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Documents disponibles dans cette catégorie (15)
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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 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 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 26831-01 THESE Livre Centre de documentation Thèses Disponible Joint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)
Titre : Joint analysis of SAR and optical satellite images time series for grassland event detection Type de document : Article/Communication Auteurs : Anatol Garioud Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] cohérence des données
[Termes IGN] détection d'événement
[Termes IGN] détection de changement
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Mâcon
[Termes IGN] prairie
[Termes IGN] puits de carboneRésumé : (auteur) Throughout Europe, grasslands are a major component of the landscape comprising 40% of agricultural land. Permanent Grassland (PM) means land used to grow herbaceous forage crops naturally (self-seeded) or through cultivation (sown) and that has not been included in the crop rotation of the holding for five years or more. PM are major ecosystems associated with high biodiversity which provide a wide range of ecosystem services (e.g. carbon sequestration, water quality, flood and erosion control). Grasslands have an important carbon storage capacity which is valuable for climate protection. Different studies have demonstrated that grassland managements such as grazing or mowing can cause significant effects on carbon storage in soils. Identifying and mapping grassland management practices over time can thus have important impact on climate studies. Remote sensing allows a synoptic and regular monitoring through systematic acquisitions of Earth Observation imagery. The emergence of free and easily Sentinel's satellite data provided by the European Copernicus program, offers new possibilities for grassland monitoring. Sentinel-1 (51) and Sentinel-2 (52) missions acquire radar and optical satellite image time series at high temporal resolution and fine spatial resolution. They fully match the requirements both for yearly and real-time monitoring. In this work, we target to jointly exploit both data sources to dynamically detect mowing events (MowEve) on permanent grasslands. Thematic related analysis of the datasets will highlight strengths and weaknesses of both optical and radar imagery. (i) 52 appears efficient for MowEve detection, with significant variations in the vegetation status that can be easily detected in the spectral signal extracted from the time series of images. But the temporal revisit of 52 although nominally 5 days is often reduced even by half due to the frequent cloud cover (ii) SAR images acquisitions being independent of illumination conditions or cloud cover allows for systematic acquisitions and revisit rate of 6 days. Data consistency makes S1 data essential during fast phenomena such as MowEve. Yet, radar data appears very sensitive to soil moisture, precipitations and geometrical properties making interpretation of their time series more challenging. MowEve detection being weakly supervised, the proposed methodology relies on applying traditional change detection strategies on a low-level fused 51 and S2 data representation. Recurrent Neural Networks will be trained to derive yearly or real-time synthetic 52 vegetation indices from both 52 and S1 observations. Furthermore, through attention mechanisms, our proposed RNN architecture will be able to take into account external data (climate, clouds, topography, etc.) so as to dynamically weight at parcel-level the contribution of optical and radar images. Such method will contribute to obtain dense temporal optical profiles without missing data and compatible with MowEve detection. An experimental evaluation will be carried out on a test site covering an area of 110x110 Km in France (Macon region). Object-oriented analysis will be presented based on permanent grasslands derived from the Land Parcel Identification System. The proposed approach will be compared with traditional MowEve methods essentially based on thresholding independently the different modalities. Numéro de notice : C2019-067 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97022
Titre : Scierie FSA : taillée sur mesure pour le douglas du Morvan Type de document : Article/Communication Auteurs : Pascal Charoy, Auteur Année de publication : 2009 Article en page(s) : p. 12-13 Langues : Français (fre) Descripteur : [Termes IGN] Autun
[Termes IGN] scierieRésumé : Installée dans les anciennes forges de la ville, la scierie Forêts et sciages d’Autun est originale à plus d’un titre. D’une part, le capital est partagé entre le scieur et les coopératives locales qui assurent son approvisionnement. D’autre part, l’outil de production, signé MEM, n’a pas d’équivalent en France. Numéro de notice : IFN_3247 Affiliation des auteurs : non IGN Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=72035
in Le Bois International : l'officiel du bois [édition verte] > vol 2009 n° 16 (25 avril 2009) . - p. 12-13[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P000348 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt
Titre : Autun récolte les bénéfices de son chauffage bois Type de document : Article/Communication Auteurs : Pascal Charoy, Auteur Année de publication : 2008 Article en page(s) : p. 17-19 Langues : Français (fre) Descripteur : [Termes IGN] Autun
[Termes IGN] biocarburant
[Termes IGN] Bourgogne
[Termes IGN] scierieRésumé : Depuis 10 ans, la ville d'Autun chauffe ses logements collectifs et bâtiments publics en brûlant des résidus de scieries. A l'époque, l'idée était de produire de la chaleur sans polluer, en utilisant un "carburant" produit localement. Aujourd'hui, les habitants tirent économiquement profit de ce choix avant-gardiste. Numéro de notice : IFN_2181 Affiliation des auteurs : non IGN Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=71005
in Le Bois International : l'officiel du bois [édition verte] > vol 2009 n ° 34 (11 octobre 2008) . - p. 17-19[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-DIR-P000173 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Inventaire forestier départemental, Saône-et-loire, résultats 4ème inventaire forestier 2003 / Inventaire forestier national (1958 - 2011) (2006)
Titre : Inventaire forestier départemental, Saône-et-loire, résultats 4ème inventaire forestier 2003 Type de document : Rapport Auteurs : Inventaire forestier national (1958 - 2011), Auteur Editeur : Paris : Ministère de l'Agriculture et de la Forêt Année de publication : 2006 Importance : 216 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Français (fre) Descripteur : [Termes IGN] forêt
[Termes IGN] inventaire forestier national (données France)
[Termes IGN] Saône-et-Loire (71)
[Vedettes matières IGN] Inventaire forestierIndex. décimale : IFD Inventaire forestier par département (rangé en réserve) Numéro de notice : 15550 Affiliation des auteurs : IFN (1958-2011) Thématique : FORET Nature : Rapport d'étude technique nature-HAL : Rapport DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=38998 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 15550-01 IFD Livre Centre de documentation Végétation - Forêt Disponible PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalink
