Climate-induced landscape evolution is resulting in changes to biogeochemical and hydrologi- cal cycling. In the Arctic and sub-Arctic permafrost zones, rising air temperatures are warming, and in some regions even thawing, the frozen ground. Permafrost is a carbon sink. The thermal state of the ground therefore has important implications on carbon exchange with the atmo- sphere. Permafrost thaw mobilises previously sequestered carbon stocks, potentially turning these high latitude regions into a net carbon source. Borehole temperature and active layer depth measurements are the traditional means for monitoring permafrost, however these point measurements cannot easily be extrapolated to the landscape-scale; Earth Observation (EO) data may be used for such purposes. It is widely recognised that changes in the thermal state of permafrost may be associated with longterm changes in surface hydrology. As the ground shifts from a frozen to a thawed state, Arctic lakes display changes in surface extent. Therefore, it has become common practice to explore lake dynamics, using these as indicators of permafrost change; dynamics being the keyword. Surface hydrology is a dynamic process. Discharge studies in the Arctic and sub-Arctic regions are associated with flashy hydrographs. Currently, however, remote sensing of permafrost lake change is done on the scale of decades without explicitly taking seasonality and rapid hydrolog- ical phenology into consideration. To examine the seasonal changes in Arctic surface hydrology on the landscape scale high temporal resolution data are necessary. Synthetic aperture radar instruments are exemplary for such a task. The PhD research focuses on establishing operational techniques for mapping open surface water using synthetic aperture radar data, investigating straightforward raster classification methods and exploring their feasibility by undertaking map accuracy and sensitivity studies (chapter 3). The results are then used to justify error propagation when developing an auto- mated procedure that creates temporal composites of water body extent. These temporal water body classifications are the main EO product used to identify and image seasonal surface water change in Arctic permafrost environments (chapter 4). Furthermore, a terrain-based hydrolog- ical study is undertaken to explore the context of the detected changes and possible links to relief and stream channel network (chapter 5). The aim of this PhD is to demonstrate a new method of dynamic monitoring using the Euro- pean Space Agency’s Envisat Advanced Synthetic Aperture Radar, recommending its incorpo- ration in longterm lake change studies. Technical feasibility is explored, the inherent trade-off vii between spatial and temporal resolution discussed. An automated surface water change de- tection algorithm is developed and its applicability to monitoring spring floods is assessed; noting possible modifications to the drainage system given present-day land-use and land- cover changes that are taking place in the study area, the hydrocarbon-rich Yamalo-Nenets Autonomous District in the North of West Siberia (chapter 6). The key significance of this research is to improve the current knowledge of Arctic lake change by including spring flood events and seasonality in the equation. Therefore, it is strongly believed that this research is of benefit to the entire permafrost community.

Submitted by ULISSES SILVA GUIMARÃES null (ulissesguimaraes21@yahoo.com.br) on 2017-04-23T14:46:14Z No. of bitstreams: 1 PPGCC_Tese_Guimaraes_mar2017.pdf: 11881192 bytes, checksum: b616aff4dd851b26e6151f6a753a3e62 (MD5) / Rejected by Luiz Galeffi (luizgaleffi@gmail.com), reason: Solicitamos que realize uma nova submissão seguindo a orientação abaixo: O arquivo submetido não contém o certificado de aprovação. Corrija esta informação e realize uma nova submissão com o arquivo correto. Agradecemos a compreensão. on 2017-04-25T19:47:14Z (GMT) / Submitted by ULISSES SILVA GUIMARÃES null (ulissesguimaraes21@yahoo.com.br) on 2017-04-27T00:35:55Z No. of bitstreams: 1 PPGCC_DR_UG.pdf: 11945196 bytes, checksum: 6a00a3b24776e561994802637366de42 