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A new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello in Journal of the Royal Statistical Society: Series C Applied Statistics, vol inconnu ([01/01/2021])  

Public [article]  inJournal of the Royal Statistical Society: Series C Applied Statistics > vol inconnu [01/01/2021] Titre : A new segmentation method for the homogenisation of GNSS-derived IWV time-series Type de document : Article/Communication Auteurs : Annarosa Quarello, Auteur ; Olivier Bock , Auteur ; Emilie Lebarbier, Auteur Année de publication : 2021 Projets : VEGA (LEFE/INSU) / Bock, Olivier Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] programmation dynamique [Termes descripteurs IGN] R (langage) [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] teneur intégrée en vapeur d'eau [Termes descripteurs IGN] variance Résumé : (auteur) Homogenization is an important and crucial step to improve the usage of observational data for climate analysis. This work is motivated by the analysis of long series of GNSS Integrated Water Vapour (IWV) data which have not yet been used in this context. This paper proposes a novel segmentation method that integrates a periodic bias and a heterogeneous, monthly varying, variance. The method consists in estimating first the variance using a robust estimator and then estimating the segmentation and periodic bias iteratively. This strategy allows for the use of the dynamic programming algorithm that remains the most efficient exact algorithm to estimate the change-point positions. The statistical performance of the method is assessed through numerical experiments. An application to a real data set of 120 global GNSS stations is presented. The method is implemented in the R package GNSSseg that will be available on the CRAN. Numéro de notice : A2021-061 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Autre URL associée : vers HAL Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : https://arxiv.org/pdf/2005.04683.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96617 [article]

fusionImage: An R package for pan‐sharpening images in open source software / Fulgencio Cánovas‐García in Transactions in GIS, Vol 24 n° 5 (October 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1185-1207 Titre : fusionImage: An R package for pan‐sharpening images in open source software Type de document : Article/Communication Auteurs : Fulgencio Cánovas‐García, Auteur ; Paúl Pesántez‐Cobos, Auteur ; Francisco Alonso‐Sarría, Auteur Année de publication : 2020 Article en page(s) : pp 1185-1207 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de Gram-Schmidt [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] filtre passe-haut [Termes descripteurs IGN] logiciel libre [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] pouvoir de résolution géométrique [Termes descripteurs IGN] R (langage) Résumé : (Auteur) The objective of this article is to evaluate the performance of three pan‐sharpening algorithms (high‐pass filter, principal component analysis and Gram–Schmidt) to increase the spatial resolution of five types of multispectral images and to evaluate the results in terms of color, coherence and spatial sharpness, both qualitatively and quantitatively. A secondary objective is to present an implementation of the aforementioned pan‐sharpening techniques within the open source software R. From a qualitative point of view, pan‐sharpening of images with a high spatial resolution ratio give better results than those whose spatial resolution ratio is 2. According to the quantitative evaluation, there is no pan‐sharpening methodology that obtains optimal results simultaneously for all types of images used. The results of the spectral and spatial ERGAS index vary for four out of the five types of images analyzed. The results show that none of the methods implemented in this work can be considered a priori better than the others. At the same time, this work indicates the importance of both qualitative and quantitative assessment. Numéro de notice : A2020-499 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12676 date de publication en ligne : 15/09/2020 En ligne : https://doi.org/10.1111/tgis.12676 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96206 [article]

Python software tools for GNSS interferometric reflectometry (GNSS-IR) / Angel Martín in GPS solutions, Vol 24 n° 4 (October 2020)  

Public [article]  inGPS solutions > Vol 24 n° 4 (October 2020) . - 7 p. 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 descripteurs IGN] antenne GNSS [Termes descripteurs IGN] format RINEX [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] réflectométrie par GNSS Résumé : (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 [article]

Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 88 (June 2020) . - 10 p. Titre : Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data Type de document : Article/Communication Auteurs : Rochelle Schneider dos Santos, Auteur Année de publication : 2020 Article en page(s) : 10 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme du gradient [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] chaleur [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] Londres [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] mortalité [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] politique publique [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] santé [Termes descripteurs IGN] station météorologique [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] température de l'air [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urban–rural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heat-related mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatio-temporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and satellite-derived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (R² = 0.68, MAE = 1.60 °C, and RMSE = 2.03 °C). This methodological approach is capable of being replicated in other UK cities, benefiting national heat-related mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatio-temporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policy-making by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decision-making process towards the prioritisation of actions to mitigate heat-related mortality amongst the vulnerable population. Numéro de notice : A2020-448 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2020.102066 date de publication en ligne : 10/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102066 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95524 [article]

Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070 Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] atmosphère terrestre [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] faisceau laser [Termes descripteurs IGN] feuille (végétation) [Termes descripteurs IGN] foresterie [Termes descripteurs IGN] forêt de feuillus [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Python (langage de programmation) [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] transfert radiatif Résumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970 [article]

Creating a web mapping portal to manage Malta’s underwater cultural heritage / Mélissa Dupuis (2020) 

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Creation of inspirational Web Apps that demonstrate the functionalities offered by the ArcGIS API for JavaScript / Arthur Genet (2020) 

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Création d’un outil d’interrogation du référentiel régional pédologique de Bretagne pour estimation du stock de carbone organique du sol / Louise Grall (2020) 

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Développement d’outils web de data visualization / Edson Olivares Medina (2020) 

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Distributed and parallel architectures for spatial data / Alberto Belussi (2020)  

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Mise en place d’une visionneuse de photographies immersives dans QGIS / Thibault Petiet (2020) 

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Restitution de profils verticaux de la distribution de gouttes de pluie à partir de mesures au sol et en altitude / Christophe Samboun (2020) 

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A factor model approach for the joint segmentation with between‐series correlation / Xavier Collilieux in Scandinavian Journal of Statistics, vol 46 n° 3 (September 2019)  

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PpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? / Sandra Bujan in Photogrammetric record, vol 34 n° 167 (September 2019)  

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Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model / Mariana Madruga de bruto in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)  

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