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Intertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France / Edward Salameh in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 98 - 120 Titre : Intertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France Type de document : Article/Communication Auteurs : Edward Salameh, Auteur ; Frédéric Frappart, Auteur ; Imen Turki, Auteur ; Benoit Laignel, Auteur Année de publication : 2020 Article en page(s) : pp 98 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] aménagement du littoral [Termes descripteurs IGN] Arcachon (bassin d') [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] Cotentin [Termes descripteurs IGN] estran [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] hydrodynamique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] niveau de l'eau [Termes descripteurs IGN] sédiment [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] trait de côte [Termes descripteurs IGN] zone tampon Résumé : (auteur) Intertidal flats lying as a buffer zone between land and sea provide critical services including protection against storm surges and coastal flooding. These environments are characterized by a continuous redistribution of sediment and changes in topography. Sea level rise, anthropogenic pressures, and their related stressors have a considerable impact on these areas and are expected to put them under more stress; hence the increased need for frequent and updated topography maps. Comparing to traditional surveying approaches, spaceborne remote sensing is able to provide topography maps more frequently with a lower cost and a higher coverage. The latter is currently considered as an established tool for measuring intertidal topography. In this study, an improved approach of the waterline method was developed to derive intertidal Digital Elevation Models (DEMs). The changes include a faster, more efficient and quasi-automatic detection and post-processing of waterlines. The edge detection technique consists in combining a k-means based segmentation and an active contouring procedure. This method was designed to generate closed contours in order to enable an automatization of the post-processing of the extracted waterlines. The waterlines were extracted from Sentinel-1 and Sentinel-2 images for two bays located on the French Coast: the Arcachon lagoon and the Bay of Veys. DEMs were generated for the Arcachon Bay between 2015 and 2018, and for the Bay of Veys between 2016 and 2018 using satellite acquisitions made during summer (low storm activity period). The comparison of the generated DEMs with lidar observations showed an error of about 19–25 cm. This study also demonstrated that the waterline method applied to Sentinel images is suitable for monitoring the morpho-sedimentary evolution in intertidal areas. By comparing the DEMs generated between 2016 and 2018, the Arcachon Bay and the Bay of Veys experienced net volume losses of 1.12 × 106 m3 and 0.70 × 106 m3 respectively. The generated DEMs provide useful and needed information for several scientific applications (e.g., sediment balance, hydrodynamic modelling), but also for authorities and stakeholders for coastal management and implementation of ecosystem protection policies. Numéro de notice : A2020-138 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.003 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94756 [article]

A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1568 -1586 Titre : A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data Type de document : Article/Communication Auteurs : Qingwang Wang, Auteur ; Yanfeng Gu, Auteur Année de publication : 2020 Article en page(s) : pp 1568 -1586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Amérique du nord [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] calcul tensoriel [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification multibande [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] modèle géométrique [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] tenseur [Termes descripteurs IGN] vectorisation [Termes descripteurs IGN] voisinage (topologie) Résumé : (Auteur) Multispectral light detection and ranging (MS-LiDAR) systems open the door to the possibility in the 3-D land cover classification at a finer scale using only point cloud data. This article proposes a model based on the tensor representation for multispectral point cloud classification. The proposed method combines the 3-D local spatial structure of each multispectral point by characterizing the point with a second-order tensor. The first mode of the tensor indicates the spatial location and spectral information of each point (i.e., the row of the second-order tensor) and the second mode denotes the neighborhood geometric and spectral structures (i.e., the column of the second-order tensor). Then we develop a novel tensor manifold discriminant embedding (TMDE) algorithm to extract the geometric–spectral features for multispectral point clouds classification. TMDE solves the mapping matrices of each mode by preserving the intraclass samples’ distribution further making it more compact and maximizing the distance of different classes. Finally, the support vector machine classifier with the extracted features as input is used to implement the classification of multispectral point clouds. Experiments are conducted on two real multispectral point cloud data sets. The experimental results demonstrate that the proposed method can achieve significant improvements in classification accuracies in comparison with several state-of-the-art algorithms. Numéro de notice : A2020-086 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947081 date de publication en ligne : 30/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947081 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94660 [article]