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-05-03T13:21:12Z (GMT) No. of bitstreams: 1 guimaraes_us_dr_prud.pdf: 11945196 bytes, checksum: 6a00a3b24776e561994802637366de42 (MD5) / Made available in DSpace on 2017-05-03T13:21:12Z (GMT). No. of bitstreams: 1 guimaraes_us_dr_prud.pdf: 11945196 bytes, checksum: 6a00a3b24776e561994802637366de42 (MD5) Previous issue date: 2017-03-06 / A Zona Costeira Amazônica (ZCA) é marcada por uma alta descarga de sedimentos e água doce sob a influência do rio Amazonas, possui ampla plataforma continental, extensas planícies de inundação e planaltos rebaixados. É uma região de clima tropical, caracterizando-se por chuvas e nebulosidade severas, além da influência de macromarés. Este estudo propõe-se a avaliar a precisão de Modelos Digitais de Superfícies (MDSs), elaborados a partir de dados de radar de abertura sintética (SAR) Cosmo-SkyMed (CSK) e TerraSAR-X (TSX), utilizando abordagens de reconstrução tridimensional por interferometria e radargrametria, para caracterizar esse relevo plano e dinâmico da costa amazônica. O estudo foi desenvolvido em quatro experimentos contemplando: i) as variações de linha de costa por meio de detecção de mudanças a partir de imagens ópticas; ii) mapeamento de ambientes costeiros; iii) elaboração e análise de MDSs interferométricos e iv) radargramétricos, por meio das suas respectivas cadeias de processamento SAR. A ZCA teve forte dinâmica nos últimos 15 anos, com acresção total de 5.582,18 km2 e sob a taxa de 372,15 km2.ano-1, erosão total de 5.475,90 km² e sob taxa de 365,06 km2.ano-1, resultando no balanço sedimentar de 106,27 km², com taxa de 7,08 km2.ano-1. O setor Insular Estuarino apresentou a maior dinâmica de linha de costa, com mudanças costeiras de 213,17±56,46 km2 e balanço sedimentar de 20,65±73,59 km2. Os ambientes costeiros amazônicos foram descritos pelo retroespalhamento e pela coerência, os quais compartilharam alta ambiguidade e dispersão elevada, sendo o pior caso de separabilidade e baixa coerência registrado para Planície Costeira. O mapeamento dos diferentes ambientes costeiros resultou em coeficiente Kappa entre 0,46 a 0,51, apontando os ângulos de incidência rasantes e o período seco como mais apropriados para o estudo. Os MDSs interferométricos e radargramétricos foram elaborados em passagens múltiplas de única revisita com compromissos entre ângulos de incidência, linha de base espacial e descorrelação temporal. A acurácia vertical foi realizada por testes estatísticos pareados com levantamentos de campo que resultou em discrepâncias, viés e precisão compatíveis com o Padrão de Exatidão Cartográfica Brasileiro para Produtos Cartográficos Digitais (PEC-PCD), em adição, os MDS foram comparados por meio dos diagramas de Taylor e Alvo. Os MDSs interferométricos alcançaram RMSE entre 9,57 e 25,18 m, com melhor desempenho para o MDS CSK, adquirido com 1 dia de revisita, ângulo de incidência íngreme, no período chuvoso e compatível a escala de 1:50.000, classe A. Entretanto, a abordagem interferométrica não foi capaz de solucionar a reconstrução tridimensional de ambientes que se mostraram incoerentes. Os modelos radargramétricos obtidos pelas abordagens do SARscape e Toutin alcançaram RMSE entre 4,34 e 7,76 m, com melhor desempenho para os modelos de Toutin, que foram compatíveis com a escala 1:50.000, classe A. A radargrametria permitiu a reconstrução tridimensional contínua, incluindo a Planície Costeira de comportamento incoerente. A comparação dos MDSs por meio dos diagramas de Taylor e Alvo, mostrou variações de precisão entre os sistemas CSK e TSX, e suas respectivas condições de aquisição e modelos, com destaque à menor variabilidade e ajuste da correlação para MDSs do sistema TSX, em incidências rasantes, no período seco e gerados pelo modelo de Toutin. O Tabuleiro Costeiro e Terraço Fluviomarinho apresentaram menor erro vertical entre 3,89 e 28,59 m, e entre 3,79 e 20,33 m, respectivamente, enquanto que a Planície Costeira teve maior RMSE entre 4,16 e 26,24 m. O Tabuleiro Costeiro foi o ambiente costeiro mais adequado para estimar altura, com posições plotadas próximas as referências de campo. Os dados CSK e TSX