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Python software tools for GNSS interferometric reflectometry (GNSS-IR) / Angel Martín in GPS solutions, Vol 24 n° 4 (October 2020)
[article]
Titre : Python software tools for GNSS interferometric reflectometry (GNSS-IR) Type de document : Article/Communication Auteurs : Angel Martín, Auteur ; Raquel Luján, Auteur ; Ana Belén Anquela, Auteur Année de publication : 2020 Article en page(s) : 7 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] antenne GNSS
[Termes IGN] format RINEX
[Termes IGN] humidité du sol
[Termes IGN] Python (langage de programmation)
[Termes IGN] rapport signal sur bruit
[Termes IGN] réflectométrie par GNSSRésumé : (auteur) Global Navigation Satellite System (GNSS) interferometric reflectometry, also known as the GNSS-IR, uses data from geodetic-quality GNSS antennas to extract information about the environment surrounding the antenna. Soil moisture monitoring is one of the most important applications of the GNSS-IR technique. This manuscript presents the main ideas and implementation decisions needed to write the Python code for software tools that transform RINEX format observation and navigation files into an appropriate format for GNSS-IR (which includes the SNR observations and the azimuth and elevation of the satellites) and to determine the reflection height and the adjusted phase and amplitude values of the interferometric wave for each individual satellite track. The main goal of the manuscript is to share the software with the scientific community to introduce new users to the GNSS-IR technique. Numéro de notice : A2020-523 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01010-0 Date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1007/s10291-020-01010-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95696
in GPS solutions > Vol 24 n° 4 (October 2020) . - 7 p.[article]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]Global Climate [in “State of the Climate in 2019"] / A. Ades in Bulletin of the American Meteorological Society, vol 101 n° 8 (August 2020)
[article]
Titre : Global Climate [in “State of the Climate in 2019"] Type de document : Article/Communication Auteurs : A. Ades, Auteur ; R. Adler, Auteur ; et al., Auteur ; Olivier Bock , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp S9 - S128 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Atmosphère
[Termes IGN] changement climatique
[Termes IGN] circulation atmosphérique
[Termes IGN] climat terrestre
[Termes IGN] cryosphère
[Termes IGN] données météorologiques
[Termes IGN] gaz à effet de serre
[Termes IGN] humidité de l'air
[Termes IGN] humidité du sol
[Termes IGN] précipitation
[Termes IGN] sécheresse
[Termes IGN] température de l'airRésumé : (auteur) [introduction] The assessments and analyses presented in this chapter focus predominantly on the measured differences of climate and weather observables from previous conditions, years, and decades to place 2019 in context. Many of these differences have direct impacts on people, for example, their health and environment, as well as the wider biosphere, but are beyond the scope of these analyses. For the last few State of the Climate reports, an update on the number of warmer-than-average years has held no surprises, and this year is again no different. The year 2019 was among the three warmest years since records began in the mid-to-late 1800s. Only 2016, and for some datasets 2015, were warmer than 2019; all years after 2013 have been warmer than all others back to the mid-1800s. Each decade since 1980 has been successively warmer than the preceding decade, with the most recent (2010–19) being around 0.2°C warmer than the previous (2000–09). This warming of the land and ocean surface is reflected across the globe. For example, lake and permafrost temperatures have increased; glaciers have continued to lose mass, becoming thinner for the 32nd consecutive year, with the majority also becoming shorter during 2019. The period during which Northern Hemisphere (NH) lakes were covered in ice was seven days shorter than the 1981–2010 long-term average, based on in situ phenological records. There were fewer cool extremes and more warm extremes on land; regions including Europe, Japan, Pakistan, and India all experienced heat waves. More strong than moderate marine heat waves were recorded for the sixth consecutive year. And in Australia (discussed in more detail in section 7h4), moisture deficits and prolonged high temperatures led to severe impacts during late austral spring and summer, including devastating wildfires. Smoke from these wildfires was detected across large parts of the Southern Hemisphere (SH). [...] Numéro de notice : A2020-798 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1175/BAMS-D-20-0104.1 Date de publication en ligne : 12/08/2020 En ligne : https://doi.org/10.1175/BAMS-D-20-0104.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96582
in Bulletin of the American Meteorological Society > vol 101 n° 8 (August 2020) . - pp S9 - S128[article]Regionalization of flood magnitudes using the ecological attributes of watersheds / Bahman Jabbarian Amiri in Geocarto international, vol 35 n° 9 ([01/07/2020])
[article]
Titre : Regionalization of flood magnitudes using the ecological attributes of watersheds Type de document : Article/Communication Auteurs : Bahman Jabbarian Amiri, Auteur ; Bahareh Baheri, Auteur ; Nicola Fohrer, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 917 - 933 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] bassin hydrographique
[Termes IGN] Caspienne, mer
[Termes IGN] crue
[Termes IGN] débit
[Termes IGN] estimation quantitative
[Termes IGN] humidité du sol
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] prévention des risques
[Termes IGN] régionalisation (segmentation)
[Termes IGN] ressources en eau
[Termes IGN] utilisation du sol
[Termes IGN] zone inondableRésumé : (auteur) Estimating flood discharge at ungauged sites is a significant challenge facing water resources planners and engineers during the planning and design of hydraulic structures, managing flood prone zones, and operating artificial waterbodies. Developing more robust models to improve the reliability of flood discharge estimations is thus very useful. The role of ecological attributes including land use/land cover (LULC), hydrologic soil groups (HSG), and watershed physical characteristics (area, main stream length, average slope), and watershed shape coefficients (form, compactness, circularity, and elongation) in explaining the overall variation in flood magnitude in 39 watersheds, located in the southern basin of the Caspian Sea, was investigated. As the LULC and HSG were found to play a significant role in explaining total variation (40–89%) in flood magnitudes, their inclusion in the estimation of flood magnitudes can provide more reliable estimates of flood risk and magnitude. Numéro de notice : A2020-428 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1552321 Date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1552321 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95494
