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Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM / Jiehua Cai in Engineering Geology, vol 305 (August 2022)  

Public [article]  inEngineering Geology > vol 305 (August 2022) . - n° 106730 Titre : Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM Type de document : Article/Communication Auteurs : Jiehua Cai, Auteur ; Lu Zhang, Auteur ; Jie Dong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 106730 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie des risques [Termes IGN] déformation de surface [Termes IGN] données lidar [Termes IGN] données multisources [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie [Termes IGN] image ALOS-PALSAR [Termes IGN] image optique [Termes IGN] image Sentinel-SAR [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] MNS lidar [Termes IGN] MNS SRTM [Termes IGN] séisme [Termes IGN] Setchouan (Chine) [Termes IGN] surveillance géologique Résumé : (auteur) On 8th August 2017, a catastrophic Ms. 7.0 earthquake with a focal depth of 20 km struck the Jiuzhaigou County in Sichuan Province, China. It exerted a strong influence on the slope stability within the surrounding areas and triggered numerous secondary geohazards including rockfalls and other co-seismic landslides, which incurred drastic surface changes, and thus can be easily identified from cloud-free high-resolution optical imagery. Most of such landslides became stabilized shortly after the earthquake while others moving very slowly for years. In contrast, some slopes were destabilized without significant surface change into slow-moving landslides, which may pose long-term potential threats to people's life and property. Therefore, it is crucial to accurately identify these slow-moving landslides and regularly monitor their post-seismic activity. In this study, we employed the synthetic aperture radar interferometry (InSAR) techniques to detect and monitor slow-moving landslides after the earthquake in the Jiuzhaigou area, and analyzed the impacts of the earthquake on these landslides through integration of multi-source data (InSAR, Lidar, optical image, and field survey). As a result, 16 slow-moving landslides were detected by InSAR in the Jiuzhaigou area, including several historical landslides. The results of time-series InSAR analyses enabled identification of three kinds of landslide evolution modes affected by the earthquake, i.e. acceleration of deformation of pre-existing landslides, reactivation of dormant landslide, and remobilization of earthquake-triggered landslide. Each mode is supported by detailed analyses of multi-source data. The results demonstrated that satellite InSAR combined with high-resolution Lidar and optical data can provide a cost-effective approach of post-earthquake geohazards detection and monitoring. Numéro de notice : A2022-469 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.enggeo.2022.106730 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.1016/j.enggeo.2022.106730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100811 [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]

Volunteered geographic information mobile application for participatory landslide inventory mapping / Raden Muhammad Anshori in Computers & geosciences, vol 161 (April 2022)  

Public [article]  inComputers & geosciences > vol 161 (April 2022) . - n° 105073 Titre : Volunteered geographic information mobile application for participatory landslide inventory mapping Type de document : Article/Communication Auteurs : Raden Muhammad Anshori, Auteur ; Guruh Samodra, Auteur ; Djati Mardiatno, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105073 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] approche participative [Termes IGN] base de données [Termes IGN] cartographie thématique [Termes IGN] données localisées des bénévoles [Termes IGN] effondrement de terrain [Termes IGN] géopositionnement [Termes IGN] inventaire [Termes IGN] Java (île de) [Termes IGN] téléphonie mobile Résumé : (auteur) Participatory landslide inventory mapping using the Volunteered Geographic Information (VGI) mobile app is a promising method to produce a landslide inventory map. The aim of this research is to describe the development and implementation of the VGI mobile app for participatory landslide inventory mapping. The architecture VGI mobile app is developed on the basis of Free Open-source Software for Geospatial Application server-client software to ensure reproducibility and flexibility, and to reduce cost. Anyone can reproduce, modify, and share the code, which suggests improvement in the collective ability to use, prepare, and landslide inventory update. Landslide inventory using VGI mobile app shows that the tool and method successfully map landslides in the landslide prone area (Magelang Regency, Central Java Province, Indonesia) with fairly high levels of effectiveness and convenience. Magelang Regency, one of the landslide prone areas in Java, is located in the intermountain basin surrounded by Menoreh Mountain, Merapi, Merbabu, Suropati-Telomoyo Complex, and Sumbing Volcano. In this study, landslide inventory mapping using VGI mobile app was applied in Magelang Regency by 17 volunteers from BPBD (Regional Agency for Disaster Management) Magelang Regency for three days. Landslides area occurred from 2017 to 2019 were properly identified and mapped by the volunteers. The sizes of landslides varied from 5.2 m2 to 4,632.5 m2, and the average was 208.2 m2. A team of volunteer was able to map 7-10 landslides per day. Participatory mapping using VGI mobile app reduces the time in transferring field data to a GIS database, in contrast to conventional participatory landslide inventory mapping. VGI mobile app allows users to provide new geographical landslide data, share landslide data rapidly, ensure consistency of landslide data, and improve accessibility of landslide data. The use of the VGI mobile app for participatory landslide inventory mapping provides new opportunities to improve risk assessment, preparedness, and early action and warning to landslide hazard. Numéro de notice : A2022-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.cageo.2022.105073 Date de publication en ligne : 22/02/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105073 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99918 [article]

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)  

