Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth?s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

A new approach to change detection in multispectral images by means of ERGAS index

In this letter, we propose a novel method for unsupervised change detection (CD) in multitemporal Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) satellite images by using the relative dimensionless global error in synthesis index locally. In order to obtain the change image, the index is calculated around a pixel neighborhood (3x3 window) processing simultaneously all the spectral bands available. With the objective of finding the binary change masks, six thresholding methods are selected. A comparison between the proposed method and the change vector analysis method is reported. The accuracy CD showed in the experimental results demonstrates the effectiveness of the proposed method.

Optimizing panchromatic image change detection based on change index multiband image analysis

This work proposes an optimization of a semi-supervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem.

Anomaly Detection Using Remote Sensing for the Archaeological Heritage Registration

The aim of this work is an approach using multisensor remote sensing techniques to recognize the potential remains and recreate the original landscape of three archaeological sites. We investigate the spectral characteristics of the reflectance parameter and emissivity in the pattern recognition of archaeological materials in several hyperspectral scenes of the prehispanic site in Palmar Sur (Costa Rica), the Jarama Valley site and the celtiberian city of Segeda in Spain. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of HyMAP, AHS, MASTER and ATM have been used. Several experiments on natural scenarios of Costa Rica and Spain of different complexity, have been designed. Spectral patterns and thermal anomalies have been calculated as evidences of buried remains and change detection. First results, land cover change analyses and their consequences in the digital heritage registration are discussed.

Transformada Wavelet Compleja de Doble Árbol (DT-CWT) y su aplicación en Teledetección. Nuevos modelos y aplicaciones

La teledetección o percepción remota (remote sensing) es la ciencia que abarca la obtención de información (espectral, espacial, temporal) sobre un objeto, área o fenómeno a través del análisis de datos adquiridos por un dispositivo que no está en contacto con el elemento estudiado. Los datos obtenidos a partir de la teledetección para la observación de la superficie terrestre comúnmente son imágenes, que se caracterizan por contar con un sinnúmero de aplicaciones que están en continua evolución, por lo cual para solventar los constantes requerimientos de nuevas aplicaciones a menudo se proponen nuevos algoritmos que mejoran o facilitan algún proceso en particular. Para el desarrollo de dichos algoritmos, es preciso hacer uso de métodos matemáticos que permitan la manipulación de la información con algún fin específico. Dentro de estos métodos, el análisis multi-resolución se caracteriza por permitir analizar una señal en diferentes escalas, lo que facilita trabajar con datos que puedan tener resoluciones diferentes, tal es el caso de las imágenes obtenidas mediante teledetección. Una de las alternativas para la implementación de análisis multi-resolución es la Transformada Wavelet Compleja de Doble Árbol (DT-CWT). Esta transformada se implementa a partir de dos filtros reales y se caracteriza por presentar invariancia a traslaciones, precio a pagar por su característica de no ser críticamente muestreada. A partir de las características de la DT-CWT se propone su uso en el diseño de algoritmos de procesamiento de imagen, particularmente imágenes de teledetección. Estos nuevos algoritmos de procesamiento digital de imágenes de teledetección corresponden particularmente a fusión y detección de cambios. En este contexto esta tesis presenta tres algoritmos principales aplicados a fusión, evaluación de fusión y detección de cambios en imágenes. Para el caso de fusión de imágenes, se presenta un esquema general que puede ser utilizado con cualquier algoritmo de análisis multi-resolución; este algoritmo parte de la implementación mediante DT-CWT para luego extenderlo a un método alternativo, el filtro bilateral. En cualquiera de los dos casos la metodología implica que la inyección de componentes pueda realizarse mediante diferentes alternativas. En el caso del algoritmo de evaluación de fusión se presenta un nuevo esquema que hace uso de procesos de clasificación, lo que permite evaluar los resultados del proceso de fusión de forma individual para cada tipo de cobertura de uso de suelo que se defina en el proceso de evaluación. Esta metodología permite complementar los procesos de evaluación tradicionales y puede facilitar el análisis del impacto de la fusión sobre determinadas clases de suelo. Finalmente, los algoritmos de detección de cambios propuestos abarcan dos enfoques. El primero está orientado a la obtención de mapas de sequía en datos multi-temporales a partir de índices espectrales. El segundo enfoque propone la utilización de un índice global de calidad espectral como filtro espacial. La utilización de dicho filtro facilita la comparación espectral global entre dos imágenes, esto unido a la utilización de umbrales, conlleva a la obtención de imágenes diferencia que contienen la información de cambio. ABSTRACT Remote sensing is a science relates to information gathering (spectral, spatial, temporal) about an object, area or phenomenon, through the analysis of data acquired by a device that is not in contact with the studied item. In general, data obtained from remote sensing to observe the earth’s surface are images, which are characterized by having a number of applications that are constantly evolving. Therefore, to solve the constant requirements of applications, new algorithms are proposed to improve or facilitate a particular process. With the purpose of developing these algorithms, each application needs mathematical methods, such as the multiresolution analysis which allows to analyze a signal at different scales. One of the options is the Dual Tree Complex Wavelet Transform (DT-CWT) which is implemented from two real filters and is characterized by invariance to translations. Among the advantages of this transform is its successful application in image fusion and change detection areas. In this regard, this thesis presents three algorithms applied to image fusion, assessment for image fusion and change detection in multitemporal images. For image fusion, it is presented a general outline that can be used with any multiresolution analysis technique; this algorithm is proposed at first with DT-CWT and then extends to an alternative method, the bilateral filter. In either case the method involves injection of components by various means. For fusion assessment, the proposal is focused on a scheme that uses classification processes, which allows evaluating merger results individually for each type of land use coverage that is defined in evaluation process. This methodology allows complementing traditional assessment processes and can facilitate impact analysis of the merger on certain kinds of soil. Finally, two approaches of change detection algorithms are included. The first is aimed at obtaining drought maps in multitemporal data from spectral indices. The second one takes a global index of spectral quality as a spatial filter. The use of this filter facilitates global spectral comparison between two images and by means of thresholding, allows imaging containing change information.

