Fall Detection Using Channel State Information from WiFi Devices

Falls among the independently living elderly population are a major public health worry, leading to injuries, loss of confidence to live independently and even to death. Each year, one in three people aged 65 and older falls and one in five of them suffers fatal or non fatal injuries. Therefore, detecting a fall early and alerting caregivers can potentially save lives and increase the standard of living. Existing solutions, e.g. push-button, wearables, cameras, radar, pressure and vibration sensors, have limited public adoption either due to the requirement for wearing the device at all times or installing specialized and expensive infrastructure. In this thesis, a device-free, low cost indoor fall detection system using commodity WiFi devices is presented. The system uses physical layer Channel State Information (CSI) to detect falls. Commercial WiFi hardware is cheap and ubiquitous and CSI provides a wealth of information which helps in maintaining good fall detection accuracy even in challenging environments. The goals of the research in this thesis are the design, implementation and experimentation of a device-free fall detection system using CSI extracted from commercial WiFi devices. To achieve these objectives, the following contributions are made herein. A novel time domain human presence detection scheme is developed as a precursor to detecting falls. As the next contribution, a novel fall detection system is designed and developed. Finally, two main enhancements to the fall detection system are proposed to improve the resilience to changes in operating environment. Experiments were performed to validate system performance in diverse environments. It can be argued that through collection of real world CSI traces, understanding the behavior of CSI during human motion, the development of a signal processing tool-set to facilitate the recognition of falls and validation of the system using real world experiments significantly advances the state of the art by providing a more robust fall detection scheme

Retrieval of biophysical parameters from multi-sensoral remote sensing data, assimilated into the crop growth model CERES-Wheat

This study investigated the possibilities and constraints for an integrated use of a crop growth model (CERES-Wheat) and earth observation techniques. The assimilation of information derived from earth observation sensors into crop growth models enables regional applications and may also help to improve the profound knowledge of the different involved processes and interactions. Both techniques can contribute to improved use of resources, reduced crop production risks, minimised environmental degradation, and increased farm income. Up to now, crop growth modelling and remote sensing techniquices mostly have been used separately for the assessment of agricultural applications. Crop growth models have made valuable contributions to, e.g., yield forecasting or to management decision support systems. Likewise, remote sensing techniques were successfully utilized in classification of agricultural areas or in the quantification of vegetation characteristics at various spatial and temporal scales. Multisensoral remote sensing approaches for the quantification biophysical variables are rarely realized. Normally the fusion of the data sources is based on the use of one sensor for classification purposes and the other one for the extraction of the desired parameters, based on the map classified previously. Pixel-based fusions between multispectral and SAR data is seldom realised for the assessment of quantitative parameters. The integration of crop growth models and remote sensing techniques by assimilating remotely sensed parameters into the models, is also still an issue of research. Especially, the integration of, e.g., multi-sensor biophysical parameter time-series for the improvement of the model performance, might feature a high potential. The starting point of the presented study was the question, if it is possible to derive the values of important crop variables from various remote sensing data? For the retrieval of these quantitative parameters by the use of various multispectral remote sensing sensors, intercalibration issues between the different retrieved vegetation indices had to be taken into account, in order to assure the comparability. Features influencing the vegetation indices are, e.g., the sensor geometry (like viewing- and solar-angle), atmospherical conditions, topography and spatial or radiometric resolution. However, the factors taken into account within this study are the spectral characteristics of the different sensors, like band position, bandwidth and centre wavelengths, which are described by the relative spectral response functions. Due to different RSR functions of the sensor bands, measured spectral differences occur, because the sensors record different components of the reflectance’s spectra