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Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology / Aura Salmivaara in Forestry, an international journal of forest research, vol 93 n° 5 (October 2020)
[article]
Titre : Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology Type de document : Article/Communication Auteurs : Aura Salmivaara, Auteur ; Samuli Launiainen, Auteur ; Jari Perttunen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 662 - 674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement
[Termes IGN] apprentissage automatique
[Termes IGN] chemin forestier
[Termes IGN] classification barycentrique
[Termes IGN] dégradation des sols
[Termes IGN] dommage
[Termes IGN] données localisées libres
[Termes IGN] exploitation forestière
[Termes IGN] Finlande
[Termes IGN] humidité du sol
[Termes IGN] modèle dynamique
[Termes IGN] modèle hydrographiqueRésumé : (auteur) Forest harvesting operations with heavy machinery can lead to significant soil rutting. Risks of rutting depend on the soil bearing capacity which has considerable spatial and temporal variability. Trafficability prediction is required in the selection of suitable operation sites for a given time window and conditions, and for on-site route optimization during the operation. Integrative tools are necessary to plan and carry out forest operations with minimal negative ecological and economic impacts. This study demonstrates a trafficability prediction framework that utilizes a spatial hydrological model and a wide range of spatial data. Trafficability was approached by producing a rut depth prediction map at a 16 × 16 m grid resolution, based on the outputs of a general linear mixed model developed using field data from Southern Finland, modelled daily soil moisture, spatial forest inventory and topography data, along with field measured rolling resistance and information on the mass transported through the grid cells. Dynamic rut depth prediction maps were produced by accounting for changing weather conditions through hydrological modelling. We also demonstrated a generalization of the rolling resistance coefficient, measured with harvester CAN-bus channel data. Future steps towards a nationwide prediction framework based on continuous data flow, process-based modelling and machine learning are discussed. Numéro de notice : A2020-790 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpaa010 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1093/forestry/cpaa010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96559
in Forestry, an international journal of forest research > vol 93 n° 5 (October 2020) . - pp 662 - 674[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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible Near-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)
[article]
Titre : Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics Type de document : Article/Communication Auteurs : Jasper A. Slingsby, Auteur ; Glenn R. Moncrieff, Auteur ; Adam M. Wilson, Auteur Année de publication : 2020 Article en page(s) : pp 15 - 25 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] approche hiérarchique
[Termes IGN] biodiversité
[Termes IGN] classification bayesienne
[Termes IGN] détection de changement
[Termes IGN] écosystème
[Termes IGN] incendie
[Termes IGN] internet interactif
[Termes IGN] Le Cap
[Termes IGN] milieu naturel
[Termes IGN] modèle dynamique
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] surveillance de la végétation
[Termes IGN] surveillance écologiqueRésumé : (auteur) Managing fire, water, biodiversity and carbon stocks can greatly benefit from early warning of changes in the state of vegetation. While near-real time tools to detect forest change based on satellite remote sensing exist, these ecosystems have relatively stable natural vegetation dynamics. Open (i.e. non-forest) ecosystems like grasslands, savannas and shrublands are more challenging as they show complex natural dynamics due to factors such as fire, postfire recovery, greater contribution of bare soil to observed vegetation indices, as well as high sensitivity to rainfall and strong seasonality. Tools to aid the management of open ecosystems are desperately required as they dominate much of the globe and harbour substantial biodiversity and carbon. We present an innovative approach that overcomes the difficulties posed by open ecosystems by using a spatio-temporal hierarchical Bayesian model that uses data on climate, topography, soils and fire history to generate ecological forecasts of the expected land surface signal under natural conditions. This allows us to monitor and detect abrupt or gradual changes in the state of an ecosystem in near-real time by identifying areas where the observed vegetation signal has deviated from the expected natural variation. We apply our approach to a case study from the hyperdiverse fire-dependent African shrubland, the fynbos of the Cape Floristic Region, a Global Biodiversity Hotspot and UNESCO World Heritage Site that faces a number of threats to vegetation health and ecosystem function. The case study demonstrates that our approach is useful for identifying a range of change agents such as fire, alien plant species invasions, drought, pathogen outbreaks and clearing of vegetation. We describe and provide our full workflow, including an interactive web application. Our approach is highly versatile, allowing us to collect data on the impacts of change agents for research in ecology and earth system science, and to predict aspects of ecosystem structure and function such as biomass, fire return interval and the influence of vegetation on hydrology Numéro de notice : A2020-349 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.017 Date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.017 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95231
in ISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 15 - 25[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020081 RAB Revue Centre de documentation En réserve L003 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 Novel communication channel model for signal propagation and loss through layered earth / David O. LeVan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)
[article]
