Dissertations / Theses on the topic 'Remote sensing'

Image analysis plays a crucial role in many computer vision applications in which images of the same scene with different geometrical orientations need to be compared for further processing. This thesis describes the design and implementation of a model-based vision system for the recognition of aerial images. The main objective is to register two remote sensing images taken at different times. First, some distinctive features are extracted and matched then, these matched features are used as marking points in defining a geometric mapping function. Once registered, the reference image can be used as an aid to automatic interpretation and as a framework for detecting changes between successive images. A two stage matching procedure is used for this task. In the first part, corners are extracted and matched in both images and an initial estimation of the mapping function is computed. This initial function is then used in the second part to estimate the parameters of a global mapping function for the entire image. The process ends when all the extracted features in one image are either mapped to features in the other image, or rejected if no match could be found.

A remote sensing near infrared suspended sediment algorithm is developed from first principles and applied to Compact Airborne Spectrographic Imagery (CASI) data flown over the Humber Estuary. Laboratory measurements were used as the basis for the algorithm development, with the resulting spectra indicating that the ideal wavelength for a suspended sediment algorithm is the near infrared. The resulting algorithm took the form of a waveband ratio which was subsequently validated with a semi-analytical water optics model based on the absorption/scattering properties of the optically active constituents. The model was then used to derive a global water-leaving radiance algorithm, which is independent of the sediment type. The algorithm was applied to the CASI data collected during August and September 1993, and the resulting SPM maps were compared with contemporaneous in-situ measurements. The in-situ measurements include calculations of the diffuse attenuation coefficient (Kd), which was correlated with the SPM concentration. Further developments to the algorithm through the use of an atmospheric correction are outlined.

Pendant les 20 dernières années, notre connaissance de la déformation de la Terre a été complètement bouleversée par l’introduction de deux techniques de Géodésie spatiale. D’une part, ce que l’on appelle positionnement satellitaire (‘point positioning’) pas seulement à partir du système GPS (Global Positioning System) mais également à partir du système DORIS (Doppler Orbitography and Radio-positioning Integrated from Space). D’autre part, ont été développées des techniques d’imagerie satellitaire de corrélation d’images et d’interférométrie SAR (Synthetic Aperture Radar) ainsi que les méthodes de mesures de décalages sur des images panchromatiques à haute résolution spatiale. Ces nouvelles techniques, ont permis une série d’avancement scientifiques notamment la confirmation et l’amélioration de la théorie de la tectonique de plaques, la cartographie fine de déplacements sismiques et asismiques, l’amélioration de la compréhension des phénomènes de relaxation post sismiques, la détection de séismes lents et ‘silencieux’, la détection de signaux précurseurs de séismes ou d’éruptions volcaniques. Actuellement des nombreuses questions clés restent ouvertes. Notamment : l’importance relative de la déformation accommodée sismiquement en bords de plaques par rapport à la déformation totale et à la contrainte tectonique ; la contribution des séismes à la déformation par rapport au déplacement asismique sur les discontinuités. Le but de la présente Thèse de Doctorat, est d’étudier le potentiel, les limitations et la complémentarité des données issues de l’Observation de la Terre pour prendre en compte et essayer de répondre à certains aspects des questions exposées ci-dessus.

This study researches the potential for using hyperspectral remote sensing to identify the phytoplanktonic composition of a freshwater bloom. Six novel analytical techniques were developed to identify phytoplankton class from reflectance spectra. These techniques offer the water manager a variety of means to identify the dominant phytoplankton class in a target water body. Identification of phytoplankton class is possible because certain photosynthetic pigments contained within phytoplankton cells are taxonomically significant, being indicative of a particular class. The detection of these pigments can be used to identify the presence of a particular phytoplanktonic class in an aquatic system. It is possible to identify these pigments using optical methods because they exhibit unique spectral absorption signatures. Such pigment absorption features are manifest in the composite reflectance signature from water bodies as measured by remote sensing instruments. However, due to the presence of the spectral features from other photosynthetic pigments and the other optically active components of water bodies, extracting from reflectance spectra the spectral information pertaining to individual class marker pigments can be difficult. The phytoplankton class identification techniques presented in this study were developed using absorption and reflectance spectra from pure cultures of phytoplankton. The reflectance spectra were measured in the controlled environment of a laboratory based experimental tank designed for this study. The class identification techniques were tested on field and airborne reflectance spectra measured from a eutrophic inland lake.

In order to reduce the problems of clouds, atmospheric variations, view angle effects, and the soil background variations in the high temporal frequency AVHRR data, a compositing technique is usually employed. Current compositing techniques use a single pixel selection criterion of outputting the input pixel of maximum value NDVI. Problems, however, exist due to the use of the NDVI classifier and to the imperfection of the pixel selection criteria of the algorithm itself. The NDVI was found not to have the maximum value under an ideal observation condition, while the single pixel selection criterion favors the large off-nadir sensor view angles. Consequently, the composited data still consist of substantial noise. To further reduce the noise, several data sets were obtained to study these external factor effects on the NDVI classifier and other vegetation indices. On the basis of the studies of these external factors, a new classifier was developed to further reduce the soil noise. Then, a new set of pixel selection criteria was proposed for compositing. The new compositing algorithm with the new classifier was used to composite two AVHRR data sets. The alternative approach showed that the high frequency noises were greatly reduced, while more valuable data were retained. The proposed alternative compositing algorithm not only further reduced the external factor related noises, but also retained more valuable data. In this dissertation, studies of external factor effects on remote sensing data and derived vegetation indices are presented in the first four chapters. Then the development of the new classifier and the alternative compositing algorithm were described. Perspectives and limitations of the proposed algorithms are also discussed.

Salinity represents the major environmental threat to arable land in Western Australia and many other parts of the world. This study was designed to establish criteria for a practical remote sensing system using the visible, reflected and shortwave infrared for the early detection and mapping of salinity. The results are principally from a group of study sites on the CSIROs Yalanbee Experiment Station, and from other significant sites during the agricultural cycles of 1985-7.Analysis of imagery from the Geoscan Multispectral Airborne Scanner showed that best discrimination between study sites affected by salinity, and those not affected, was provided by bands 3 (650-700 nm), 4 (830-870nm) and band 6 (1980-2080nm). The maximum discrimination occurred in a September 1986 flight (spring-flush). Although excellent discrimination was also evident in August and November in 1985, this could not be reproduced in November 1986. The visible and reflected infrared bands 3 and 4 featured prominently, but the significance of the short wave infrared bands was evident especially when vegetative ground cover became a less dominant factor.Field spectra collected over the same period with the Geoscan Portable Field Spectroradiometer (PFS) supported the aircraft data to a certain extent. Detailed analysis of the fine non-correlated structure of narrow constructed bands, from PFS data, indicated that improved discrimination between sites could be provided over a wider time window extending into the summer and autumn. This is when weather-related conditions, i.e. cloud, soil moisture and sun angle, are more conducive to extensive surveys.The importance of at least one narrow band centred near 1985 nm was determined. Laboratory spectra of bare soil from sites measured on an Hitachi Spectrophotometer also provided the importance of the shortwave region adjacent to the 1900 nm water absorption.The study evaluated the spatial and spectral characteristics of existing satellite systems such as Thematic Mapper and the Multispectral Scanner on the Landsat series and determined that a spatial resolution of about 20-30 metres was most appropriate for detection of salinity at a scale whereby management could be implemented.Ground electromagnetic techniques were evaluated during the study and the EM-38 Ground Conductivity Unit proved valuable for characterizing salinity status of the sites. The Lowtran Computer Code was used to model atmospheric attenuation and results indicated that the positioning of a narrow shortwave infrared waveband, centred at 1985 nm, is possible.

Salinity represents the major environmental threat to arable land in Western Australia and many other parts of the world. This study was designed to establish criteria for a practical remote sensing system using the visible, reflected and shortwave infrared for the early detection and mapping of salinity. The results are principally from a group of study sites on the CSIROs Yalanbee Experiment Station, and from other significant sites during the agricultural cycles of 1985-7.Analysis of imagery from the Geoscan Multispectral Airborne Scanner showed that best discrimination between study sites affected by salinity, and those not affected, was provided by bands 3 (650-700 nm), 4 (830-870nm) and band 6 (1980-2080nm). The maximum discrimination occurred in a September 1986 flight (spring-flush). Although excellent discrimination was also evident in August and November in 1985, this could not be reproduced in November 1986. The visible and reflected infrared bands 3 and 4 featured prominently, but the significance of the short wave infrared bands was evident especially when vegetative ground cover became a less dominant factor.Field spectra collected over the same period with the Geoscan Portable Field Spectroradiometer (PFS) supported the aircraft data to a certain extent. Detailed analysis of the fine non-correlated structure of narrow constructed bands, from PFS data, indicated that improved discrimination between sites could be provided over a wider time window extending into the summer and autumn. This is when weather-related conditions, i.e. cloud, soil moisture and sun angle, are more conducive to extensive surveys.The importance of at least one narrow band centred near 1985 nm was determined. Laboratory spectra of bare soil from sites measured on an Hitachi Spectrophotometer also provided the importance of the shortwave region adjacent to the 1900 nm water ++
absorption.The study evaluated the spatial and spectral characteristics of existing satellite systems such as Thematic Mapper and the Multispectral Scanner on the Landsat series and determined that a spatial resolution of about 20-30 metres was most appropriate for detection of salinity at a scale whereby management could be implemented.Ground electromagnetic techniques were evaluated during the study and the EM-38 Ground Conductivity Unit proved valuable for characterizing salinity status of the sites. The Lowtran Computer Code was used to model atmospheric attenuation and results indicated that the positioning of a narrow shortwave infrared waveband, centred at 1985 nm, is possible.

