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Implementation of the log-transformed band ratio algorithm on images of WorldView-3 and Sentinel-2 for bathymetry mapping of a pocket beach of Malta / Antoine Cornu (2022)
Titre : Implementation of the log-transformed band ratio algorithm on images of WorldView-3 and Sentinel-2 for bathymetry mapping of a pocket beach of Malta Type de document : Article/Communication Auteurs : Antoine Cornu, Auteur ; Luciano Galone, Auteur ; Arnaud Le Bris , Auteur ; Sebastiano d' Amico, Auteur ; Adam Gauci, Auteur ; Manchun Lei , Auteur ; Emanuele Colica, Auteur Editeur : New-York : IEEE Computer society Année de publication : 2022 Conférence : MetroSea 2022, IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters 03/10/2022 05/10/2022 Milazzo Italie Importance : pp 493 - 496 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bathymétrie
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Worldview
[Termes IGN] littoral
[Termes IGN] profondeurMots-clés libres : log-transformed band ratio depth retrieval Résumé : (auteur) Several methods are in place to calculate shallow water bathymetry from satellite images, such as Worldview 3 or Sentinel-2, which have differences in their resolution and their accessibility. The method used in this research is the log- transformed band ratio between the blue channel and the green channel. This document compares the results of this method with the Worldview 3 and Sentinel-2 images. Numéro de notice : C2022-045 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/MetroSea55331.2022.9950982 En ligne : https://doi.org/10.1109/MetroSea55331.2022.9950982 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102290 Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data / Marta Chiesi in European journal of remote sensing, vol 55 n° 1 (2022)
[article]
Titre : Monitoring and analysis of crop irrigation dynamics in Central Italy through the use of MODIS NDVI data Type de document : Article/Communication Auteurs : Marta Chiesi, Auteur ; Luca Angeli, Auteur ; Piero Battista, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 23 - 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] bilan hydrique
[Termes IGN] carte agricole
[Termes IGN] cultures irriguées
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] irrigation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Toscane (Italie)Résumé : (auteur) A recent study has proposed and tested a semi-empirical method to estimate crop irrigation based on a water balance logic and Sentinel-2 Multi Spectral Instrument (MSI) NDVI imagery. The current paper aims at extending the same approach to the analysis of the main irrigation patterns occurred in Tuscany (Central Italy) during the 2000–2019 period. This operation was made possible by feeding the irrigation water (IW) estimation method with 250-m spatial resolution Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images. The results of this operation were first assessed versus various reference datasets available for the region; next, the annual maps of IW estimated for the 20 study years were analyzed at province scale in conjunction with relevant agricultural statistics. The use of MODIS in place of MSI images reduces the IW estimation accuracy irregularly at local scale, depending on the size and spatial arrangement of irrigated and non-irrigated fields; the reduction in accuracy is, however, marginal over relatively large areas. Irrigated crops are decreasing throughout most Tuscany provinces, while they are increasing in the most southern and driest province. The possible reasons and implications of these findings are finally discussed in relation to the main environmental issues affecting the region. Numéro de notice : A2022-099 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1080/22797254.2021.2013735 Date de publication en ligne : 05/01/2022 En ligne : https://doi.org/10.1080/22797254.2021.2013735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99549
in European journal of remote sensing > vol 55 n° 1 (2022) . - pp 23 - 36[article]Monitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)
Titre : Monitoring grassland dynamics by exploiting multi-modal satellite image time series Titre original : Suivi de la dynamique des prairies permanentes par analyse des séries temporelles multi-modales Type de document : Thèse/HDR Auteurs : Anatol Garioud , Auteur ; Clément Mallet , Directeur de thèse ; Silvia Valero, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2022 Importance : 194 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée et soutenue en vue de l'obtention du Doctorat de l'Université Gustave Eiffel, Spécialité Sciences et Technologies de l'Information GéographiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse multivariée
[Termes IGN] apprentissage profond
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] données auxiliaires
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Mâcon
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prairie
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] seuillage d'image
[Termes IGN] superpixel
[Termes IGN] surveillance agricole
