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Investigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations / E-Ping Rau (2022)
Titre : Investigating the role of wind disturbance in tropical forests through a forest dynamics model and satellite observations Type de document : Thèse/HDR Auteurs : E-Ping Rau, Auteur ; Jérôme Chave, Directeur de thèse Editeur : Toulouse : Université de Toulouse 3 Paul Sabatier Année de publication : 2022 Importance : 184 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse 3 Paul SabatierLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse forestière
[Termes IGN] canopée
[Termes IGN] chablis (sylviculture)
[Termes IGN] cyclone
[Termes IGN] forêt tropicale
[Termes IGN] Guyane française
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle dynamique
[Termes IGN] perturbation écologique
[Termes IGN] précipitation
[Termes IGN] risque naturel
[Termes IGN] sécheresse
[Termes IGN] traitement d'image radar
[Termes IGN] ventIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Natural disturbances have an important influence on the structure, composition and functioning of tropical forests and a role in the regulation of biogeochemical cycles. The frequency and intensity of natural disturbances are modified by climate change: a better knowledge of their mechanism of action is necessary to predict the consequences of this modification. Modeling allows us to evaluate the role of each of the ecological processes and their link with environmental factors. Remote sensing tools inform us about the structure and functioning of forests at large scales, and can be useful for the calibration and validation of vegetation models. In this thesis, I employed both approaches to examine how tropical forests are shaped by natural disturbances, particularly wind, which is a major disturbance factor in many tropical regions. First, I evaluated the transferability of a spatially explicit, individual-based model via sensitivity testing and calibration of global parameters. The model correctly predicts forest structure at two contrasting sites, and its response is consistent with variations in climate forcing. Calibration of a small number of key parameters was required, including the parameter controlling mortality and crown allometry. To investigate the sensitivity of the model to mortality, I implemented a wind damage module based on biophysical principles and coupled with wind speed to model forest responses to extreme wind events. With increasing disturbance level, canopy height decreased steadily but biomass showed a non-linear response. Wind intensity had a strong impact on canopy height and biomass, but not the frequency of extreme wind events. Finally, I tested whether radar data from Sentinel-1 satellites could be used to detect gaps due to natural disturbances in French Guiana. The Sentinel-1 data detected more natural gaps above 0.2 ha than the optical satellite data, and they showed a spatial pattern consistent with the optical images. The level of disturbance did not vary with altitude. We found more disturbance during dry seasons, which could be due to the delayed response of precipitation rather than the direct response of drought. In conclusion, this thesis demonstrates that the integration between modeling and remote sensing sheds light on the effects of natural disturbances on tropical forests. The resulting results can be used to study other types of disturbances and their interactions on a large scale. Note de contenu : General introduction
General methods
1: Transferability of an individual- and trait-based forest dynamics model: a test case across the tropics
1.1 Abstract
1.2 Introduction
1.3 Materials and methods
1.4 Results
1.5 Discussion
1.6 Acknowledgements and author contributions
1.7 Supplementary data
2: Wind speed controls forest structure in subtropical forests exposed to cyclones: a case study using an individual-based model
2.1 Abstract
2.2 Introduction
2.3 Material and methods
2.4 Results
2.5 Discussion
2.6 Acknowledgments and author contributions
2.7 Supplementary data
3: Detecting Natural Disturbances in Tropical Forests Using Sentinel-1 SAR Data: a Test in French Guiana
3.1 Abstract
3.2 Introduction
3.3 Methods
3.4 Results
3.5 Discussions
3.6 Acknowledgments and author contributions
3.7 Supplementary data
General discussion and conclusionsNuméro de notice : 26836 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Ecologie, biodiversité et évolution : Toulouse 3 : 2022 nature-HAL : Thèse DOI : sans Date de publication en ligne : 20/06/2022 En ligne : https://tel.hal.science/tel-03699667 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101075 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 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 26831-01 THESE Livre Centre de documentation Thèses Disponible
Titre : Radar backscatter contribution to tropical forest disturbance monitoring Type de document : Thèse/HDR Auteurs : Bertrand Ygorra, Auteur ; Jean-Pierre Wigneron, Directeur de thèse ; Serge Riazanoff, Directeur de thèse ; Frédéric Frappart, Directeur de thèse Editeur : Bordeaux : Université de Bordeaux Année de publication : 2022 Importance : 253 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de BordeauxLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] couvert forestier
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] forêt tropicale
[Termes IGN] image Sentinel-SAR
[Termes IGN] nébulosité