Ce travail présente l’algorithme GARRLiC (Generalized Aerosol Retrieval from Radiometer and Lidar Combined data). Le but de cet algorithme est d’inverser simultanément les mesures co-localisées d’un LiDAR et d’un photomètre solaire. Cet algorithme original permet de déduire un ensemble très complet de paramètres descriptifs de l’aérosol atmosphérique, paramètres à la fois intégrés sur la colonne atmosphérique et résolus verticalement. GARRLiC est basée sur la recherche du meilleur ajustement de données multi-sources avec contraintes a priori. Il est basée sur la recherche de la meilleure solution selon un ensemble de critères statistiques. Les paramètres déduits sont de 2 types. Certains sont des quantités intégrées sur la colonne atmosphériques tandis que d’autres sont fonction de l’altitude comme la concentration en particules pour les deux modes dimensionnels fin et grossier. Une étude de sensibilité a montré que l’ensemble des paramètres peut être restitué avec une bonne précision dans toutes les situations considérées. L’étude indique une précision moins bonne pour le mode fin et qu’en général la précision est moindre à faible épaisseur optique. Elle a également montré que la précision sur l’indice de réfraction pouvait être accrue si l’on prenait en compte la mesure de polarisation issue du photomètre solaire. L’impact de la prise en compte de mesure LiDAR et de bruit sur les mesures a été étudié. GARRLiC a été appliqué à des mesures réelles obtenues à Minsk (Biélorussie) et Lille (France). L’approche employée ici peut être facilement modifiée pour retrouver les propriétés de l’aérosol à partir de multiples combinaisons d’instruments de télédétection passif et actif
This thesis presents the GARRLiC algorithm (Generalized Aerosol Retrieval from Ra- diometer and Lidar Combined data) that simultaneously inverts co-incident lidar and sun-photometer observations and derives a united set of aerosol parameters that describe both columnar and vertical aerosol properties. GARRLiC searches for the best fit of the multi-source measurements together with a priori constraints on aerosol characteristics through the continuous space of all possi- ble solutions under statistically formulated criteria. It retrieves height independent size distribution, complex refractive index and fraction of spherical particles together with vertically resolved aerosol concentration, all differentiated between fine and coarse aerosol modes. The potential and limitations of the method are demonstrated by sensitivity tests. The tests showed that the complete set of aerosol parameters for each aerosol component can be robustly derived with acceptable accuracy in all considered situations. Limited sen- sitivity to the properties of the fine mode and dependence of retrieval accuracy on the aerosol optical thickness for both modes were found. It was shown that sensitivity to fine mode refractive index could be improved by accounting for polarization data provided by passive instruments. The effects of the presence of lidar data and random noise on aerosol retrievals were studied. The algorithm was also applied to the real lidar and radiometer observations obtained over Minsk (Belarus) and Lille (France) AERONET sites. Suggested approach could be easily modified to retrieve aerosol properties from all possible combinations of existing passive and active remote sensing instruments

Many lakes, coastal zones and oceans are directly or indirectly influenced by human activities. Through the outlet of a vast amount of substances in the air and water, we are changing the natural conditions on local and global levels.

Remote sensing sensors, on satellites or airplanes, can collect image data, providing the user with information about the depicted area, object or phenomenon. Three different applications are discussed in this thesis. In the first part, we have used a bio-optical model to derive information about water quality parameters from remote sensing data collected over Swedish lakes. In the second part, remote sensing data have been used to locate and map wastewater plumes from pulp and paper industries along the east coast of Sweden. Finally, in the third part, we have investigated to what extent satellite data can be used to monitor coral reefs and detect coral bleaching.

Regardless of application, it is important to understand the limitations of this technique. The available sensors are different and limited in terms of their spatial, spectral, radiometric and temporal resolution. We are also limited with respect to the objects we are monitoring, as the concentration of some substances is too low or the objects are too small, to be identified from space. However, this technique gives us a possibility to monitor our environment, in this case the aquatic environment, with a superior spatial coverage. Other advantages with remote sensing are the possibility of getting updated information and that the data is collected and distributed in digital form and therefore can be processed using computers.

In an area of interest- Sivas Basin, Turkey- where most of the units are sedimentary and show similar spectral characteristics, spectral settings of ASTER sensor may not be enough by itself. Therefore, considering other aspects, such as morphological variables, is reasonable in addition to spectral classifiers. The main objective of this study is to test usefulness of integration of spectral analysis and morphological information for geological mapping. Remotely sensed imagery obtained from ASTER sensor is used to classify different lithological units while DEM is used to characterize landforms related to these lithological units. Maximum Likelihood Classification (MLC) is used to integrate data streaming from different sources. The methodology involves integrating the surface properties of the classified geological units in addition to the spectral reflectances. Seven different classification trials were conducted: : 1. MLC using only nine ASTER bands, 2. MLC using ASTER bands and DEM, 3. MLC using ASTER bands and slope, 4. MLC using ASTER bands and plan curvature, 5. MLC using ASTER bands and profile curvature, 6. MLC using ASTER bands and drainage density and finally 7. MLC using ASTER bands and all ancillary data. The results revealed that integrating topographical parameters aid in improvement of classification where spectral information is not sufficient to discriminate between classes of interest. An increase of more than 5% is observed in overall accuracy for the all ancillary data integration case. Moreover more than 10% improvement for most of the classes was identified. However from the results it is evident that the areal extent of the classified units causes constraints on application of the methodology.

Reconstruire des champs géophysiques à partir d'observations bruitées et partielles est un problème classique bien étudié dans la littérature. L'assimilation de données est une méthode populaire pour aborder ce problème, et se fait par l'utilisation de techniques classiques, comme le filtrage de Kalman d’ensemble ou des filtres particulaires qui procèdent à une évaluation online du modèle physique afin de fournir une prévision de l'état. La performance de l'assimilation de données dépend alors fortement de du modèle physique. En revanche, la quantité de données d'observation et de simulation a augmenté rapidement au cours des dernières années. Cette thèse traite l'assimilation de données d'une manière data-driven et ce, sans avoir accès aux équations explicites du modèle. Nous avons développé et évalué l'assimilation des données par analogues (AnDA), qui combine la méthode des analogues et des méthodes de filtrage stochastiques (filtres Kalman, filtres à particules, chaînes de Markov cachées). Des applications aux modèles chaotiques simplifiés et à des études de cas de télédétection réelle (température de surface de lamer, anomalies du niveau de la mer), nous démontrons la pertinence d'AnDA pour l'interpolation de données manquantes des systèmes dynamiques non linéaires et à haute dimension à partir d'observations irrégulières et bruyantes.Motivé par l'essor du machine learning récemment, la dernière partie de cette thèse est consacrée à l'élaboration de modèles deep learning pour la détection et de tourbillons océaniques à partir de données de sources multiples et/ou multi temporelles (ex: SST-SSH), l'objectif général étant de surpasser les approches dites expertes
Reconstructing geophysical fields from noisy and partial remote sensing observations is a classical problem well studied in the literature. Data assimilation is one class of popular methods to address this issue, and is done through the use of classical stochastic filtering techniques, such as ensemble Kalman or particle filters and smoothers. They proceed by an online evaluation of the physical modelin order to provide a forecast for the state. Therefore, the performanceof data assimilation heavily relies on the definition of the physical model. In contrast, the amount of observation and simulation data has grown very quickly in the last decades. This thesis focuses on performing data assimilation in a data-driven way and this without having access to explicit model equations. The main contribution of this thesis lies in developing and evaluating the Analog Data Assimilation(AnDA), which combines analog methods (nearest neighbors search) and stochastic filtering methods (Kalman filters, particle filters, Hidden Markov Models). Through applications to both simplified chaotic models and real ocean remote sensing case-studies (sea surface temperature, along-track sea level anomalies), we demonstrate the relevance of AnDA for missing data interpolation of nonlinear and high dimensional dynamical systems from irregularly-sampled and noisy observations. Driven by the rise of machine learning in the recent years, the last part of this thesis is dedicated to the development of deep learning models for the detection and tracking of ocean eddies from multi-source and/or multi-temporal data (e.g., SST-SSH), the general objective being to outperform expert-based approaches

The aim of this research was to investigate the use of high spectral resolution sensors to recover more detailed information on the biochemical composition of forest canopies. The research was divided into two broad areas: laboratory and field studies. The laboratory studies were undertaken to determine the relationships between reflected radiation and the biochemical concentrations of foliage. A number of sample sets of slash pine, Sitka spruce, lodgepole pine and Japanese larch were analysed for their chlorophyll, water, nitrogen, lignin, cellulose, sugar and starch concentrations. Reflectance measurements were made for whole green, wet needles and dried whole needles. Stepwise regression analyses showed strong relationships between reflectance spectra and chlorophyll concentration. Wavelengths in the region of the red edge often had the strongest correlation with chlorophyll concentration. For the remaining biochemicals the relationships between concentration and reflectance were weaker. Also, for these biochemicals absorption features with the larget correlation with reflectance values could not be readily related to the wavelengths of absorption features determined from other work using near infrared spectroscopy techniques on dry ground samples. Field studies at two sites used similar statistical techniques to relate reflectance spectra (measured for canopies using the AVIRIS) to the biochemical concentration and content of the foliage. Except for chlorophyll and nitrogen the results of both the concentration and content data showed a weak comparison with laboratory results. Difference spectra between control plot canopy samples and fertilized plot canopy samples revealed a large number of apparent absorption features which could be related to the known absorption features of foliar biochemicals.

