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The 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. Rukhovitch in Remote sensing, vol 13 n° 1 (January-1 2021)
[article]
Titre : The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution Type de document : Article/Communication Auteurs : Dimitri I. Rukhovitch, Auteur ; Polina V. Koroleva, Auteur ; Danila D. Rukhovitch, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation des sols
[Termes IGN] distribution spatiale
[Termes IGN] érosion
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Russie
[Termes IGN] surface cultivée
[Termes IGN] système d'information géographiqueRésumé : (auteur) Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps. Numéro de notice : A2021-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010155 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010155 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96810
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 155[article]Understanding the geodetic signature of large aquifer systems: Example of the Ozark Plateaus in Central United States / Stacy Larochelle (2021)
Titre : Understanding the geodetic signature of large aquifer systems: Example of the Ozark Plateaus in Central United States Type de document : Article/Communication Auteurs : Stacy Larochelle, Auteur ; Kristel Chanard , Auteur ; Luce Fleitout, Auteur ; Jérôme Nicolas Fortin, Auteur ; Adriano Gualandi, Auteur ; Laurent Longuevergne, Auteur ; Paul Rebischung , Auteur ; Sophie Violette, Auteur ; Jean-Philippe Avouac, Auteur Editeur : Washington DC [Etats-Unis] : Earth and Space Science Open Archive ESSOAr Année de publication : 2021 Projets : 1-Pas de projet / Importance : 29 p. Note générale : bibliographie
soumis au Journal of Geophysical Research - Solid EarthLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] aquifère
[Termes IGN] Arkansas (Etats-Unis)
[Termes IGN] déformation de la croute terrestre
[Termes IGN] élasticité
[Termes IGN] Kansas (Etats-Unis ; état)
[Termes IGN] masse d'eau
[Termes IGN] Missouri (Etats-Unis)
[Termes IGN] Oklahoma (Etats-Unis)
[Termes IGN] série temporelle
[Termes IGN] surcharge hydrologiqueRésumé : (auteur) The continuous redistribution of water mass involved in the hydrologic cycle leads to deformation of the solid Earth. On a global scale, this deformation is well explained by redistribution in surface loading and can be quantified to first order with space-based gravimetric and geodetic measurements. At the regional scale, however, aquifer systems also undergo poroelastic deformation in response to groundwater fluctuations. Disentangling these related but distinct 3D deformation fields from geodetic time series is essential to accurately invert for changes in continental water mass, to understand the mechanical response of aquifers to internal pressure changes as well as to correct time series for these known effects. Here, we demonstrate a methodology to accomplish this task by considering the example of the well-instrumented Ozark Plateaus Aquifer System (OPAS) in central United States. We begin by characterizing the most important sources of signal in the spatially heterogeneous groundwater level dataset using an Independent Component Analysis. Then, to estimate the associated poroelastic displacements, we project geodetic time series corrected for surface loading effects onto orthogonalized versions of the groundwater temporal functions. We interpret the extracted displacements in light of analytical solutions and a 2D model relating groundwater level variations to surface displacements. In particular, the relatively low estimates of elastic moduli inferred from the poroelastic displacements and groundwater fluctuations may be indicative of surficial layers with a high fracture density. Our findings suggest that OPAS undergoes significant poroelastic deformation, including highly heterogeneous horizontal poroelastic displacements. Numéro de notice : P2021-006 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Preprint nature-HAL : Préprint DOI : 10.1002/essoar.10507870.1 Date de publication en ligne : 02/09/2021 En ligne : https://doi.org/10.1002/essoar.10507870.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98994 Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)
[article]
Titre : Application of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy Type de document : Article/Communication Auteurs : Vasil Yordanov, Auteur ; Maria Antonia Brovelli, Auteur Année de publication : 2020 Article en page(s) : 23 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] cartographie géomorphologique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] effondrement de terrain
[Termes IGN] figuré linéaire
[Termes IGN] indice de risque
[Termes IGN] inventaire
[Termes IGN] Lombardie
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] modèle statistique
[Termes IGN] régression logistiqueRésumé : (auteur) Landslide susceptibility mapping is a crucial initial step in risk mitigation strategies. Landslide hazards are widely spread all over the world and, as such, mapping the relevant susceptibility levels is in constant research and development. As a result, numerous modelling techniques and approaches have been adopted by scholars, implementing these models at different scales and with different terrains, in search of the best-performing strategy. Nevertheless, a direct comparison is not possible unless the strategies are implemented under the same environmental conditions and scenarios. The aim of this work is to implement three statistical-based models (Statistical Index, Logistic Regression, and Random Forest) at the basin scale, using various scenarios for the input datasets (terrain variables), training samples and ratios, and validation metrics. A reassessment of the original input data was carried out to improve the model performance. In total, 79 maps were obtained using different combinations with some highly satisfactory outcomes and others that are barely acceptable. Random Forest achieved the highest scores in most of the cases, proving to be a reliable modelling approach. While Statistical Index passes the evaluation tests, most of the resulting maps were considered unreliable. This research highlighted the importance of a complete and up-to-date landslide inventory, the knowledge of local conditions, as well as the pre- and post-analysis evaluation of the input and output combinations. Numéro de notice : A2020-695 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00344-1 Date de publication en ligne : 09/11/2020 En ligne : https://doi.org/10.1007/s12518-020-00344-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96244