permitiram mapear o relevo plano e dinâmico da ZCA, por meio da banda X, alta resolução espacial e revisita, o que demostrou o suporte para cartografar em detalhe de escala espacial (1:50.000) e frequente atualização (semestral a anual). / The Amazon Coastal Zone (ACZ) is marked by a high discharge of sediments and fresh water under the influence of the Amazon River, which has a wide continental shelf, extensive flood plain and lowered plateaus. It is a region of tropical climate, rainfall, severe cloudiness and macrotidal influence. This study proposes to assess the performance of Digital Surface Models (DSM) based on Cosmo-SkyMed (CSK) and TerraSAR-X (TSX) dataset, using threedimensional reconstruction by interferometry and radargrammetry approaches on the flat and dynamic relief of Amazonian coast. The method focused on four experiments: i) shoreline variations through change detection of optical images, ii) mapping of coastal environments; iii) elaboration and analyses of interferometric DMSs and iv) radargrammetric through their respective Synthetic Aperture Radar (SAR) processing chains. The ACZ had high dynamic in the last 15 years with total sediment deposition of 5,582.18 km2 and under a rate of 372.15 km2 .yr -1 , and with total erosion of 5,475.90 km² and under a rate of 365.06 km2 .yr-1 . Besides, it was obtained a sedimentary balance of 106.27 km² and under a rate of 7.08 km2 .yr-1 . The Estuarine Insular sector presented the greater dynamics of shoreline, registering coastal changes of 213.17 ± 56.46 km2 and sedimentary balance of 20.65 ± 73.59 km2 . The Amazonian coastal environments were described by backscattering and coherence which shared ambiguity and high dispersion, with the lowest separability and coherence noted for Coastal Flat. The mapping of the coastal environments obtained Kappa coefficients between 0.46 and 0.51, indicating the shallow incidence angles during the dry season as more appropriated for the study. The interferometric and radargrammetric DSMs were elaborated in multi-pass and single revisit with commitment between incidence angles, spatial baseline and temporal decorrelation. A vertical accuracy assessment was performed with paired statistical tests at surveyed elevations in the field that resulted in discrepancies, bias and precision, in accordance to the Brazilian Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD), in addition, the DSMs were compared throughout Taylor and Target diagrams. The interferometric DMSs achieved RMSE between 9.57 and 25.18 m, with better performance for the DMS CSK, acquired with 1 day of revisit, steeper incidence, in rainy season and compatible at a scale of 1: 50,000, class A. However, the interferometric approach was not able to solve the threedimensional reconstruction in incoherent environments. The radargrammetric models of SARscape and Toutin achieved a RMSE between 4.34 and 7.76, and the best performances were for the Toutin’s models compatible at a scale of 1: 50,000, class A. The advantage of radargrammetry was to provide continuous three-dimensional reconstruction, including the Coastal Flat of incoherent behavior. The comparison of the DMSs through the Taylor and Target diagrams showed fluctuations of precision between CSK and TSX systems and their respective acquisition conditions and models, but it is remarkable the stability of the lowest variability and the correlation well fitted for the DMSs given by TSX system, shallow incidences, dry season and Toutin model. The Coastal Plateau and Fluvial Marine Terrace had the lowest vertical error between 3.89 and 28.59 m, and between 3.79 and 20.33 m, respectively. On the contrary, the Coastal Flat had the highest RMSE between 4.16 and 26.24 m. The Coastal Plateau was the most suitable coastal environment to estimate the height following the Taylor and Target diagrams, with the plotted positions close to the field references. The CSK and TSX data allowed to map the ZCA precisely, based on X-band perspective, high spatial resolution and revisit, which has demonstrated the support for detailed cartography of spatial scale (1: 50,000) and frequent updating (semiannual up to annual).