Synthetic Aperture Radar (SAR) has been identified as a good candidate to provide high-resolution soil moisture information over extended areas. SAR data could be used as observations within a global Data Assimilation (DA) approach to benefit applications such as hydrology and agriculture. Prior to developing an operational DA system, one must tackle the following challenges of soil moisture estimation with SAR: (1) the dependency of the measured radar signal on both soil moisture and soil surface roughness which leads to an ill-conditioned inverse problem, and (2) the difficulty in characterizing spatially/temporally surface roughness of natural soils and its scattering contribution. The objectives of this project are (1) to develop a roughness measurement method to improve the spatial/temporal characterization of soil surface roughness, and (2) to investigate to what extent the inverse problem can be solved by combining multipolarization, multi-incidence, and/or multi-frequency radar measurements. The first objective is achieved with a measurement method based on Structure from Motion (SfM). It is tailored to monitor natural surface roughness changes which have often been assumed negligible although without evidence. The measurement method is flexible, a.ordable, straightforward and generates Digital Elevation Models (DEMs) for a SAR-pixel-size plot with mm accuracy. A new processing method based on band-filtering of the DEM and its 2D Power Spectral Density (PSD) is proposed to compute the classical roughness parameters. Time series of DEMs show that non-negligible changes in surface roughness can happen within two months at scales relevant for microwave scattering. The second objective is achieved using maximum likelihood fitting of the Oh backscattering model to (1) full-polarimetric Radarsat-2 data and (2) simulated multi-polarization / multi-incidence / multi-frequency radar data. Model fitting with the Radarsat-2 images leads to poor soil moisture retrieval which is related to inaccuracy of the Oh model. Model fitting with the simulated data quantifies the amount of multilooking for di.erent combinations of measurements needed to mitigate the critical e.ect of speckle on soil moisture uncertainty. Results also suggest that dual-polarization measurements at L- and C-bands are a promising combination to achieve the observation requirements of soil moisture. In conclusion, the SfM method along with the recommended processing techniques are good candidates to improve the characterization of surface roughness. A combination of multi-polarization and multi-frequency radar measurements appears to be a robust basis for a future Data Assimilation system for global soil moisture monitoring.

Shallow lakes are an important ecological and socio-economic resource. However, the impact of human pressures, both at the lake and catchment scale, has precipitated a decline in the ecological status of many shallow lakes, both in the UK, and throughout Europe. There is now, as direct consequence, unprecedented interest in the assessment and monitoring of ecological status and trajectory in shallow lakes, not least in response to the European Union Water Framework Directive (2000/60/EC). In this context, the spatially-resolving and panoramic data provided by remote sensing platforms may be of immense value in the construction of effective and efficient strategies for the assessment and monitoring of ecological status in shallow lakes and, moreover, in providing new, spatially-explicit, insights into the function of these ecosystems and how they respond to change. This thesis examined the use of remote sensing data for the assessment of (i) phytoplankton abundance and species composition and (ii) aquatic vegetation distribution and ecophysiological status in shallow lakes with a view to establishing the credence of such an approach and its value in limnological research and monitoring activities. High resolution in-situ and airborne remote sensing data was collected during a 2-year sampling campaign in the shallow lakes of the Norfolk Broads. It was demonstrated that semi-empirical algorithms could be formulated and used to provide accurate and robust estimations of the concentration of chlorophyll-a, even in these optically-complex waters. It was further shown that it was possible to differentiate and quantify the abundance of cyanobacteria using the biomarker pigment C-phycocyanin. The subsequent calibration of the imagery obtained from the airborne reconnaissance missions permitted the construction of diurnal and seasonal regional-scale time-series of phytoplankton dynamics in the Norfolk Broads. This approach was able to deliver unique spatial insights into the migratory behaviour of a potentially-toxic cyanobacterial bloom. It was further shown that remote sensing can be used to map the distribution of aquatic plants in shallow lakes, importantly including the extent of submerged vegetation, which is central to the assessment of ecological status. This research theme was subsequently extended in an exploration of the use of remote sensing for assessing the ecophysiological response of wetland plants to nutrient enrichment. It was shown that remote sensing metrics could be constructed for the quantification of plant vigour. The extrapolation of these techniques enabled spatial heterogeneity in the ecophysiological response of Phragmites australis to lake nutrient enrichment to be characterised and assisted the formulation of a mechanistic explanation for the variation in reedswamp performance in these shallow lakes. It is therefore argued that the spatially synoptic data provided by remote sensing has much to offer the assessment, monitoring and policing of ecological status in shallow lakes and, in particular, for facilitating the development of pan-European scale lake surveillance capabilities for the Water Framework Directive (2000/60/EC). It is also suggested that remote sensing can make a valuable contribution to furthering ecological understanding and, most significantly, in enabling ecosystem processes and functions to be examined at the lake-scale.

Remote sensing of the Earth's atmosphere, typically performed in the infrared region of the spectrum, plays an important role in scientific research. In the past the instruments used to perform these observations have been large, massive devices and correspondingly have only been able to be placed on large satellites. There is currently a trend toward smaller Earth observing platforms, so-called micro-satellites, and there is therefore a need for smaller, less massive instruments. Typically these instruments utilise a semiconductor device that responds to incoming infrared radiation in a known way. Such devices are subject to a number of noise sources that reduce their performance. By cooling them to temperatures around 80K it is possible to significantly reduce the amplitude of this noise compared to the incoming radiation of interest, thus increasing the signal-to-noise ratio (SNR). Typically this cooling is performed by a mechanical cooler, but currently many of them are too massive and require too much power to be suitable for use on a small remote sensing satellite. By considering a typical application, a performance target for a miniature cooler was determined to be a heat lift of 200mW at a cold tip temperature of 80K. Hardware has been created to investigate the feasibility of achieving this aim with a hybrid cooler/radiator. The cooler is a miniature integral Stirling machine and uses flat spiral flexures with a newly designed linear motor to drive the compressor piston; the displacer is driven pneumatically. The prototype initially underwent characterisation without first being pre-cooled by the radiator. Although significant cooling was observed (to below 170K), initial characterisation highlighted a low thermal resistance between the warm end of the cooler and the cold tip. With pre-cooling the cold tip was able to reach a minimum no-load cold tip temperature of 92K, and with 200mW applied to the cold tip a temperature of 122.4 K was sustainable. Attempts were made to increase the thermal resistance between the warm end and cold tip by introducing thermal breaks into the regenerator, and whilst these did increase the thermal resistance, the overall performance of the cooler decreased. The concept of a hybrid miniature cooler/radiator has been shown to be feasible. To achieve the target performance of a heat lift of 200mW at 80K further work needs to be performed to characterise loss processes within the cooler and increase the thermal resistance between the warm end and cold tip.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Classificação de imagens é o processo de extração de informação em imagens digitais para reconhecimento de padrões e objetos homogêneos, que em sensoriamento remoto propõe-se a encontrar padrões entre os pixels pertencentes a uma imagem digital e áreas da superfície terrestre, para uma análise posterior por um especialista. Nesta dissertação, utilizamos a metodologia de aprendizado de máquina support vector machines para o problema de classificação de imagens, devido a possibilidade de trabalhar com grande quantidades de características. Construímos classificadores para o problema, utilizando imagens distintas que contém as informações de espaços de cores RGB e HSB, dos valores altimétricos e do canal infravermelho de uma região. Os valores de relevo ou altimétricos contribuíram de forma excelente nos resultados, uma vez que esses valores são características fundamentais de uma região e os mesmos não tinham sido analisados em classificação de imagens de sensoriamento remoto. Destacamos o resultado final, do problema de classificação de imagens, para o problema de identificação de piscinas com vizinhança dois. Os resultados obtidos são 99 por cento de acurácia, 100 por cento de precisão, 93,75 por cento de recall, 96,77 por cento de F-Score e 96,18 por cento de índice Kappa.
Image Classification is an information extraction process in digital images for pattern and homogeneous objects recognition. In remote sensing it aims to find patterns from digital images pixels, covering an area of earth surface, for subsequent analysis by a specialist. In this dissertation, to this images classification problem we employ Support Vector Machines, a machine learning methodology, due the possibility of working with large quantities of features. We built classifiers to the problem using different image information, such as RGB and HSB color spaces, altimetric values and infrared channel of a region. The altimetric values contributed to excellent results, since these values are fundamental characteristics of a region and they were not previously considered in remote sensing images classification. We highlight the final result, for the identifying swimming pools problem, when neighborhood is two. The results have 99 percent accuracy, 100 percent precision, 93.75 percent of recall, 96.77 percent F-Score and 96.18 percent of Kappa index.

