Centre dedocumentation
scientifique

Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions / Amit Batar in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 3 (March 2021) . - n° 114 Titre : Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the indian Himalayan region: Recent developments, gaps, and future directions Type de document : Article/Communication Auteurs : Amit Batar, Auteur ; Teiji Watanabe, Auteur Année de publication : 2021 Article en page(s) : n° 114 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse bivariée [Termes IGN] analyse de sensibilité [Termes IGN] bassin hydrographique [Termes IGN] cartographie des risques [Termes IGN] effondrement de terrain [Termes IGN] géomorphologie locale [Termes IGN] Google Earth [Termes IGN] Himalaya [Termes IGN] Inde [Termes IGN] inventaire [Termes IGN] système d'information géographique [Termes IGN] théorème de Bayes Résumé : (auteur) The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection. Numéro de notice : A2021-233 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10030114 date de publication en ligne : 27/02/2021 En ligne : https://doi.org/10.3390/ijgi10030114 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97228 [article]

Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)  

Public [article]  inApplied geomatics > vol 12 n° 4 (December 2020) . - 23 p. Titre : Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy Type de document : Article/Communication Auteurs : Vasil Yordanov, Auteur ; Maria Antonia Brovelli, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse de sensibilité [Termes IGN] cartographie des risques [Termes IGN] cartographie géomorphologique [Termes IGN] classification par forêts aléatoires [Termes IGN] effondrement de terrain [Termes IGN] figuré linéaire [Termes IGN] indice de risque [Termes IGN] inventaire [Termes IGN] Lombardie [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] modèle statistique [Termes IGN] régression logistique Résumé : (auteur) Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations. Numéro de notice : A2020-695 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00344-1 date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1007/s12518-020-00344-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96244 [article]

Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])  

Public [article]  inGeocarto international > vol 35 n° 15 [01/11/2020] . - pp 1706 - 1721 Titre : Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México Type de document : Article/Communication Auteurs : Nelly L. Ramirez Serrato, Auteur ; Fabiola D. Yepez-Rincon, Auteur ; Adrian L. Ferrino Fierro, Auteur Année de publication : 2020 Article en page(s) : pp 1706 - 1721 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse diachronique [Termes IGN] cartographie des risques [Termes IGN] chaîne de traitement [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] effondrement de terrain [Termes IGN] image satellite [Termes IGN] inventaire [Termes IGN] Mexique [Termes IGN] modèle numérique de surface [Termes IGN] semis de points [Termes IGN] visualisation 3D Résumé : (auteur) By nature, slopes are conformed by forces that are in constant balance. Altering this natural balance causes the sliding of soil towards lower zones. Landslides are a constant danger that compromises the general welfare of society. Landslides mapping is especially important for urban areas or development plans. The innovative aspect of this study is the creation of the Topographic Connection Method (TPCM) to automatically map landslides using two types of landslides 1) falls and 2) flows. TPCM cartography results were compared to a previously proven method (Contour Connection Method), as well as to the manual inventory method. Each method was run four times to locate changes through time by using satellite imagery, digital elevations models and 3D relief visualizations with data covering a period from 2012 to 2017. Results showed both falls and flows with all three methods and demonstrated that TPCM can improve mapping accuracy by up to 14%. Numéro de notice : A2020-659 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581269 date de publication en ligne : 01/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96132 [article]