from the monitored targets. These are then also introduced into the derived vegetation indices. The chosen cross-calibration method, intercalibrated the assessed Normalized Difference Vegetation Index and the Weighted Difference Vegetation Index between the various sensor pairs by regression, based on simulated multispectral sensors. Differences between the various assessed remote sensing sensors decreased form around 7% to below 1%. The intercalibration also had a positive impact on the later biophysical retrieval performance, producing sounder retrieval results. For the retrieval of the biophysical parameters empirical and semi-empirical models were assessed. The results indicate that the semi-empirical CLAIR model outperforms the empirical approaches. Not only for the Leaf Area Index retrieval, but also in the cases of all other assessed parameters. Concerning the other remote sensing data type used, the SAR data, it was analysed what potential different polarizations and incidence angles have for the extraction of the quantitative parameters. It became obvious that especially high incidence angles, as provided by the satellite Envisat ASAR, produce sounder retrieval results than lower incidence angles, due to a smaller amount of received soil signal. In the context of the assessed polarizations, sound results for the VV polarization could only be achieved for the retrieval of fresh biomass and the plant water content. For the ASAR sensor modelling fresh biomass and LAI using the HV polarization or the dry biomass using the ratio (HH/HV) was appropriate. As roughness aspects also have an influence on the retrieval performance from biophysical parameters using SAR data, the impact of soil surface and vegetation roughness was additionally considered. Best results were achieved, when also considering roughness features, however due to the need of regional modelling it is more appropriate not to consider them. For the calibration and re-tuning of crop growth models information about important phenological events such as heading/flowering is rather important. After this stage reproductive growth begins, whereby the number of kernels per plant is often calculated from plant weight at flowering and kernel weight is calculated from time and temperature available for dry matter distribution. By the use of the SAR VV time-series this important stage could be successfully extracted. Further methods for pixel-based fused biophysical parameter estimations, using SAR and multispectral data were analysed. By this approach the different features, being monitored of the two systems, are combined for sounder parameter retrieval. The assessed method of combining the multi-sensoral information by linear regression did not bring sound results and was outperformed by single sensor use, only taking into account the multispectral information. Only for the parameter fresh biomass, modelling based on the NDIV and the ASAR ratio slightly outperformed the single sensor modelling approaches. The complex combined modelling by the use of the CLAIR and the Water Cloud Model featured no valid results. For the combination, by using the CLAIR model and multiple regression slight improvements, in contrast to the single multispectral sensor use, were achieved. Especially, during late phenological stages, the assessed VV information improved the modelling results, in comparison to only using the CLAIR model. All the findings could finally be successfully applied for regional estimations. Only the roughness features could not be applied, due to the fact, that it is hard to regionally assess this needed model input parameter. Regional parameter on the basis of remote sensing data, is the major advantage of this technique, due to the large spatial overview given. The second main question was, if it is possible to integrate the crop variables gained from multisensoral data into a crop growth model, increasing the final yield estimation accuracy. Thus far, beneficial linkages between both techniques have been often limited to land use classification via remote sensing for choosing the adequate model and quantification of crop growth and development curves using biophysical parameters derived from remote sensing images for model calibration. Only a few studies actually considered the potentials of remote sensing for model re-initialization of growth and development characteristics of a specific crop, as the here studied winter wheat. Overall, the integration of remotely sensed variables into the crop growth model CERES-Wheat led to an improved final yield estimation accuracy in comparison to an automatic input parameter setting. The assessed final yield bias for the automatic input parameter setting summed up to 6.6%. When re-initializing the most sensitive input parameters (sowing date and fertilizer application date) by the use of remotely sensed biophysical variables the biases ranged from 0.56% overestimation to 5.4% understimation, in dependence of the