Made available in DSpace on 2014-06-12T18:29:09Z (GMT). No. of bitstreams: 2 arquivo4274_1.pdf: 5027595 bytes, checksum: 37a31f281a0f888465edbdc60cb2db39 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2008 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Nessa tese estudamos a estimação por máxima verossimilhança (MV) do parâmetro de aspereza da distribuição G 0 A de imagens com speckle (Frery et al., 1997). Descobrimos que, satisfeita uma certa condição dos momentos amostrais, a função de verossimilhança é monótona e as estimativas MV são infinitas, implicando uma região plana. Implementamos quatro estimadores de correção de viés em uma tentativa de obter estimativas MV finitas. Três dos estimadores são obtidos da literatura sobre verossimilhança monótona (Firth, 1993; Jeffreys, 1946) e um, baseado em reamostragem, é proposto pelo autor. Fazemos experimentos numéricos de Monte Carlo para comparar os quatro estimadores e encontramos que não existe um favorito claro, a menos quando um parâmetro (dado a priori da estimação) toma um valor específico. Também aplicamos os estimadores a dados reais de radar de abertura sintética. O resultado desta análise mostra que os estimadores precisam ser comparados com base em suas habilidades de classificar regiões corretamente como ásperas, planas, ou intermediárias e não pelos seus vieses e erros quadráticos médios

Dans le cycle de carbone à l'échelle de la planète, la contribution des forêts tropicales, en tant que stock de carbone, est déterminante. Les études actuelles montrent que la connaissance précise de la biomasse forestière globale est nécessaire pour les modèles de prévision. C'est dans ce contexte que le projet BIOMASS est choisi par l'Agence spatiale européenne (ESA) comme une phase A du programme «Earth Core Mission». L'objectif de cette mission innovatrice est l'utilisation d'un système d'imagerie polarimétrique fonctionnant en bande P (435 MHz) pour la mesure de la biomasse forestière. La définition actuelle de la mission prévoit un mode tomographique rassurant une imagerie tri-dimentionnelle (3-D) de la forêt. Dans le cadre du projet BIOMASS, cette thèse de doctorat vise à développer une nouvelle stratégie pour la télédétection de la biomasse dans les forêts tropicales en utilisant des données multi-baseline acquises par le radar à ouverture synthétique (SAR) en bande P. Une approche originale consite à combiner la tomographie et le modèle RvoG (Random-Volume-over-Ground) établi et vérifié avec la technique PolInSAR (polarimetric SAR Interferometry). L'environnement forestier peut être décrit avec précision par un modèle polarimétrique multicouche (sol et succession de couches végétales). Une généralisation multi-baseline du modèle RVoG implique un certain nombre de paramètres qui peuvent être estimés à partir des données SAR en utilisant des méthodes spectrales haute résolution. Ainsi, une cartographie de la forêt et du sol peut être réalisée à l'aide de données tomographiques. De plus, la capacité des techniques tomographiques permet d'estimer la distribution verticale de la puissance rétrodiffusée. Ainsi, une information précise sur la biomasse peut être extraite de la puissance mesurée dans un domaine adapté à la couche de végétation. Cependant, cette puissance mesurée peut être fortement affectée par l'écho du sol dû à la contribution de double rebond. Et par suite, le principal défi peut être résumé par l'élaboration d'un nouvel estimateur de la biomasse forestière lié à une puissance rétrodiffusée mesurée avec une polarisation et un domaine vertical, tous les deux sont adaptés à la couche de végétation. Les algorithmes développés pour la cartographie de la forêt, l'estimation et la simulation de la biomasse sont appliqués et validés sur des données SAR aéroportées réalisées lors de la campagne TROPISAR en Guyane. / Forested areas cover one third of earth's land surface and their contribution in the storage of carbon is decisive. Current studies show that the accurate knowledge of global forest biomass is necessary for the prediction of climate changes on the planet. In this context, the BIOMASS project is selected by the European Space Agency (ESA) as Phase A of the 'Earth Core Mission' program. This highly innovative mission consists