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Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)
Titre : Deep learning for wildfire progression monitoring using SAR and optical satellite image time series Type de document : Thèse/HDR Auteurs : Puzhao Zhang, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 100 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-91-7873-935-6 Note générale : bibliographie
Doctoral Thesis in GeoinformaticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alberta (Canada)
[Termes IGN] apprentissage profond
[Termes IGN] bande C
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] détection de changement
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie de forêt
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] zone sinistréeRésumé : (auteur) Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even real-time. Satellite remote sensing enables cost-effective, accurate, and timely monitoring on the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8 and Sentinel-1/2 data opens the new era for global land surface monitoring, providing an opportunity to analyze wildfire impacts around the globe. The advances in both cloud computing and deep learning empower the automatic interpretation of spatio-temporal remote sensing big data on a large scale. The overall objective of this thesis is to investigate the potential of modern medium resolution earth observation data, especially Sentinel-1 C-Band synthetic aperture radar (SAR) data, in wildfire monitoring and develop operational and effective approaches for real-world applications. This thesis systematically analyzes the physical basis of earth observation data for wildfire applications, and critically reviews the available wildfire burned area mapping methods in terms of satellite data, such as SAR, optical, and SAR-Optical fusion. Taking into account its great power in learning useful representations, deep learning is adopted as the main tool to extract wildfire-induced changes from SAR and optical image time series. On a regional scale, this thesis has conducted the following four fundamental studies that may have the potential to further pave the way for achieving larger scale or even global wildfire monitoring applications. To avoid manual selection of temporal indices and to highlight wildfire-induced changes in burned areas, we proposed an implicit radar convolutional burn index (RCBI), with which we assessed the roles of Sentinel-1 C-Band SAR intensity and phase in SAR-based burned area mapping. The experimental results show that RCBI is more effective than the conventional log-ratio differencing approach in detecting burned areas. Though VV intensity itself may perform poorly, the accuracy can be significantly improved when phase information is integrated using Interferometric SAR (InSAR). On the other hand, VV intensity also shows the potential to improve VH intensity-based detection results with RCBI. By exploiting VH and VV intensity together, the proposed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the 2017 Thomas Fire and the 2018 Carr Fire. For the scenario of near real-time application, we investigated and demonstrated the potential Sentinel-1 SAR time series for wildfire progression monitoring with Convolutional Neural Networks (CNN). In this study, the available pre-fire SAR time series were exploited to compute temporal average and standard deviation for characterizing SAR backscatter behaviors over time and highlighting the changes with kMap. Trained with binarized kMap time series in a progression-wise manner, CNN showed good capability in detecting wildfire burned areas and capturing temporal progressions as demonstrated on three large and impactful wildfires with various topographic conditions. Compared to the pseudo masks (binarized kMap), CNN-based framework brought an 0.18 improvement in F1 score on the 2018 Camp Fire, and 0.23 on the 2019 Chuckegg Creek Fire. The experimental results demonstrated that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals. For continuous wildfire progression mapping, we proposed a novel framework of learning U-Net without forgetting in a near real-time manner. By imposing a temporal consistency restriction on the network response, Learning without Forgetting (LwF) allows the U-Net to learn new capabilities for better handling with newly incoming data, and simultaneously keep its existing capabilities learned before. Unlike the continuous joint training (CJT) with all available historical data, LwF makes U-Net learning not dependent on the historical training data any more. To improve the quality of SAR-based pseudo progression masks, we accumulated the burned areas detected by optical data acquired prior to SAR observations. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019-2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also found that the SAR cross-polarization ratio (VH/VV) can be very useful in highlighting burned areas when VH and VV have diverse temporal change behaviors. SAR-based change detection often suffers from the variability of the surrounding background noise, we proposed a Total Variation (TV)-regularized U-Net model to relieve the influence of SAR-based noisy masks. Considering the small size of labeled wildfire data, transfer learning was adopted to fine-tune U-Net from pre-trained weights based on the past wildfire data. We quantified the effects of TV regularization on increasing the connectivity of SAR-based areas, and found that TV-regularized U-Net can significantly increase the burned area mapping accuracy, bringing an improvement of 0.0338 in F1 score and 0.0386 in IoU score on the validation set. With TV regularization, U-Net trained with noisy SAR masks achieved the highest F1 (0.6904) and IoU (0.5295), while U-Net trained with optical reference mask achieved the highest F1 (0.7529) and IoU (0.6054) score without TV regularization. When applied on wildfire progression mapping, TV-regularized U-Net also worked significantly better than vanilla U-Net with the supervision of noisy SAR-based masks, visually comparable to optical mask-based results. On the regional scale, we demonstrated the effectiveness of deep learning on SAR-based and SAR-optical fusion based wildfire progression mapping. To scale up deep learning models and make them globally applicable, large-scale globally distributed data is needed. Considering the scarcity of labelled data in the field of remote sensing, weakly/self-supervised learning will be our main research directions to go in the near future. Note de contenu : 1- Introduction