data series used for assimilation. Whereby, it was assessed that the combined dense data series, considering SAR and multispectral information, slightly outperformed the performance of the full multispectral data series. However, when analysing the assimilation of the multispectral data series in further detail, it became clear that the actually information from the phenological stage ripening declines the modelling performance and thus the final yield estimation accuracy. When neglecting the information from this phenological stage the reduced multispectral data series performed as sound as the dense data series containing SAR and multispectral information. Thus, when the appropriate phenological stages are monitored by multispectral data, additional SAR information does not lead to a model improvement. However, when important dates are not monitored by multispectral images, e.g., due to cloud coverage, the additionally considered SAR information was not able to appropriatly fill these important multispectral time gaps. They even had a more negeative influence on the modelling performance. Overall, the best results could be obtained by assimilating a multispectral data series, covering the crop development during the important phenological stages stem elongation and flowering (without ripening stage), into the CERES-Wheat model. Finally, the integration of remote sensing data in the point-based crop growth model allowed it‘s spatial application for prediction of wheat production at a more regional scale. This approach also outperformed another evaluated method of direct multi-sensoral regional yield estimation. This study has demonstrated that biophysical parameters can be retrieved from remote sensing data and led, when assimilated into a crop growth model, to an improved final yield estimation. However, overall the SAR information did not really have a significant positive effect on the multi-sensoral biophysical parameter retrieval and on the later assimilation process. Thus, overall SAR information should only be considered, when multispectral data acquisitions are tremendously hampered by cloud coverage. The assessed assimilation of remote sensing information into a crop growth model had a positive effect on the final yield estimation performance. The analysed method, combining remote sensing and crop growth model techniques, was succsessfully demonstrated and will gain even more importance in the future for, e.g., decision support systems fine-tuning fertilizer regimes and thus contributing to more environmentally sound and sustained agricultural production

Estimation of Stage-Area-Storage Relationships in Reservoirs and Stage-Discharge Relationships in Rivers Using Remotely Sensed Data

This thesis estimates the relationships between the water surface levels and quantities of water in reservoirs and rivers using remotely sensed data without any field measurements. However, the accuracies of the estimates were validated using the field measurements from ground stations. The relationships between the water surface levels and quantities are fundamental for monitoring water quantities for the operation of hydraulic structures, as well as analyzing variability and changes in hydrology. Accessibility and transparency are big issues in establishing a monitoring system that can provide field validation of efforts to model global climate systems. Remote sensing has a capability of global spatial coverage and stable temporal frequency in data acquisition, and hence can be helpful. Two types of remotely sensed data are used: satellite images, and satellite altimeter elevations. The thesis has two parts. First, the relationships among water surface levels, areas and volumes were estimated for reservoirs. A strategic procedure, which is missing in the current literature, was formulated to estimate the water surface area, and then the water volume. Water levels were derived from Hydroweb, a satellite altimetry database. Areas were estimated from Landsat surface reflectance images by classifying the Modified Normalized Difference Water Index (MDNWI) into binary images using an internally calibrated threshold. Internal calibration of the threshold was performed by computing the overall accuracies of classification from confusion matrices created for selected regions in the classified image. Finally, water surface heights from the lowest levels and areas were used to estimate volumes assuming an inverted pyramidal shape; then, second-order polynomials were fitted to compute relationships. The fits were tested to be statistically significant by performing t-tests for coefficients and F-test for overall significance at α = 0.05. Stage-area-storage relationships were developed for Lake Mead (LM) and Lake Powell (LP) that are reservoirs formed in the Colorado River. The study estimated the areas of LM with a Root Mean Square Difference (RMSD) of 17.8 km2 and LP with an RMSD of 53.7 km2 compared with in-situ measurements. The RMSD