The remote real time identification of road surfaces is an increasingly important task in the automotive world. The development of automotive active safety system requires a remote sensing technology that alerts drivers to potential hazards such as slippery surfaces caused by water, mud, ice, snow etc. This will improve the safety of driving and reduce the road accidents all over the world. This thesis is dedicated to the experimental study of the feasibility of an affordable short-range ultrasonic and radar system for road surface recognition ahead of a vehicle. It introduces a developed novel system which can recognize the surfaces for all terrains (both on-road and off-road) based on the analysis of backscattered signals. Fundamental theoretical analysis, extensive modelling and practical experiments demonstrated that the use of pattern recognition techniques allows for reliable discrimination of the surfaces of interest. The overall classification system is described, including features extraction and their number reduction, as well as optimization of the algorithms. The performance of 4 classification algorithms was assessed and evaluated to confirm the effectiveness of the system. Several aspects like the complexity of the classification algorithms and the priori knowledge of the environment were investigated to explore the potential of this research and the possibility of introducing the surface classification system into the automotive market in the nearest future.

Thesis (PhD)--Stellenbosch University, 2013.
ENGLISH ABSTRACT: Concrete evidence of dryland salinity was observed in the Berg River catchment in the Western Cape Province of South Africa. Soil salinization is a global land degradation hazard that negatively affects the productivity of soils. Timely and accurate detection of soil salinity is crucial for soil salinity monitoring and mitigation. It would be restrictive in terms of costs to use traditional wet chemistry methods to detect and monitor soil salinity in the entire Berg River catchment. The goal of this study was to investigate less tedious, accurate and cost effective techniques for better monitoring. Firstly, hyperspectral remote sensing (HRS) techniques that can best predict electrical conductivity (EC) in the soil using individual bands, a unique normalized difference soil salinity index (NDSI), partial least squares regression (PLSR) and bagging PLSR were investigated. Spectral reflectance of dry soil samples was measured using an analytical spectral device FieldSpec spectrometer in a darkroom. Soil salinity predictive models were computed using a training dataset (n = 63). An independent validation dataset (n = 32) was used to validate the models. Also, field-based regression predictive models for EC, pH, soluble Ca, Mg, Na, Cl and SO4 were developed using soil samples (n = 23) collected in the Sandspruit catchment. These soil samples were not ground or sieved and the spectra were measured using the sun as a source of energy to emulate field conditions. Secondly, the value of NIR spectroscopy for the prediction of EC, pH, soluble Ca, Mg, Na, Cl, and SO4 was evaluated using 49 soil samples. Spectral reflectance of dry soil samples was measured using the Bruker multipurpose analyser spectrometer. “Leave one out” cross validation (LOOCV) was used to calibrate PLSR predictive models for EC, pH, soluble Ca, Mg, Na, Cl, and SO4. The models were validated using R2, root mean square error of cross validation (RMSECV), ratio of prediction to deviation (RPD) and the ratio of prediction to interquartile distance (RPIQ). Thirdly, owing to the suitability of land components to map soil properties, the value of digital elevation models (DEMs) to delineate accurate land components was investigated. Land components extracted from the second version of the 30-m advanced spaceborne thermal emission and reflection radiometer global DEM (ASTER GDEM2), the 90-m shuttle radar topography mission DEM (SRTM DEM), two versions of the 5-m Stellenbosch University DEMs (SUDEM L1 and L2) and a 5-m DEM (GEOEYE DEM) derived from GeoEye stereo-images were compared. Land components were delineated using the slope gradient and aspect derivatives of each DEM. The land components were visually inspected and quantitatively analysed using the slope gradient standard deviation measure and the mean slope gradient local variance ratio for accuracy. Fourthly, the spatial accuracy of hydrological parameters (streamlines and catchment boundaries) delineated from the 5-m resolution SUDEM (L1 and L2), the 30-m ASTER GDEM2 and the 90-m SRTM was evaluated. Reference catchment boundary and streamlines were generated from the 1.5-m GEOEYE DEM. Catchment boundaries and streamlines were extracted from the DEMs using the Arc Hydro module for ArcGIS. Visual inspection, correctness index, a new Euclidean distance index and figure of merit index were used to validate the results. Finally, the value of terrain attributes to model soil salinity based on the EC of the soil and groundwater was investigated. Soil salinity regression predictive models were developed using CurveExpert software. In addition, stepwise multiple linear regression soil salinity predictive models based on annual evapotranspiration, the aridity index and terrain attributes were developed using Statgraphics software. The models were validated using R2, standard error and correlation coefficients. The models were also independently validated using groundwater hydro-census data covering the Sandspruit catchment. This study found that good predictions of soil salinity based on bagging PLSR using first derivative reflectance (R2 = 0.85), PLSR using untransformed reflectance (R2 = 0.70), a unique NDSI (R2 = 0.65) and the untransformed individual band at 2257 nm (R2 = 0.60) predictive models were achieved. Furthermore, it was established that reliable predictions of EC, pH, soluble Ca, Mg, Na, Cl and SO4 in the field are possible using first derivative reflectance. The R2 for EC, pH, soluble Ca, Mg, Na, Cl and SO4 predictive models are 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 and 0.58 respectively. Regarding NIR spectroscopy, validation R2 for all the PLSR predictive models ranged from 0.62 to 0.87. RPD values were greater than 1.5 for all the models and RMSECV ranged from 0.22 to 0.51. This study affirmed that NIR spectroscopy has the potential to be used as a quick, reliable and less expensive method for evaluating salt-affected soils. As regards hydrological parameters, the study concluded that valuable hydrological parameters can be derived from DEMs. A new Euclidean distance ratio was proved to be a reliable tool to compare raster data sets. Regarding land components, it was concluded that higher resolution DEMs are required for delineating meaningful land components. It seems probable that land components may improve salinity modelling using hydrological modelling and that they can be integrated with other data sets to map soil salinity more accurately at catchment level. In the case of terrain attributes, the study established that promising soil salinity predictions could be made based on slope, elevation, evapotranspiration and terrain wetness index (TWI). Stepwise multiple linear regressions soil salinity predictive model based on elevation, evapotranspiration and TWI yielded slightly more accurate prediction of soil salinity. Overall, the study showed that it is possible to enhance soil salinity monitoring using HRS, NIR spectroscopy, land components, hydrological parameters and terrain attributes.
AFRIKAANSE OPSOMMING: Konkrete bewyse van droëland sout is waargeneem in die Bergrivier opvanggebied in die Wes- Kaap van Suid-Afrika. Verbrakking van grond is 'n wêreldwye probleem wat ‘n negatiewe invloed op die produktiwiteit van grond kan hê. Tydige en akkurate herkenning van verandering in grond soutgehalte is ‘n noodsaaklike aksie vir voorkoming. Dit sou beperkend wees in terme van koste om konvensionele nat chemiese metodes te gebruik vir die opsporing en monitering daarvan in die hele Bergrivier opvanggebied. Die doel van hierdie studie was om ondersoek in te stel na minder tydsame, akkurate en koste-effektiewe tegnieke vir beter monitering. Eerstens, is hiperspektrale afstandswaarnemings (HRS) tegnieke wat die beste in staat is elektriese geleidingsvermoë (EG) in die grond te kan voorspel deur gebruik te maak van individuele bande, 'n unieke genormaliseerde grond soutindeks verskil (NDSI), parsiële kleinste kwadratiese regressie (PLSR) en afwyking in PLSR, is ondersoek. Spektrale reflektansie van droë grondmonsters is gemeet deur gebruik te maak van 'n spektrale analitiese toestel: FieldSpec spektrometer in 'n donkerkamer. Voorspellings modelle vir grond soutgehalte is bereken met behulp van 'n toets datastel (n = 63). 'n onafhanklike validasie datastel (n = 32) is gebruik om die modelle te evalueer. Daarbenewens is veld-gebaseerde regressie voorspellings modelle vir EG, pH oplosbare Ca, Mg, Na, Cl and SO4 ontwikkel deur gebruik te maak van grondmonsters (n = 23) versamel in the Sandpruit opvangsgebied. Hierdie grondmonsters is nie gemaal of gesif nie en die spectra is gemeet deur gebruik te maak van die son as ‘n bron van energie om veld toestande na te boots. Tweedens, is die waarde van NIR spektroskopie vir die voorspelling van die EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 met behulp van 49 grondmonsters geëvalueer. Spektrale reflektansie van droë grondmonsters is gemeet deur gebruik te maak van die Bruker NIR veeldoelige analiseerder . Kruisvalidering (LOOCV) is gebruik om PLSR voorspellings modelle vir EG, pH, oplosbare Ca, Mg, Na, Cl, en SO4 te kalibreer. Hierdie modelle is gevalideer: R2, wortel-gemiddelde-kwadraat fout kruisvalidering (RMSECV), verhouding van voorspellings afwyking (RPD) en die verhouding van die voorspelling se inter-kwartiel afstand (RPIQ). Derdens is land komponente gekarteer vanweë die nut daat van tov grondeienskappe, en die waarde van DEMs is ondersoek om akkurate land komponente af te baken. Land komponente uit die tweede weergawe van die 30 m gevorderde ruimte termiese emissie en refleksie radio globale DEM (ASTER GDEM2), die 90-m ruimtetuig radar topografie sending DEM (SRTM DEM), twee weergawes van die 5 m Universiteit van Stellenbosch DEMs (SUDEM L1 en L2) en 'n 5 m DEM (GEOEYE DEM) afgelei van GeoEye stereo-beelde, is vergelyk. Land komponente is afgebaken met behulp van helling, gradiënt en aspek afgeleides van elke DEM. Die land komponente is visueel geïnspekteer en kwantitatief ontleed met behulp van die helling gradiënt standaardafwyking te meet en die gemiddelde helling-gradiënt-plaaslike variansie verhouding vir akkuraatheid. Vierdens, is die ruimtelike akkuraatheid van hidrologiese parameters (stroomlyn en opvanggebied grense) geëvalueer soos afgelei vanaf die 5 m resolusie SUDEM (L1 en L2), die 30 m ASTER GDEM2 en die 90 m SRTM . Die verwysings opvanggebied grens en stroomlyn is gegenereer vanaf die 1,5-m GEOEYE DEM. Opvanggebied grense en stroomlyn uit die DEMs is bepaal deur gebruik te maak van die Arc Hydro module in ArcGIS. Visuele inspeksie, korrektheid indeks, 'n nuwe Euklidiese afstand indeks en die indikasie-van-meriete indeks is gebruik om die resultate te valideer. Laastens is die waarde van die terrein eienskappe om grond southalte te modeleer ondersoek, gebaseer op die EG van die grond en grondwater. Grond soutgehalte regressie voorspellings modelle is ontwikkel met behulp van CurveExpert sagteware. Verder, stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings modelle gebaseer op jaarlikse evapotranspirasie, die dorheids indeks en terrein eienskappe is ontwikkel met behulp van Statgraphics sagteware. Die modelle is gevalideer deur gebruik te maak van R2, standaardfout en korrelasiekoëffisiënte. Die modelle is ook onafhanklik bekragtig deur die gebruik van grondwater hidro-sensus-data wat die Sandspruit opvanggebied insluit. Hierdie studie het bevind dat 'n goeie voorspelling van grond soutgehalte gebaseer op uitsak PLSR met behulp van eerste orde afgeleide reflektansie (R2 = 0,85), PLSR deur gebruik te maak van ongetransformeerde reflektansie (R2 = 0,70), 'n unieke NDSI (R2 = 0,65) en die ongetransformeerde individuele band op 2257 nm (R2 = 0,60) voorspellings modelle verkry is. Verder is vasgestel dat betroubare voorspellings van die EG, pH, oplosbare Ca, Mg, Na, Cl en SO4 in die veld moontlik is met behulp van eerste afgeleide reflektansie. Die R2 van EG, pH, oplosbare Ca, Mg, Na, Cl en SO4 is 0.85, 0.50, 0.65, 0.84, 0.79, 0.81 en 0.58 onderskeidelik. Ten opsigte van NIR spektroskopie het die validasie van R2 vir al die PLSR voorspellings modelle gewissel tussen 0,62-0,87. Die RPD waardes was groter as 1,5 vir al die modelle en RMSECV het gewissel tussen 0,22-0,51. Hierdie studie het bevestig dat NIR spektroskopie die potensiaal het om gebruik te word as 'n vinnige, betroubare en goedkoper metode vir die analise van soutgeaffekteerde gronde. T.o.v. hidrologiese parameters, het die studie tot die gevolgtrekking gekom dat waardevolle hidrologiese parameters afgelei kan word uit DEMs. 'n nuwe Euklidiese afstand verhouding is bevestig as 'n betroubare hulpmiddel om raster datastelle te vergelyk. Ten opsigte van grond komponente, is daar tot die gevolgtrekking gekom dat hoër resolusie DEMs nodig is vir die bepaling van sinvolle land komponente. Dit lyk waarskynlik dat die land komponent soutgehalte modellering hidrologiese modellering verbeter en dat hulle geïntegreer kan word met ander datastelle vir meer akkurate kaarte op opvangsgebied skaal. In die geval van die terrein eienskappe het, die studie vasgestel dat belowende grond soutgehalte voorspellings gemaak kan word gebaseer op helling, elevasie, evapotranspirasie en terrein natheid indeks (TWI). 'n stapsgewyse meervoudige lineêre regressie grond soutgehalte voorspellings model wat gebaseer is op elevasie, evapotranspirasie en TWI het effens meer akkurate voorspellings van die grond soutgehalte gelewer. In geheel gesien, het die studie getoon dat dit moontlik is om grond soutgehalte monitering te verbeter met behulp van HRS, NIR spektroskopie, land komponente, hidrologiese parameters en terrein eienskappe.
The Agricultural Research Council (ARC), Water Research Commission and the National Research Foundation for funding.