[Termes IGN] ToulouseIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The vast grassland surfaces as well as the growing recognition of the ecosystem services thez provide have revealed urgent needs for their conservation and sutainable management. Despite the acknowledged importance of grassland management practices, there are currently no large-scale efforts reporting on their frequency and nature. Satellite remote sensing time series appear to be a suitable tool for efficient grassland monitoring and allow synoptic and regular analysis. The research conducted in this PhD aims to develop methods for the detection of grassland management practices from complementary optical and SAR multivariate time series. Advances in deep learning are employed to regress multivariate SAR time series and contextual knowledge towards optical NDVI. Resulting gap-free time series are used to efficiently explore methods aiming to detect vegetation status changes related to management practices on grasslands. Note de contenu : INTRODUCTION
1. Grasslands and remote sensing: context, diversity and challenges
1.1 Definition, extent and importance of grasslands
1.2 Earth observation from space: principles and applications over grasslands
1.3 Problem statement and objectives
1.4 Outline of the manuscript
2. Study areas and datasets
2.1 Study areas
2.2 Satellite data
2.3 Reference and ancillary datasets
2.4 Feature derived from sentinel images for grassland monitoring
2.5 Description of the feature engineering steps
2.6 Exploring the relationships between derived satellite features
2.7 Concluding remarks
HIGH-TEMPORAL SAMPLED TIME-SERIES
3. Sentinels regression for vegetation monitoring
3.1 Monitoring vegetation through optical-SAR synergy
3.2 Retrieving missing data in optical time series
3.3 SenRVM: a deep learning-based regression framework
3.4 Concluding remarks
4. Outcomes of the SenRVM approach
4.1 Experimental design for training and evaluating SenRVM models
4.2 Assessment of SenRVM predictions
4.3 Empirical analysis of the SenRVM results
4.4 Generalization capabilities of single-class grassland SenRVM models
4.5 Further post-processing of SenRVM results
4.6 Concluding remarks
MONITORING GRASSLANDS
5. Detecting and quantifying grassland management practices
5.1 Challenges and related work
5.2 The proposed methodology
5.3 Description of validation data
5.4 Experimental setup
5.5 Assessment of the proposed method
5.6 Potential outcomes
5.7 Concluding remarks
GENERAL CONCLUSION
6. Conclusion and perspectives
6.1 Summary
6.2 PerspectivesNuméro de notice : 26831 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences et Technologies de l'Information Géographique : Gustave Eiffel : 2022 Organisme de stage : LASTIG (IGN) nature-HAL : Thèse DOI : sans En ligne : https://theses.hal.science/tel-03843683 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100728 Réservation
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Titre : Remote sensing in applications of geoinformation Type de document : Monographie Auteurs : Silas Michaelides, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2022 Importance : 174 p. ISBN/ISSN/EAN : 978-3-0365-2325-5 Note générale : Bibliographie
This book is a printed edition of the Special Issue Remote Sensing in Applications of Geoinformation that was published in Remote SensingLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] écosystème forestier
[Termes IGN] écosystème urbain
[Termes IGN] image Sentinel-MSI
[Termes IGN] inondation
[Termes IGN] modèle 3D de l'espace urbainIndex. décimale : 35.40 Applications de télédétection - généralités Résumé : (Editeur) Remote sensing, especially from satellites, is a source of invaluable data which can be used to generate synoptic information for virtually all parts of the Earth, including the atmosphere, land, and ocean. In the last few decades, such data have evolved as a basis for accurate information about the Earth, leading to a wealth of geoscientific analysis focusing on diverse applications. Geoinformation systems based on remote sensing are increasingly becoming an integral part of the current information and communication society. The integration of remote sensing and geoinformation essentially involves combining data provided from both, in a consistent and sensible manner. This process has been accelerated by technologically advanced tools and methods for remote sensing data access and integration, paving the way for scientific advances in a broadening range of remote sensing exploitations in applications of geoinformation. This volume hosts original research focusing on the exploitation of remote sensing in applications of geoinformation. The emphasis is on a wide range of applications, such as the mapping of soil nutrients, detection of plastic litter in oceans, urban microclimate, seafloor morphology, urban forest ecosystems, real estate appraisal, inundation mapping, and solar potential analysis. Note de contenu : - Vis-NIR Spectroscopy and Satellite Landsat-8 OLI Data to Map Soil Nutrients in Arid Conditions: A Case Study of the Northwest Coast of Egypt / Elsayed Said Mohamed, A. A El Baroudy, T. El-beshbeshy, M. Emam, A. A. Belal, Abdelaziz Elfadaly, Ali A. Aldosari, Abdelraouf. M. Ali and Rosa Lasaponara
- Investigating Detection of Floating Plastic Litter from Space Using Sentinel-2 Imagery / Kyriacos Themistocleous, Christiana Papoutsa, Silas Michaelides and Diofantos Hadjimitsis