of the use of a polarimetric imaging radar operating at P band (435 MHz) for the measurement of forest biomass. The current definition of the mission provides a three-dimensional imaging (3-D) of the forest with both tomographic and multi-pass interferometric modes. In the framework of this project, this PHD thesis aims to develop a novel strategy for the remote sensing of the biomass within the dense tropical forests by processing on multi-baseline P-band Synthetic Aperture Radar (SAR) data. An original approach combines the possibilities of 3-D exploration tomography and the Random-Volume- over-Ground (RVoG) model established and verified with PolInSAR technique (Polarimetric Interferometry SAR). The forested environment can be accurately described by a polarimetric multi-layer model (soil and a succession of vegetationlayers). A multi-baseline generalization of the RVoG model involves a certain number of parameters which must be estimated from radar observation data by using High- Resolution spectral estimation tomographic methods. Thereby, a cartography of the forest and its underlying ground can be made using tomographic data. Furthermore, the capacity of the tomographic techniques on 3-D imaging allows an estimation of the vertical distribution of the backscattered power. Thus, an accurate biomass information may be extracted from the power measured at a domain adapted to the canopy layer. However, this measured backscattered may be strongly affected by the ground echo due to the double bounce contribution. The main challenge of this thesis is to establish a novel biomass estimator related to a backscattered powermeasured with a polarimetric channel and at a vertical domain, both adapted to the canopy layer. The proposed algorithms of forest cartography and biomass estimation are applied and validated on Airborne P-band SAR data realized on the TROPISAR campaign in French Guyana.

The sea surface microlayer is a millimeter-scale interfacial layer between the atmosphere and the ocean. A number of studies have suggested that there is a unique ecosystem for marine bacteria in the sea surface microlayer, but little information exists on the microbial community composition of this ecosystem due to sampling complexities. In this work, we present an improved method to sample and compare the bacterial diversity of the sea surface microlayer with that of subsurface water at the same site. Bacterial samples were collected from the sea surface microlayer with a sampling method, which minimized sample contamination from the research platform and the subsurface water. Sampling was conducted using a polycarbonate membrane filter to obtain the bacterial community structure at open water and coastal water sites in the Straits of Florida. The microlayer sampling was planned to coincide with synthetic aperture radar satellite overpasses (COSMO SkyMed), which capture a range of fine-scale features on the sea surface. The presence of surfactants affect the synthetic aperture radar imaging process because surfactants in the sea surface microlayer suppress short gravity-capillary ocean surface waves, thereby decreasing the backscatter and allowing the radar to detect surfactant-covered areas. Although sources of surfactants vary, certain marine bacteria are known to produce and transform surfactants, which suggest that these surfactant-related marine bacteria have an important biological influence on fine-scale synthetic aperture radar satellite imagery. Therefore, the comparison between synthetic aperture radar satellite images and in situ field samples may be used for interpreting and studying fine-scale features on the sea surface. The surfactant-associated bacterial composition of the sampling sites was determined using high-throughput, 454 pyrosequencing methods. A total of 61,663 sequences were analyzed and the results indicated the presence of surfactant-associated bacteria such as Moraxellaceae, Halomonadaceae, Enterobacteriaceae, Bacillaceae, and Nocardiaceae. By establishing these bacterial groups that influence the presence of surfactants, remote sensing techniques which involve monitoring the microlayer are expected to be enhanced and may provide additional information on the state of the upper ocean ecosystem.

