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Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach / Bisong Hu in International journal of geographical information science IJGIS, vol 35 n° 3 (March 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 3 (March 2021) . - pp 466 - 489 Titre : Space-time disease mapping by combining Bayesian maximum entropy and Kalman filter: the BME-Kalman approach Type de document : Article/Communication Auteurs : Bisong Hu, Auteur ; Pan Ning, Auteur ; Yi Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 466 - 489 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] carte sanitaire [Termes descripteurs IGN] Chine [Termes descripteurs IGN] entropie maximale [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] régression Résumé : (auteur) In this work, a synthesis of the Bayesian maximum entropy (BME) and the Kalman filter (KF) methods, which enhances their individual strengths and overcomes certain of their weaknesses for spatiotemporal mapping purposes, is proposed in a spatiotemporal disease mapping context. The proposed BME-Kalman synthesis allows BME to use information from both parametric regression modeling and KF estimation leading to enhanced knowledge bases. The BME-Kalman synthetic approach is used to study the space-time incidence mapping of the hand, foot and mouth disease (HFMD) in Shandong province (China) during the period May 1st, 2008 to March 19th, 2009. The results showed that the BME-Kalman approach exhibited very good regressive and predictive accuracies, maintained a very good performance even during low-incidence and extremely low-incidence periods, offered an improved description of hierarchical disease characteristics compared to traditional mapping techniques, and provided a clear explanation of the spatial stratified incidence heterogeneity at unsampled locations. The BME-Kalman approach is versatile and flexible so that it can be modified and adjusted according to the needs of the application. Numéro de notice : A2021-165 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1795177 date de publication en ligne : 22/07/2021 En ligne : https://doi.org/10.1080/13658816.2020.1795177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97098 [article]

A dynamic bidirectional coupled surface flow model for flood inundation simulation / Chunbo Jiang in Natural Hazards and Earth System Sciences, Vol 21 n° 2 (February 2021)  

Public [article]  inNatural Hazards and Earth System Sciences > Vol 21 n° 2 (February 2021) . - pp 497 - 515 Titre : A dynamic bidirectional coupled surface flow model for flood inundation simulation Type de document : Article/Communication Auteurs : Chunbo Jiang, Auteur ; Qi Zhou, Auteur ; Wangyang Yu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 497 - 515 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Chine [Termes descripteurs IGN] crue [Termes descripteurs IGN] inondation [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] modèle hydrographique [Termes descripteurs IGN] prévention des risques Résumé : (auteur) Flood disasters frequently threaten people and property all over the world. Therefore, an effective numerical model is required to predict the impacts of floods. In this study, a dynamic bidirectional coupled hydrologic–hydrodynamic model (DBCM) is developed with the implementation of characteristic wave theory, in which the boundary between these two models can dynamically adapt according to local flow conditions. The proposed model accounts for both mass and momentum transfer on the coupling boundary and was validated via several benchmark tests. The results show that the DBCM can effectively reproduce the process of flood propagation and also account for surface flow interaction between non-inundation and inundation regions. The DBCM was implemented for the floods simulation that occurred at Helin Town located in Chongqing, China, which shows the capability of the model for flood risk early warning and future management. Numéro de notice : A2021-168 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5194/nhess-21-497-2021 date de publication en ligne : 03/02/2021 En ligne : https://doi.org/10.5194/nhess-21-497-2021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97107 [article]

Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 121 -1 36 Titre : Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling Type de document : Article/Communication Auteurs : Stefanos Georganos, Auteur ; Tais Grippa, Auteur ; Assane Niang Gadiaga, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 121 -1 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Dakar [Termes descripteurs IGN] densité de population [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] population [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still ‘aspatial’ and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity. Numéro de notice : A2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595177 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96822 [article]