Detecting abandoned farmland using harmonic analysis and machine learning / Heeyeun Yoon in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 201 - 212 Titre : Detecting abandoned farmland using harmonic analysis and machine learning Type de document : Article/Communication Auteurs : Heeyeun Yoon, Auteur ; Soyoun Kim, Auteur Année de publication : 2020 Article en page(s) : pp 201 - 212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse harmonique [Termes IGN] apprentissage automatique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Corée du sud [Termes IGN] gestion des ressources [Termes IGN] inventaire [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Normalized Difference Water Index [Termes IGN] phénologie [Termes IGN] production agricole [Termes IGN] Soil Adjusted Vegetation Index [Termes IGN] surface cultivée Résumé : (auteur) It is critical to inventory abandoned farmland soon after it is generated, to better manage agricultural resources and to prevent negative consequences that would otherwise follow. This study aims to distinguish abandoned farmlands from active croplands—rice paddy and agricultural fields—by discerning the phenological trajectories over a short-term period of three years (Jan. 2016 to Dec. 2018) in Gwanyang City in South Korea. For Support Vector Machine (SVM) classification, we fully utilized parameters derived from harmonic analyses of the three vegetation indices (VIs: NDVI, NDWI, and SAVI) extracted from Sentinel-2A imagery. The harmonic analyses proved that higher-order sinusoid components produced better fitting to explain the trajectory of the VIs—the maximum adjusted was 95.23%—and the multiple VIs diversified the attributes for the classifications. Consequently, the higher-order harmonic components and the additional VIs increased the accuracy when used in SVM classification. The best performing classification was achieved with a composite of harmonic terms derived from the three VIs, yielding overall accuracy of 90.72%, Kappa index of 0.858, and user’s accuracy for abandoned farmland of 93.40%. The proposed method here would greatly improve the process of detecting abandoned farmland, despite a relatively short observation period, and enable a rapid response to the occurrence of abandonment. Numéro de notice : A2020-356 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.021 date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95243 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020081	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2020083	DEP-RECP	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2020082	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 125 - 136 Titre : Geocoding of trees from street addresses and street-level images Type de document : Article/Communication Auteurs : Daniel Laumer, Auteur ; Nico Lang, Auteur ; Natalie Van Doorn, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des correspondances [Termes IGN] apprentissage profond [Termes IGN] arbre urbain [Termes IGN] Californie (Etats-Unis) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'arbres [Termes IGN] détection d'objet [Termes IGN] géocodage par adresse postale [Termes IGN] géomatique [Termes IGN] image panoramique [Termes IGN] image Streetview [Termes IGN] inventaire [Termes IGN] service écosystémique [Termes IGN] zone urbaine Résumé : (auteur) We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale. Numéro de notice : A2020-124 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.001 date de publication en ligne : 21/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94749 [article]

Restitution 4D du Château du Kagenfels par combinaison de l’existant et d’hypothèses archéologiques pour une visite virtuelle du site / Théo Benazzi (2018)  

Permalink

Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models / Kai Cui in Geocarto international, vol 32 n° 9 (September 2017)  

Permalink

Deux systèmes d’évaluation du statut de conservation des espèces en France : complémentarité ou redondance ? Cas de la liste rouge et du rapport sur l’état de conservation pour la directive habitats-faune-flore / Renaud Puissauve in Revue d'écologie, vol 71 n° 4 (octobre - décembre 2016)

Permalink

Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)  

Permalink

Spatial analysis of coastal chalk cliff falls in upper Normandy (France). From Veules-les-Roses to Le Treport (2002-2009) / Pauline Letortu in Revue internationale de géomatique, vol 24 n° 3 (septembre - novembre 2014)  

Permalink

Usage et lecture des cartes / Ferjan Ormeling in Cartes & Géomatique, n° 221 (septembre 2014)

Permalink

Active learning in the spatial domain for remote sensing image classification / André Stumpf in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)  

Permalink

The online TÉKA database, an integrated approach to landmark inventorization / László Kollányi in Geocarto international, vol 28 n° 1-2 (February - May 2013)  

Permalink

Apports des données ALOS PALSAR polarimétriques à la détection des zones humides littorales (Sassandra, Côte d'Ivoire) / Kouakou Hervé Kouassi in Photo interpretation, European journal of applied remote sensing, vol 48 n° 4 (décembre 2012)

Permalink

SIG et RésALIS / Nadine Cadiou (2012)

Permalink