Chapter 2 The sea surface microlayer is a biogenic thin layer, comprising less than one millimeter of the ocean surface. This surface layer has gained much attention due to its dampening effect on ocean capillary ripples. The chemistry of the air-sea interface has been studied for decades; however, the structure and function of the marine bacterial community within the sea surface microlayer are still understudied. Although various sea surface microlayer sampling techniques were developed over the past decades, aseptic bacterial sampling in the open ocean is a rather challenging task. In this study, a new approach is presented. It is designed for bacterial sampling of the sea surface microlayer, which intends to reduce sampling contamination from the vessel, subsurface water and the investigators. A 47mm polycarbonate membrane was utilized at each sampling site. In addition, the metagenomic approach using the new generation 454 high-throughput DNA sequencing system was employed to compensate for the small sample size. Two sample sets were collected in summer 2010 and fall 2011 from the sea surface microlayer and underlying water (20 cm deep). A contamination assessment was carried out to determine that contamination might have been caused during the use of the sampling techniques. A total of 14,120 bacterial 16S rRNA gene sequences with an average length of 437.8 bp were obtained. A total of 1,254 Operational Taxonomic Units (OTUs) were constructed and 268 genera were identified. The results indicated that the bacterial compositions of the sea surface microlayer samples were distinct from those of the underlying water samples. This experiment demonstrated that the new generation sequencing platform and microbial metagenomics analysis software together served as powerful tools to gain a deeper understanding of microbial communities within the sea surface microlayer. Furthermore, it is suggested that the newly employed sampling methods could be used to obtain a snapshot of bacterial community structure as well as environmental conditions. Chapter 3 Synthetic aperture radar (SAR) remote sensing captures various fine-scale features on the ocean surface such as coastal discharge, oil pollution, vessel traffic, algal blooms and sea slicks. Although numerous factors potentially affect the SAR imaging process, the influence of biogenic and anthropogenic surfactants has been suggested as one of the primary parameters, especially under relatively low wind conditions. Surfactants have a tendency to dampen the short gravity-capillary ocean waves causing the sea surface to smoothen, thus allowing the radar to detect areas of surfactants. Surfactants are found in sea slicks, which are the accumulation of organic material shaped as elongated bands on the ocean’s surface. Sea slicks are often observable with the naked eye due to their glassy appearance and can also be seen on SAR images as dark scars. While the sources of surfactants can vary, some are known to be associated with marine bacteria. Countless numbers of marine bacteria are present in the oceanic environment, and their biogeochemical contributions cannot be overlooked. Not only do marine bacteria produce surfactants, but they also play an important role in the transformation of surfactants. In this study, we profiled the surfactant-associated bacteria composition within the biogenic thin layer of the ocean surface more commonly referred as the sea surface microlayer (SML). Bacterial samples were collected from the SML for comparative analysis from both within and outside of sea slick areas as well as the respective underlying subsurface water. The bacterial microlayer sampling coincided with SAR satellite, RADARSAT-2, overpasses to demonstrate the simultaneous in-situ measurements during a satellite image capture. The SML sampling method was designed to enable aseptic bacterial sampling. A 47 mm polycarbonate membrane was utilized at each sampling site to obtain a snapshot of the bacterial community structure at a specific space and time. Also, a new generation high-throughput sequencing method was employed to compensate for the small sample size acquired. A total of 27,006 nucleotide sequences (16S rRNA genes) with an average 437.8 bp in length were analyzed. The results revealed the presence of industrially important surfactant-producing marine bacteria, Acinetobacter, Bacillus, Corynebacterium and surfactant-degrading marine bacteria, Escherichia. In addition, Pseudomonas was detected which can be either a producer, decomposer or both. Recognizing that there is still a large number of marine bacterial species that have not been taxonomically classified nor recognized as surfactant-associated species, the effects on SAR imaging due to a high number of surfactant-associated marine bacteria is expected. This study has provided the basis for the biological importance for fine-scale synthetic aperture satellite imaging. Moreover, this new approach is expected to have applications in monitoring biological and chemical properties of the sea surface across the globe.

Satellite geodesy plays an important role in earth observation. This dissertation presents three applications of satellite geodesy in environmental and climate change. Three satellite geodesy techniques are used: high-precision Global Positioning System (GPS), the Gravity Recovery and Climate Experiment (GRACE) and Interferometric Synthetic Aperture Radar (InSAR). In the first study, I use coastal uplift observed by GPS to study the annual changes in mass loss of the Greenland ice sheet. The data show both spatial and temporal variations of coastal ice mass loss and suggest that a combination of warm atmospheric and oceanic condition drove these variations. In the second study, I use GRACE monthly gravity change estimates to constrain recent freshwater flux from Greenland. The data show that Arctic freshwater flux started to increase rapidly in the mid-late 1990s, coincident with a decrease in the formation of dense Labrador Sea Water, a key component of the deep southward return flow od the Atlantic Meridional Overturning Circulation (AMOC). Recent freshening of the polar oceans may be reducing formation of Labrador Sea Water and hence may be weakening the AMOC. In the third study, I use InSAR to monitor ground deformation caused by CO2 injection at an enhanced oil recovery site in west Texas. Carbon capture and storage can reduce CO2 emitted from power plants, and is a promising way to mitigate anthropogenic warming. From 2007 to 2011, ~24 million tons of CO2 were sequestered in this field, causing up to 10 MPa pressure buildup in a reservoir at depth, and surface uplift up to 10 cm. This study suggests that surface displacement observed by InSAR is a cost-effective way to estimate reservoir pressure change and monitor the fate of injected fluids at waste disposal and CO2 injection sites.

