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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]

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)  

Public [article]  inIranian Journal of Science and Technology - Transactions of Civil Engineering > vol 46 n° 2 (April 2022) . - pp 1481 - 1490 Titre : Flood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco Type de document : Article/Communication Auteurs : Brahim Benzougagh, Auteur ; Pierre-Louis Frison , Auteur ; Sarita Gajbhiye Meshram, Auteur ; Larbi Boudad, Auteur ; Abdallah Dridri, Auteur ; Driss Sadkaoui, Auteur ; Khalid Mimich, Auteur ; Khaled Mohamed Khedher, Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : pp 1481 - 1490 Note générale : bibliographie This research work was supported by the Deanship of Scientific Research at King Khalid University under Grant number RGP. 2/173/42. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bassin hydrographique [Termes IGN] cartographie des risques [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] inondation [Termes IGN] Maroc [Termes IGN] plan de prévention des risques [Termes IGN] prévention des risques [Termes IGN] risque naturel Résumé : (auteur) Natural disasters like floods are happening worldwide. Due to their negative impact on different social, economic and environmental aspects need to monitor and map these phenomena have increased. In fact, to access the zones affected by the flood, we use open source remote sensing (RS) images acquired by optical and radar sensors. Furthermore, we present a method using Sentinel-1 images; we suggest applying Ground Range Detected (GRD) images. For this purpose, pre-processed built and provided by the European Space Agency (ESA), preserved by free software Sentinel Application Platform (SNAP) for data extraction around appropriate demand. Moreover, the principal objective of this article is to assess the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images in order to visualize flood areas in the Inaouene watershed located in north-eastern of Morocco. The origin of this natural hazard is the combination of natural and anthropogenic factors that makes the watershed vulnerable with a sub-annual frequency. The results of this work help decision-makers and managers in the field of natural risk management and land-use planning to implement a strategy and action plan for the protection of the populations and the environment against the negative impact of floods. Numéro de notice : A2021-937 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1007/s40996-021-00683-y Date de publication en ligne : 18/06/2021 En ligne : https://doi.org/10.1007/s40996-021-00683-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99581 [article]

Multi-parameter risk mapping of Qazvin aquifer by classic and fuzzy clustering techniques / Saman Javadi in Geocarto international, vol 37 n° 4 (April 2022)  

Public [article]  inGeocarto international > vol 37 n° 4 (April 2022) . - pp 1160-1182 Titre : Multi-parameter risk mapping of Qazvin aquifer by classic and fuzzy clustering techniques Type de document : Article/Communication Auteurs : Saman Javadi, Auteur ; Seied Mehdy Hashemy Shahdany, Auteur ; Hashemy Shahdany, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1160-1182 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] aquifère [Termes IGN] arsenic [Termes IGN] cartographie des risques [Termes IGN] contamination [Termes IGN] eau souterraine [Termes IGN] Iran [Termes IGN] logique floue [Termes IGN] nitrate [Termes IGN] pollution des eaux [Termes IGN] vulnérabilité Résumé : (auteur) This study proposes a new approach to establish a multi-parameter risk mapping method by employing the K-Means clustering technique. Accordingly, spatial assessment of arsenic (As), nitrate (NO3) and total dissolved solids (TDS) were carried out based on the type of land use to estimate contamination potential in an aquifer. Since risk mapping is always associated with the occurrence probability of a phenomenon, pollution occurrence probability was then obtained using the fuzzy C-means clustering. The results reveal that NO3 and As contamination levels increase from the first cluster (C1), covers 22.3% of the aquifer, to C5 encompassing 35.1% of the aquifer devoted to extensive industrial and agricultural activities. Fuzzy clustering results show that the pollution occurrence probability in each aquifer cell varied from less than 30 to more than 90%. Moreover, the results show, industrial and agricultural land uses cover about 70% of the areas with high risk of contamination. Numéro de notice : A2022-396 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1778099 Date de publication en ligne : 23/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1778099 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100690 [article]

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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A user-centric optimization of emergency map symbols to facilitate common operational picture / Tomasz Opach in Cartography and Geographic Information Science, vol 49 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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CIME: Context-aware geolocation of emergency-related posts / Gabriele Scalia in Geoinformatica [en ligne], vol 26 n° 1 (January 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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Remote sensing in applications of geoinformation / Silas Michaelides (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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A GIS-remote sensing approach for forest fire risk assessment: case of Bizerte region, Tunisia / Salwa Saidi in Applied geomatics, vol 13 n° 4 (December 2021)  

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