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Aux sources institutionnelles de l’enregistrement et du cadastre fonciers au Québec / Francis Roy in XYZ, n° 164 (septembre 2020)
[article]
Titre : Aux sources institutionnelles de l’enregistrement et du cadastre fonciers au Québec Type de document : Article/Communication Auteurs : Francis Roy, Auteur ; Yaïves Ferland, Auteur Année de publication : 2020 Article en page(s) : pp 43-55 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cadastre étranger
[Termes IGN] arpentage
[Termes IGN] cadastre ancien
[Termes IGN] droit civil
[Termes IGN] droit foncier
[Termes IGN] géomètre
[Termes IGN] histoire
[Termes IGN] plan cadastral
[Termes IGN] Québec (Canada)Résumé : (auteur) Les arpenteurs-géomètres du Québec ont dressé à la fin du XIXe siècle un cadastre graphique, dit “originaire”. Une fois aboli le régime seigneurial hérité de la Coutume de Paris, le Code civil du Bas-Canada (1866) a combiné la tenure des terres en censive avec les lots de canton concédés sous le Common Law, établissant la propriété foncière “absolue”. Chaque plan local de cadastre servit à identifier et numéroter les lots pour l’enregistrement des transactions et hypothèques. La désuétude des plans cadastraux, accrue par la multiplication de parties de lot
non officialisées, devint un problème majeur. Le nouveau Code civil du Québec (1994) amorça la réforme du registre foncier et du cadastre du Québec, en version informatisée, pour immatriculer tous les lots du territoire à l’aide
d’un plan présumé exact. Les mêmes questions organisationnelles et pratiques qu’à l’époque de l’institution du cadastre originaire se posèrent, dans un contexte technologique performant.Numéro de notice : A2020-554 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95822
in XYZ > n° 164 (septembre 2020) . - pp 43-55[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2020031 RAB Revue Centre de documentation En réserve L003 Disponible A spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)
[article]
Titre : A spaceborne SAR-based procedure to support the detection of landslides Type de document : Article/Communication Auteurs : Giuseppe Esposito, Auteur ; Ivan Marchesini, Auteur ; Alessandro Cesare Mondini, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2379 - 2395 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] algorithme de décalage moyen
[Termes IGN] cartographie des risques
[Termes IGN] correction d'image
[Termes IGN] détection de changement
[Termes IGN] effondrement de terrain
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] ligne de rupture de pente
[Termes IGN] modèle de simulation
[Termes IGN] Papouasie-Nouvelle-Guinée
[Termes IGN] risque naturel
[Termes IGN] segmentation d'image
[Termes IGN] traitement automatique de donnéesRésumé : (auteur) The increasing availability of free-access satellite data represents a relevant opportunity for the analysis and assessment of natural hazards. The systematic acquisition of spaceborne imagery allows for monitoring areas prone to geohydrological disasters, providing relevant information for risk evaluation and management. In cases of major landslide events, for example, spaceborne radar data can provide an effective solution for the detection of slope failures, even in cases with persistent cloud cover. The information about the extension and location of the landslide-affected areas may support decision-making processes during emergency responses. In this paper, we present an automatic procedure based on Sentinel-1 Synthetic Aperture Radar (SAR) images, aimed at facilitating the detection of landslides over wide areas. Specifically, the procedure evaluates changes of radar backscattered signals associated with land cover modifications that may be also caused by mass movements. After a one-time calibration of some parameters, the processing chain is able to automatically execute the download and preprocessing of images, the detection of SAR amplitude changes, and the identification of areas potentially affected by landslides, which are then displayed in a georeferenced map. This map should help decision makers and emergency managers to organize field investigations. The process of automatization is implemented with specific scripts running on a GNU/Linux operating system and exploiting modules of open-source software. We tested the processing chain, in back analysis, on an area of about 3000 km2 in central Papua New Guinea that was struck by a severe seismic sequence in February–March 2018. In the area, we simulated a periodic survey of about 7 months, from 12 November 2017 to 6 June 2018, downloading 36 Sentinel-1 images and performing 17 change detection analyses automatically. The procedure resulted in statistical and graphical evidence of widespread land cover changes that occurred just after the most severe seismic events. Most of the detected changes can be interpreted as mass movements triggered by the seismic shaking. Numéro de notice : A2020-611 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/nhess-20-2379-2020 Date de publication en ligne : 10/09/2020 En ligne : https://doi.org/10.5194/nhess-20-2379-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95976
in Natural Hazards and Earth System Sciences > vol 20 n° 9 (September 2020) . - pp 2379 - 2395[article]A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery / Bo Yang in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
[article]
Titre : A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery Type de document : Article/Communication Auteurs : Bo Yang, Auteur ; Lin Liu, Auteur ; Minxuan Lan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1740 - 1764 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] coefficient de corrélation
[Termes IGN] criminalité
[Termes IGN] données spatiotemporelles
[Termes IGN] géostatistique
[Termes IGN] historique des données
[Termes IGN] image NPP-VIIRS
[Termes IGN] krigeage
[Termes IGN] modèle dynamique
[Termes IGN] nuit
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] prédiction
[Termes IGN] prévention des risques
[Termes IGN] prise de vue nocturne
[Termes IGN] test statistique