Le bruit de chatoiement (speckle) lié aux systèmes d'imagerie cohérente a des conséquences sur l'analyse et l'interprétation des images radar à synthèse d'ouverture (RSO). Pour corriger ce défaut, nous profitons de séries temporelles d'images RSO bien recalées. Nous améliorons le filtre adaptatif temporel non-local à l'aide de méthodes performantes de débruitage adaptatif et proposons un filtrage temporel adaptatif basé sur les patchs. Pour réduire le biais du débruitage, nous proposons une méthode originale, rapide et efficace de débruitage multitemporel. L'idée principale de l'approche proposée est d'utiliser l'image dite "de ratio", donnée par le rapport entre l'image et la moyenne temporelle de la pile. Cette image de ratio est plus facile à débruiter qu'une image isolée en raison de sa meilleure stationnarité. Par ailleurs, les structures fines stables dans le temps sont bien préservées grâce au moyennage multitemporel. Disposant d'images débruitées, nous proposons ensuite d'utiliser la méthode du rapport de vraisemblance généralisé simplifié pour détecter les zones de changement ainsi que l'amplitude des changements et les instants de changements intéressants dans de longues séries d'images correctement recalées. En utilisant le partitionnement spectral, on applique le rapport de vraisemblance généralisé simplifié pour caractériser les changements des séries temporelles. Nous visualisons les résultats de détection en utilisant l'échelle de couleur 'jet' et une colorisation HSV. Ces méthodes ont été appliquées avec succès pour étudier des zones cultivées, des zones urbaines, des régions portuaires et des changements dus à des inondations. / The inherent speckle which is attached to any coherent imaging system affects the analysis and interpretation of synthetic aperture radar (SAR) images. To take advantage of well-registered multi-temporal SAR images, we improve the adaptive nonlocal temporal filter with state-of-the-art adaptive denoising methods and propose a patch based adaptive temporal filter. To address the bias problem of the denoising results, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well-preserved thanks to the multi-temporal mean. Without reference image, we propose to use a patch-based auto-covariance residual evaluation method to examine the residual image and look for possible remaining structural contents. With speckle reduction images, we propose to use simplified generalized likelihood ratio method to detect the change area, change magnitude and change times in long series of well-registered images. Based on spectral clustering, we apply the simplified generalized likelihood ratio to detect the time series change types. Then, jet colormap and HSV colorization may be used to vividly visualize the detection results. These methods have been successfully applied to monitor farmland area, urban area, harbor region, and flooding area changes.

<div>Synthetic Aperture Radar (SAR) is a technique to synthesize a large antenna array using the motion of a small antenna. When it comes to remote sensing, mapping, and change detection, SAR has been shown to be a good candidate by its ability to penetrate moisture and vegetation, and the avilibility of phase information for precise interferometric measurements [1] [13].</div><div><br></div><div><div>This study was motivated by the fact that satellite and high-altitude SAR has limited data availability in terms of temporal resolution and the cost of every measurement. It is believed that SAR systems mounted on smaller UAV or ground vehicles could provide a much better coverage of the target in time, and in dierent geometry.</div></div><div><br></div><div><div>We proposed a L-band SAR system based on Software-Defined Radio to be mounted on automotive platform. Novel motion estimation and compensation, as well as autofocusing techniques were developed to aid the SAR signal processing under much more demanding environment - the instability of radar platforms. It is expected this research development could bring down the cost of SAR being used as a remote sensing solution, and allow SAR system to be mounted on much smaller platforms by overcoming the instability of the track using novel signal processing methods, and eventually making SAR measurement available in places and times that was previously impossible.</div></div>

