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Multiscale cartographic visualization of harmonized datasets / Peter Kunz in International journal of cartography, vol 5 n° 2-3 (July - November 2019)  

Public [article]  inInternational journal of cartography > vol 5 n° 2-3 (July - November 2019) . - pp 178 - 194 Titre : Multiscale cartographic visualization of harmonized datasets Type de document : Article/Communication Auteurs : Peter Kunz, Auteur ; Joachim Bobrich, Auteur Congrès : Congrès: ICC 2019, 29th International Cartographic Conference ICA Mapping everything for everyone (15 - 20 July 2019; Tokyo, Japan), Commanditaire Année de publication : 2019 Article en page(s) : pp 178 - 194 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] cartographie topographique [Termes descripteurs IGN] échelle cartographique [Termes descripteurs IGN] geoportail [Termes descripteurs IGN] intégration de données [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] PostGIS [Termes descripteurs IGN] représentation multiple [Termes descripteurs IGN] stockage de données [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) With the product TopPlusOpen, the Federal Agency for Cartography and Geodesy (BKG) has for the first time provided a freely accessible web map, which is based entirely on open or free data sources. For this purpose, the BKG processed and combined many different data sources in order to achieve the best possible map presentation. Among others, free official spatial data of the Federal Government and the Open Data states Berlin, Hamburg, North Rhine-Westphalia and Thuringia are presented in the product. Furthermore, Brandenburg, Mecklenburg-Western Pomerania and Rhineland-Palatinate provide their official spatial data for TopPlusOpen based on a cooperation agreement so that also these states are presented exclusively by official data. The services of the TopPlusOpen were released in September 2017. The product was very well received by the public, private and scientific sectors and is already used as a background map in many geoportals. The free access and the cross-border map presentation are especially mentioned. Numéro de notice : A2019-236 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2019.1610931 date de publication en ligne : 13/05/2019 En ligne : https://doi.org/10.1080/23729333.2019.1610931 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92935 [article]

CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 415 - 424 Titre : CNN-based dense image matching for aerial remote sensing images Type de document : Article/Communication Auteurs : Shunping Ji, Auteur ; Jin Liu, Auteur ; Meng Lu, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 424 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chine [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] Munich [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] Stuttgart [Termes descripteurs IGN] ville [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) Dense stereo matching plays a key role in 3D reconstruction. The capability of using deep learning in the stereo matching of remote sensing data is currently uncertain. This article investigated the application of deep learning–based stereo methods in aerial image series and proposed a deep learning–based multi-view dense matching framework. First, we applied three typical convolutional neural network models, MC-CNN, GC-Net, and DispNet, to aerial stereo pairs and compared the results with those of the SGM and a commercial software, SURE. Second, on different data sets, the generalization ability of each network is evaluated by using direct transfer learning with models pretrained on other data sets and by fine-tuning with a small number of target training data. Third, we present a deep learning–based multi-view dense matching framework where the multi-view geometry is introduced to further refine matching results. Three sets of aerial images as the main data sets and two open-source sets of street images as auxiliary data sets are used for testing. Experiments show that, first, the performance of deep learning–based stereo methods is slightly better than traditional methods. Second, both the GC-Net and the MC-CNN have demonstrated good generalization ability and can obtain satisfactory results on aerial images using a pretrained model on several available stereo benchmarks. Third, multi-view geometry constraints can further improve the performance of deep learning–based methods, which is better than that of the multi-view–based SGM and SURE. Numéro de notice : A2019-246 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.415 date de publication en ligne : 01/06/2019 En ligne : https://doi.org/10.14358/PERS.85.6.415 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93002 [article]

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Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 42 - 58 Titre : Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery Type de document : Article/Communication Auteurs : Christian Geiss, Auteur ; Patrick Aravena Pelizari, Auteur ; Lukas Blickensdörfer, Auteur ; Hannes Taubenböck, Auteur Année de publication : 2019 Article en page(s) : pp 42 - 58 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] Cologne [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] invariant [Termes descripteurs IGN] Kenya [Termes descripteurs IGN] séparateur à vaste marge Résumé : (Auteur) We follow the idea of learning invariant decision functions for remote sensing image classification with Support Vector Machines (SVM). To do so, we generate artificially transformed samples (i.e., virtual samples) from available prior knowledge. Labeled samples closest to the separating hyperplane with maximum margin (i.e., the Support Vectors) are identified by learning an initial SVM model. The Support Vectors are used for generating virtual samples by perturbing the features to which the model should be invariant. Subsequently, the model is relearned using the Support Vectors and the virtual samples to eventually alter the hyperplane with maximum margin and enhance generalization capabilities of decision functions. In contrast to existing approaches, we establish a self-learning procedure to ultimately prune non-informative virtual samples from a possibly arbitrary invariance generation process to allow for robust and sparse model solutions. The self-learning strategy jointly considers a similarity and margin sampling constraint. In addition, we innovatively explore the invariance generation process in the context of an object-based image analysis framework. Image elements (i.e., pixels) are aggregated to image objects (as represented by segments/superpixels) with a segmentation algorithm. From an initial singular segmentation level, invariances are encoded by varying hyperparameters of the segmentation algorithm in terms of scale and shape. Experimental results are obtained from two very high spatial resolution multispectral data sets acquired over the city of Cologne, Germany, and the Hagadera Refugee Camp, Kenya. Comparative model accuracy evaluations underline the favorable performance properties of the proposed methods especially in settings with very few labeled samples. Numéro de notice : A2019-203 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.001 date de publication en ligne : 12/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92666 [article]