in volumes were 699 Million Cubic Meters (MCM) for LM and 1330 MCM for LP. The second-order polynomial fits between water surface heights and volumes were established with R2 = 0.999 for both LM and LP. The coefficients of the fit and the overall fit were tested to be statistically significant at α = 0.05. The RMSD is higher in LP than LM and were explained by comparatively more shadows and a higher number of mixed pixels in the LP Landsat images than LM. Secondly, the relationships between the water surface levels and discharges for rivers were estimated. Two major rivers, the Mississippi River and the Colorado River, representing an alluvial and rocky terrain, were selected to highlight the differences in estimates between varied terrain and size of the river. A variant of Manning’s equation was used that required a channel cross-section, water surface slope, and roughness coefficient as input parameters. A parabolic cross-section was fitted for each river using the width of river estimated from the Landsat images at several water levels. Water surface slopes were estimated from water elevations at different locations on each river using two sources. For the Mississippi River, water surface elevations were obtained at virtual stations from the DAHITI database. For the Colorado River, elevations were extracted using the MAPS at river crossings. Roughness coefficients were estimated using empirical models that utilized meander length. Results showed that discharges were estimated to within 31.4% of the average discharge with root mean square error of 5700 cu.m/sec for the Mississippi River. Colorado River discharges were estimated within 30.5% of the average discharge with RMSE of 50 cu.m/sec. A linear relationship was fitted between the water surface elevation and discharges in the Mississippi River with R2 = 0.62. For the Colorado River, second-order polynomial was fitted for a relationship between water surface elevations and discharges with R2 = 0.99. The coefficients of the fits and the overall significance of the fit were statistically significant at α = 0.05 tested by performing t-tests and F-test respectively. It was difficult to estimate a cross-section for rivers with smaller channel widths or smaller changes in width with water level as in the case of the Colorado River. However, estimated accuracies were similar in both the cases in terms of percentage of error

Estimation of water resources on continental surfaces by multi-sensor microwave remote sensing

L'estimació dels recursos hídrics de les superfícies continentals a escala regional i global és fonamental per a una bona gestió dels recursos hídrics. Aquesta estimació cobreix una àmplia gamma de temes i camps, incloent-hi la caracterització dels sòls i dels recursos hídrics a l’escala de la conca, la modelització hidrològica i la predicció i la cartografia d'inundacions. En aquest context, la caracterització dels estats de la superfície continental, per a obtenir millors paràmetres d’entrada als models hidrològics, és essencial per millorar la precisió en la simulació de cabals, sequeres i inundacions. L’estimació del contingut d’aigua en el sistema, incloses les diferents masses d’aigua i l’aigua lliure en el sòl, és especialment necessària per a una descripció precisa dels processos hidrològics i, en general, del cicle de l’aigua a les superfícies continentals. Per caracteritzar millor els processos hidrològics, les intervencions antropogèniques no es poden negligir. L'home influeix en el cicle de l'aigua, principalment mitjançant el reg i la construcció de preses, fet que s’ha de quantificar correctament. L’objectiu de la tesi és la millora de l’estimació remota dels recursos hídrics, incloent-hi la quantificació dels factors antròpics, mitjançant l’ús de diversos sensors llançats recentment, aprofitant recents desenvolupaments en la tecnologia de teledetecció. Amb l'arribada de les constel·lacions Sentinel (Sentinel-1, 2, 3), disposem de millors eines per estimar els recursos hídrics, incloent-hi els impactes humans, amb una major precisió i cobertura. Aquest treball de tesi consta principalment de dues línies de recerca on s’estimen les intervencions humanes en el cicle hidrològic: la cartografia del reg (com a aplicació en humitat del sòl), i el forçament d’embassaments en simulacions hidrològiques (com a aplicació de l’altimetria). En la primera linia s’estima la humitat del sòl a partir de l’anàlisi estadística de les dades SAR de Sentinel-1. Es desenvolupen dues metodologies per obtenir la humitat del sòl amb una resolució espacial de 100 m basant-se en la interpretació de les dades de Sentinel-1 obtingudes amb la polarització VV (vertical-vertical), que es combina amb dades òptiques Sentinel-2 per a l'anàlisi dels efectes de la vegetació. Com aplicació de la humitat del sòl, es cartografia el reg en diverses condicions meteorològiques, i amb una alta resolució espacial i temporal. Es proposa una metodologia per a la cartografia del reg