The European Union (EU) has openly solicited advice on the development of EU bathing water quality policy and made calls for the development of remotely sensed operational real world solutions. This research demonstrates a new approach to estimating water quality using remote sensing and specifically to monitoring bathing water quality by using remote sensing to "flag" failing areas for manual survey. This method meets the environmental demands of the EU, the tourist industry, the water industry and environmental monitoring agencies throughout the world. The results show the genuine potential for a remotely sensed monitoring system that could, with further research, lead to an efficient and effective method of monitoring bathing water quality. These findings are particularly important given the imminent changes in EU Bathing Water policy, an expected increase in monitoring costs (currently estimated by the EU to be 15 million euros for 2001 (EU, 2002)) and the widespread availability of airborne sensors and satellites. Simultaneous water quality and spectral data were collected at Southend-on-Sea pier with a Natural Environment Research Council (NERC) loaned spectroradiometer and water sampling equipment. Simultaneous data enabled the accurate analysis of the relationship between water quality and reflectance, avoiding the normal delays experienced with flown or satellite data. The thesis successfully proposes and investigates a remotely sensed flagging system for bathing water quality monitoring using both statistical and visual analysis to identify optimum wavelengths which identify threshold levels of E.coli, suspended sediments, low pH, nitrates, chlorophyll, faecal coliform and temperature. The findings demonstrate that remote sensing could be used to monitor several of the water quality parameters that are relevant to the EU Bathing Water Directives and, in particular, the monitoring of effluent in bathing waters through the successful identification of high E.coli counts. Through the creation and integration of a localised water quality model, it demonstrates that it is possible to predict when water quality parameters exceed a threshold level through direct remote sensing or through the use of remotely sensed indirect water quality parameters. The success rate of remotely sensed "flagging" of samples above a threshold level was tested and used to yield a "predictor" rating for each parameter. Finally, a spectral physical model was constructed that identifies the parameters, wavelengths and secondary parameters that could be used to flag failing water quality areas. This model could be used to improve monitoring coverage and reduce overall costs. The application of the model, which was based on Case 2 coastal water, to other types of coastal area is suggested as needing further research before it could be widely exploited. Remote sensing information could lead to a greater understanding of the coastal environment and offers potential near real time monitoring, allowing for the first time reactive management of coastal water quality in failing water quality areas. This would provide a solution to many of the issues raised by the EU regarding the current bathing water quality directives and provides the remote sensing community with a practical solution to a real world problem.

This thesis presents and examines evidence of Jupiter’s magnetospheric dynamics, pri- marily using Hubble Space Telescope images of the planet’s ultraviolet aurora. The first two studies discuss the radial transport of hot plasma injections from reconnection return flow. The auroral evidence for a global magnetospheric disturbance is examined and it is found that the aurora showed a significantly super-rotating polar spot poleward of the main emission on the dawnside. The spot transitioned from the polar to main emission region in the presence of a locally broad, bright dawnside main emission feature and two large equatorward emission features. This is taken to be evidence of a prolonged period of reconnection and plasma injection, taking place over several hours. The second chapter examines the effect of hot plasma injection in the middle to inner magnetosphere. The lo- cation of the second oval feature lies between the Ganymede and Europa moon footprint contours between 150 and 240 degrees system III longitude, corresponding to a source in the inner magnetosphere between 9 and 13 RJ . The feature is enhanced in both brightness and longitudinal spread 1-3 days after large hot plasma injections. It was suggested that wave-particle interactions are responsible for the scattering of electrons in this region. A third study investigated the auroral response to co-rotating interaction regions and solar wind compression. It was shown that in there are both high latitude and mid latitude features that arise - large scale flashes appear at high latitudes, sometimes along side significant branching of the main emission region. It is suggested that the arcs in the main emission are a result of strong compression either due to enhanced flow structures at the dusk side or a stepwise co-rotation breakdown in response to increase of azimuthal plasma velocity.