in Geocarto international > vol 35 n° 9 [01/07/2020] . - pp 917 - 933[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020091 RAB Revue Centre de documentation En réserve L003 Disponible A web-based spatial decision support system for monitoring the risk of water contamination in private wells / Yu Lan in Annals of GIS, vol 26 n° 3 (July 2020)
[article]
Titre : A web-based spatial decision support system for monitoring the risk of water contamination in private wells Type de document : Article/Communication Auteurs : Yu Lan, Auteur ; Wenwu Tang, Auteur ; Samantha Dye, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 293 - 309 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] arsenic
[Termes IGN] base de données localisées
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] contamination
[Termes IGN] eau souterraine
[Termes IGN] interpolation spatiale
[Termes IGN] krigeage
[Termes IGN] pollution des eaux
[Termes IGN] prévention des risques
[Termes IGN] puits
[Termes IGN] santé
[Termes IGN] surveillance sanitaire
[Termes IGN] système d'aide à la décision
[Termes IGN] système d'information géographique
[Termes IGN] WebSIGRésumé : (auteur) Long-term exposure to contaminated water can cause health effects, such as cancer. Accurate spatial prediction of inorganic compounds (e.g. arsenic) and pathogens in groundwater is critical for water supply management. Ideally, environmental health agencies would have access to an early warning system to alert well owners of risks of such contamination. The estimation and dissemination of these risks can be facilitated by the combination of Geographic Information Systems and spatial analysis capabilities – i.e., spatial decision support system (SDSS). However, the use of SDSS, especially web-based SDSS, is rare for spatially explicit studies of drinking water quality of private wells. In this study, we introduce the interactive Well Water Risk Estimation(iWWRE), a web-based SDSS to facilitate the monitoring of water contamination in private wells across Gaston County, North Carolina (US). Our system implements geoprocessing web services and generates dynamic spatial analysis results based on a database of private wells. Environmental health scientists using our system can conduct fine-grained spatial interpolation on 1) a particular type of contaminant such as arsenic, 2) on various subsets through a temporal query. Visuals consist of an estimation map, cross validation information, Kriging variance and contour lines that delineate areas with maximum contaminant levels (MCL), as set by the US Environmental Protection Agency (EPA). Our web-based SDSS was developed jointly with environmental health specialists who found it particularly critical for the monitoring of local contamination trends, and a useful tool to reach out to private well users in highly elevated contaminated areas. Numéro de notice : A2020-583 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1798508 Date de publication en ligne : 30/07/2020 En ligne : https://doi.org/10.1080/19475683.2020.1798508 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95905
in Annals of GIS > vol 26 n° 3 (July 2020) . - pp 293 - 309[article]Hydrogeology of the western Po plain (Piedmont, NW Italy) / Domenico Antonio De Luca in Journal of maps, vol 16 n° 2 ([01/06/2020])PermalinkImproved SMAP dual-channel algorithm for the retrieval of soil moisture / Mario Julian Chaubell in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkLack of effect of admixture proportion and tree density on water acquisition depth for European beech (Fagus sylvatica L.) and sycamore maple (Acer pseudoplatanus L.) / Alexandre Fruleux in Annals of Forest Science, Vol 77 n° 2 (June 2020)PermalinkSoil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkSpectral Interference of Heavy Metal Contamination on Spectral Signals of Moisture Content for Heavy Metal Contaminated Soils / Haein Shin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkIntegrated edge detection and terrain analysis for agricultural terrace delineation from remote sensing images / Wen Dai in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)PermalinkUsing Ranked Probability Skill Score (RPSS) as Nonlocal Root-Mean-Square Errors (RMSEs) for Mitigating Wet Bias of Soil Moisture Ocean Salinity (SMOS) Soil Moisture / Ju Hyoung Lee in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)PermalinkEstimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner / Kai Tan in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkPermalinkModélisation des effets de la compétition interspécifique et des pratiques sylvicoles sur la croissance de jeunes plants forestiers / Jean-Charles Miquel (2020)PermalinkSurface soil moiture retrieval over irrigated wheat crops in semi-arid areas using Sentinel-1 data / Nadia Ouaadi (2020)PermalinkSoil and vegetation scattering contributions in L-Band and P-Band polarimetric SAR observations / S. Hamed Alemohammad in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkCombining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (October-2 2019)PermalinkComparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkLes eaux de pluie maîtrisées ou en excès / Pierre Clergeot in Géomètre, n° 2173 (octobre 2019)PermalinkThe relationship between climate and the intra-annual oxygen isotope patterns from pine trees: a case study along an elevation gradient on Corsica, France / Sonja Szymczak in Annals of Forest Science, Vol 76 n° 3 (September 2019)PermalinkUsing LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)PermalinkComprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])PermalinkDéveloppement d’un « ModelBuilder » pour l’évaluation de la recharge nette : cas de la nappe phréatique de Zéramdine Beni Hassène (Tunisie) / Imen Hentati in Géomatique expert, n° 128 (juin - juillet 2019)PermalinkIncluding Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data / Abdelhakim Amazirh in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)Permalink