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Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam / Khanh Giang Le in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 2 (June 2020) . - pp 153 - 164 Titre : Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam Type de document : Article/Communication Auteurs : Khanh Giang Le, Auteur ; Pei Liu, Auteur ; Liang-Tay Lin, Auteur Année de publication : 2020 Article en page(s) : pp 153 - 164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes descripteurs IGN] accident de la route [Termes descripteurs IGN] base de données spatiotemporelles [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] Hanoï [Termes descripteurs IGN] indice de risque [Termes descripteurs IGN] nuit [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] variation diurne [Termes descripteurs IGN] variation saisonnière Résumé : (auteur) This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index (SI) on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons. Road Traffic Accident (RTA) data in 3 years (2015 − 2017) in Hanoi, Vietnam were used to analyze and test this approach. Firstly, the RTA data were divided into four seasons in accordance with Hanoi’s weather conditions and the time intervals such as the daytime, nighttime, or peak hours. Then, the Kernel Density Estimation (KDE) method was applied to analyze hotspots according to the time intervals and seasons. Finally, the results were presented by using the comap technique. This study considered both analyses with and without SI. The accident SI measures the seriousness of an accident. The approach method is to give higher weights to the more serious accidents, but not with the extremely high values calculated on a direct rate to the accident expenditures. The results showed that both analyses determined the relatively similar hotspots, but the rankings of some hotspots were quite different due to the integration of SI. It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate. From there, the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately. This is also the first study about this issue in Vietnam, so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities. Numéro de notice : A2020-317 Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10095020.2019.1683437 date de publication en ligne : 02/12/2019 En ligne : https://doi.org/10.1080/10095020.2019.1683437 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95176 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 106 - 124 Titre : Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation Type de document : Article/Communication Auteurs : Eslam Ali, Auteur ; Wenbin Xu, Auteur ; Xiao-Li Ding, Auteur Année de publication : 2020 Article en page(s) : pp 106 - 124 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] correction des ombres [Termes descripteurs IGN] COSI-Corr [Termes descripteurs IGN] déplacement d'objet géographique [Termes descripteurs IGN] désert [Termes descripteurs IGN] désertification [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] dune [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] modèle d'inversion [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] prévention des risques [Termes descripteurs IGN] sable [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] SINAI [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] vent de sable Résumé : (auteur) Sand dune migration poses a potential threat to desert infrastructure, vegetation, and atmospheric conditions. Capturing the patterns of long-term dune migration is useful for predicting probable desertification issues and wind conditions across vast desert areas. In this study, we employed optical image matching and a singular value decomposition approach to estimate the rates of dune migration in the North Sinai Sand Sea using the free Landsat 8 and Sentinel-2 archives. Our optical image matching time-series selection and inversion (OPTSI) algorithm limited the difference in the solar illumination of correlated pairs to decrease shadows and seasonal variability. We found that the maximum annual dune migration rates were 9.4 m/a and 15.9 m/a for Landsat 8 and Sentinel-2 data, respectively, and the results of time-series analysis revealed the existence of seasonal variations in dune migration controlled by wind regimes. The directions of sand movement extracted from the mean velocity solution agreed strongly with each other and with the drift directions estimated using wind data from meteorological stations. We assessed the uncertainty of each solution based on the variance of stable areas. Our results showed that the proposed inversion decreased uncertainty by up to 25% and increased the spatial coverage by up to 20%. This algorithm is also promising for the retrieval of historical time series on the ground displacements of glaciers and slow-moving landslides employing free archives that provide high-frequency images. Numéro de notice : A2020-253 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.004 date de publication en ligne : 27/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94997 [article]

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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)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 4 (April 2020) . - 17 p. Titre : Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China Type de document : Article/Communication Auteurs : Mingyue Wang, Auteur ; Jun’e Fu, Auteur ; Zhitao Wu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] Fleuve jaune (Chine) [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image SPOT [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température Résumé : (auteur) Research on vegetation variation is an important aspect of global warming studies. The quantification of the relationship between vegetation change and climate change has become a central topic and challenge in current global change studies. The source region of the Yellow River (SRYR) is an appropriate area to study global change because of its unique natural conditions and vulnerable terrestrial ecosystem. Therefore, we chose the SRYR for a case study to determine the driving forces behind vegetation variation under global warming. Using the Normalized Difference Vegetation Index (NDVI) and climate data, we investigated the NDVI variation in the growing season in the region from 1998 to 2016 and its response to climate change based on trend analysis, the Mann–Kendall trend test and partial correlation analysis. Finally, an NDVI–climate mathematical model was built to predict the NDVI trends from 2020 to 2038. The results indicated the following: (1) over the past 19 years, the NDVI showed an increasing trend, with a growth rate of 0.00204/a. There was an upward trend in NDVI over 71.40% of the region. (2) Both the precipitation and temperature in the growing season showed upward trends over the last 19 years. NDVI was positively correlated with precipitation and temperature. The areas with significant relationships with precipitation covered 31.01% of the region, while those with significant relationships with temperature covered 56.40%. The sensitivity of the NDVI to temperature was higher than that to precipitation. Over half (56.58%) of the areas were found to exhibit negative impacts of human activities on the NDVI. (3) According to the simulation, the NDVI will increase slightly over the next 19 years, with a linear tendency of 0.00096/a. From the perspective of spatiotemporal changes, we combined the past and future variations in vegetation, which could adequately reflect the long-term vegetation trends. The results provide a theoretical basis and reference for the sustainable development of the natural environment and a response to vegetation change under the background of climate change in the study area. Numéro de notice : A2020-262 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9040282 date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.3390/ijgi9040282 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95022 [article]

Advanced GNSS tropospheric products for monitoring severe weather events and climate / Jonathan Jones (2020)  

Public Titre : Advanced GNSS tropospheric products for monitoring severe weather events and climate : COST action ES1206 final action dissemination report Type de document : Actes de congrès Auteurs : Jonathan Jones, Editeur scientifique ; Guergana Guerova, Auteur ; Jan Douša, Auteur ; Galina Dick, Auteur ; Siebren de Haan, Auteur ; Eric Pottiaux, Auteur ; Olivier Bock , Auteur ; Rosa Pacione, Auteur ; Roeland Van Malderen, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Importance : 563 p. ISBN/ISSN/EAN : 978-3-030-13901-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] climat terrestre [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] propagation troposphérique [Termes descripteurs IGN] surveillance météorologique [Termes descripteurs IGN] vapeur d'eau Résumé : (Editeur) [Introduction] The book (COST Action Final report) summarises the proceedings from COST Action ES1206. COST Action ES1206, Advanced GNSS Tropospheric Products for Severe Weather Events and Climate (GNSS4SWEC), was a 4-year project, running from 2013 to 2017, which coordinated new and improved capabilities from concurrent developments in GNSS, meteorological and climate communities. For the first time, the synergy of multi-GNSS constellations was used to develop new, more advanced tropospheric products, exploiting the full potential of multi-GNSS on a wide range of temporal and spatial scales - from real-time products monitoring and forecasting severe weather, to the highest quality post-processed products suitable for climate research. The Action also promoted the use of meteorological data as an input to real-time GNSS positioning, navigation, and timing services and has stimulated knowledge and data transfer throughout Europe and beyond. Note de contenu : - Front Matter - General Background - Advanced GNSS Processing Techniques (Working Group 1) - Use of GNSS Tropospheric Products for High-Resolution, Rapid-Update NWP and Severe Weather Forecasting (Working Group 2) - Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3) - National Status Reports - STSM Reports - Back Matter Numéro de notice : 26248 Thématique : POSITIONNEMENT Nature : Actes nature-HAL : RappRech DOI : 10.1007/978-3-030-13901-8 date de publication en ligne : 14/09/2019 En ligne : https://doi.org/10.1007/978-3-030-13901-8 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94503

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)  

Public [article]  inISPRS International journal of geo-information > Vol 9 n° 1 (January 2020) Titre : Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods Type de document : Article/Communication Auteurs : Wolfgang B. Hamer, Auteur ; Tim Birr, Auteur ; Joseph-Alexander Verreet, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] diffusion spatiale [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] maladie phytosanitaire [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] modèle dynamique [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] temps réel Résumé : (auteur) Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted Numéro de notice : A2020-116 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9010044 date de publication en ligne : 15/01/2020 En ligne : https://doi.org/10.3390/ijgi9010044 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94723 [article]

Spatiotemporal variation in the relationship between boreal forest productivity proxies and climate data / Clémentine Ols in Dendrochronologia, vol 58 (December 2019)  

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Les eaux de pluie maîtrisées ou en excès / Pierre Clergeot in Géomètre, n° 2173 (octobre 2019)

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A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n° 5 (October 2019)  

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Quantifying the impact of trees on land surface temperature: a downscaling algorithm at city-scale / Elena Barbierato in European journal of remote sensing, sans n° (2019)  

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Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB‐II for 135 eastern U.S. trees / Matthew P. Peters in Ecology and evolution, vol 9 n° 15 (August 2019)  

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The AROME-WMED reanalyses of the first special observation period of the Hydrological cycle in the Mediterranean experiment (HyMeX) / Nadia Fourrié in Geoscientific Model Development, vol 12 n° 7 (July 2019)  

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High-resolution models of tropospheric delays and refractivity based on GNSS and numerical weather prediction data for alpine regions in Switzerland / Karina Wilgan in Journal of geodesy, vol 93 n°6 (June 2019)  

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Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia / Enkhjargal Natsagdorj in Geocarto international, vol 34 n° 7 ([01/06/2019])  

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Champs et objets pour mieux représenter les phénomènes dans leur contexte géographique / Anne Ruas in Revue internationale de géomatique, vol 29 n° 2 (avril - juin 2019)  

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Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI) / Helge Dietrich in Annals of Forest Science [en ligne], vol 76 n° 1 (March 2019)  

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