Synthetic aperture radar imaging is an effective tool for imaging the sea surface because of its response to changes in sea surface roughness. This allows for the remote sensing of features on the sea surface, which modulate se surface roughness. In this work, 18 synthetic aperture radar images were collected from the TerraSAR-X and RADARSAT-2 satellites in the Port Everglades, Florida area. In-situ measurements were collected in conjunction with the satellite images in order to provide more information on the features visible in the imagery, and aid in identification of the origin of the features. Information on ships in the area of the satellite image footprints was collected using an automatic information system. Weather conditions were recorded by a meteorological station and a National Oceanic and Atmospheric Administration weather radar station. Waves and currents in the observational area were recorded with acoustic Doppler current profilers and wave gauges. Sonar systems and conductivity, depth, and salinity profilers were used to identify stratification in the water column. Surfactant release experiments were also conducted to explore the affects of surface active materials. Results of the experiment show the manifestation of atmospheric effects, oceanic fronts and eddies, wind shadowing, natural and artificial slicks, and ships and ship wakes on the synthetic aperture radar imagery. Atmospheric conditions were found to play a significant role in the visibility of features on the sea surface, and sometimes masked the appearance of features on the ocean surface. Overall the most reliable feature capable of being imaged on the sea surface by the synthetic aperture radar satellites was the signatures of ships and their wakes.

Under low wind speed conditions, surfactants accumulate at the air-sea interface, dampen short-gravity capillary (Bragg) waves, and form natural sea slicks that are detectable visually and in synthetic aperture radar (SAR) imagery. Marine organisms, such as phytoplankton, zooplankton, seaweed, and bacteria, produce and degrade surfactants during various life processes. This study coordinates in situ sampling with TerraSAR-X satellite overpasses in order to help guide microbiological analysis of the sea surface microlayer (SML) and associated subsurface water (SSW). Samples were collected in the Gulf of Mexico during a research cruise (LASER) in February 2016 to determine abundance of surfactant associated bacteria in the sea surface microlayer and subsurface water column. By using real time polymerase chain reaction (quantitative PCR, or qPCR) to target Bacillus spp. associated with surfactant production, results indicate that more surfactant-associated bacteria reside in the subsurface water in low wind speed conditions. Sequencing results suggest that Bacillus and Pseudomonas are more abundant in the SSW in low wind speed conditions. These results indicate that these bacteria reside in the SSW, presumably producing surfactants that move to the surface via physical processes, accumulate on and enrich the sea surface microlayer.

Le suivi de l’évolution du manteau neigeux est directement lié à des enjeux socio-économiques majeurs en zone de montagne. Parmi ces enjeux figure la prévision du risque d’avalanche qui s’appuie principalement sur des observations et sur la connaissance de l’état du manteau neigeux et de son évolution dans le temps. Dans cette thèse, co-financée par le CNES et par Météo- France, nous avons évalué l’apport d’observations de télédétection spatiale active micro-ondes issues du radar à synthèse d’ouverture (SAR) de Sentinel-1, pour suivre l’évolution de certaines propriétés du manteau neigeux. Dans un premier temps, nous avons évalué la chaîne de modélisation SAFRAN-ISBA/Crocus-MEMLS par rapport aux données Sentinel-1 pré-traitées sur 3 saisons hivernales de 2014 à 2017, sur une zone de 2310 km2 à 20 m de résolution dans les Alpes du Nord françaises. Nous avons montré que les données SAR étaient pertinentes pour suivre l’évolution du manteau neigeux et, avons démontré la capacité de la chaîne de modélisation à reproduire les variations du signal observé dans le temps malgré de forts biais négatifs en cas de neige humide. Nous nous sommes intéressés à la valeur ajoutée des observations SAR de Sentinel-1 pour cartographier la neige humide, c’est-à-dire, la neige avec un taux élevé d’eau liquide. Des comparaisons ont été effectuées entre les produits neige humide obtenus par Sentinel-1 et les produits neige de Sentinel-2 distribués par Theia. Cette étude a été menée sur la saison hivernale 2017-2018, qui a connu un enneigement exceptionnel. Ces travaux ouvrent la voie à l’assimilation de données de télédétection SAR dans le modèle de neige Crocus ainsi qu’à une plus grande exploitation de ces données dans le cadre du suivi de l’enneigement pour de multiples applications. / Monitoring snowpack properties in moutainous areas is directly related to major socio-economic issues. Among these issues, avalanche prediction works through a range of tools based on meteorological and snow observations and modeling. In this thesis, co-funded by the CNES and Météo-France, we evaluated the contribution of Sentinel-1 synthetic aperture radar (SAR) remote sensing observations to study the snowpack properties and the quality of the simulations for assimilation in a snowpack model. As a first step, we evaluated the SAFRAN-ISBA/Crocus- MEMLS modeling chain against pre-processed Sentinel-1 data for 3 winter seasons from 2014 to 2017 over an area of 2310 km2 in the Northern French Alps. We have shown that SAR data are relevant for monitoring snowpack evolution and demonstrated the ability of the modeling chain to reproduce observed signal variations despite strong negative bias in wet snow conditions. We focused on wet snow products derived from Sentinel-1 SAR observations in synergy with snow absence/presence products derived from visible Sentinel-2 observations. This study was conducted on the winter season 2017-2018, which was remarkable for its snow and avalanche conditions. Such combined products make it possible to follow the spatio-temporal variability of mountain wet snow and dry snow at high elevation. This work opens the way for the assimilation of SAR remote sensing data into the Crocus snowpack model as well as greater exploitation of this data in the context of avalanche snow monitoring and prediction for a variety of purposes.