mitjançant dades SAR obtingudes en polaritzacions VV (vertical-vertical) i VH (vertical-horitzontal). A partir de la sèrie temporal Sentinel-1, s’analitzen diferents estadístiques i mètriques, incloent-hi el valor mitjà, la variància del senyal, la longitud de la correlació i la dimensió fractal, a partir dels quals es classifiquen els arbres irrigats, els cultius irrigats i els cultius no irrigats. En la segona línia, s’estima el nivell dels embassaments a partir de les dades d’altimetria de Sentinel-3, amb l’altímetre SAR (SRAL), basant-se en diferents algorismes per millorar la precisió. Aquest estudi presenta tres algorismes especialitzats o retrackers destinats a obtenir el nivell de la superfície dels cossos d’aigua estudiats, minimitzant la contaminació de les formes d’ona degut al sòl que els envolta. Es compara el rendiment del mètode proposat de selecció de la porció d’ona amb tres retrackers, és a dir, un retracker de llindar, el retracker del centre de gravetat (OCOG) i un retracker de base física de dos passos. S’obtenen sèries temporals del nivell de la làmina d’aigua d’embassaments situats a la conca del riu Ebre (Espanya). Com aplicació, les sèries de nivell dels embassaments obtingudes s’utilitzen per a forçar els embassaments en simulacions hidrològiques.La estimación de los recursos hídricos de las superficies continentales a escala regional y global es fundamental para una buena gestión de los recursos hídricos. Esta estimación cubre una amplia gama de temas y campos, incluyendo la caracterización de los suelos y de los recursos hídricos a escala de cuenca, la modelización hidrológica y la predicción y la cartografía de inundaciones. En este contexto, la caracterización de los estados de la superficie continental, para obtener mejores parámetros de entrada para los modelos hidrológicos, es esencial para mejorar la precisión en la simulación de caudales, sequías e inundaciones. La estimación del contenido de agua en el sistema, incluidas las diferentes masas de agua y el agua libre en el suelo, es especialmente necesaria para una descripción precisa de los procesos hidrológicos y, en general, del ciclo del agua en las superficies continentales. Una caracterización precisa de los procesos hidrológicos requiere no descuidar las intervenciones humanas. El hombre influye en el ciclo del agua, principalmente mediante el riego y la construcción de embalses, lo que se debe cuantificar correctamente. El objetivo de la tesis es la mejora de la estimación remota de los recursos hídricos, incluyendo la cuantificación de los factores humanos, mediante el uso de varios sensores lanzados recientemente, aprovechando recientes desarrollos en la tecnología de teledetección. Con la llegada de las constelaciones Sentinel (Sentinel-1, 2, 3), disponemos de mejores herramientas para estimar los recursos hídricos, incluyendo los impactos humanos, con una mayor precisión y cobertura. Este trabajo de tesis consta principalmente en dos ejes de investigación donde se estiman las intervenciones humanas en el ciclo hidrológico: la cartografía del riego (como aplicación en humedad del suelo), y el forzamiento de embalses en simulaciones hidrológicas (como aplicación de la altimetría). En relación al primer eje, se estima la humedad del suelo a partir del análisis estadístico de los datos SAR de Sentinel-1. Se desarrollan dos metodologías para obtener la humedad del suelo con una resolución espacial de 100 m basándose en la interpretación de los datos de Sentinel-1 obtenidas con la polarización VV (vertical-vertical), que se combina con datos ópticas Sentinel-2 para el análisis de los efectos de la vegetación. Como aplicación de la humedad del suelo, se cartografía el riego en diversas condiciones meteorológicas, y con una alta resolución espacial y temporal. Se propone una metodología para la cartografía del riego mediante datos SAR obtenidos en polarizaciones VV (vertical-vertical) y VH (vertical-horizontal). A partir de la serie temporal Sentinel-1, se analizan diferentes estadísticas y métricas, incluyendo el valor medio, la varianza de la señal, la longitud de la correlación y la dimensión fractal, a partir de los cuales se clasifican los árboles irrigados, los cultivos irrigados y los cultivos no irrigados. En el segundo eje, se estima el nivel de los embalses a partir de los datos de altimetría de Sentinel-3, con el altímetro SAR (SRAL), basándose en diferentes algoritmos para mejorar la precisión. Este estudio presenta tres algoritmos especializados o retrackers destinados a obtener el nivel de la superficie de los cuerpos de agua estudiados, minimizando la contaminación de las formas de onda debido al suelo que los rodea. Se compara el rendimiento del método propuesto de selección de la porción de onda con tres retrackers, es decir, un retracker de umbral, el retracker del centro de gravedad (OCOG) y un retracker de base física de dos pasos. Se obtienen series temporales del nivel de la lámina de agua de embalses situados