[Termes IGN] télédétection en hyperfréquenceIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Earth Observations are increasingly used to monitor environmental problems. Its interests lie in the ability of sensors aboard satellites to provide information at global, regional and local scales. Optical remote sensing has shown great potential for the monitoring of forest disturbances. Until recently, deforestation monitoring systems were mainly based on remotely sensed optical images. In the intertropical latitudes, such images often face limitations of frequent cloud cover, leading to late detection or misdetections due to the low temporal availability of new images uncontaminated by clouds. In tropical humid forests, regrowth can close canopy gaps between two non-cloud-contaminated optical images used for detection.New SAR (Synthetic Aperture Radar) systems have opened new perspectives for forest disturbance monitoring in tropical humid forests (Sentinel-1, PALSAR-2). These active sensors penetrate the clouds. The availability of Sentinel-1 C-band images at high spatial and temporal resolutions makes it a potential substitute of optical systems for monitoring disturbances in forest covers.This work is articulated around three parts. The first part consists in the development of a new change detection method for monitoring disturbances in forest cover, based on the Cumulative Sum algorithm (CuSum) combined with a bootstrap analysis. The method was applied to time-series of Sentinel-1 Ground-Range Detected (GRD) dual polarization (VV, VH) images obtained in a legal forest concession near Kisangani in the Democratic Republic of the Congo. The results from VV and VH polarization were intersected in VV x VH result map, and a spatial recombination of a high Critical Threshold (Tc) with a low critical threshold was performed. The second part of this work is to develop a multiple-breakpoints version of the CuSum cross-Tc called ReCuSum to further enhance the ability to monitor changes in forest cover. The development was made by applying the CuSum cross-Tc over a time-series in an iterative manner, in the State of Parà, Brazilian Amazon. The third axis of this thesis is to develop a Near-Real-Time (NRT) version of the CuSum cross-Tc and to compare it with the state-of-the-art NRT algorithms (RADD, JJ-FAST GLAD, DETER-B, DETER-R). Note de contenu :
Chapter 1. General introduction
1.1. Introduction
1.2. Thesis objectives and outline
Chapter 2. Radar remote sensing
2.1. The RADAR technique
2.2. Instrumental parameters
2.3. Scattering mechanisms
2.4. Synthetic Aperture Radar
2.5. Sentinel-1
Chapter 3. Methods for monitoring forest cover change using spaceborne SAR sensors
3.1. Introduction
3.2. Publication
3.3. Contribution and perspectives
Chapter 4. Monitoring forest disturbances from Sentinel-1 time-series: a CuSum?based approach
4.1. Introduction
4.2. Publication
4.3. Conference note: IGARSS 2021
4.4. Contribution to this work and perspectives in the PhD course
Chapter 5. Multiple breakpoints Evolution of the cross-Tc CuSum: ReCuSum
5.1. Introduction
5.2. Publication
5.3. Conference note: IGARSS 2022
5.4. Contribution to this work and perspective
Chapter 6. Development of the CuSum cross-Tc as an NRT algorithm
6.1. Introduction
6.2. Publication
6.3. Contribution and perspectives
Chapter 7. Conclusion and perspectives
7.1. Conclusion
7.2. PerspectivesNuméro de notice : 26964 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Physique de l’environnement : Bordeaux : 2022 Organisme de stage : INRAE nature-HAL : Thèse DOI : sans Date de publication en ligne : 16/02/2023 En ligne : https://theses.hal.science/tel-03991973v1/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103001 Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission / Nicolas Gasnier (2022)
Titre : Use of multi-temporal and multi-sensor data for continental water body extraction in the context of the SWOT mission Type de document : Thèse/HDR Auteurs : Nicolas Gasnier, Auteur ; Florence Tupin, Directeur de thèse ; Loïc Denis, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2022 Importance : 213 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de doctorat présentée à l’Institut Polytechnique de Paris, spécialité ImagesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] base de données localisées
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données hydrographiques
[Termes IGN] hauteurs de mer
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] image SWOT
[Termes IGN] lac
[Termes IGN] rivière
[Termes IGN] série temporelle
[Termes IGN] télédétection en hyperfréquenceIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Spaceborne remote sensing provides hydrologists and decision-makers with data that are essential for understanding the water cycle and managing the associated resources and risks. The SWOT satellite, which is a collaboration between the French (CNES) and American (NASA, JPL) space agencies, is scheduled for launch in 2022 and will measure the height of lakes, rivers, and oceans with high spatial resolution. It will complement existing sensors, such as the SAR and optical constellations Sentinel-1 and 2, and in situ measurements. SWOT represents a technological breakthrough as it is the first satellite to carry a near-nadir swath altimeter. The estimation of water levels is done by interferometry on the SAR images acquired by SWOT. Detecting water in these images is therefore an essential step in processing SWOT data, but it can be very difficult, especially with low signal-to-noise ratios, or in the presence of unusual radiometries. In this thesis, we seek to develop new methods to make water detection more robust. To this end, we focus on the use of exogenous data to guide detection, the combination of multi-temporal and multi-sensor data and denoising approaches. The first proposed method exploits information from the river database used by SWOT (derived from GRWL) to detect narrow rivers in the image in a way that is robust to both noise in the image, potential errors in the database, and temporal changes. This method relies on a new linear structure detector, a least-cost path algorithm, and a new Conditional Random Field segmentation method that combines data attachment and regularization terms adapted to the problem. We also proposed a method derived from GrabCut that uses an a priori polygon containing a lake to detect it on a SAR image or a time series of SAR images. Within this framework, we also studied the use of a multi-temporal and multi-sensor combination between Sentinel-1 SAR and Sentinel-2 optical images. Finally, as part of a preliminary study on denoising methods applied to water detection, we studied the statistical properties of the geometric temporal mean and proposed an adaptation of the variational method MuLoG to denoise it. Note de contenu : 1. Introduction
