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Forest cover mapping based on a combination of aerial images and Sentinel-2 satellite data compared to National Forest Inventory data / Selina Ganz in Forests, vol 11 n° 12 (December 2020)
[article]
Titre : Forest cover mapping based on a combination of aerial images and Sentinel-2 satellite data compared to National Forest Inventory data Type de document : Article/Communication Auteurs : Selina Ganz, Auteur ; Petra Adler, Auteur ; Gerald Kändler, Auteur Année de publication : 2020 Article en page(s) : n° 1322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] carte forestière
[Termes IGN] image aérienne
[Termes IGN] image Sentinel-MSI
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle numérique de surface
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Research Highlights: This study developed the first remote sensing-based forest cover map of Baden-Württemberg, Germany, in a very high level of detail.
Background and Objectives: As available global or pan-European forest maps have a low level of detail and the forest definition is not considered, administrative data are often oversimplified or out of date. Consequently, there is an important need for spatio-temporally explicit forest maps. The main objective of the present study was to generate a forest cover map of Baden-Württemberg, taking the German forest definition into account. Furthermore, we compared the results to NFI data; incongruences were categorized and quantified. Materials and
Methods: We used a multisensory approach involving both aerial images and Sentinel-2 data. The applied methods are almost completely automated and therefore suitable for area-wide forest mapping.
Results: According to our results, approximately 37.12% of the state is covered by forest, which agrees very well with the results of the NFI report (37.26% ± 0.44%). We showed that the forest cover map could be derived by aerial images and Sentinel-2 data including various data acquisition conditions and settings. Comparisons between the forest cover map and 34,429 NFI plots resulted in a spatial agreement of 95.21% overall. We identified four reasons for incongruences: (a) edge effects at forest borders (2.08%), (b) different forest definitions since NFI does not specify minimum tree height (2.04%), (c) land cover does not match land use (0.66%) and (d) errors in the forest cover layer (0.01%).
Conclusions: The introduced approach is a valuable technique for mapping forest cover in a high level of detail. The developed forest cover map is frequently updated and thus can be used for monitoring purposes and for assisting a wide range of forest science, biodiversity or climate change-related studies.Numéro de notice : A2020-845 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f11121322 Date de publication en ligne : 12/12/2020 En ligne : https://doi.org/10.3390/f11121322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98633
in Forests > vol 11 n° 12 (December 2020) . - n° 1322[article]Intercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations / Shaoqi Gong in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)
[article]
Titre : Intercomparisons of precipitable water vapour derived from radiosonde, GPS and sunphotometer observations Type de document : Article/Communication Auteurs : Shaoqi Gong, Auteur ; Wenqin Chen, Auteur ; Cunjie Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 562 - 577 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse comparative
[Termes IGN] atmosphère terrestre
[Termes IGN] coefficient de corrélation
[Termes IGN] photomètre
[Termes IGN] photométrie
[Termes IGN] positionnement par GNSS
[Termes IGN] précipitation
[Termes IGN] radiosondage
[Termes IGN] station d'observation
[Termes IGN] valeur aberrante
[Termes IGN] vapeur d'eauRésumé : (Auteur) The atmospheric precipitable water vapour (PWV) plays a crucial role in the hydrological cycle and energy transfer on a global scale. Radiosonde (RS), sunphotometer (SP) and GPS (as well as broader GNSS) receivers have gradually been the principal instruments for ground-based PWV observation. This study first co-locates the observation stations configured the three instruments in the globe and in three typical latitudinal climatic regions respectively, then the PWV data from the three instruments are matched each other according to the observing times. After the outliers are removed from the matched data pairs, the PWV intercomparisons for any two instruments are performed. The results show that the PWV estimates from any two instruments have a good agreement with very high correlation coefficients. The latitude and climate have no significant influence on the PWV measurements from the three instruments, indicating that the instruments are very stable and depend on their performance. The PWV differences of any two instruments display the normal distribution, indicating non-systematic biases among the two PWV datasets. The relative differences between SP and GPS are the smallest, the middle between SP and RS, and those between GPS and RS are the largest. This study will be useful to promote GPS (GNSS) and SP PWV to be a substitute for RS PWV as a benchmark because of their high temporal resolutions. Numéro de notice : A2020-778 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.562-577 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96709
in Geodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 562 - 577[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2020041 RAB Revue Centre de documentation En réserve L003 Disponible Mapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles / Ned Horning in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)
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Titre : Mapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles Type de document : Article/Communication Auteurs : Ned Horning, Auteur ; Erika Fleishman, Auteur ; Peter J. Ersts, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 487 - 497 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image aérienne
[Termes IGN] image captée par drone
[Termes IGN] Orfeo Tool Box
[Termes IGN] orthoimage