Le Synthetic Aperture Radar (SAR) aéroporté permet d’obtenir des images hautes résolutions, en compensant un déphasage lié au déplacement de l’avion. Il n’est cependant pas adapté à l’imagerie des cibles mobiles, celles-ci introduisant un déphasage supplémentaire, dépendant de leur vitesse et de leur accélération. En utilisant un système SAR multivoies, il est cependant possible de réaliser des traitements adaptés aux cibles mobiles, dont les principes sont proches du Space-Time Adaptive Processing (STAP). Le Synthetic Aperture Radar (SAR) aéroporté permet d’obtenir des images hautes résolutions, en compensant un déphasage lié au déplacement de l’avion. Il n’est cependant pas adapté à l’imagerie des cibles mobiles, celles-ci introduisant un déphasage supplémentaire, dépendant de leur vitesse et de leur accélération. En utilisant un système SAR multivoies, il est cependant possible de réaliser des traitements adaptés aux cibles mobiles, dont les principes sont proches du Space-Time Adaptive Processing (STAP). / Airborne Synthetic Aperture Radar (SAR) provides high-resolution images, by compensating a phase shift linked to the platform movement. However, this processing is not suited for imaging moving target, for they introduce an additional phase shift, depending on their velocity and acceleration. By using a multichannel SAR system, it is possible to correctly process moving targets. Such a processing is closely related to Space-Time Adaptive Processing (STAP) principles. Airborne Synthetic Aperture Radar (SAR) provides high-resolution images, by compensating a phase shift linked to the platform movement. However, this processing is not suited for imaging moving target, for they introduce an additional phase shift, depending on their velocity and acceleration. By using a multichannel SAR system, it is possible to correctly process moving targets. Such a processing is closely related to Space-Time Adaptive Processing (STAP) principles.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21089号 / 工博第4453号 / 新制||工||1692(附属図書館) / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小池 克明, 教授 立川 康人, 准教授 後藤 忠徳 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

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Interferometry and coherent change detection (CCD) utilize phase differences between complex SAR images to find terrain height and to detect small changes between images, respectively. A new method for improving interferometry and CCD using multiple sub-apertures is proposed. Using backprojection processing, multiple sub-aperture images are created for a pair of flights. An interferogram and coherence map is made from each sub-aperture. For CCD, each sub-aperture coherence map offers an independent estimate of the coherence over the same area. By combining coherence maps, low coherence areas associated with residual motion errors are reduced, shadowed areas are minimized, and the overall coherence of stationary objects between images is increased. For interferometry, combining independent estimates of a scene's height offers a more accurate height estimate. For repeat-pass interferometry, multiple apertures are shown to increase the coverage of valid height estimates. The benefits of multi-aperture interferometry and CCD are shown using examples with real data.

[pt] A captura de cenas de cobertura da Terra com sensores óticos de satélite é frequentemente limitada pela presença de nuvens que corrompem as imagens coletadas. Entre os métodos para recuperar imagens óticas de satélite corrompidas por nuvens, várias abordagens de tradução de imagemimagem usando Redes Adversárias Generativas (GANs) têm surgido com bons resultados, conseguindo criar imagens óticas realistas a partir de imagens de Radar de Abertura Sintética (SAR). Os métodos baseados em GANs condicionais (cGAN) propostos até agora para a síntese de imagens SAR-óticas tendem a produzir imagens ruidosas e com pouca nitidez. Neste trabalho, propomos a atrous-cGAN, uma nova arquitetura que melhora a transformação de imagem SAR em ótica. As redes propostas para o gerador e discriminador contam com convolusões dilatadas (atrous) e incorporam o módulo Pirâmide Espacial Atrous Pooling (ASPP) para realçar detalhes finos na imagem ótica gerada, explorando o contexto espacial em várias escalas. Este trabalho apresenta experimentos realizados para avaliar o desempenho da atrous-cGAN na síntese de imagens Landsat a partir de dados Sentinel-1A, usando quatro bases de dados públicas. A análise experimental indicou que a atrous-cGAN supera o modelo clássico pix2pix como uma ferramenta de aprendizado de atributos para segmentação semântica. A proposta também gera imagens com maior qualidade visual, e em geral com maior semelhança com a verdadeira imagem ótica. / [en] The capture of land cover scenes with optical satellite sensors is often constrained by the presence of clouds that corrupt the collected images. Among the methods for recovering satellite optical images corrupted by clouds, several image to image translation approaches using Generative Adversarial Networks (GANs) have emerged with profitable results, managing to create realistic optical images from Synthetic Aperture Radar (SAR) data. Conditional GAN (cGAN) based methods proposed so far for SAR-to-optical image synthesis tend to produce noisy and unsharp optical outcomes. In this work, we propose the atrous-cGAN, a novel cGAN architecture that improves the SAR-to-optical image translation. The proposed generator and discriminator networks rely on atrous convolutions and incorporate the Atrous Spatial Pyramid Pooling (ASPP) module to enhance fine details in the generated optical image by exploiting spatial context at multiple scales. This work reports experiments carried out to assess the performance of atrouscGAN for the synthesis of Landsat images from Sentinel-1A data based on four public datasets. The experimental analysis indicated that the atrouscGAN overcomes the classical pix2pix model as a feature learning tool for semantic segmentation. The proposal also generates higher visual quality images, in general with higher similarity with the true optical image.