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Climate change and mixed forests: how do altered survival probabilities impact economically desirable species proportions of Norway spruce and European beech? / Carola Paul in Annals of Forest Science [en ligne], vol 76 n° 1 (March 2019)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 76 n° 1 (March 2019) Titre : Climate change and mixed forests: how do altered survival probabilities impact economically desirable species proportions of Norway spruce and European beech? Type de document : Article/Communication Auteurs : Carola Paul, Auteur ; Susanne Brandl, Auteur ; Stefan Friedrich, Auteur ; Wolfgang Falk, Auteur ; Fabian Härtl, Auteur ; Thomas Knoke, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] aménagement forestier [Termes descripteurs IGN] analyse de survie [Termes descripteurs IGN] biodiversité végétale [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] reboisement [Termes descripteurs IGN] régénération (sylviculture) [Termes descripteurs IGN] restauration écologique [Vedettes matières IGN] Economie forestière Résumé : (auteur) Context : Climate change is expected to increase natural hazards in European forests. Uncertainty in expected tree mortality and resulting potential economic consequences complicate regeneration decisions. Aims : This study aims to analyze the economic consequences of altered survival probabilities for mixing Norway spruce (Picea abies L. Karst.) and European beech (Fagus sylvatica L.) under different climate change scenarios. We investigate whether management strategies such as species selection and type of mixture (mixed stands vs. block mixture) could mitigate adverse financial effects of climate change. Methods : The bio-economic modelling approach combines a parametric survival model with modern portfolio theory. We estimate the economically optimal species mix under climate change, accounting for the biophysical and economic effects of tree mixtures. The approach is demonstrated using an example from Southeast Germany. Results : The optimal tree species mixtures under simulated climate change effects could buffer but not completely mitigate undesirable economic consequences. Even under optimally mixed forest stands, the risk-adjusted economic value decreased by 28%. Mixed stands economically outperform block mixtures for all climate scenarios. Conclusion : Our results underline the importance of mixed stands to mitigate the economic consequences of climate change. Mechanistic bio-economic models help to understand consequences of uncertain input variables and to design purposeful adaptation strategies. Numéro de notice : A2019-041 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0793-8 date de publication en ligne : 08/02/2019 En ligne : https://doi.org/10.1007/s13595-018-0793-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92039 [article]

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)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 76 n° 1 (March 2019) Titre : Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI) Type de document : Article/Communication Auteurs : Helge Dietrich, Auteur ; Thilo Wolf, Auteur ; Tobias Kawohl, Auteur ; Jan Wehberg, Auteur ; Gerald Kändler, Auteur ; Tobias Mette, Auteur ; Arno Röder, Auteur ; ürgen Böhner, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] historique des données [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] largeur de bande [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] productivité [Termes descripteurs IGN] rayonnement solaire [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] station météorologique [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) The “NFI 2012 environmental data base climate” is part of the environmental database of the German National Forest Inventory. It contains climate information for 26,450 inventory points generated from gridded daily climate data for 1961–2100 at a spatial resolution of 250 m. Grids are based on DWD-Observations and REMO EURO-CORDEX climate projections. Access to the databases is provided via the URL: https://doi.org/10.3220/DATA/20180823-102429. Associated metadata are available at https://agroenvgeo.data.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/d0789030-c94e-4883-8d38-2a7332c98673. Numéro de notice : A2019-043 Thématique : FORET Nature : Article DOI : 10.1007/s13595-018-0788-5 date de publication en ligne : 23/01/2019 En ligne : https://doi.org/10.1007/s13595-018-0788-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92049 [article]

Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)  

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Forest conversion from Norway spruce to European beech increases species richness and functional structure of aboveground macrofungal communities / Peggy Heine in Forest ecology and management, vol 432 (15 January 2019)  

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Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands / Fabio Castaldi in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)  

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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

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Land cover mapping at very high resolution with rotation equivariant CNNs : Towards small yet accurate models / Diego Marcos in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

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Estimation and uncertainty of the mixing effects on Scots pine—European beech productivity from national forest inventories data / Sonia Condés in Forests, vol 9 n° 9 (September 2018)  

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Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests / Nina Amiri in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)  

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Static site indices from different national forest inventories: harmonization and prediction from site conditions / Susanne Brandl in Annals of Forest Science [en ligne], vol 75 n° 2 (June 2018)  

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The German Forest Strategy 2020: Target achievement control using national forest inventory results / Martin Lorenz in Annals of forest research, vol 61 n° 2 (July - December 2018)  

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Integration of aerial oblique imagery and terrestrial imagery for optimized 3D modeling in urban areas / Bo Wu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)  

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