[Termes IGN] zone urbaineRésumé : (auteur) Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test. Numéro de notice : A2020-475 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1737701 Date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1737701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95622
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1740 - 1764[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible Use of Bayesian modeling to determine the effects of meteorological conditions, prescribed burn season, and tree characteristics on litterfall of pinus nigra and pinus pinaster stands / Juncal Espinosa in Forests, vol 11 n° 9 (September 2020)
[article]
Titre : Use of Bayesian modeling to determine the effects of meteorological conditions, prescribed burn season, and tree characteristics on litterfall of pinus nigra and pinus pinaster stands Type de document : Article/Communication Auteurs : Juncal Espinosa, Auteur ; Óscar Rodríguez de Rivera, Auteur ; Javier Madrigal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse
[Termes IGN] classification bayesienne
[Termes IGN] données météorologiques
[Termes IGN] Espagne
[Termes IGN] estimation bayesienne
[Termes IGN] incendie de forêt
[Termes IGN] intégrale de Laplace
[Termes IGN] modèle linéaire
[Termes IGN] Pinus nigra
[Termes IGN] Pinus pinaster
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Research Highlights: Litterfall biomass after prescribed burning (PB) is significantly influenced by meteorological variables, stand characteristics, and the fire prescription. Some of the fire-adaptive traits of the species under study (Pinus nigra and Pinus pinaster) mitigate the effects of PB on litterfall biomass. The Bayesian approach, tested here for the first time, was shown to be useful for analyzing the complex combination of variables influencing the effect of PB on litterfall.
Background and Objectives: The aims of the study focused on explaining the influence of meteorological conditions after PB on litterfall biomass, to explore the potential influence of stand characteristic and tree traits that influence fire protection, and to assess the influence of fire prescription and fire behavior.
Materials and Methods: An experimental factorial design including three treatments (control, spring, and autumn burning), each with three replicates, was established at two experimental sites (N = 18; 50 × 50 m2 plots). The methodology of the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP forests) was applied and a Bayesian approach was used to construct a generalized linear mixed model.
Results: Litterfall was mainly affected by the meteorological variables and also by the type of stand and the treatment. The effects of minimum bark thickness and the height of the first live branch were random. The maximum scorch height was not high enough to affect the litterfall. Time during which the temperature exceeded 60 °C (cambium and bark) did not have an important effect. Conclusions: Our findings demonstrated that meteorological conditions were the most significant variables affecting litterfall biomass, with snowy and stormy days having important effects. Significant effects of stand characteristics (mixed and pure stand) and fire prescription regime (spring and autumn PB) were shown. The trees were completely protected by a combination of low-intensity PB and fire-adaptive tree traits, which prevent direct and indirect effects on litterfall. Identification of important variables can help to improve PB and reduce the vulnerability of stands managed by this method.Numéro de notice : A2020-753 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091006 Date de publication en ligne : 18/09/2020 En ligne : https://doi.org/10.3390/f11091006 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96433
in Forests > vol 11 n° 9 (September 2020) . - N° 1006[article]Using OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)
[article]
Titre : Using OpenStreetMap data and machine learning to generate socio-economic indicators Type de document : Article/Communication Auteurs : Daniel Feldmeyer, Auteur ; Claude Meisch, Auteur ; Holger Sauter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] Allemagne
[Termes IGN] apprentissage automatique
[Termes IGN] arbre aléatoire
[Termes IGN] base de données spatiotemporelles
[Termes IGN] changement climatique
[Termes IGN] chômage
[Termes IGN] classification par réseau neuronal
[Termes IGN] collectivité territoriale
[Termes IGN] données localisées des bénévoles
[Termes IGN] données socio-économiques
[Termes IGN] inégalité
[Termes IGN] limite administrative
[Termes IGN] modèle de régression
[Termes IGN] modèle de simulation
[Termes IGN] OpenStreetMapRésumé : (auteur) Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities. Numéro de notice : A2020-663 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9090498 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.3390/ijgi9090498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96139
in ISPRS International journal of geo-information > vol 9 n° 9 (September 2020) . - 16 p.[article]Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience / Yingwei Yan in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkWater level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkPermalinkEvaluation of single-frequency receivers for studying crustal deformation at the longitudinal Valley fault, eastern Taiwan / Horng-Yue Chen in Survey review, vol 52 n° 374 (August 2020)PermalinkExploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique / Hao Li in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkGeneration of crowd arrival and destination locations/times in complex transit facilities / Brian Ricks in The Visual Computer, vol 36 n° 8 (August 2020)PermalinkHistory of laser scanning, part 2: the later phase of industrial and heritage applications / Adam P. Spring in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 8 (August 2020)PermalinkLanduse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkA name‐led approach to profile urban places based on geotagged Twitter data / Juntao Lai in Transactions in GIS, Vol 24 n° 4 (August 2020)PermalinkNear-real time forecasting and change detection for an open ecosystem with complex natural dynamics / Jasper A. Slingsby in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)Permalink