[Termes IGN] R (langage)Résumé : (auteur) The use of unmanned aerial vehicles (UAVs) to map and monitor the environment has increased sharply in the last few years. Many individuals and organizations have purchased consumer-grade UAVs, and commonly acquire aerial photographs to map land cover. The resulting ultra-high-resolution (sub-decimeter-resolution) imagery has high information content, but automating the extraction of this information to create accurate, wall-to-wall land-cover maps is quite difficult. We introduce image-processing workflows that are based on open-source software and can be used to create land-cover maps from ultra-high-resolution aerial imagery. We compared four machine-learning workflows for classifying images. Two workflows were based on random forest algorithms. Of these, one used a pixel-by-pixel approach available in ilastik, and the other used image segments and was implemented with R and the Orfeo ToolBox. The other two workflows used fully connected neural networks and convolutional neural networks implemented with Nenetic. We applied the four workflows to aerial photographs acquired in the Great Basin (western USA) at flying heights of 10 m, 45 m and 90 m above ground level. Our focal cover type was cheatgrass (Bromus tectorum), a non-native invasive grass that changes regional fire dynamics. The most accurate workflow for classifying ultra-high-resolution imagery depends on diverse factors that are influenced by image resolution and land-cover characteristics, such as contrast, landscape patterns and the spectral texture of the land-cover types being classified. For our application, the ilastik workflow yielded the highest overall accuracy (0.82–0.89) as assessed by pixel-based accuracy. Numéro de notice : A2020-853 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.144 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1002/rse2.144 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98682
in Remote sensing in ecology and conservation > vol 6 n° 4 (December 2020) . - pp 487 - 497[article]A novel intelligent classification method for urban green space based on high-resolution remote sensing images / Zhiyu Xu in Remote sensing, vol 12 n° 22 (December-1 2020)
[article]
Titre : A novel intelligent classification method for urban green space based on high-resolution remote sensing images Type de document : Article/Communication Auteurs : Zhiyu Xu, Auteur ; Yi Zhou, Auteur ; Shixin Wang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 3845 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] espace vert
[Termes IGN] image à haute résolution
[Termes IGN] image Gaofen
[Termes IGN] milieu urbain
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Pékin (Chine)
[Termes IGN] phénologie
[Termes IGN] précision de la classification
[Termes IGN] urbanismeRésumé : (auteur) The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification. Numéro de notice : A2020-792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223845 Date de publication en ligne : 23/11/2020 En ligne : https://doi.org/10.3390/rs12223845 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96565
in Remote sensing > vol 12 n° 22 (December-1 2020) . - n° 3845[article]Les stations virtuelles au service de la cartographie mobile / Mathieu Regul in XYZ, n° 165 (décembre 2020)
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Titre : Les stations virtuelles au service de la cartographie mobile Type de document : Article/Communication Auteurs : Mathieu Regul, Auteur ; Florian Birot, Auteur Année de publication : 2020 Article en page(s) : pp 41 - 46 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] auscultation d'ouvrage
[Termes IGN] base de données localisées 3D
[Termes IGN] calcul d'itinéraire
[Termes IGN] données GNSS
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] instrumentation Riegl
[Termes IGN] jumeau numérique
[Termes IGN] semis de points
[Termes IGN] station virtuelle
[Termes IGN] surveillance d'ouvrage
[Termes IGN] système de numérisation mobile
[Termes IGN] voie ferréeRésumé : (Auteur) SNCF Réseau utilise les scanners laser 3D depuis plus de dix ans. L'utilisation des nuages de points et des scanners laser (statiques et dynamiques) a permis d'augmenter les rendements des relevés et de fiabiliser bon nombre de traitements. En 2013, la division Assistance Travaux et Topographie (ATT) s’est équipée d’un scanner laser dynamique RiEGL VMX-450, devenant ainsi le premier gestionnaire d’infrastructures au monde à s’équiper de cette technologie. En 2019, SNCF Réseau a équipé trois engins de mesures avec des systèmes scanners laser dynamique RiEGL New VMX-Rail afin de permettre une numérisation continue et cyclique du réseau ferré national. Les données ainsi récoltées permettent de maintenir à jour la base des obstacles du réseau ainsi que de cartographier ce dernier. Ces données forment également le socle du jumeau numérique du réseau ferré national. Le volume de données collecté grandissant sans cesse jusqu’à atteindre 200 000 km par an depuis 2020, il a été nécessaire de faire évoluer toutes les méthodologies d’acquisition et de calcul des nuages 3D issus des systèmes de cartographie mobile. Numéro de notice : A2020-772 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96664
in XYZ > n° 165 (décembre 2020) . - pp 41 - 46[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2020041 RAB Revue Centre de documentation En réserve L003 Disponible Is field-measured tree height as reliable as believed – Part II, A comparison study of tree height estimates from conventional field measurement and low-cost close-range remote sensing in a deciduous forest / Luka Jurjević in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)PermalinkComparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])PermalinkA comparative user study of visualization techniques for cluster analysis of multidimensional data sets / Elio Ventocilla in Information visualization, vol 19 n° 4 (October 2020)PermalinkComparing features of single and multi-photon lidar in boreal forests / Xiaowei Yu in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkA LiDAR aiding ambiguity resolution method using fuzzy one-to-many feature matching / Chuang Qian in Journal of geodesy, vol 94 n° 10 (October 2020)PermalinkOpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkSee the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkTree species classification using structural features derived from terrestrial laser scanning / Louise Terryn in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkUse of visible and near-infrared reflectance spectroscopy models to determine soil erodibility factor (K) in an ecologically restored watershed / Qinghu Jiang in Remote sensing, vol 12 n° 18 (September-2 2020)PermalinkComparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)Permalink