Titre : Novel communication channel model for signal propagation and loss through layered earth Type de document : Article/Communication Auteurs : David O. LeVan, Auteur Année de publication : 2020 Article en page(s) : pp 5393 - 5399 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] atténuation du signal
[Termes IGN] champ électromagnétique
[Termes IGN] cible cachée
[Termes IGN] géophysique
[Termes IGN] mine de charbon
[Termes IGN] modèle de simulation
[Termes IGN] modèle mathématique
[Termes IGN] modélisation
[Termes IGN] propagation du signal
[Termes IGN] Terre (planète)Résumé : (auteur) Signal propagation through-the-Earth (TTE) is of great importance to geophysicists searching for underground resources such as oil and gas, homeland defense searching for tunnels and underground structures, and mining operations. The Earth is a conductive medium, unlike air or space, which tends to “short-out” electromagnetic fields traditionally used for wireless communications. The magnitude of this effect depends on many factors, such as frequency and the type of Earth-material. Mathematical models of energy propagation have been developed to help us understand the signal propagation issues, and some models can be used to predict the performance of the specific electromagnetic energies being used. There are numerous ways of modeling the Earth to study energy propagation. Some early literature presented models of signal propagation through a homogeneous Earth. These were fairly accurate for signals traveling from one point in the Earth to another point. However, signals traveling from below the ground to the surface of the Earth encounter many different layers of the Earth. This realization led to the development of models of a layered Earth. A novel layered-Earth communication model is presented and evaluated as to its accuracy by using measured data gathered during TTE communication tests from 2007 to 2012. Evaluations show that the new layered-EARTH model provides improved accuracy for the prediction of signal propagation performance from within a subterranean space, such as a mine, to and from the surface of the Earth. Numéro de notice : A2020-472 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2965398 Date de publication en ligne : 28/01/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2965398 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95575
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 8 (August 2020) . - pp 5393 - 5399[article]Learning evolving user’s behaviors on location-based social networks / Ruizhi Wu in Geoinformatica, vol 24 n° 3 (July 2020)
[article]
Titre : Learning evolving user’s behaviors on location-based social networks Type de document : Article/Communication Auteurs : Ruizhi Wu, Auteur ; Guangchun Luo, Auteur ; Qi jin, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 713 – 743 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] comportement
[Termes IGN] données localisées des bénévoles
[Termes IGN] filtrage d'information
[Termes IGN] géopositionnement
[Termes IGN] interaction homme-milieu
[Termes IGN] modèle dynamique
[Termes IGN] réseau social géodépendant
[Termes IGN] utilisateurRésumé : (auteur) With the popularity of smart phones, users’ activities on location-based social networks (LBSNs) evolve faster than traditional social networks. Existing models focus on modeling users’ long-term preferences, leveraging social collaborative filtering to enhance prediction performance. However, the dynamic mobility mechanism of user’s check-in behaviors on LBSNs is seldom considered. In this paper, we propose a new dynamic model that considers both geo-aware user preferences and the social interaction excitation arising from social connections to learn the dynamic mobility mechanism of user’s behaviors on LBSNs. Geo-aware location features, such as semantic features, latent features and dynamic features, are utilized to characterize the location information and reveal the evolution of the geographical impact of location. These geo-aware location features enable us to exploit user’s personal preferences. Meanwhile, we integrate a user’s social connections and friends’ preferences for modeling social interaction excitations. Finally, we jointly incorporate geo-aware user preference learning and social interaction excitation modeling to create a conditional intensity function for temporal point processes with which to explore the dynamic mobility mechanism of evolving user’s check-in behaviors on LBSNs. Extensive experiments on several real-world check-in datasets confirm that our proposed algorithm performs better than existing state-of-the-art methods. Numéro de notice : A2020-372 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00400-3 Date de publication en ligne : 16/03/2020 En ligne : https://doi.org/10.1007/s10707-020-00400-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95267
in Geoinformatica > vol 24 n° 3 (July 2020) . - pp 713 – 743[article]Modéliser ce qui résiste à la modélisation / Aurélien Bénel in Revue ouverte d'intelligence artificielle, ROIA, vol 1 n° 1 ([01/07/2020])PermalinkUsing machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests / Jiaxin Chen in Forest ecology and management, Vol 466 (15 June 2020)PermalinkFine-scale dasymetric population mapping with mobile phone and building use data based on grid Voronoi method / Zhenzhong Peng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkImproved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkCity information modeling, Rennes s'offre un jumeau numérique / Marielle Mayo in Géomètre, n° 2180 (mai 2020)PermalinkVisualizing when, where, and how fires happen in U.S. parks and protected areas / Nicole C. Inglis in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)PermalinkA global analysis of cities’ geosocial temporal signatures for points of interest hours of operation / Kevin Sparks in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)PermalinkOnline flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)PermalinkSpatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)PermalinkIntegration of remote sensing and GIS to extract plantation rows from a drone-based image point cloud digital surface model / Nadeem Fareed in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)Permalink