- A New Approach for Understanding Urban Microclimate by Integrating Complementary Predictors at Different Scales in Regression and Machine Learning Models /8 Lucille Alonso and Florent Renard
- Automatic Pattern Recognition of Tectonic Lineaments in Seafloor Morphology to Contribute in the Structural Analysis of Potentially Hydrocarbon-Rich Areas / Eleni Kokinou and Costas Panagiotakis
- Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services / Elena Barbierato, Iacopo Bernetti, Irene Capecchi and Claudio Saragosa
- Sensitivity Analysis of Machine Learning Models for the Mass Appraisal of Real Estate. Case Study of Residential Units in Nicosia, Cyprus / Thomas Dimopoulos, Nikolaos P. Bakas
- Automatic Inundation Mapping Using Sentinel-2 Data Applicable to Both Camargue and Donana Biosphere Reserves / Georgios A. Kordelas, Ioannis Manakos, Gaëtan Lefebvre and Brigitte Poulin
- The Application of LiDAR Data for the Solar Potential Analysis Based on Urban 3D Model / I˜naki Prieto, Jose Luis Izkara and Elena UsobiagaNuméro de notice : 26796 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-2326-2 En ligne : https://doi.org/10.3390/books978-3-0365-2326-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100057 SenRVM: A multi-modal deep learning regression methodology for continuous vegetation monitoring with dense temporal NDVI time series / Anatol Garioud (2022)
Titre : SenRVM: A multi-modal deep learning regression methodology for continuous vegetation monitoring with dense temporal NDVI time series Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Silvia Valero, Auteur ; Clément Mallet , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2022 Conférence : LPS 2022, ESA Living Planet Symposium 22/05/2022 27/05/2022 Bonn Allemagne programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] dynamique de la végétation
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] phénologie
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) The Earth's biosphere and the phenology of vegetation are at the heart of climatic, economic and social concerns. Human activities have led to a significant degradation of ecosystem services (e.g. carbon sequestration, biodiversity, water quality, flood, and erosion regulation) provided by various extensive ecosystems such as forests, grasslands or crops.
A key parameter for relevant climate modeling, public policy implementations or commercial applications is the temporal resolution at which vegetation is observed. As a tool providing synoptic and regular coverage of Earth’s surfaces, satellite Earth Observation has been increasingly adopted, among others, for estimating biomass, yields, modeling different fluxes or detecting changes. Optical images have been historically used for vegetation monitoring, considering their efficient discrimination of phenomena related to photosynthetic activity.
To deal with missing data due to clouds, many interpolation strategies integrating one or more optical sensors have been developed. Most of these strategies are based on trend modelling that does not reflect the real evolution of the vegetation cover in many cases (sudden climatic impact, man-made effects). As a result, data that may be weeks or months apart are often interpolated on areas suffering from high cloud cover.
Copernicus Sentinels provide new opportunities and unprecedented observations for the monitoring of vegetation’s dynamics. In particular, concordant optical and SAR data sets provided by the Sentinel-1 and 2 satellites open the door to new multi-sensor methodologies aiming at the reconstruction of missing information.
Taking into account the still numerous non-cloudy observations provided by the Sentinel-2 satellites, a deep learning regression methodology, namely the Sentinels Regression for Vegetation Monitoring (SenRVM), has been developed. Its goal is the translation of SAR features acquired regardless of the climatic conditions into NDVI. The developed architecture integrates several deep learning architectures such as Multilayer Perceptron and Recurrent Neural Networks. The SenRVM regression strategy proposes the integration of auxiliary data such as climatic and topographic features. This allows accurate NDVI time series to be predicted by minimizing effects exogenous to the vegetation’s phenology through SAR acquisitions contextualization.
Object-oriented analysis of the results is carried out on large scale areas for various vegetation types with distinct phenologies (grasslands, crops and forests). The results are analyzed by taking into account spatial and temporal aspects or with an ablation study of the Network’s inputs. The proposed approach is further compared with traditional interpolation methods exploiting monomodal (Whittaker smoothing, linear weighted interpolation) or multimodal (Random Forest, Gaussian Regression Processes, single Multilayer Perceptron) features.
The potential of high-temporal NDVI time series obtained by the SenRVM method for several vegetation-related applications is subsequently illustrated. In particular, the interest of the obtained time series to observe the phenology and its associated parameters of the three main vegetation classes is presented.Numéro de notice : C2022-011 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100786 Documents numériques
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