This thesis is concerned with the problem of mixed pixel classification in Remote sensing applications and attempts to find accurate and robust solutions to this problem. The application we are interested in, is to monitor burned forest regions for a few years after the fire in order to identify the type of vegetation present in these areas and consequently assess the danger of desertification. The areas of interest are semi-arid where the vegetation tends to vary at smaller scales than the area covered by a single Landsat TM pixel, thus mixed pixels are quite common. In this thesis we considered whole sets of mixed pixels. First, an overview of the methods currently used to solve the mixed pixel classification problem is presented, focused on the linear mixing model which is adopted in this thesis. Then a method that incorporates higher order moments of the distributions of the pure and the mixed classes is proposed. This method is shown to augment the number of equations used for the classification and theoretically it allows the specification of more cover classes than there are bands available, without compromising the accuracy of the results. The problem of deterioration of the classification performance, due to inaccuracies in calculation of the statistics when outliers are present, is also examined. The use of the Hough Transform is proposed for the linear unmixing in order to provide robust estimates even in cases where outliers are present. The Hough transform method though, is an exhaustive method and therefore has higher computational complexity. Furthermore, its performance, in the absence of outliers, is not as good as the solution obtained by the Least Squares Error method. Hence, the Randomized Hough Transform is proposed in order to improve the computational speed and maintain the same level of performance, while the Hypothesis Testing Hough Transform is proposed to improve the accuracy of the classification results. All the methods proposed in this thesis have been compared with the Least Squares Error method using simulated and real Landsat TM image data, in order to illustrate the validity and usefulness of the proposed algorithms.

This study concerns with classification techniques in high dimensional space such as that of Hyperspectral Imaging (HSI) data sets, with objectives of understanding the strength and weakness of various classifiers and at the same time to study how their performances can be assessed particularly when there is an absence of ground truth target map in the data set. The thesis summaries the work that carried out during the course of this study and it encompasses a brief survey of machine learning and classification theories, an outline of the HSI instrumentations, data sets that collected in the study and classification analysis. It is found that the supervised classifiers such as the Maximum Likelihood (QD) and the Mahalanobis Distance (FD) classifiers, especially when they are coupled with techniques like Regularised Discriminant Analysis (RDA) or leave-one-out covariance estimations (LOOC), have demonstrated excellent performances comparable to that of the more complicated and computational costly classifiers like the Support Vector Machine (SVM). This work has also revealed that separability measures such as the Total Transformed Divergence (TTD) and Total Jeffries-Matusita Distance (TJM) can be an invaluable method for assessing the goodness of classification in principle. However, the present methods for the evaluation of the separability measures are insufficient for achieving this goal and further work in this area is needed. This study has also confirmed the effectiveness for using RDA and LOOC techniques for a better estimation of the covariance when the sample size is small, ie when the sample size per class to band ratio is less than 100. Through team work this study has contributed partially a number of publications in the area of hyperspectral imaging and machine visions.

Thesis (Ph.D.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (November 15, 2006) Vita. Includes bibliographical references.

An experiment was conducted to determine whether the water depth (above soil) and soil type would have any influence on the multispectral reflectances of paddy rice, and their calculated vegetation index values. The results showed that, when vegetation cover was low (below 600 grams of dry biomass per square meter), the near infrared (NIR) reflectances decreased very little with water depth. The same was true for red reflectances, but to a lesser degree. Overall the changes were not significant at 0.05 level of significance when the water depth was increased from 2.5 centimeters to 10 centimeters. When the vegetation cover became higher most NIR and red reflectances did not show a significant decrease with the increase of the water depth, and sometimes they even increased slightly up to a water depth of 6.4 cm. Nevertheless both rice cover and water depth as well as soils played an important role in the reflectance pattern in red and NIR bands. Some index values increased and some decreased depending on water depth and rice cover. Statistical analysis of the data showed that rice multispectral responses were mainly controlled by vegetation and minimally influenced by soil and water depths.

Orientador: Jairo Roberto Jiménez-Rueda
Banca: Marcos Estevan Del Prette
Banca: Eraldo Aparecido Trondoli Matricardi
Banca: Daniel Marcos Bonotto
Banca: Archimedes Perez Filho
Resumo: A presente pesquisa objetivou elaboração de uma proposta de ordenamento da ocupação territorial da Sub-bacia Hidrográfica do Baixo Rio Candeias, localizada no Estado de Rondônia, utilizando ferramentas de Sensoriamento Remoto e Sistemas de Informações Geográficas na avaliação do processo de mudanças de uso da terra para subsidiar o planejamento de bacias hidrográficas. Nesta pesquisa, utilizou-se imagens de satélite digitais e analógicas e Sistema Processamento de Informações Georreferenciadas - SPRING, disponibilizados pelo Instituto Nacional de pesquisas Espaciais - INPE, no qual foram armazenadas, processadas e analisadas todas as informações inerentes a pesquisa. Inicialmente foi realizado um Diagnóstico Zero da sub-bacia, que serviu de base de dados para estabelecer e identificar as deficiências técnicas que necessitam ser complementadas em função das necessidades das comunidades abrangidas. Através da análise da rede de drenagem foi possível analisar a morfoestrutura e morfotectonica da área, identificando as falhas e fraturas, bem como, anomalias do tipo alto/baixo estrutural. Foi realizada uma caracterização das unidades fisiográficas, definidas a partir da interpretação das imagens orbitais, com identificação das formas, reconhecimento e deduções dos fenômenos na elaboração da paisagem atual e subatual. A estas informações foram agregadas, informações de pedologia de fundamental importância para entender a dinâmica e evolução da paisagem e consequentemente, na elaboração do mapa de subzonas. Os limites das Subzonas coincidiram com os limites das unidades de solos incrementadas a unidades geológicas, e como resultado definiu-se dezenove subzonas, que agruparam todas as informações (morfoestrutura e morfotectonica, fisiografia, solos, vegetação e litologia) produzidas e pesquisadas... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: This research objective was to prepare a proposal of suitable land uses for the Lower Candeias River Watershed, geographically located within the State of Rondônia, Brazil, using Remote Sensing and Geographic Information Systems approaches to assess land use and land cover change processos and to provide information to support preparation of a sustainable watershed occupation plan. Satellite imagery and a Geographic Information System (SPRING) developed by the National Space Research Institute (INPE) were used to store, process, and analyze digital datasets. Initially, a "Zero Diagnostic" of the Lower Candeias River Watershed was prepared. This diagnostic was used as supporting information to identify technical weakness in the methodological approaches, which required complementary efforts given the local community and environmental characteristics. In addition, based on the river network analysis, it was possible to define the morphostructure and morphotectonic of the study area, which made possible to identify geologic faults and fractures, and low/high structural anomalies. Physiographic units were identified by analyzing satellite imagery, which included form identification, recognition and deduction of the phenomenon that were shaping current and previous landscape. The critical pedologic information were aggregated to support analysis of the dynamic and evolution of the landscape and, subsequently, to support preparation of the subzoning map of the Lower Candeias River Watershed. The subzones limits overlapped the soil unit limits and, by merging them with the geologic units, it resulted in 19 new subzones. Therefore, these new 19 subzones incorporated all information (morfoestrutura and morfotectonica, fisiografia, ground, vegetation and litologia) derived from this dissertation research. Therefore, the land use map... (Complete abstract click electronic access below)
Doutor

In order to assess the influence of litter cover on soil background spectral response, trays of dry Lehmann Lovegrass (Eragrostis lehmanniana) were used at three different densities (635, 1015, 2815 Kg/ha) over three different soil backgrounds (Whitehouse sandy clay loam, Superstition sand, and Cloversprings loam). After analysis, spectral measurements made with a Barnes Multi-Modular Radiometer revealed that, soil-litter mixtures exhibit an oil like spectral behavior in the (0.45-2.30 m) waveband range. Mulched soils could not be discriminated from bare soils solely on the basis of the spectral response. However, mulched and bare soil spectral responses differed in amplitude depending on the difference in brightness between the bare soil and the litter cover. In addition, the results showed that while an increase of litter cover density on the soil surface decreased RVI, NDVI and PVI predicted greenness, it increased the GVI based greenness for all soils except the Superstition sand where the GVI showed a reversed trend. The PVI increased at low and intermediate litter densities and decreased at higher ones for the Superstition sand.