1.1 Context
1.2 Contributions
1.3 Organization of the manuscript
I BACKGROUND ON SAR REMOTE SENSING AND WATER SURFACE MONITORING WITH SAR IMAGES
2. SAR images
2.1 Physics and statistics of SAR images
2.2 The SWOT mission
2.3 Sentinel-1
3. SAR water detection and hydrological prior
3.1 Water detection in SAR images
3.2 SWOT processing and products
3.3 Prior water masks and databases
4. Methodological background
4.1 Markov random fields
4.2 Variational methods for image denoising
PROPOSED APPROACHES
5. Guided extraction of narrow rivers on SAR images using an exogenous river database
5.1 Introduction
5.2 Proposed river segmentation pipeline
5.3 Experimental results
5.4 Conclusion
6. Adaptation of the GrabCut method to SAR images: lake detection from a priori polygon
6.1 Single-date GrabCut method for lake detection from a priori polygon
6.2 Multitemporal and multi-sensor adaptations of the method
6.3 2D+T GrabCut of SAR images with temporal regularization for lake detection within an a priori mask
6.4 Joint 2D+T segmentation of SAR and optical images
7. Denoising of the temporal geometric mean
7.1 Introduction
7.2 Statistics of the temporal geometric mean of SAR intensities
7.3 Denoising method
7.4 Experiments
7.5 Application to change detection
7.6 Application to ratio-based denoising of single SAR images within a time series
7.7 Conclusion
8 Conclusion and perspectivesNuméro de notice : 26762 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Images : Palaiseau : 2022 Organisme de stage : Télécom Paris nature-HAL : Thèse DOI : sans Date de publication en ligne : 17/02/2022 En ligne : https://tel.hal.science/tel-03578831/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99823 Land subsidence in Beijing’s sub-administrative center and its relationship with urban expansion inferred from Sentinel-1/2 observations / Jin Cao in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
[article]
Titre : Land subsidence in Beijing’s sub-administrative center and its relationship with urban expansion inferred from Sentinel-1/2 observations Titre original : Affaissement du sol dans le centre sous administratif de Beijing et sa relation avec l’expansion urbaine déduits des observations de Sentinel-1/2 Type de document : Article/Communication Auteurs : Jin Cao, Auteur ; Huili Gong, Auteur ; Beibei Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 802 - 817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] croissance urbaine
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Pékin (Chine)
[Termes IGN] subsidenceRésumé : (auteur) Beijing’s Sub-Administrative Center (BSAC) is located in the South-eastern Beijing Plain, which exhibits severe subsidence. The rapid urban expansion in recent years has aggravated land subsidence and threatens the safe operation of Beijing. First, this study applied the persistent scatterer-interferometric synthetic aperture radar (PS-InSAR) to extract BSAC subsidence time series data. Second, combined with the index-based built-up index (IBI), expansion intensity index (EII), and expansion gradient index (EGI), the spatiotemporal characteristics of urban expansion were retrieved from optical data. Finally, we examined the urban expansion effects on land subsidence at the regional and single-building scales. The results showed that the maximum subsidence velocity in the BSAC reached 121 mm/year from 2015 to 2018, and the urban construction land area increased by 22%. At the regional scale, there existed a positive correlation between land subsidence and EGI or EII. This indicated that urban expansion had a certain impact on land subsidence. Therefore, we further explored the relationship between construction and land subsidence at the single-building scale. The engineering construction effects on land subsidence were divided into three periods, namely, rapid settlement, rebound, and stable periods. Although construction had a significant influence on land subsidence, it did not cause subsidence mutation. Numéro de notice : A2021-955 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1080/07038992.2021.1964944 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1080/07038992.2021.1964944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99981
in Canadian journal of remote sensing > vol 47 n° 6 [01/11/2021] . - pp 802 - 817[article]Multi-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])PermalinkA novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkPersistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images / Hari Shankar in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkA repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France / Arnaud Cerbelaud in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)PermalinkSuperpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)PermalinkBi- and three-dimensional urban change detection using sentinel-1 SAR temporal series / Meiqin Che in Geoinformatica, vol 25 n° 4 (October 2021)PermalinkDeep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)PermalinkEvaluation of methods for connecting InSAR to a terrestrial reference frame in the Latrobe Valley, Australia / P.J. Johnston in Journal of geodesy, vol 95 n° 10 (October 2021)PermalinkInvestigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)PermalinkInvestigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)Permalink