en la cuenca del río Ebro (España). Como aplicación, las series de nivel de los embalses obtenidas se utilizan para forzar los embalses en simulaciones hidrológicas.The estimation of the water resources of the continental surfaces at a regional and global scale is fundamental for good water resources management. This estimation covers a wide range of topics and fields, including the characterisation of soils and water resources at the basin scale, hydrological modelling and flood prediction and mapping. In this context, the characterisation of the states of the continental surface, to obtain better input parameters for hydrological models, is essential to improve the precision in the simulation of flows, droughts, and floods. The estimation of the water content in the system, including the different water bodies and the free water in the soil, is especially necessary for a precise description of the hydrological processes and, in general, of the water cycle on the continental surfaces. To better characterise hydrological processes, human interventions cannot be neglected. Humans influence the water cycle, mainly through irrigation and the construction of reservoirs, which must be correctly quantified. The objective of the thesis is the improvement of the remote estimation of water resources, including the quantification of human factors, using several sensors recently launched, taking advantage of recent developments in remote sensing technology. With the arrival of the Sentinel constellations (Sentinel-1, 2, 3), we have better tools to estimate water resources, including human impacts, with greater precision and coverage. This thesis consists mainly of two parts where human interventions in the water cycle are considered: irrigation cartography (as an application of soil moisture), and the forcing of reservoirs in hydrological simulations (as an application of altimetry). Firstly, soil moisture is estimated from the statistical analysis of Sentinel-1 SAR data. Two methodologies are developed to obtain soil moisture with a spatial resolution of 100 m based on the interpretation of Sentinel-1 data collected with the VV polarization (vertical-vertical), which is combined with optical data of Sentinel-2 for the analysis of the effects of vegetation. Secondly, irrigation is mapped under various meteorological conditions, including high spatial and temporal resolution. A methodology for irrigation mapping is proposed using SAR data obtained in VV (vertical-vertical) and VH (vertical-horizontal) polarizations. With Sentinel-1 time series, different statistics and metrics are analysed, including the mean value, the variance of the signal, the correlation length and the fractal dimension, based on which the classification of irrigated trees, irrigated crops, and non-irrigated crops are derived. Finally, the level of the reservoirs is estimated from the Sentinel-3 altimetry data, with the SAR altimeter (SRAL), based on different algorithms to improve the accuracy. This study presents three specialised algorithms or retrackers designed to obtain the level of the surface of the studied inland bodies of water, minimising the contamination of the waveforms due to the surrounding soil. The performance of the selection method of the proposed wave portion is compared with three retrackers, that is, the centre of gravity retracker (OCOG) and the two-step physical-based retracker. Temporal series of the water level of reservoirs located in the basin of the Ebro River (Spain) are obtained. As an application, the level series of the reservoirs obtained are used to force the reservoirs in hydrological simulations.L'estimation et le suivi des ressources en eau des surfaces continentales aux niveaux régional et global est essentielle pour la gestion du bilan hydrique, particulièrement dans le contexte des changements climatiques et anthropiques. Ils couvrent un large éventail de thèmes et de domaines, incluant la caractérisation des ressources en eau à l'échelle du bassin, la modélisation hydrologique ainsi que la prévision et la cartographie des inondations. Dans ce contexte, la caractérisation des états de surface, en tant que paramètres d’entrée dans les modèles hydrologiques, est essentielle pour obtenir une meilleure précision de la simulation, qui est liée à la précision prévisionnelle des débits des cours d’eau et le suivi des sécheresses et des inondations. L'estimation de la teneur en eau des surfaces continentales, incluant l’état hydrique du sol et les niveaux des surfaces couvertes d’eau, est particulièrement nécessaire pour une description précise des processus hydrologiques et plus généralement du cycle de l'eau sur les surfaces continentales. Afin de mieux comprendre les processus hydrologiques, l'influence humaine (l’effet anthropique) sur le cycle de l'eau nécessite une évaluation fine. Elle est particulièrement liée à la gestion de l’irrigation et la construction de barrages. L'objectif de la thèse était d'améliorer