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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)
[article]
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] aléa
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] éboulement
[Termes IGN] hétérogénéité spatiale
[Termes IGN] prévention des risquesRésumé : (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
in Natural Hazards > vol 114 n° 3 (December 2022) . - pp 2709 - 2738[article]Ground deformation monitoring of the eruption offshore Mayotte / Aline Peltier in Comptes rendus : Géoscience Sciences de la planète, vol 354 n° S2 (2022)
[article]
Titre : Ground deformation monitoring of the eruption offshore Mayotte Titre original : Suivi des déformations liées à l’éruption au large de Mayotte Type de document : Article/Communication Auteurs : Aline Peltier, Auteur ; Sébastien Saur , Auteur ; Valérie Ballu, Auteur ; François Beauducel, Auteur ; Pierre Briole, Auteur ; Kristel Chanard , Auteur ; et al., Auteur ; Perrine Rouffiac , Auteur ; Pierre Valty , Auteur Année de publication : 2022 Article en page(s) : pp 1 - 23 Note générale : bibliographie
REVOSIMA (Réseau de surveillance volcanologique et sismologique de Mayotte)Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] coordonnées GNSS
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données GRACE
[Termes IGN] Mayotte
[Termes IGN] séismeRésumé : (auteur) En mai 2018, l’île de Mayotte a été touchée par une crise sismique sans précédent, suivie en juillet 2018 par des déplacements de surface à terre anormaux. En cumulé, du 1er juillet 2018 au 31 décembre 2021, les déplacements horizontaux étaient d’environ 21 à 25 cm vers l’est, et la subsidence d’environ 10 à 19 cm. L’étude des données GNSS à terre, et leur modélisation couplée aux données des capteurs de pression en mer, ont permis de conclure à une origine magmatique de la crise sismique avec la déflation d’une source profonde à l’est de Mayotte, confirmée en mai 2019 par la découverte d’une éruption sous-marine, à 50 km au large de Mayotte ([Feuillet et al., 2021]). Malgré une géométrie de réseau non optimale et des récepteurs éloignés de la source, les données GNSS ont permis de suivre la dynamique profonde du transfert magmatique, via la surveillance des flux volumiques. Numéro de notice : A2022-917 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article DOI : 10.5802/crgeos.176 Date de publication en ligne : 19/12/2022 En ligne : https://doi.org/10.5802/crgeos.176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102409
in Comptes rendus : Géoscience Sciences de la planète > vol 354 n° S2 (2022) . - pp 1 - 23[article]Impact of skidding operations on forest soils: a narrative review / Monica Cecilia Zurita Vintimilla in Revista Padurilor, vol 137 n° 4 (2022)
[article]
Titre : Impact of skidding operations on forest soils: a narrative review Type de document : Article/Communication Auteurs : Monica Cecilia Zurita Vintimilla, Auteur Année de publication : 2022 Langues : Anglais (eng) Descripteur : [Termes IGN] débardage
[Termes IGN] impact sur l'environnement
[Termes IGN] sol forestier
[Vedettes matières IGN] ForesterieNuméro de notice : A2022-584 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : sans Date de publication en ligne : 16/12/2022 En ligne : http://revistapadurilor.com/wp-content/uploads/2017/09/2.-IMPACT-OF-SKIDDING-OPE [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103252
in Revista Padurilor > vol 137 n° 4 (2022)[article]Vertical deformation and residual altimeter systematic errors around continental Australia inferred from a Kalman-based approach / Mohammad-Hadi Rezvani in Journal of geodesy, vol 96 n° 12 (December 2022)
[article]
Titre : Vertical deformation and residual altimeter systematic errors around continental Australia inferred from a Kalman-based approach Type de document : Article/Communication Auteurs : Mohammad-Hadi Rezvani, Auteur ; Christopher S. Watson, Auteur ; Matt A. King, Auteur Année de publication : 2022 Article en page(s) : n° 96 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] altimètre
[Termes IGN] Australie occidentale (Australie)
[Termes IGN] déformation verticale de la croute terrestre
[Termes IGN] données altimétriques
[Termes IGN] données marégraphiques
[Termes IGN] erreur systématique
[Termes IGN] filtre de Kalman
[Termes IGN] montée du niveau de la mer
[Termes IGN] série temporelle
[Termes IGN] variabilitéRésumé : (auteur) We further developed a space–time Kalman approach to investigate time-fixed and time-variable signals in vertical land motion (VLM) and residual altimeter systematic errors around the Australian coast, through combining multi-mission absolute sea-level (ASL), relative sea-level from tide gauges (TGs) and Global Positioning System (GPS) height time series. Our results confirmed coastal subsidence in broad agreement with GPS velocities and unexplained by glacial isostatic adjustment alone. VLM determined at individual TGs differs from spatially interpolated GPS velocities by up to ~ 1.5 mm/year, yielding a ~ 40% reduction in RMSE of geographic ASL variability at TGs around Australia. Our mission-specific altimeter error estimates are small but significant (typically within ~ ± 0.5–1.0 mm/year), with negligible effect on the average ASL rate. Our circum-Australia ASL rate is higher