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Titre : Étude de l’évolution du couvert forestier dans le Haut-Béarn Type de document : Mémoire Auteurs : Céline Toussaint, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2021 Importance : 81 p. Format : 21 x 30 cm Note générale : Bibliographie
Mémoire de fin d'études, cycle des ingénieurs ENSG 3ème année, Master DDMEG Développement Durable, Management Environnemental et GéomatiqueLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] BD forêt
[Termes IGN] BD Topo
[Termes IGN] Béarn
[Termes IGN] carte d'Etat-Major
[Termes IGN] carte de Cassini
[Termes IGN] couvert forestier
[Termes IGN] densité de la végétation
[Termes IGN] données vectorielles
[Termes IGN] image aérienne
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] orthoimage géoréférencée
[Termes IGN] peuplement forestier
[Termes IGN] QGISIndex. décimale : DDMEG Mémoires du Master Développement Durable, Management Environnemental et Géomatique Résumé : (Auteur) En Asie de l’Est et en Afrique subsaharienne, la superficie de la forêt régresse, tandis qu’en Europe de l’Est, une augmentation de la surface forestière est constatée depuis le XIXe siècle. Dans le Haut-Béarn, zone montagnarde à faible densité urbaine (environ 7 000 habitants pour 100 000 hectares), cette impression d’envahissement par les arbres est forte parmi la population, depuis le début de l’exode rural et le manque de main d’œuvre pour maintenir les paysages ouverts. C’est dans ce cadre que le sujet de stage « étude de l’évolution du couvert forestier dans le Haut-Béarn » a été proposé par l’Institution Patrimoniale du Haut-Béarn. Pour analyser ces changements, il a été question d’utiliser la télédétection, après avoir vectorisé les forêts sur les cartes d’État-Major, sur d’anciennes photographies aériennes (au préalable géoréférencées) sur le logiciel de SIG QGIS. L’utilisation de données forestières vectorielles, telles que les BD Forêt V1 (1992) et V2 (2008), et la BD Topographique (2019) de l’IGN, a permis d’établir qu’une augmentation d’environ 65 % est observable entre le XIXe siècle et 2019 sur le Haut-Béarn. La classification d’images satellites et le calcul de leur NDVI a enrichi la courbe d’évolution de la forêt sur ce territoire, qui a laissé apparaître une accélération de la progression de la forêt ces dernières années. Au cours de ce stage, des travaux annexes ont également pu être réalisés, comme la préparation de cartes pour des héliportages, ou l’utilisation d’algorithmes pour détecter les zones écobuées de 2021, et ont contribué à ma compréhension des enjeux du territoire des valléens. Note de contenu :
Introduction
1. Présentation et contexte
2. La caractérisation des forêts dans le Haut-Béarn au cours du temps
3. Les résultats et leurs enseignements
4. Travaux annexes principaux réalisés pendant le stage
ConclusionNuméro de notice : 26663 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Mémoire de fin d'études IT Organisme de stage : Institution Patrimoniale du Haut-Béarn Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98873 Documents numériques
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Étude de l’évolution du couvert forestier dans le Haut-Béarn - pdf auteurAdobe Acrobat PDF Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping / Mthembeni Mngadi in Geocarto international, vol 36 n° 1 ([01/01/2021])
[article]
Titre : Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping Type de document : Article/Communication Auteurs : Mthembeni Mngadi, Auteur ; John Odindi, Auteur ; Kabir Peerbhay, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse discriminante
[Termes IGN] carte forestière
[Termes IGN] Eucalyptus (genre)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] KwaZulu-Natal (Afrique du Sud)
[Termes IGN] Pinus (genre)
[Termes IGN] télédétection spatialeRésumé : (Auteur) The successful launch and operation of the Sentinel satellite platform has provided access to freely available remotely sensed data useful for commercial forest species discrimination. Sentinel – 1 (S1) with a synthetic aperture radar (SAR) sensor and Sentinel – 2 (S2) multi-spectral sensor with additional and strategically positioned bands offer great potential for providing reliable information for discriminating and mapping commercial forest species. In this study, we sought to determine the value of S1 and S2 data characteristics in discriminating and mapping commercial forest species. Using linear discriminant analysis (LDA) algorithm, S2 multi-spectral imagery showed an overall classification accuracy of 84% (kappa = 0.81), with bands such as the red-edge (703.9–740.2 nm), narrow near infrared (835.1–864.8 nm), and short wave infrared (1613.7–2202.4 nm) particularly influential in discriminating individual forest species stands. When Sentinel 2’s spectral wavebands were fused with Sentinel 1’s (SAR) VV and VH polarimetric modes, overall classification accuracies improved to 87% (kappa = 0.83) and 88% (kappa = 0.85), respectively. These findings demonstrate the value of combining Sentinel’s multispectral and SAR structural information characteristics in improving commercial forest species discrimination. These, in addition to the sensors free availability, higher spatial resolution and larger swath width, offer unprecedented opportunities for improved local and large scale commercial forest species discrimination and mapping. Numéro de notice : A2021-050 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1585483 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1585483 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96719
in Geocarto international > vol 36 n° 1 [01/01/2021] . - pp 1 - 12[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2021011 RAB Revue Centre de documentation En réserve L003 Disponible Extraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : Extraction of street pole-like objects based on plane filtering from mobile LiDAR data Type de document : Article/Communication Auteurs : Jingming Tu, Auteur ; Jian Yao, Auteur ; Li Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 749 - 768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse d'image orientée objet
[Termes IGN] carte routière
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forme caractéristique
[Termes IGN] méthode robuste
[Termes IGN] octree
[Termes IGN] réseau routierRésumé : (auteur) Pole-like objects provide important street infrastructure for road inventory and road mapping. In this article, we proposed a novel pole-like object extraction algorithm based on plane filtering from mobile Light Detection and Ranging (LiDAR) data. The proposed approach is composed of two parts. In the first part, a novel octree-based split scheme was proposed to fit initial planes from off-ground points. The results of the plane fitting contribute to the extraction of pole-like objects. In the second part, we proposed a novel method of pole-like object extraction by plane filtering based on local geometric feature restriction and isolation detection. The proposed approach is a new solution for detecting pole-like objects from mobile LiDAR data. The innovation in this article is that we assumed that each of the pole-like objects can be represented by a plane. Thus, the essence of extracting pole-like objects will be converted to plane selecting problem. The proposed method has been tested on three data sets captured from different scenes. The average completeness, correctness, and quality of our approach can reach up to 87.66%, 88.81%, and 79.03%, which is superior to state-of-the-art approaches. The experimental results indicate that our approach can extract pole-like objects robustly and efficiently. Numéro de notice : A2021-042 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2993454 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2993454 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96758
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 749 - 768[article]From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
[article]
Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] base de données forestières
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert forestier
[Termes IGN] échantillonnage
[Termes IGN] France (administrative)
[Termes IGN] image Sentinel-MSI
[Termes IGN] mise à jour de base de données
[Termes IGN] Populus (genre)
[Termes IGN] série temporelleRésumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 Date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100[article]Réservation
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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 Generative adversarial networks to generalise urban areas in topographic maps / Azelle Courtial (2021)
Titre : Generative adversarial networks to generalise urban areas in topographic maps Type de document : Article/Communication Auteurs : Azelle Courtial , Auteur ; Guillaume Touya , Auteur ; Xiang Zhang, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2021 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B4-2021 Projets : 1-Pas de projet / Conférence : ISPRS 2021, Commission 4, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice Virtuel France OA Archives Commission 4 Importance : pp 15 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] carte topographique
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] réseau antagoniste génératif
[Termes IGN] zone urbaine