l'estimation des ressources en eau et une meilleure caractérisation des interventions anthropiques à travers l'utilisation de nouveaux capteurs satellitaires multi-configurations du programme européen Copernicus. Avec le développement de la technologie de télédétection spatiale, et plus particulièrement avec l’arrivée des constellations Sentinel (Sentinel-1, 2, 3) à haute résolution spatiale et temporelle, il existe un meilleur outil pour estimer les états des surfaces continentales. Ce travail de thèse comprend principalement deux priorités liées à des interventions humaines dans le cycle hydrologique:la cartographie de l'irrigation en tant que action humaine liée directement à l'humidité du sol et le forçage des barrages dans un modèle de simulation de rivière en tant qu'application liée à l’estimation du niveau de l'eau libre. Un premier axe de recherche a été basé sur une analyse statistique des données SAR Sentinel-1 pour caractériser l’état hydrique du sol. Deux méthodes ont été développées pour estimer ce paramètre avec une résolution spatiale de 100 m. Elles sont basées sur des approches de détection de changement à partir des données Sentinel-1 acquises en polarisation VV (verticale-verticale), combinées aux données optiques Sentinel-2 pour corriger les effets de la végétation. L’application consistait à cartographier l'irrigation, avec des résolutions spatiale et temporelle élevées. Une méthodologie de cartographie de l'irrigation utilisant des données SAR Sentinel-1 a été proposée. Elle estbasée sur les acquisitions en polarisations VV (vertical-vertical) et VH (vertical-horizontal). A partir de la série temporelle des mesures Sentinel-1, des paramètres statistiques tel que la valeur moyenne, la variance du signal, la longueur de corrélation temporelle et la dimension fractale, sont analysées, en fonction du type de culture; cultures annuelles irriguées, arbres irrigués et cultures pluviales. Des classifications supervisées utilisant les approches Random Forest et SVM sont testées. En deuxième axe, l'estimation de la hauteur de la surface de l'eau à partir des données altimétriques de Sentinel-3 avec l’altimètre SAR (SRAL) a été réalisée à l'aide de différents algorithmes afin d'améliorer la précision sur des petites surfaces. Cette étude présente trois algorithmes spécialisés (ou retrackers) dédiées à la minimisation de la contamination des sols par les formes d’ondes permettant de récupérer les niveaux d’eau à partir de données altimétriques SAR sur des masses d’eaux intérieures. Les performances de la méthode de sélection de portion de forme d'onde proposée avec trois retrackers, à savoir, le retracker à seuil, le retracker à centre de gravité décalé (OCOG) et le retracker à base physique à 2 étapes, sont comparées. Des séries chronologiques de niveaux d'eau sont extraites pour les masses d'eau du bassin de l'Èbre (Espagne). Une application des produits altimétriques est proposée. Le produit de niveau d’eau a été utilisé comme paramètre d’entrée pour analyser l’effet tampon des barrages dans les simulations de débits fluviaux

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Monitoring Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of Snowmelt

The warming climate is threatening to alter inland water resources on a global scale. Within all waterbody types, lake and river systems are vital not only for natural ecosystems but, also, for human society. Snowmelt phenology is also altered by global warming, and snowmelt is the primary water supply source for many river and lake systems around the globe. Hence, (1) monitoring snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced river and lake systems, and (3) quantifying the causal effect of snowmelt conditions on these waterbodies are critical to understand the cryo-hydrosphere interactions under climate change. Previous studies utilized in-situ or multispectral sensors to track either the surface areas or water levels of waterbodies, which are constrained to small-scale regions and limited by cloud cover, respectively. On the contrary, in the present study, we employed the latest Sentinel-1 synthetic aperture radar (SAR) and Sentinel-3 altimetry data to grant a high-resolution, cloud-free, and illumination-independent comprehensive inland water dynamics monitoring strategy. Moreover, in contrast to previous studies utilizing in-house algorithms, we employed freely available cloud-based services to ensure a broad applicability with high efficiency. Based on altimetry and SAR data, the water level and the water-covered extent (WCE) (surface area of lakes and the flooded area of rivers) can be successfully measured. Furthermore, by fusing the water level and surface area information, for Lake Urmia, we can estimate the hypsometry and derive the water volume change. Additionally, for the Brahmaputra River, the variations of both the water level and the flooded area can be tracked. Last, but not least, together with the wet snow cover extent (WSCE) mapped with SAR imagery, we can analyze the influence of snowmelt conditions on water resource variations. The distributed lag model (DLM) initially developed in the econometrics discipline was employed, and the lagged causal effect of snowmelt conditions on inland water resources was eventually assessed