2- Literature review
3- Study areas and data
4- Metodology
5- Results and discussionNuméro de notice : 28309 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics : RTK Stockholm : 2021 DOI : sans En ligne : http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1557429 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98130 Développement d’outils d’exploitation des archives photographiques aériennes de l’IGN pour caractériser l’évolution pluridécennale du littoral sur l’île de la Réunion / Adinane Oladjidé Ayichemi (2021)
Titre : Développement d’outils d’exploitation des archives photographiques aériennes de l’IGN pour caractériser l’évolution pluridécennale du littoral sur l’île de la Réunion Type de document : Mémoire Auteurs : Adinane Oladjidé Ayichemi, Auteur Editeur : Le Mans : Ecole Supérieure des Géomètres et Topographes ESGT Année de publication : 2021 Importance : 87 p. Format : 21 x 30 cm Note générale : Bibliographie
Mémoire présenté en vue d'obtenir le diplome d'Ingénieur CNAM Spécialité Géomètre et TopographeLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] catastrophe naturelle
[Termes IGN] détection de changement
[Termes IGN] géomorphologie locale
[Termes IGN] géoréférencement
[Termes IGN] image ancienne
[Termes IGN] modèle numérique de surface
[Termes IGN] orthoimage
[Termes IGN] photographie aérienne
[Termes IGN] prévention des risques
[Termes IGN] Réunion, île de la
[Termes IGN] risque naturel
[Termes IGN] superposition d'imagesIndex. décimale : ESGT Mémoires d'ingénieurs de l'ESGT Résumé : (auteur) Pour anticiper l’ampleur des futures catastrophes naturelles, il est courant de revisiter les changements morphologiques liés aux événements passés enregistrés. La Réunion est une île très exposée aux risques naturels majeurs, notamment les cyclones et les mouvements de terrain, qui perturbent sa vie sociale et économique. Les photographies aériennes historiques offrent aujourd’hui une opportunité pour suivre et décrire l’évolution du paysage grâce à la photogrammétrique moderne. Nous exploitons les archives disponibles pour créer et analyser des modèles numériques de surface en vue de quantifier les effets cycloniques dans la rivière des Galets à la Réunion. Dans ce processus de chasse aux changements locaux, un enregistrement robuste des séquences de campagne et un géoréférencement précis sont des facteurs limitatifs clés. Le co-alignement des photographiques issues de deux différentes missions encadrant un cyclone est effectué afin de limiter les erreurs liées à la distorsion des modèles générés lorsqu’ils seront comparés. À l’aide de la carte des zones stéréo-optimales des missions, que nous avons créée, les régions les plus prioritaires ont été repérées pour identifiés des détails topographiques persistants. Ces détails sont relevés par GNSS pour géoréférencer nos modèles. Une évaluation de la qualité des modèles créés est effectuée afin de garantir dans quelle mesure ils sont exploitables pour détecter des changements morphologiques dans la zone d’intérêt. Note de contenu : 1- Contexte scientifique
2- Rapatriement des données brutes
3- Préparation des données nécessaires pour le calcul photogrammétrique
4- Création des MNS et orthophtos
ConclusionNuméro de notice : 28696 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire ingénieur ESGT Organisme de stage : Bureau de recherches géologiques et minières BRGM En ligne : https://dumas.ccsd.cnrs.fr/MEMOIRES-CNAM/dumas-03526338v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100467 Drought propagation and its impact on groundwater hydrology of wetlands: a case study on the Doode Bemde nature reserve (Belgium) / Buruk Kitachew Wossenyeleh in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)
[article]
Titre : Drought propagation and its impact on groundwater hydrology of wetlands: a case study on the Doode Bemde nature reserve (Belgium) Type de document : Article/Communication Auteurs : Buruk Kitachew Wossenyeleh, Auteur ; Kaleb Asnake Worku, Auteur ; Boud Verbeiren, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 39 - 51 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Belgique
[Termes IGN] carte hydrographique
[Termes IGN] données météorologiques
[Termes IGN] eau souterraine
[Termes IGN] hydrogéologie
[Termes IGN] réserve naturelle
[Termes IGN] sécheresse
[Termes IGN] surveillance hydrologique
[Termes IGN] zone humideRésumé : (auteur) Drought can be described as a temporary decrease in water availability over a significant period that affects both surface and groundwater resources. Droughts propagate through the hydrological cycle and may impact vulnerable ecosystems. This paper investigates drought propagation in the hydrological cycle, focusing on assessing its impact on a groundwater-fed wetland ecosystem. Meteorological drought indices were used to analyze meteorological drought severity. Moreover, a method for assessing groundwater drought and its propagation in the aquifer was developed and applied. Groundwater drought was analyzed using the variable threshold method. Furthermore, meteorological drought and groundwater drought on recharge were compared to investigate drought propagation in the hydrological cycle. This research is carried out in the Doode Bemde wetland in central Belgium. The results of this research show that droughts are attenuated in the groundwater system. The number and severity of drought events on groundwater discharge were smaller than for groundwater recharge. However, the onset of both drought events occurred at the same time, indicating a quick response of the groundwater system to hydrological stresses. In addition, drought propagation in the hydrological cycle indicated that not all meteorological droughts result in groundwater drought. Furthermore, this drought propagation effect was observed in the wetland. Numéro de notice : A2021-133 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/nhess-21-39-2021 Date de publication en ligne : 08/01/2021 En ligne : https://doi.org/10.5194/nhess-21-39-2021 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96994