Soil moisture and vegetation biomass are two essential parameters from a scienti c and economical point of view. On one hand, they are key for the understanding of the hydrological and carbon cycle. On the other hand, soil moisture is essential for agricultural applications and water management, and vegetation biomass is crucial for regional development programs. Several remote sensing techniques have been used to measure these two parameters. However, retrieving soil moisture and vegetation biomass with the required accuracy, and the appropriate spatial and temporal resolutions still remains a major challenge. The use of Global Navigation Satellite Systems (GNSS) reflected signals as sources of opportunity for measuring soil moisture and vegetation biomass is assessed in this PhD Thesis. This technique, commonly known as GNSS-Reflectometry (GNSS-R), has gained increasing interest among the scienti c community during the last two decades due to its unique characteristics. Previous experimental works have already shown the capabilities of GNSS-R to sense small reflectivity changes on the surface. The use of the co- and cross-polarized reflected signals was also proposed to mitigate nuisance parameters, such as soil surface roughness, in the determination of soil moisture. However, experimental evidence of the suitability of that technique could not be demonstrated. This work analyses from a theoretical and an experimental point of view the capabilities of polarimetric observations of GNSS reflected signals for monitoring soil moisture and vegetation biomass. The Thesis is structured in four main parts. The fi rst part examines the fundamental aspects of the technique and provides a detailed review of the GNSS-R state of the art for soil moisture and vegetation monitoring. The second part deals with the scattering models from land surfaces. A comprehensive description of the formation of scattered signals from rough surfaces is provided. Simulations with current state of the art models for bare and vegetated soils were performed in order to analyze the scattering components of GNSS reflected signals. A simpli ed scattering model was also developed in order to relate in a straightforward way experimental measurements to soil bio-geophysical parameters. The third part reviews the experimental work performed within this research. The development of a GNSS-R instrument for land applications is described, together with the three experimental campaigns carried out in the frame of this PhD Thesis. The analysis of the GNSS-R and ground truth data is also discussed within this part. As predicted by models, it was observed that GNSS scattered signals from natural surfaces are a combination of a coherent and an incoherent scattering components. A data analysis technique was proposed to separate both scattering contributions. The use of polarimetric observations for the determination of soil moisture was demonstrated to be useful under most soil conditions. It was also observed that forests with high levels of biomass could be observed with GNSS reflected signals. The fourth and last part of the Thesis provides an analysis of the technology perspectives. A GNSS-R End-to-End simulator was used to determine the capabilities of the technique to observe di erent soil reflectivity conditions from a low Earth orbiting satellite. It was determined that high accuracy in the estimation of reflectivity could be achieved within reasonable on-ground resolution, as the coherent scattering component is expected to be the predominant one in a spaceborne scenario. The results obtained in this PhD Thesis show the promising potential of GNSS-R measurements for land remote sensing applications, which could represent an excellent complementary observation for a wide range of Earth Observation missions such as SMOS, SMAP, and the recently approved ESA Earth Explorer Mission Biomass.
La humedad del suelo y la biomasa de la vegetaci on son dos parametros clave desde un punto de vista tanto cient co como econ omico. Por una parte son esenciales para el estudio del ciclo del agua y del carbono. Por otra parte, la humedad del suelo es esencial para la gesti on de las cosechas y los recursos h dricos, mientras que la biomasa es un par ametro fundamental para ciertos programas de desarrollo. Varias formas de teledetección se han utilizado para la observaci on remota de estos par ametros, sin embargo, su monitorizaci on con la precisi on y resoluci on necesarias es todav a un importante reto tecnol ogico. Esta Tesis evalua la capacidad de medir humedad del suelo y biomasa de la vegetaci on con señales de Sistemas Satelitales de Posicionamiento Global (GNSS, en sus siglas en ingl es) reflejadas sobre la Tierra. La t ecnica se conoce como Reflectometr í a GNSS (GNSS-R), la cual ha ganado un creciente inter es dentro de la comunidad científ ca durante las dos ultimas d ecadas. Experimentos previos a este trabajo ya demostraron la capacidad de observar cambios en la reflectividad del terreno con GNSS-R. El uso de la componente copolar y contrapolar de la señal reflejada fue propuesto para independizar la medida de humedad del suelo de otros par ametros como la rugosidad del terreno. Sin embargo, no se pudo demostrar una evidencia experimental de la viabilidad de la t ecnica. En este trabajo se analiza desde un punto de vista te orico y experimental el uso de la informaci on polarim etrica de la señales GNSS reflejadas sobre el suelo para la determinaci on de humedad y biomasa de la vegetaci on. La Tesis se estructura en cuatro partes principales. En la primera parte se eval uan los aspectos fundamentales de la t ecnica y se da una revisi on detallada del estado del arte para la observaci on de humedad y vegetaci on. En la segunda parte se discuten los modelos de dispersi on electromagn etica sobre el suelo. Simulaciones con estos modelos fueron realizadas para analizar las componentes coherente e incoherente de la dispersi on de la señal reflejada sobre distintos tipos de terreno. Durante este trabajo se desarroll o un modelo de reflexi on simpli cado para poder relacionar de forma directa las observaciones con los par ametros geof sicos del suelo. La tercera parte describe las campañas experimentales realizadas durante este trabajo y discute el an alisis y la comparaci on de los datos GNSS-R con las mediciones in-situ. Como se predice por los modelos, se comprob o experimentalmente que la señal reflejada est a formada por una componente coherente y otra incoherente. Una t ecnica de an alisis de datos se propuso para la separacióon de estas dos contribuciones. Con los datos de las campañas experimentales se demonstr o el bene cio del uso de la informaci on polarim etrica en las señales GNSS reflejadas para la medici on de humedad del suelo, para la mayor a de las condiciones de rugosidad observadas. Tambi en se demostr o la capacidad de este tipo de observaciones para medir zonas boscosas densamente pobladas. La cuarta parte de la tesis analiza la capacidad de la t ecnica para observar cambios en la reflectividad del suelo desde un sat elite en orbita baja. Los resultados obtenidos muestran que la reflectividad del terreno podr a medirse con gran precisi on ya que la componente coherente del scattering ser a la predominante en ese tipo de escenarios. En este trabajo de doctorado se muestran la potencialidades de la t ecnica GNSS-R para observar remotamente par ametros del suelo tan importantes como la humedad del suelo y la biomasa de la vegetaci on. Este tipo de medidas pueden complementar un amplio rango de misiones de observaci on de la Tierra como SMOS, SMAP, y Biomass, esta ultima recientemente aprobada para la siguiente misi on Earth Explorer de la ESA.

Hoy en día, los datos de teledetección son esenciales para muchas aplicaciones dirigidas a la observación de la tierra. El potencial de los datos de teledetección en ofrecer información valiosa permite entender mejor las características de la tierra y las actividades humanas. Los desarrollos recientes en los sensores de satélites permiten cubrir amplias áreas geográficas, produciendo imágenes con resoluciones espaciales, espectrales y temporales sin precedentes. Esta cantidad de datos producidos implica una necesidad requiere técnicas de compresión eficientes para mejorar la transmisión y la capacidad de almacenamiento. La mayoría de estas técnicas se basan en las transformadas o en los métodos de predicción. Con el fin de entender la independencia no lineal y la compactación de datos para las imágenes hiperespectrales, empezaos por investigar la mejora de la transformada “Principa Component Analysis” (PCA) que proporciona una decorrelación optima para fuentes Gausianas. Analizamos la eficiencia en compresión sin perdida de “Principal Polynomial Analysis” (PPA) que generaliza PCA con la eliminación de las dependencias non lineales a través de regresión polinomial. Mostramos que las componentes principales no son capaces de predecirse con la regresión polinomial y por tanto no se mejora la independencia del PCA. Este análisis nos permite entender mejor el concepto de la predicción en el dominio de la transformada para fines de compresión. Por tanto, en lugar de utilizar transformadas sofisticadas y costosas como PCA, centramos nuestro interés en transformadas más simples como “CDiscrete Wavelet Transform”(DWT). Mientras tanto, adoptamos técnicas de predicción para explotar cualquier dependencia restante entre las componentes transformadas. Así, introducimos un nuevo esquema llamado “Regression Wavelet Analysis” (RWA) para aumentar la independencia entre los coeficientes de las imágenes hiperespectrales. El algoritmo utiliza la regresión multivariante para explotar las relaciones entre los coeficientes de las transformada DWT. El algoritmo RWA ofrece muchas ventajas, como el bajo coste computacional y la no expansión del rango dinámico. Sin embargo, la propiedad más importante es la eficiencia en compresión sin perdida. Experimentaos extensivos sobre un conjunto amplio de imanes indican que RWA supera las técnicas mas competitivas en el estado del arte com. PCA o el estándar CCSDS-123. Extendemos los beneficios de RWA para la compresión progresiva “ Lossy-to-lossless “. Mostramos que RWA puede alcanzar una relación rate-distorsión mejor que las obtenidas por otras técnicas del estado del arte como PCA. Para este fin, proponemos un esquema de pesos que captura la significancia predictiva de las componentes. Para un análisis más profundo, también analizamos el sesgo en los parámetros de regresión cuando se aplica una compresión con perdida. Mostramos que los parámetros de RWA no son sesgados cuando los modelos de regresión se aplican con los datos recuperados que carecen información. Finalmente, introducimos una versión del algoritmo RWA de muy bajo coste computacional. Con este nuevo enfoque, la predicción solo se basa en muy pocas componentes, mientras que el rendimiento se mantiene. Mientras que la complejidad de RWA se lleva a su bajo extremo, un método de selección eficiente es necesario. A diferencia de otros métodos de selección costosos, proponemos una estrategia simple pero eficiente llamada “ neighbor selection” para seleccionar modelos con pocas componentes predictivas. Sobre un amplio conjunto de imágenes hiperespectrales, estos modelos mantienen el excelente rendimiento de RWA con el modelo máximo, mientras que el coste computacional es reducido al
Today remote sensing is essential for many applications addressed to Earth Observation. The potential capability of remote sensing in providing valuable information enables a better understanding of Earth characteristics and human activities. Recent advances in satellite sensors allow recovering large areas, producing images with unprecedented spatial, spectral and temporal resolution. This amount of data implies a need for efficient compression techniques to improve the capabilities of storage and transmissions. Most of these techniques are dominated by transforms or prediction methods. This thesis aims at deeply analyzing the state-of-the-art techniques and at providing efficient solutions that improve the compression of remote sensing data. In order to understand the non-linear independence and data compaction of hyperspectral images, we investigate the improvement of Principal Component Analysis (PCA) that provides optimal independence for Gaussian sources. We analyse the lossless coding efficiency of Principal Polynomial Analysis (PPA), which generalizes PCA by removing non-linear relations among components using polynomial regression. We show that principal components are not able to predict each other through polynomial regression, resulting in no improvement of PCA at the cost of higher complexity and larger amount of side information. This analysis allows us to understand better the concept of prediction in the transform domain for compression purposes. Therefore, rather than using expensive sophisticated transforms like PCA, we focus on theoretically suboptimal but simpler transforms like Discrete Wavelet Transform (DWT). Meanwhile, we adopt predictive techniques to exploit any remaining statistical dependence. Thus, we introduce a novel scheme, called Regression Wavelet Analysis (RWA), to increase the coefficient independence in remote sensing images. The algorithm employs multivariate regression to exploit the relationships among wavelet-transformed components. The proposed RWA has many important advantages, like the low complexity and no dynamic range expansion. Nevertheless, the most important advantage consists of its performance for lossless coding. Extensive experimental results over a wide range of sensors, such as AVIRIS, IASI and Hyperion, indicate that RWA outperforms the most prominent transforms like PCA and wavelets, and also the best recent coding standard, CCSDS-123. We extend the benefits of RWA to progressive lossy-to-lossless. We show that RWA can attain a rate-distortion performance superior to those obtained with the state-of-the-art techniques. To this end, we propose a Prediction Weighting Scheme that captures the prediction significance of each transformed components. The reason of using a weighting strategy is that coefficients with similar magnitude can have extremely different impact on the reconstruction quality. For a deeper analysis, we also investigate the bias in the least squares parameters, when coding with low bitrates. We show that the RWA parameters are unbiased for lossy coding, where the regression models are used not with the original transformed components, but with the recovered ones, which lack some information due to the lossy reconstruction. We show that hyperspectral images with large size in the spectral dimension can be coded via RWA without side information and at a lower computational cost. Finally, we introduce a very low-complexity version of RWA algorithm. Here, the prediction is based on only some few components, while the performance is maintained. When the complexity of RWA is taken to an extremely low level, a careful model selection is necessary. Contrary to expensive selection procedures, we propose a simple and efficient strategy called \textit{neighbor selection} for using small regression models. On a set of well-known and representative hyperspectral images, these small models maintain the excellent coding performance of RWA, while reducing the computational cost by about 90\%.