Public [article]  inComputers & geosciences > vol 158 (January 2022) . - n° 104966 Titre : 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 Type de document : Article/Communication Auteurs : Huijuan Zhang, Auteur ; Yingxu Song, Auteur ; Shiluo Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104966 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aléa [Termes IGN] apprentissage automatique [Termes IGN] base de données localisées [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] effondrement de terrain [Termes IGN] modèle de simulation [Termes IGN] régression géographiquement pondérée [Termes IGN] régression logistique [Termes IGN] risque naturel [Termes IGN] Trois Gorges, barrage des Résumé : (auteur) This study aims to investigate the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation. Wanzhou section of the Three Gorges Reservoir area, China, frequently suffering numerous landslides, is chosen as an example. The class-weighted algorithm focuses on the class-imbalanced issue of landslide and non-landslide samples, and it can turn the class-imbalanced issue into a cost-sensitive machine learning by setting unequal weights for different classes, which contribute to improving the accuracy of landslide susceptibility evaluation. The landslide inventory database was produced by field investigation and remote sensing images derived from Google Earth. Of the 233 landslides in the inventory, 40% were used for validation, and the remaining 60% were used for training purposes. Twelve environmental parameters (elevation, slope, aspect, curvature, distance to river, NDVI, NDWI, rainfall, seismic intensity, land use, TRI, lithology) were treated as inputs of the models to produce a landslide susceptibility map (LSM). The AUC value, Balanced accuracy, and Geometric mean score were utilized to estimate the quality of models. The result shows that the weighted models (weighted logistic regression (WLR), weighted LightGBM (WLightGBM), weighted random forest (WRF) have higher AUC values, Balanced accuracy, and Geometric mean scores than those of unweighted methods, which demonstrates that the weighted models exhibit better than unweighted models, with the WRF model having the best performance. The landslide susceptibility map of the Wanzhou section displays that the high and very high landslide susceptibility zones are mainly distributed on both sides of the river. The insights from this research will be useful for ameliorating the landslide susceptibility mapping and the prevention and mitigation for the Wanzhou section. Numéro de notice : A2022-029 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104966Get rights and content Date de publication en ligne : 27/10/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104966Get rights and content Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99268 [article]

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)  

Public [article]  inRemote sensing > vol 14 n° 1 (January-1 2022) . - n° 211 Titre : A GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods Type de document : Article/Communication Auteurs : Pengxiang Zhao, Auteur ; Zohreh Masoumi, Auteur ; Maryam Kalantari, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aléa [Termes IGN] analyse comparative [Termes IGN] apprentissage profond [Termes IGN] cartographie des risques [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] effondrement de terrain [Termes IGN] Iran [Termes IGN] modèle numérique de surface [Termes IGN] régression logistique [Termes IGN] réseau neuronal artificiel [Termes IGN] risque naturel [Termes IGN] système d'information géographique Résumé : (auteur) Landslides often cause significant casualties and economic losses, and therefore landslide susceptibility mapping (LSM) has become increasingly urgent and important. The potential of deep learning (DL) like convolutional neural networks (CNN) based on landslide causative factors has not been fully explored yet. The main target of this study is the investigation of a GIS-based LSM in Zanjan, Iran and to explore the most important causative factor of landslides in the case study area. Different machine learning (ML) methods have been employed and compared to select the best results in the case study area. The CNN is compared with four ML algorithms, including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). To do so, sixteen landslide causative factors have been extracted and their related spatial layers have been prepared. Then, the algorithms were trained with related landslide and non-landslide points. The results illustrate that the five ML algorithms performed suitably (precision = 82.43–85.6%, AUC = 0.934–0.967). The RF algorithm achieves the best result, while the CNN, SVM, the ANN, and the LR have the best results after RF, respectively, in this case study. Moreover, variable importance analysis results indicate that slope and topographic curvature contribute more to the prediction. The results would be beneficial to planning strategies for landslide risk management. Numéro de notice : A2022-056 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3390/rs14010211 Date de publication en ligne : 04/01/2022 En ligne : https://doi.org/10.3390/rs14010211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99459 [article]

Landslide evolution pattern revealed by multi-temporal DSMs obtained from historical aerial images / Michele Santangelo (2022)  

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A rapid assessment method for earthquake-induced landslide casualties based on GIS and logistic regression model / Yuqian Dai in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (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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Persistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images / Hari Shankar in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)  

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Investigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)  

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Landslide susceptibility prediction based on image semantic segmentation / Bowen Du in Computers & geosciences, vol 155 (October 2021)  

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Mise en place d'un dispositif expérimental numérique pour l'enseignement des risques naturels avec le jeu vidéo Minetest / Jérôme Staub in Cartes & Géomatique, n° 245-246 (septembre - décembre 2021)

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Orogenic collapse and stress adjustments revealed by an intense seismic swarm following the 2015 Gorkha earthquake in Nepal / Lok Bijaya Adhikari in Frontiers in Earth Science, vol 9 (2021)  

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Shoreline changes along Northern Ibaraki Coast after the great East Japan earthquake of 2011 / Quang Nguyen Hao in Remote sensing, vol 13 n° 7 (April-1 2021)  

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Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide / Chaoyang Niu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

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