-resolution remote sensing and street view images Type de document : Article/Communication Auteurs : Ziyao Xing, Auteur ; Shuai Yang, Auteur ; Xuli Zan, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 104467 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] bâtiment [Termes IGN] Chine [Termes IGN] gestion des risques [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] milieu urbain [Termes IGN] planification urbaine [Termes IGN] Quickbird [Termes IGN] segmentation sémantique [Termes IGN] vulnérabilité Résumé : (auteur) Urban flood risk management requires an extensive investigation of the vulnerability characteristics of buildings. Large-scale field surveys usually cost a lot of time and money, while satellite remote sensing and street view images can provide information on the tops and facades of buildings respectively. Thereupon, this paper develops a building vulnerability assessment framework using remote sensing and street view features. Specifically, a UNet-based semantic segmentation model, FSA-UNet (Fusion-Self-Attention-UNet) is proposed to integrate remote sensing and street view features and the vulnerability information contained in the images is fully exploited. And the building vulnerability index is generated to provide the spatial distribution characteristics of urban building vulnerability. The experiment shows that the mIoU of the proposed model can reach 82% for building vulnerability classification in Hefei, China, which is more accurate than the traditional semantic segmentation models. The results indicate that the integration of street view and remote sensing image features can improve the ability of building vulnerability assessment, and the model proposed in this study can better capture the correlation features of multi-angle images through the self-attention mechanism and combines hierarchy features and edge information to improve the classification effect. This study can support for disaster management and urban planning. Numéro de notice : A2023-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2023.104467 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.1016/j.scs.2023.104467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102826 [article]

Seismic deformation in the Adriatic Sea region / B. Orecchio in Journal of geodynamics, vol 155 (March 2023)  

Public [article]  inJournal of geodynamics > vol 155 (March 2023) . - n°101956 Titre : Seismic deformation in the Adriatic Sea region Type de document : Article/Communication Auteurs : B. Orecchio, Auteur ; D. Presti, Auteur ; S. Scolaro, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n°101956 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] Adriatique, mer [Termes IGN] déformation de la croute terrestre [Termes IGN] faille géologique [Termes IGN] forme d'onde [Termes IGN] histogramme [Termes IGN] inversion [Termes IGN] sismologie [Termes IGN] surveillance géologique [Termes IGN] tectonique des plaques Résumé : (auteur) We present an overall analysis of the recent seismic activity occurred in the Adriatic Sea region, a strongly debated sector of the Mediterranean area, where several authors have proposed different models of plate configuration and kinematics. In the past, seismic investigations of this marine area have been strongly hampered by non-optimal network geometries, but data quality increase and recent methodological improvements lay the groundwork to attempt more accurate analyses including proper evaluations of result reliability. On these grounds, we investigated the seismic activity of the last decades by means of new hypocenter locations, waveform inversion focal mechanisms and seismogenic stress fields. We used the Bayloc non-linear probabilistic algorithm to compute hypocenter locations for the most relevant seismic sequences by carefully evaluating location quality and seismolineaments reliability. We also provided an updated database of waveform inversion focal mechanisms including original solutions estimated by applying the waveform inversion method Cut And Paste and data available from official catalogs. Then, focal mechanism solutions have been used to estimate seismogenic stress fields through different inversion algorithms. Seismic results indicate a relevant degree of fragmentation and different patterns of deformation in the Central Adriatic region. In particular, our analyses depicted two NW-SE oriented, adjacent volumes: (i) a pure compressive domain with NNE-trending axis of maximum compression characterizes the northeastern volume where the seismic activity occurs on W-to-NW oriented seismic sources; (ii) a transpressive domain with NW-trending axis of maximum compression characterizes the southwestern sector where thrust faulting preferentially occurs on ENE-to-NE oriented planes and strike-slip faulting on E-W ones. Joint evaluation of seismic findings of the present study and kinematic models proposed in the literature indicates just in the Central Adriatic region the presence of a broad deformation zone, accommodating a still evolving fragmentation of the Adriatic domain in two blocks rotating in opposite directions. On these grounds, the obtained results not only furnish new seismological evidence supporting the "two-blocks model" proposed by previous authors, but they also provide additional constraints, useful for better understanding and modeling the seismotectonic processes occurring in the Adriatic region. Numéro de notice : A2023-051 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.jog.2022.101956 Date de publication en ligne : 30/11/2022 En ligne : https://doi.org/10.1016/j.jog.2022.101956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102379 [article]

Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 228 - 240 Titre : Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models Type de document : Article/Communication Auteurs : Xikun Hu, Auteur ; Puzhao Zhang, Auteur ; Yifang Ban, Auteur Année de publication : 2023 Article en page(s) : pp 228 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dommage [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] incendie de forêt [Termes IGN] jeu de données localisées [Termes IGN] segmentation sémantique [Termes IGN] surveillance forestière [Termes IGN] zone sinistrée Résumé : (auteur) Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial and comprehensive information for fire stabilization and recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g., dNBR, and RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort using prior knowledge and visual inspection to determine burn severity thresholds. In this study, we aim to employ highly automated approaches to provide spatially explicit damage level estimates. We first reorganize a large-scale Landsat-based bi-temporal burn severity assessment dataset (Landsat-BSA) by visual data cleaning based on annotated MTBS data (approximately 1000 major fire events in the United States). Then we apply state-of-the-art deep learning (DL) based methods to map burn severity based on the Landsat-BSA dataset. Experimental results emphasize that multi-class semantic segmentation algorithms can approximate the threshold-based techniques used extensively for burn severity classification. UNet-like models outperform other region-based CNN and Transformer-based models and achieve accurate pixel-wise classification results. Combined with the online hard example mining algorithm to reduce class imbalance issue, Attention UNet achieves the highest mIoU (0.78) and the highest Kappa coefficient close to 0.90. The bi-temporal inputs with ancillary spectral indices work much better than the uni-temporal multispectral inputs. The restructured dataset will be publicly available and create opportunities for further advances in remote sensing and wildfire communities. Numéro de notice : A2023-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.026 Date de publication en ligne : 11/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102498 [article]

Perspectives: Critical zone perspectives for managing changing forests / Marissa Kopp in Forest ecology and management, vol 528 (January-15 2023)  

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Cartographier l'anthropocène 2023 / IGN (2023)    

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La forêt progresse mais la mortalité des arbres s’accroît / Anonyme in Géomètre, n° 2209 (janvier 2023)

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Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)  

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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)  

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Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) / Rolando Carbonari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

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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)  

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

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

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Spatio-temporal patterns of wildfires in Siberia during 2001–2020 / Oleg Tomshin in Geocarto international, vol 37 n° 25 ([01/12/2022])  

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