[Vedettes matières IGN] GénéralisationRésumé : (auteur) This article presents how a generative adversarial network (GAN) can be employed to produce a generalised map that combines several cartographic themes in the dense context of urban areas. We use as input detailed buildings, roads, and rivers from topographic datasets produced by the French national mapping agency (IGN), and we expect as output of the GAN a legible map of these elements at a target scale of 1:50,000. This level of detail requires to reduce the amount of information while preserving patterns; covering dense inner cities block by a unique polygon is also necessary because these blocks cannot be represented with enlarged individual buildings. The target map has a style similar to the topographic map produced by IGN. This experiment succeeded in producing image tiles that look like legible maps. It also highlights the impact of data and representation choices on the quality of predicted images, and the challenge of learning geographic relationships. Numéro de notice : C2021-016 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B4-2021-15-2021 Date de publication en ligne : 30/06/2021 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-15-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98062 PermalinkPermalinkPermalinkPermalinkPermalinkA method of hydrographic survey technology selection based on the decision tree supervised learning / Ivana Golub Medvešek (2021)PermalinkMise en place d’une infrastructure de données spatiales sur le risque de piqures de tiques / Lilian Calas (2021)PermalinkModelling landslide hazards under global changes: the case of a Pyrenean valley / Séverine Bernardie in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)PermalinkMulti-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot (2021)PermalinkParticiper à la construction de la base de données des toponymes maritimes du SHOM / Solenn Tual (2021)PermalinkProposition d’un référentiel de description et de détection de la végétation dans une agglomération / Mathilde Segaud (2021)PermalinkPermalinkRemotely-sensed rip current dynamics and morphological control in high-energy beach environments / Isaac Rodriguez Padilla (2021)PermalinkSemantic segmentation of sea ice type on Sentinel-1 SAR data using convolutional neural networks / Alissa Kouraeva (2021)PermalinkSherloc: a knowledge-driven algorithm for geolocating microblog messages at sub-city level / Laura Di Rocco in International journal of geographical information science IJGIS, vol 35 n° 1 (January 2021)PermalinkSpatial characterization and distribution modelling of Ensete ventricosum (wild and cultivated) in Ethiopia / Meron Awoke Eshetae in Geocarto international, vol 36 n° 1 ([01/01/2021])PermalinkSuivi de la rotation des cultures à partir de séries temporelles d’images satellite / Félix Quinton (2021)PermalinkSuivi des vignes par télédétection de proximité : le deep learning au service de l’agriculture de précision / Sami Beniaouf (2021)PermalinkPermalinkPermalinkPermalinkTélédétection et intégration de connaissances via la modélisation spatiale pour une cartographie plus cohérente des systèmes agricoles complexes / Arthur Crespin-Boucaud (2021)PermalinkPermalinkThe use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. 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Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkExploratory bivariate and multivariate geovisualizations of a social vulnerability index / Georgianna Strode in Cartographic perspectives, n° 95 (July 2020)PermalinkLa gratuité, une histoire ancienne... / Anonyme in Géomètre, n° 2182 (juillet - août 2020)PermalinkImproved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)PermalinkMapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkMapping the French green infrastructure – an exercise in homogenizing heterogeneous regional data / Lucille Billon in International journal of cartography, Vol 6 n° 2 (July 2020)PermalinkRoles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkSimulating urban land use change by integrating a convolutional neural network with vector-based cellular automata / Yaqian Zhai in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)PermalinkThe image of subsurface geology / Ane Bang-Kittilsen in International journal of cartography, Vol 6 n° 2 (July 2020)PermalinkTriangulation network of 1929–1944 of the first 1:500 urban map of València / Miriam Villar-Cano in Survey review, vol 52 n° 373 (July 2020)PermalinkAn empirical study on the intra-urban goods movement patterns using logistics big data / Pengxiang Zhao in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)PermalinkAn integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkCartographic inference: a peircean perspective / Gordon A. Cromley in Cartographica, vol 55 n° 2 (Summer 2020)PermalinkA change of theme: the role of generalization in thematic mapping / Paulo Raposo in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkFine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)PermalinkHydrogeology of the western Po plain (Piedmont, NW Italy) / Domenico Antonio De Luca in Journal of maps, vol 16 n° 2 ([01/06/2020])Permalink