Dynamic mechanism of blown sand hazard formation at the Jieqiong section of the Lhasa–Shigatse railway / Shengbo Xie in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 12 n° 1 (2021) . - pp 154 - 166 Titre : Dynamic mechanism of blown sand hazard formation at the Jieqiong section of the Lhasa–Shigatse railway Type de document : Article/Communication Auteurs : Shengbo Xie, Auteur ; Jianjun Qu, Auteur ; Yingjun Pang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 154 - 166 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] météorologie locale [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] prévention des risques [Termes descripteurs IGN] risque naturel [Termes descripteurs IGN] sable [Termes descripteurs IGN] Tibet [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] vent de sable [Termes descripteurs IGN] vitesse [Termes descripteurs IGN] voie ferrée Résumé : (auteur) Blown sand hazards at the Jieqiong section of the Lhasa–Shigatse railway are severe, and their formation mechanism is unclear. Moreover, sand prevention and control work cannot be carried out. Therefore, the dynamic mechanism of blown sand at the Jieqiong section of the Lhasa–Shigatse Railway was investigated by field observation, laboratory analysis, and calculation. Results show that the yearly sand–moving wind at the Jieqiong section commonly originates from the SW direction. The yearly resultant drift direction and the yearly resultant angle of the maximum possible sand transport quantity are NE direction. The angle between railway trend and sand transport direction is 5°–30°. During dry season, sand materials are blown up by the wind, forming wind–sand flow and movement to the NE direction, at which they are blocked by the railway roadbed. Consequently, accumulation occurs and causes serious damage. Strong wind and dryness are synchronous within a season. The directions of sand source and prevailing wind are consistent, thereby aggravating the blown sand dynamic further. The present results provide a reference for controlling sand hazards in the locale. Numéro de notice : A2021-109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2020.1863268 date de publication en ligne : 28/12/2020 En ligne : https://doi.org/10.1080/19475705.2020.1863268 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96906 [article]

Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates / Robert E. Keane in Forest ecology and management, vol 477 ([01/12/2020])  

Public [article]  inForest ecology and management > vol 477 [01/12/2020] . - 12 p. Titre : Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates Type de document : Article/Communication Auteurs : Robert E. Keane, Auteur ; Lisa M. Holsinger, Auteur ; Rachel Loehman, Auteur Année de publication : 2020 Article en page(s) : 12 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] habitat forestier [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] modélisation de la forêt [Termes descripteurs IGN] Montana (Etats-Unis) [Termes descripteurs IGN] substitution [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Land managers need new tools for planning novel futures due to climate change. Species distribution modeling (SDM) has been used extensively to predict future distributions of species under different climates, but their map products are often too coarse for fine-scale operational use. In this study we developed a flexible, efficient, and robust method for mapping current and future distributions and abundances of vegetation species and communities at the fine spatial resolutions that are germane to land management. First, we mapped Potential Vegetation Types (PVTs) using conventional statistical modeling techniques (Random Forests) that used bioclimatic ecosystem process and climate variables as predictors. We obtained over 50% accuracy across 13 mapped PVTs for our study area. We then applied future climate projections as climate input to the Random Forest model to generate future PVT maps, and used field data describing the occurrence of tree and non-tree species in each PVT category to model and map species distribution for current and future climate. These maps were then compared to two previous SDM mapping efforts with over 80% agreement and equivalent accuracy. Because PVTs represent the biophysical potential of the landscape to support vegetation communities as opposed to the vegetation that currently exists, they can be readily linked to climate forecasts and correlated with other, climate-sensitive ecological processes significant in land management, such as fire regimes and site productivity. Numéro de notice : A2020-624 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118498 date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118498 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96022 [article]

Semantic trajectory segmentation based on change-point detection and ontology / Yuan Gao in International journal of geographical information science IJGIS, vol 34 n° 12 (December 2020)  

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Analyzing the joint effect of forest management and wildfires on living biomass and carbon stocks in Spanish forests / Patricia Adame in Forests, vol 11 n°11 (November 2020)  

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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)  

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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)  

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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)  

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Learning evolving user’s behaviors on location-based social networks / Ruizhi Wu in Geoinformatica [en ligne], vol 24 n° 3 (July 2020)  

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Using 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)  

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Fine-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)  

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Improved 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)  

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Visualizing 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)  

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A 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)  

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Spatiotemporal 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)  

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Integration 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)  

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Spectral–spatial–temporal MAP-based sub-pixel mapping for land-cover change detection / Da He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process / Biswajit Nath in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)  