in Natural Hazards and Earth System Sciences > vol 21 n° 1 (January 2021) . - pp 39 - 51[article]Dynamic mechanism of blown sand hazard formation at the Jieqiong section of the Lhasa–Shigatse railway / Shengbo Xie in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)
[article]
Titre : Dynamic mechanism of blown sand hazard formation at the Jieqiong section of the Lhasa–Shigatse railway Type de document : Article/Communication Auteurs : Shengbo Xie, Auteur ; Jianjun Qu, Auteur ; Yingjun Pang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 154 - 166 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] météorologie locale
[Termes IGN] modèle dynamique
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] sable
[Termes IGN] Tibet
[Termes IGN] variation saisonnière
[Termes IGN] vent de sable
[Termes IGN] vitesse
[Termes IGN] voie ferréeRésumé : (auteur) Blown sand hazards at the Jieqiong section of the Lhasa–Shigatse railway are severe, and their formation mechanism is unclear. Moreover, sand prevention and control work cannot be carried out. Therefore, the dynamic mechanism of blown sand at the Jieqiong section of the Lhasa–Shigatse Railway was investigated by field observation, laboratory analysis, and calculation. Results show that the yearly sand–moving wind at the Jieqiong section commonly originates from the SW direction. The yearly resultant drift direction and the yearly resultant angle of the maximum possible sand transport quantity are NE direction. The angle between railway trend and sand transport direction is 5°–30°. During dry season, sand materials are blown up by the wind, forming wind–sand flow and movement to the NE direction, at which they are blocked by the railway roadbed. Consequently, accumulation occurs and causes serious damage. Strong wind and dryness are synchronous within a season. The directions of sand source and prevailing wind are consistent, thereby aggravating the blown sand dynamic further. The present results provide a reference for controlling sand hazards in the locale. Numéro de notice : A2021-109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2020.1863268 Date de publication en ligne : 28/12/2020 En ligne : https://doi.org/10.1080/19475705.2020.1863268 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96906
in Geomatics, Natural Hazards and Risk > vol 12 n° 1 (2021) . - pp 154 - 166[article]Flood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)
Titre : Flood mapping from radar remote sensing using automated image classification techniques Type de document : Thèse/HDR Auteurs : Lisa Landuyt, Auteur ; Niko Verhoest, Directeur de thèse ; Frieke Vancoillie, Directeur de thèse Editeur : Gand [Belgique] : Universiteit Gent Année de publication : 2021 Importance : 227 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-94-6357-415-0 Note générale : bibliographie
Dissertation submitted in fulfillment of the requirements for the degree of Doctor (PhD) of Bioscience EngineeringLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bande C
[Termes IGN] cartographie des risques
[Termes IGN] détection de changement
[Termes IGN] extraction de la végétation
[Termes IGN] Flandre (Belgique)
[Termes IGN] gestion de l'eau
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle de simulation
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Floods are a hazard of major concern, causing substantial fatalities and eco-nomic losses. These losses are expected to further accumulate in the future, as both the frequency and magnitude of flood events are projected to increase dueto climate change. Insights into the occurrence and dynamics of these disastrous events are thus of paramount importance for the protection of livelihoods across the world, both in the near and far future.Synthetic Aperture Radar (SAR) satellite imagery is particularly suited to observe floods due to the synoptic view, low cost and timely availability ofsatellite imagery and the all-weather imaging capabilities of SAR sensors. The resulting observations are crucial for various purposes, including emergency relief, post-disaster damage assessment, the calibration and validation of floodprediction models, and risk assessment.Despite the clear advantages of SAR imagery, several factors complicate the flood extent retrieval from this imagery type. These include surfaces or land dynamics characterized by a SAR backscatter similar to that of water/flooding,as well as the presence of urban features and vegetation. Moreover, existing approaches often lack the robustness and automation necessary for operational purposes. This thesis aims to contribute to the accuracy and automation of SAR-based flood mapping approaches, by elaborating on several of theremaining challenges. More specifically, the objectives of this thesis are:
1.to investigate the state of the art in SAR-based flood mapping andidentify the strengths and limitations of existing methods, as well as possible trends;
2.to assess the potential of C-band SAR for the delineation of floodedvegetation, and suggested an approach for doing so in an automated way;
3.to identify the main obstacles with respect to automated flood monitoring,and develop an approach that allows putting science into practice.