abstract: The objective of this work is to design a novel method for imaging targets and scenes which are not directly visible to the observer. The unique scattering properties of terahertz (THz) waves can turn most building surfaces into mirrors, thus allowing someone to see around corners and various occlusions. In the visible regime, most surfaces are very rough compared to the wavelength. As a result, the spatial coherency of reflected signals is lost, and the geometry of the objects where the light bounced on cannot be retrieved. Interestingly, the roughness of most surfaces is comparable to the wavelengths at lower frequencies (100 GHz – 10 THz) without significantly disturbing the wavefront of the scattered signals, behaving approximately as mirrors. Additionally, this electrically small roughness is beneficial because it can be used by the THz imaging system to locate the pose (location and orientation) of the mirror surfaces, thus enabling the reconstruction of both line-of-sight (LoS) and non-line-of-sight (NLoS) objects. Back-propagation imaging methods are modified to reconstruct the image of the 2-D scenario (range, cross-range). The reflected signal from the target is collected using a SAR (Synthetic Aperture Radar) set-up in a lab environment. This imaging technique is verified using both full-wave 3-D numerical analysis models and lab experiments. The novel imaging approach of non-line-of-sight-imaging could enable novel applications in rescue and surveillance missions, highly accurate localization methods, and improve channel estimation in mmWave and sub-mmWave wireless communication systems. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

In the long term it is beneficial to a country's economy to exploit the maritime environment surrounding it responsibly. It is also beneficial to protect this environment from poaching and pollution. To achieve this the responsible parties of a country must have an awareness of what is transpiring in the maritime domain. Synthetic aperture radar can provide an image, regardless of weather or light conditions, of the ocean showing most vessels therein. To monitor the ocean, using synthetic aperture radar imagery, at the lowest cost would require large swath synthetic aperture radar imagery. There exists a trade-off between large swath imagery and the image's resolution resulting in the largest swath image having the poorest resolution. Existing research has shown that it is possible to use coarse resolution synthetic aperture radar imagery to detect vessels at sea, but little work has been done on classifying those vessels. This research aims to investigate the coarse resolution classification information gap. This is done by using a dataset of matching synthetic aperture radar and ship transponder data to train a statistical classification algorithm in order to classify or estimate the length of vessels based on features extracted from their synthetic aperture radar image. The results of this research show that coarse resolution (approximately 40 m per pixel) synthetic aperture radar imagery is able to estimate vessel size for larger classes and provides insight on which vessel classes would require finer resolutions in order to be detected and classified reliably. The range of smaller vessel classes is usually limited to ports and fishing zones. These zones can be mapped using historical vessel transponder data and so a dedicated surveillance campaign can be optimised to use higher resolution products in these areas. The size estimation from the machine learning algorithm performs better than current techniques. / Dissertation (MEng)--University of Pretoria, 2017. / Electrical, Electronic and Computer Engineering / MEng / Unrestricted