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Object‐oriented tracking of thematic and spatial behaviors of urban heat islands / Rui Zhu in Transactions in GIS, Vol 24 n° 1 (February 2020)  

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Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)  

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Human mobility, spatiotemporal context, and environmental health / Mei-Po Kwan (2019)  

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A large-scale forest dynamic model to estimate wood resources in the French forests based on NFI information / Thimotée Audinot (2019)

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On constrained integrated total Kalman filter for integrated direct geo-referencing / Vahid Mahboub in Survey review, vol 51 n° 364 (January 2019)  

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Stochastic processes / Alexander Zeifman (2019)  

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Automatic cloud resource management for interactive remote geovisualization / Tong Zhang in Transactions in GIS, vol 22 n° 6 (December 2018)  

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Optimal management of larch (Larix olgensis A. Henry) plantations in Northeast China when timber production and carbon stock are considered / Wei Peng in Annals of Forest Science [en ligne], vol 75 n° 1 (March 2018)  

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Morphodynamic model for predicting beach changes based on Bagnold's concept and its applications / Takaaki Uda (2018)  

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Toward a systematic integration of optical remote sensing for inland waters studies / Vincent Maurice Nouchi (2018)  

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Vehicle dynamic model based navigation for small UAVs / Mehran Khaghani (2018)  

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VGDI – advancing the concept: Volunteered geo-dynamic information and its benefits for population dynamics modeling / Christoph Aubrecht in Transactions in GIS, vol 21 n° 2 (April 2017)  

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ASSURE : a model for the simulation of urban expansion and intra-urban social segregation / Karolien Vermeiren in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)  

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Vers la construction d'un référentiel géographique ancien / Benoit Costes (2016)  

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De l’étude des nomenclatures territoriales à la modélisation des dynamiques des territoires administratifs en France / Christine Plumejeaud in Revue internationale de géomatique, vol 25 n° 3 (septembre - novembre 2015)  

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Études des dynamiques de l’occupation du sol. Questionnements, simplifications et limites / Julien Perret in Revue internationale de géomatique, vol 25 n° 3 (septembre - novembre 2015)  

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Regards croisés sur la modélisation des dynamiques spatiales / Anne Ruas in Revue internationale de géomatique, vol 25 n° 3 (septembre - novembre 2015)  

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A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS / Abubrakr A. A. Al Sharif in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)  

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To die or not to die: Forest dynamics in Switzerland under climate change / Nicolas Bircher (2015)  

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Dynamic modeling of GNSS troposphere wet delay for estimation of precipitable water vapour / Ahmed El-Mowafy in Journal of applied geodesy, vol 8 n° 1 (April 2014)  

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Outils méthodologiques pour l'analyse d'images MSG : estimation du mouvement, suivi des masses nuageuses et détection de fronts / Thomas Corpetti in Revue Française de Photogrammétrie et de Télédétection, n° 205 (Janvier 2014)

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Paysans et pasteurs sur les marches du Levant Sud / Claudine Dauphin in Géomatique expert, n° 95 (01/11/2013)  

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Un système multi-agent pour la simulation des dynamiques urbaines / Julien Perret (2010)

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An improved empirical model for the effect of long-period ocean tides on polar motion / Richard S. Gross in Journal of geodesy, vol 83 n° 7 (July 2009)  

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Assimilation of SPOT-Vegetation NDVI data into a Sahelian vegetation dynamics model / Lionel Jarlan in Remote sensing of environment, vol 112 n° 4 (15/04/2008)

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Modelling landscape dynamics with Python / D. Karssenberg in International journal of geographical information science IJGIS, vol 21 n° 5 (may 2007)

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Implementing a new model for simulating processes / F. Reitsma in International journal of geographical information science IJGIS, vol 19 n° 10 (november 2005)

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Dynamic environmental modelling in GIS: 2. Modelling error propagation / D. Karssenberg in International journal of geographical information science IJGIS, vol 19 n° 6 (july 2005)

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Dynamic environmental modelling in GIS: 1. modelling in three spatial dimensions / D. Karssenberg in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)

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AAMAS'05, fifth European workshop on adaptive agents and multi-agent systems, March 21 - 22, 2005, Paris, France / Eduardo Alonso (2005)

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