than previous results, suggesting an acceleration in the ~ 27-year time series. Analysis of the time-variability of altimeter errors confirmed stability for most missions except for Jason-2 with an anomaly reaching ~ 2.8 mm/year in the first ~ 3.5 years of operation, supported by analysis from the Bass Strait altimeter validation facility. Data predominantly from the reference missions and located well off narrow shelf regions was shown to bias results by as much as ~ 0.5 mm/year and highlights that residual oceanographic signals remain a fundamental limitation. Incorporating non-reference-mission measurements well on the shelf helped to mitigate this effect. Comparing stacked nonlinear VLM estimates and altimeter systematic errors with the El Niño-Southern Oscillation shows weak correlation and suggests our approach improves the ability to explore nonlinear localized signals and is suitable for other regional- and global-scale studies. Numéro de notice : A2022-897 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01680-3 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1007/s00190-022-01680-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102251
in Journal of geodesy > vol 96 n° 12 (December 2022) . - n° 96[article]Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? / Arthur Sanguet in Global ecology and conservation, vol 39 (November 2022)
[article]
Titre : Beyond topo-climatic predictors: Does habitats distribution and remote sensing information improve predictions of species distribution models? Type de document : Article/Communication Auteurs : Arthur Sanguet, Auteur ; Nicolas Wyler, Auteur ; Blaise Petitpierre, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° e02286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte d'occupation du sol
[Termes IGN] changement climatique
[Termes IGN] distribution spatiale
[Termes IGN] échantillonnage de données
[Termes IGN] habitat (nature)
[Termes IGN] modèle de simulation
[Termes IGN] montagne
[Termes IGN] pédologie locale
[Termes IGN] Suisse
[Termes IGN] télédétection
[Termes IGN] topographie locale
[Termes IGN] zone humide
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Species Distribution Models (SDM) represent a powerful tool to predict species’ habitat suitability on a landscape and fill the gap between truncated observation data and all possible locations. SDMs have been widely used in theoretical studies of species niches as well as in conservation applications. Here, we evaluated the impacts of predictors’ type on models’ performances and spatial predictions using 72 plant species belonging to six ecological groups at a regional scale in the area of Geneva (Switzerland). Twelve models were created using various combinations of high-resolution (25 m) explanatory variables including topography, pedology, climate, habitats and remote sensing data. Models integrating a combination of habitats and topopedo-climatic predictors had significantly higher performances, while remote sensing predictors showed low performances. Our results suggest that the number and the level of details of habitat predictors (broad or very precise) do not fundamentally affect prediction maps. However, selecting too few, overly simplified or exceedingly complex habitat predictors tend to lower models’ performances. The use of eight habitat categories complemented with eight topopedo-climatic predictors produced models with the highest performances. Ecological groups of species responded differently to models and while alpine and ruderal species have greater average performances due to a high affinity with topopedo-climatic predictors, wetlands’ species were less performant on average. These results underline the necessity of developing or having access to habitats distribution data especially in a conservation context. Numéro de notice : A2022-815 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1016/j.gecco.2022.e02286 Date de publication en ligne : 13/09/2022 En ligne : https://doi.org/10.1016/j.gecco.2022.e02286 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101977
in Global ecology and conservation > vol 39 (November 2022) . - n° e02286[article]Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)PermalinkGraph neural networks with constraints of environmental consistency for landslide susceptibility evaluation / Haowei Zeng in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)PermalinkMachine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)PermalinkModelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach / Abebe Debele Tolche in Geocarto international, vol 37 n° 24 ([20/10/2022])PermalinkGIS and MCDMA prioritization based modeling for sub-watershed in Bastora river basin / Raid Mahmood Faisal in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkCorrecting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France / Michaela Nováková in Remote sensing of environment, vol 280 (October 2022)PermalinkDeveloping 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)PermalinkInvestigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)PermalinkModelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)PermalinkRemote sensing and GIS based Soil Loss Estimation for Bhutan, using RUSLE model / Sangay Gyeltshen in Geocarto international, Vol 37 n° 21 ([01/10/2022])Permalink