A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 97 - 106 Titre : A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery Type de document : Article/Communication Auteurs : Lucas Prado Osco, Auteur ; Mauro Dos Santos de Arruda, Auteur ; José Marcato Junior, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 97 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] agrume [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] carte de confiance [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] géolocalisation [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] verger Résumé : (Auteur) Visual inspection has been a common practice to determine the number of plants in orchards, which is a labor-intensive and time-consuming task. Deep learning algorithms have demonstrated great potential for counting plants on unmanned aerial vehicle (UAV)-borne sensor imagery. This paper presents a convolutional neural network (CNN) approach to address the challenge of estimating the number of citrus trees in highly dense orchards from UAV multispectral images. The method estimates a dense map with the confidence that a plant occurs in each pixel. A flight was conducted over an orchard of Valencia-orange trees planted in linear fashion, using a multispectral camera with four bands in green, red, red-edge and near-infrared. The approach was assessed considering the individual bands and their combinations. A total of 37,353 trees were adopted in point feature to evaluate the method. A variation of σ (0.5; 1.0 and 1.5) was used to generate different ground truth confidence maps. Different stages (T) were also used to refine the confidence map predicted. To evaluate the robustness of our method, we compared it with two state-of-the-art object detection CNN methods (Faster R-CNN and RetinaNet). The results show better performance with the combination of green, red and near-infrared bands, achieving a Mean Absolute Error (MAE), Mean Square Error (MSE), R2 and Normalized Root-Mean-Squared Error (NRMSE) of 2.28, 9.82, 0.96 and 0.05, respectively. This band combination, when adopting σ = 1 and a stage (T = 8), resulted in an R2, MAE, Precision, Recall and F1 of 0.97, 2.05, 0.95, 0.96 and 0.95, respectively. Our method outperforms significantly object detection methods for counting and geolocation. It was concluded that our CNN approach developed to estimate the number and geolocation of citrus trees in high-density orchards is satisfactory and is an effective strategy to replace the traditional visual inspection method to determine the number of plants in orchards trees. Numéro de notice : A2020-045 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.010 date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94525 [article]

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Data scale as cartography: a semi-automatic approach for thematic web map creation / Auriol Degbelo in Cartography and Geographic Information Science, vol 47 n° 2 (February 2020)  

Public [article]  inCartography and Geographic Information Science > vol 47 n° 2 (February 2020) . - pp 153 - 170 Titre : Data scale as cartography: a semi-automatic approach for thematic web map creation Type de document : Article/Communication Auteurs : Auriol Degbelo, Auteur ; Saad Sarfraz, Auteur ; Christian Kray, Auteur Année de publication : 2020 Article en page(s) : pp 153 - 170 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] accès aux données localisées [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] cartographie numérique [Termes descripteurs IGN] données ouvertes [Termes descripteurs IGN] échelle des données [Termes descripteurs IGN] géovisualisation [Termes descripteurs IGN] test statistique [Termes descripteurs IGN] web mapping Résumé : (auteur) Open government promises increased transparency by providing its citizens datasets about city processes. Open data portals have been emerging all over the world as mines of open geographic datasets. Thematic web maps are key to understanding these open geographic datasets. Current thematic web maps are created by programmers and/or cartographers, and thus are not designed to be easily reused with new geographic datasets. As a result, they pose several challenges to non-experts wanting to adapt them to new scenarios. This article introduces a semi-automatic approach for the creation of thematic web maps by and for users with no prior training in cartography. The approach relies on the mapping between Stevens’ data types and Bertin’s visual variables, to suggest (meaningful) thematic map visualizations for a given input geographic dataset. It was implemented as a web prototype in AngularJS and evaluated with 19 participants. Results from the user study suggest that despite facing a few challenges in accurately identifying Stevens’ data types, participants managed to successfully create web maps and correctly answer spatial questions. The prototype and insights gathered from the user study are relevant to making cartographic products more accessible to a broader population, and open geographic data more usable in the context of an open government. Numéro de notice : A2020-059 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2019.1677176 date de publication en ligne : 05/11/2019 En ligne : https://doi.org/10.1080/15230406.2019.1677176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94576 [article]

Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 124 - 135 Titre : Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods Type de document : Article/Communication Auteurs : Elisa Kamir, Auteur ; François Waldner, Auteur ; Zvi Hochman, Auteur Année de publication : 2020 Article en page(s) : pp 124 - 135 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] Australie [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] carte agricole [Termes descripteurs IGN] climat [Termes descripteurs IGN] estimation de précision [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle non linéaire [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] régression [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55 t ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2  Numéro de notice : A2020-046 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.008 date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94556 [article]

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Promoting environmental justice through Integrated mapping approaches: the map of water conflicts in Andalusia (Spain) / Belen Pedregal in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)  

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The "Incense Road" from Petra to Gaza: an analysis using GIS and Cost functions / Motti Zohar in International journal of geographical information science IJGIS, vol 34 n° 2 (February 2020)  

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Typology of meteorological weather forecast maps printed in world newspapers / Jaromir Kolejka in Cartographic journal (the), Vol 57 n° 1 (February 2020)  

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Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

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Validation of Marine Geoid Models by Utilizing Hydrodynamic Model and Shipborne GNSS Profiles / Sandra Varbla in Marine geodesy, Vol 43 n° 1 (janvier 2020)  

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An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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An approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping / Qi Zhou in Transactions in GIS, Vol 23 n° 6 (November 2019)  

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Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco / Ali Aydda in Geocarto international, vol 34 n° 13 ([15/10/2019])  

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Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (2 October 2019)  

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