In the process of pursuing these objectives, special attention is given to automation, as this is key for objective and timely observations, and to optimally employing available data, as additional data can substantially improve flood observations but not handling these critically may be have adverse effects. Additionally, the potential of object-based image analysis (OBIA) techniques is investigated, as they have proven their added value using optical imagery but SAR-based applications remain limited. Sentinel-1imagery is the main datasource considered in this thesis, as this medium-resolution C-band imagery is freely available and provides consistent global coverage.First, the state of the art in SAR-based flood mapping is investigated. Distin-guishing between approaches for the retrieval of open water, flooded vegetationand urban flooding, deployed input data and classification techniques are discussed. As it is difficult to draw conclusions regarding the strengths and limitations of these classification techniques based on their scientific publications, an in-depth assessment and comparison of a selection of these is carried out. This selection includes thresholding, active contour modeling and theHSBA-Flood method, and both single scene and change detection-based maps are generated. To tackle the second objective of this thesis, the detectability of both woody and herbaceous vegetation using Sentinel-1 is investigated. Moreover, an automated, object-based clustering approach, making use of globally and freely available data only, is presented and applied on four study areas with varying characteristics. The resulting flood maps discriminate between dryland, permanent water, open flooding and flooded vegetation. Forests are indicated too, in order to underline the uncertainty related to these areas where flooding cannot or only to a limited extent be detected.In the last part of this thesis, an approach for operational flood monitoringin Flanders is presented. This approach was developed for and with input of the local water manager,i.e.the Flanders Environment Agency, and makesuse of high-resolution ancillary data available for the region of interest. By combining a pixel-based and an object-based approach, a discrimination is made between dry land, permanent water, open flooding, probable flooding, flooded vegetation and probably flooded forests. The approach is extensively tested on flood events of different sizes that occurred between 2016 and 2020. Both the detectability of these flood events and the accuracy of the developed algorithm, in the presence and absence of flooding, are assessed and discussed.Note de contenu : 1- Introduction
2- Synthetic aperture radar: theoretical background
3- State of the art in SAR-based flood mapping
4- An assessment of establish
ed SAR-based flood mappingapproaches
5- Flood mapping in vegetated areas using an unsupervisedclustering approach on Sentinel-1 and -2 imagery
6- Flood monitoring in Flanders using Sentinel-1 imagery
7- Conclusion and outlookNuméro de notice : 28303 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Bioscience Engineering : Universiteit Gent : 2021 DOI : sans En ligne : https://biblio.ugent.be/publication/8709595/file/8709639.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98053 Impact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkModeling the risk of robbery in the city of Tshwane, South Africa / Nicolas Kemp in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)PermalinkPermalinkPermalinkDoes recent fire activity impact fire-related traits of Pinus halepensis Mill. and Pinus sylvestris L. in the French Mediterranean area? / Bastien Romero in Annals of Forest Science, vol 77 n° 4 (December 2020)PermalinkA framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December-1 2020)PermalinkLarge-scale stochastic flood hazard analysis applied to the Po River / A. Curran in Natural Hazards, vol 104 n° 3 (December 2020)PermalinkAnalyzing the joint effect of forest management and wildfires on living biomass and carbon stocks in Spanish forests / Patricia Adame in Forests, vol 11 n°11 (November 2020)PermalinkBayesian-deep-learning estimation of earthquake location from single-station observations / S. Mostafa Mousavi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 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)Permalink