Centre de documentation
scientifique

Geospatial assessment of urban ecosystem disservices: An example of poisonous urban trees in Berlin, Germany / Peer von Döhren in Urban Forestry & Urban Greening, vol 67 (January 2022)  

Public [article]  inUrban Forestry & Urban Greening > vol 67 (January 2022) . - n° 127440 Titre : Geospatial assessment of urban ecosystem disservices: An example of poisonous urban trees in Berlin, Germany Type de document : Article/Communication Auteurs : Peer von Döhren, Auteur ; Dagmar Haase, Auteur Année de publication : 2022 Article en page(s) : n° 127440 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] arbre urbain [Termes IGN] Berlin [Termes IGN] santé [Termes IGN] service écosystémique Mots-clés libres : allergie  poison  terrain de jeu Résumé : (auteur) Urban trees play an important role in green infrastructure planning for the ecosystem services they provide. These services include carbon sequestration, the provision of clean air through oxygen production and filtering of airborne pollutants, and the offsetting of the urban heat island effect by providing shade and cooling. In addition to the well-studied positive effects of urban trees, under specific conditions, there are some unwanted side effects that need to be considered. Such negative side effects, such as allergies caused by tree pollen, traffic hazards from falling trees or tree parts or damage from roots or branches in resource supply or waste disposal infrastructures, are termed ecosystem disservices. An ecosystem disservice that is not very often illuminated in the urban context is the presence of poisonous urban trees. This paper provides a spatially explicit view of the distribution of poisonous urban trees in the city of Berlin, relating the spatial distribution of the hazard from this urban ecosystem disservice with the conditions under which it can have the most damaging effect by considering nearby playgrounds and areas with a high population density of children under 5 years old, the most vulnerable group within the urban population. Numéro de notice : A2022-317 Affiliation des auteurs : non IGN Thématique : FORET/URBANISME Nature : Article DOI : 10.1016/j.ufug.2021.127440 En ligne : https://doi.org/10.1016/j.ufug.2021.127440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100422 [article]

Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany / Omar Seleem in Geomatics, Natural Hazards and Risk, vol 13 (2022)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 1640 - 1662 Titre : Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany Type de document : Article/Communication Auteurs : Omar Seleem, Auteur ; Georgy Ayzel, Auteur Année de publication : 2022 Article en page(s) : pp 1640 - 1662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Berlin [Termes IGN] cartographie des risques [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] inondation [Termes IGN] pouvoir de résolution géométrique [Termes IGN] vulnérabilité Résumé : (auteur) Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available. Numéro de notice : A2022-457 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2097131 Date de publication en ligne : 12/07/2022 En ligne : https://doi.org/10.1080/19475705.2022.2097131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101257 [article]

Unmixing-based Sentinel-2 downscaling for urban land cover mapping / Fei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 133 - 154 Titre : Unmixing-based Sentinel-2 downscaling for urban land cover mapping Type de document : Article/Communication Auteurs : Fei Xu, Auteur ; Ben Somers, Auteur Année de publication : 2021 Article en page(s) : pp 133 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] bande spectrale [Termes IGN] Berlin [Termes IGN] Bruxelles [Termes IGN] cartographie urbaine [Termes IGN] Cologne [Termes IGN] corrélation [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] matrice de co-occurrence [Termes IGN] occupation du sol Résumé : (auteur) With the launch of Sentinel-2 new opportunities for large scale urban mapping arise. However, the spectral information embedded in the Sentinel-2 20 m spatial resolution bands cannot yet be fully explored in heterogeneous urban landscapes. The 20 m image pixels are often composed of different land covers, resulting in a difficult to interpret mixed pixel spectrum. Here, we propose an unmixing-based image fusion algorithm (UnFuSen2) that self-adapts to the spectral variability of varying land covers and improves the image fusion accuracy by constraining the unmixing equations on the basis of spectral mixing models and the correlation between spectral bands of coarse and fine spatial resolution, respectively. When compared to alternative state-of-the-art downscaling methods UnFuSen2 consistently showed the highest accuracy when applied across test sites in three different European cities (RMSEUnFuSen2 = 203 vs RMSEalternatives = [252, 337]). In a next step, we applied Multiple Endmember Spectral Mixture Analysis (MESMA) on the downscaled Sentinel-2 image cube (i.e. ten 10 m bands) to generate subpixel urban land cover fractions. We compared our MESMA results against the traditional MESMA output as applied on the original Sentinel-2 image cube (i.e. four 10 m bands and six 20 m bands) and tested its robustness against reference data obtained over all three study sites. Results revealed an average decrease in RMSE of respectively 18% and 8% for impervious surface and vegetation fractions when our approach was compared to the traditional MESMA outcomes. Numéro de notice : A2021-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.009 Date de publication en ligne : 26/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96419 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021011	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021013	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2021012	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos / Yu Feng in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 2 (February 2018) Titre : Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos Type de document : Article/Communication Auteurs : Yu Feng, Auteur ; Monika Sester, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] apprentissage profond [Termes IGN] Berlin [Termes IGN] cartographie des risques [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] inondation [Termes IGN] Londres [Termes IGN] Paris (75) [Termes IGN] protection civile [Termes IGN] risque naturel [Termes IGN] zone sinistrée [Termes IGN] zone urbaine Résumé : (Auteur) In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens’ safety. Therefore, real-time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin. Numéro de notice : A2018-105 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7020039 En ligne : https://doi.org/10.3390/ijgi7020039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89530 [article]

Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks / Sina Montazeri in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 6868 - 6878 Titre : Three-dimensional deformation monitoring of urban infrastructure by tomographic SAR using multitrack TerraSAR-X data stacks Type de document : Article/Communication Auteurs : Sina Montazeri, Auteur ; Xiao Xiang Zhu, Auteur ; Michael Eineder, Auteur ; Richard Bamler, Auteur Année de publication : 2016 Article en page(s) : pp 6868 - 6878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse comparative [Termes IGN] Berlin [Termes IGN] déformation d'édifice [Termes IGN] image radar moirée [Termes IGN] image TerraSAR-X [Termes IGN] semis de points [Termes IGN] surveillance d'ouvrage [Termes IGN] tomographie radar Résumé : (Auteur) Differential synthetic aperture radar tomography (D-TomoSAR), similar to its conventional counterparts such as differential interferometric SAR and persistent scatterer interferometry, is only capable of capturing 1-D deformation along the satellite's line of sight. In this paper, we propose a method based on L1-norm minimization within local spatial cubes to reconstruct 3-D displacement vectors from TomoSAR point clouds available from at least three different viewing geometries. The methodology is applied on two pairs of cross-heading-combination of ascending and descending-TerraSAR-X (TS-X) spotlight image stacks over the city of Berlin. The linear deformation rate and the amplitude of seasonal deformation are decomposed, and the results from two test sites with remarkable deformation pattern are discussed in detail. The results, to our knowledge, demonstrate the first attempt for motion decomposition using TomoSAR data from multiple viewing geometries. Numéro de notice : A2016-919 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585741 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2585741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83322 [article]

Les temps de transport pour délimiter des aires urbaines fonctionnelles ? Une investigation critique à partir de trois métropoles européennes / Marianne Guérois in Belgeo, vol 2016 n° 2 (2016-2)  

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Automatic detection and reconstruction of 2-D/3-D building shapes from spaceborne TomoSAR point clouds / Muhammad Shahzad in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)  

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Open Data: Zukunftsorientierte Bereitstellung von amtlichen Geodaten im Land Berlin / Michael Friedt in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement, vol 139 n° 5 (September - Oktober 2014)

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Information content of very high resolution SAR images: study of feature extraction and imaging parameters / Corneliu Dimitru in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)  

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Home energy assessment technologies / G. Hay in GIM international, vol 24 n° 3 (March 2010)

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Solar atlas of Berlin / D. Ludwig in GIM international, vol 24 n° 3 (March 2010)

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3D builbing reconstruction from lidar based on a cell decomposition approach / Martin Kada (01/12/2009)  

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Aménagement de la qualité de l'air urbain / Florian Pfäfflin in Géomatique expert, n° 64 (01/09/2008)

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Die städtebauliche Entwicklung Berlins seit 1650 in Karten / Bruno Aust in Annuaire international de cartographie, n° 29 (1989)

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Fernerkundung : Stand und Entwicklungstendenzen 1988, Tome 1. Beiträge zur Konferenz / Zentralinstitut für Physik der Erde (1989)

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