Monitoring von Hangbewegungen mit InSAR Techniken im Gebiet Ciloto, Indonesien

In this doctoral thesis, the InSAR techniques are applied to detect the ground movement phenomenon and to assess the InSAR result geometrically in the Ciloto area, Indonesia. Mainly, one of those techniques, the SB-SDFP algorithm, overcomes the limitations of conventional InSAR in monitoring rural and agricultural areas and can observe extremely slow landslides. The InSAR strategy is positively known as a promising option to detect and quantify the kinematics of active landslides on a large areal scale. To minimize the bias of the InSAR displacement result, the correction of the tropospheric phase delay was carried out in a first step. This procedure is demonstrated in experiments both in the small study area in Ciloto and in a larger area. The latter is an area located in Northern Baja California, Mexico and is dominated by tectonic activity as well as groundwater-induced subsidence. A detailed investigation of the slope movement's behavior in the Ciloto district was conducted utilizing multi-temporal and multi-band SAR data from ERS1/2 (1996-1999), ALOS PALSAR (2007-2009) and Sentinel-1 (2014-2018) satellites. The region was successfully identified as a permanent active landslide prone area, especially in the vicinity of the Puncak Pass and Puncak Highway. The full 3D velocity field and the displacement time series were estimated using the inversion model. The velocity rate was classified from extremely slow to slow movement. To comprehend the landslide's behavior, a further examination of the relationship between InSAR results and physical characteristics of the area was carried out. For the long period of a slow-moving landslide, the relationship between precipitation and displacement trend shows a weak correlation. It is concluded that the extremely slow to slow deformation is not directly influenced by the rainfall intensity, yet it effectuates the subsurface and the groundwater flow. The run-off process with rainfall exceeding a soil's infiltration capacity was suspected as the main driver of the slow ground movement phenomenon. However, when analyzing rapid and extremely rapid landslide events at Puncak Pass, a significant increase in the correlation coefficient between precipitation and displacement rate could be observed.In dieser Doktorarbeit wird die Anwendung von erweiterten Verarbeitungsstrategien von InSAR Daten zur Erkennung und geometrischen Bewertung der Bodenbewegungen im Ciloto - Indonesien dargestellt. Dieser Ansatz überwindet die Beschränkungen konventioneller SAR-Interferometrie und ermöglicht sowohl ein kontinuierliches Monitoring dieses landwirtschaftich geprägten Gebietes als auch die Erfassung extrem langsamer Hangrutschungen. Um eine Verzerrung der InSAR Deformationsergebnisse zu minimieren, wurde zunächst eine Korrektur der troposphärischen Phase durchgeführt. Diese neuartige Strategie wird sowohl im Forschungsgebiet Ciloto als auch an einem größeren Gebiet demonstriert. Bei letzterem handelt es sich um einen Küstenstreifen im nördlichen Niederkalifornien, Mexiko, welcher durch hohe tektonische Aktivität und grundwasserinduzierte Landsetzungen charakterisiert ist. Die detaillierte Untersuchung des Verhaltens von Hangrutschungen im Ciloto erfolgte durch die Verarbeitung multi-temporaler SAR-Daten unter Nutzung verschiedener Frequenzbänder, darunter ESR1/2 (1996-1999), ALOS PALSAR (2007-2009) und Sentinel-1 (2014-2018) Daten. Die Region konnte erfolgreich als permanent aktives Hangrutschungsgebiet identifiziert werden, wobei der Puncak Pass und der Puncak Highway ein erhöhtes Gefahrenpotential aufweisen. Ein 3D- Geschwindig-keitsfeld der Deformation und die zugehörigen Zeitreihen wurden mit dem Inversionsmodell berechnet. Die Geschwindigkeitsrate wurde als langsam bis extrem langsam klassifiziert. Um das dynamische Verhalten der Hangrutschung zu verstehen wurde, in einer weiteren Untersuchung die Beziehung zwischen dem InSAR-Ergebnis und den physikalischen Begebenheiten im Forschungsgebiet analysiert. Es wird der Schluss gezogen, dass die langsame bis extrem langsame Verformung nicht direkt von der Niederschlagsintensität beeinflusst wird, diese sich aber auf den Untergrund und die Grundwasserströmung auswirkt. Es wird vermutet, dass der Oberflächenablauf, welcher die Infiltrationskapazität des Bodens übersteigt, ausschlaggebend für das Phänomen der langsamen Bodenbewegung ist. Für die schnellen und extrem schnellen Hangrutschungen jedoch konnte eine signifikante Erhöhung des Korrelationskoeffizienten zwischen Niederschlag und Verschiebungsrate bei Untersuchungen der Hangrutschung am Puncak-Pass nachgewiesen werden