En aquesta tesi doctoral es proposa utilitzar la tècnica LIDAR (Light Detection And Ranging) per estudiar la deriva de pesticides. A diferència dels col·lectors in situ, aquesta tècnica permet mesurar els aerosols de forma remota, amb elevada resolució temporal i en distància. Els objectius d’aquesta tesi són (1) dissenyar un sistema lidar específic per la mesura de la deriva i (2) avaluar la capacitat d’aquesta tècnica per quantificar la concentració en els núvols de pesticides. Per la consecució de l’objectiu (1) s’ha elaborat una metodologia de disseny, validada mitjançant la construcció d’un prototipus de ceilòmetre lidar biaxial. Partint d’aquesta metodologia s’han establert els paràmetres de disseny del sistema específic per mesurar la deriva: longitud d’ona de 1550 nm, energia per pols igual a 25 μJ, etc. Respecte a l’objectiu (2), es proposa un model teòric que relaciona les mesures lidar de la deriva amb les obtingudes utilitzant col·lectors passius. La relació entre els dos tipus de sensors també s’ha estudiat experimentalment. Les mesures van mostrar que per a cada assaig existeix una elevada correlació lineal (R2≈0.9) entre el senyal lidar i els col·lectors.
En esta tesis doctoral se propone utilizar la técnica LIDAR (LIght Detection And Ranging) para monitorizar la deriva de pesticidas. A diferencia de los colectores in situ, esta técnica permite medir los aerosoles de forma remota, con elevada resolución temporal y en distancia. Los objetivos de esta tesis son (1) diseñar un sistema lidar específico para la medida de la deriva y (2) evaluar la capacidad de esta técnica para cuantificar la concentración en las plumas de pesticidas. Para la consecución del objetivo (1) se ha elaborado una metodología de diseño, validada mediante la construcción de un prototipo de ceilómetro lidar biaxial. Partiendo de esta metodología se han establecido los parámetros de diseño del sistema lidar específico para medir la deriva: longitud de onda de 1550 nm, energía por pulso igual a 25 μJ, etc. Respecto al objetivo (2), se propone un modelo teórico que relaciona las medidas lidar de la deriva con las obtenidas utilizando colectores pasivos. La relación entre ambos tipos de sensores también ha sido estudiada experimentalmente. Las medidas mostraron que para cada ensayo existe una elevada correlación lineal (R2≈0.9) entre la señal lidar y los colectores.
This doctoral thesis proposes the use of the LIDAR (LIght Detection And Ranging) technique for spray drift monitoring. Unlike in situ collectors, this technique enables remote measurement of aerosols with high temporal and range resolution. The objectives of this thesis are as follows: (1) the design of a lidar system specifically for the remote sensing of pesticide spray drift and (2) assessment of the capacity of lidar technology to quantify droplet concentration in drift clouds. For the purposes of objective (1), a design methodology was elaborated. This methodology was validated with the construction of a biaxial lidar ceilometer prototype. Taking this methodology as a starting point the design parameters of a lidar system specifically for spray drift measurement were established: 1550 nm wavelength, 25 μJ de pulse energy, etc. As for objective (2), it is proposed a quantitative analytical model which relates the lidar spray drift measurements with those obtained using passive collectors. The relationship between the two sensor types was also studied experimentally. The measurements showed that for each test there is a high linear correlation (R2≈0.9) between the lidar signal and the collectors

The geometry, deformation mechanism and kinematics of the Sü
rgü
Fault Zone is investigated by using remotely sensed data including Landsat TM and ASTER imagery combined with SRTM, and stereo-aerial photographs. They are used to extract information related to regional lineaments and tectono-morphological characteristics of the SFZ. Various image processing and enhancement techniques including contrast enhancement, PCA, DS and color composites are applied on the imagery and three different approaches including manual, semi automatic and automatic lineament extraction methods are followed. Then the lineaments obtained from ASTER and Landsat imagery using manual and automatic methods are overlaid to produce a final lineaments map. The results have indicated that, the total number and length of the lineaments obtained from automatic is more than other methods while the percentages of overlapping lineaments for the manual method is more than the automatic method which indicate that the lineaments from automatic method does not discriminate man made features which result more lineaments and less overlapping ratio with respect to final map. It is revealed from the detail analysis that, the SFZ displays characteristic deformation patterns of strike-slip faults, such as pressure ridges, linear fault controlled valleys, deflected stream courses, rotated blocks and juxtaposition of stratigraphical horizons in macroscopic scale. In addition to these, kinematic analyses carried out using fault slip data indicated that the Sü
rgü
Fault Zone is dextral strike-slip fault zone with a reverse component of slip and cumulative displacement along the fault is more than 2 km.

The regeneration of native Scots pine forest has become an important objective of conservation management within Scotland. Management practices are being employed to encourage regeneration by natural means, but quantitatively monitoring progress is difficult to achieve on the ground. The study explores the potential application of optical remote sensing to monitoring natural regeneration. However, there is currently a lack of understanding of the detailed spectral and spatial properties of such semi-natural landscapes. The study addresses this problem through detailed analysis of the spectral and spatial properties of regions of natural regeneration at Inshriach and Rothiemurcus in the Cairngorms National Nature Reserve, Scotland. High-spectral resolution field-spectroradiometeric data (400 - 2500 nm) were collected over the growing seasons of 1997 and 1998 over sample plots. A method was developed to measure Leaf Area Index (LAI) within the sample plots and correlation analysis was used to assess the relationship of both LAI and percentage cover (PC) with reflectance and first-derivative reflectance. LAI and PC correlated most strongly with reflectance indices involving red and NIR wavelengths, such as the Simple Ratio (SR), NDVI and R₇₅₂/R₇₂₂. Derivative indices involving the red-edge, (D₇₃₀/D₇₀₀ and D₇₁₈/D₇₀₃), the green-edges (D₅₂₀ and D₅₇₀), and the NIR at D₁₁₅₀, also correlated strongly with LAI and PC. Two peaks were identified on the derivatives red-edge feature (D₇₁₉ and D₇₀₃), with D₇₁₉ becoming more prominent with increasing LAI and PC. Red-edge position correlated more strongly with PC than with LAI, but a sudden switching of REP to longer wavelengths was identified with increasing sapling amount, rather than a smooth progression. These relationships were unaffected by seasonal changes in the heather understorey reflectance at the sites investigated. A hybrid geometric-optical canopy reflectance model (FLIGHT) was used to simulate the canopy reflectance of the sample plots. Model scenarios were run to account for changing LAI, PC and crown needle density.