in Applied geomatics > vol 12 n° 4 (December 2020) . - 23 p.[article]Du drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)
[article]
Titre : Du drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique Type de document : Article/Communication Auteurs : Maxime Lafleur, Auteur ; Elliot Mugner, Auteur ; Rabine Keyetieu-Nlowe, Auteur ; Nicolas Seube, Auteur Année de publication : 2020 Article en page(s) : pp 25 -32 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] auscultation d'ouvrage
[Termes IGN] barrage
[Termes IGN] base de données localisées 3D
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] drone
[Termes IGN] exactitude des données
[Termes IGN] filtrage du bruit
[Termes IGN] géoréférencement
[Termes IGN] Haute-Loire (43)
[Termes IGN] précision des données
[Termes IGN] semis de points
[Termes IGN] sol nuRésumé : (Auteur) Le levé LiDAR présenté dans cet article a été effectué dans le cadre d’une mission d’évaluation de la chaîne de traitement mdInfinity, appliquée à des données acquises par un système drone LiDAR Microdrones. Les différents outils qui constituent cette chaîne de traitement ont été développés et intégrés sur la plateforme de traitement mdInfinity dans une version particulièrement adaptée au système de levé utilisé pour cette mission. Le site utilisé pour cette évaluation est le barrage de Labrioulette (Haute-Garonne), infrastructure située sur la Garonne et exploitée par EDF. Cette zone contient de nombreux éléments sur lesquels la précision et l’exactitude des données LiDAR est primordiale afin d’obtenir un nuage de point exploitable ; notamment la complexité structurelle du barrage (figure 1), les zones sous couvert végétal dense, l’aire de transformation électrique, etc. Pour cette raison, en plus de confirmer la bonne interopérabilité des systèmes LiDAR Microdrones avec les outils de traitement mdInfinity, nous avons tiré profit de cette acquisition pour évaluer les performances de nos algorithmes. Numéro de notice : A2020-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96662
in XYZ > n° 165 (décembre 2020) . - pp 25 -32[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2020041 RAB Revue Centre de documentation En réserve L003 Disponible Geomorphological analysis of the San Domino Island (Tremiti Islands, Southern Adriatic Sea). Results from the 2019 Geomorphological Field Camp of the MSc in Geological Science and Technology (University of Chieti-Pescara) / Marcello Buccolini in Journal of maps, vol 16 n° 3 ([01/12/2020])
[article]
Titre : Geomorphological analysis of the San Domino Island (Tremiti Islands, Southern Adriatic Sea). Results from the 2019 Geomorphological Field Camp of the MSc in Geological Science and Technology (University of Chieti-Pescara) Type de document : Article/Communication Auteurs : Marcello Buccolini, Auteur ; Cristiano Carabella, Auteur ; Giorgio Paglia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 10 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] 1:5.000
[Termes IGN] analyse des risques
[Termes IGN] archipel
[Termes IGN] cartographie géologique
[Termes IGN] données de terrain
[Termes IGN] géologie locale
[Termes IGN] géomorphologie locale
[Termes IGN] Italie
[Termes IGN] morphométrieRésumé : (auteur) The 2019 Geomorphological Field Camp at San Domino Island (Tremiti Islands, Southern Adriatic Sea) is the result of geological and geomorphological field work activities carried out by a group of students attending the Geomorphological field mapping course of the Master’s Degree in Geological Science and Technology (University of Chieti-Pescara). The main map (1:5000 scale) was obtained through an integrated approach that incorporates morphometric analysis, geological and geomorphological field mapping, and geomorphological profiles drawing. Activities were carried out by all students, divided into six working groups of three to four persons each. The field camp and field work activities made it possible to produce a detailed thematic map, as a scientific tool to depict the San Domino Island landscape, and to outline some geomorphological issues in terms of possible constraints to landscape evolution, geomorphological processes distribution, and natural hazard assessment. Numéro de notice : A2020-816 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/17445647.2020.1831979 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1080/17445647.2020.1831979 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96982
in Journal of maps > vol 16 n° 3 [01/12/2020] . - pp 10 - 18[article]A meta-analysis of changes in soil organic carbon stocks after afforestation with deciduous broadleaved, sempervirent broadleaved, and conifer tree species / Guolong Hou in Annals of Forest Science, vol 77 n° 4 (December 2020)PermalinkUnderstanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkDisplacement monitoring of upper Atbara dam based on time series InSAR / Q.Q. Wang in Survey review, vol 52 n° 375 (November 2020)PermalinkLandslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea / Sunmin Lee in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkMacrozonation of seismic transient and permanent ground deformation of Iran / Saeideh Farahani in Natural Hazards and Earth System Sciences, vol 20 n° 11 (November 2020)PermalinkSoil erosion assessment using RUSLE model and its validation by FR probability model / Amiya Gayen in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkTopographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkVNIR-SWIR superspectral mineral mapping: An example from Cuprite, Nevada / Kathleen E. Johnson in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 11 (November 2020)PermalinkAnalysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (October 2020)PermalinkApplication of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data / Mahdi Moalla in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)Permalink