Adaptive Multi-Camera System for Real Time Object Detection

In this paper we present an adaptive multi-camera system for real time object detection able to efficiently adjust the computational requirements of video processing blocks to the available processing power and the activity of the scene. The system is based on a two level adaptation strategy that works at local and at global level. Object detection is based on a Gaussian mixtures model background subtraction algorithm. Results show that the system can efficiently adapt the algorithm parameters without a significant loss in the detection accuracy.

PDR with a foot-mounted IMU and ramp detection

The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.

Investigation of AlInN barrier ISFET structures with GaN capping for pH detection

The pH response of GaN/AlInN/AlN/GaN ion-sensitive field effect transistor (ISFET) on Si substrates has been characterized. We analyzed the variation of the surface potential (ΔVsp/ΔpH) and current (ΔIds/ΔpH) with solution pH in devices with the same indium content (17%, in-plane lattice-matched to GaN) and different AlInN thickness (6 nm and 10 nm), and compared with the literature. The shrinkage of the barrier, that has the effect to increase the transconductance of the device, makes the 2-dimensional electron density (2DEG) at the interface very sensitive to changes in the surface. Although the surface potential sensitivity to pH is similar in the two devices, the current change with pH (ΔIds/ΔpH), when biasing the ISFET by a Ag/AgCl reference electrode, is almost 50% higher in the device with 6 nm AlInN barrier, compared to the device with 10 nm barrier. When measuring the current response (ΔIds/ΔpH) without reference electrode, the device with thinner AlInN layer has a larger response than the thicker one, of a factor of 140%, and that current response without reference electrode is only 22% lower than its maximum response obtained using reference electrode.

Bayesian scene analysis for multi-camera 3D tracking and camera positioning

Los sistemas de seguimiento mono-cámara han demostrado su notable capacidad para el análisis de trajectorias de objectos móviles y para monitorización de escenas de interés; sin embargo, tanto su robustez como sus posibilidades en cuanto a comprensión semántica de la escena están fuertemente limitadas por su naturaleza local y monocular, lo que los hace insuficientes para aplicaciones realistas de videovigilancia. El objetivo de esta tesis es la extensión de las posibilidades de los sistemas de seguimiento de objetos móviles para lograr un mayor grado de robustez y comprensión de la escena. La extensión propuesta se divide en dos direcciones separadas. La primera puede considerarse local, ya que está orientada a la mejora y enriquecimiento de las posiciones estimadas para los objetos móviles observados directamente por las cámaras del sistema; dicha extensión se logra mediante el desarrollo de un sistema multi-cámara de seguimiento 3D, capaz de proporcionar consistentemente las posiciones 3D de múltiples objetos a partir de las observaciones capturadas por un conjunto de sensores calibrados y con campos de visión solapados. La segunda extensión puede considerarse global, dado que su objetivo consiste en proporcionar un contexto global para relacionar las observaciones locales realizadas por una cámara con una escena de mucho mayor tamaño; para ello se propone un sistema automático de localización de cámaras basado en las trayectorias observadas de varios objetos móviles y en un mapa esquemático de la escena global monitorizada. Ambas líneas de investigación se tratan utilizando, como marco común, técnicas de estimación bayesiana: esta elección está justificada por la versatilidad y flexibilidad proporcionada por dicho marco estadístico, que permite la combinación natural de múltiples fuentes de información sobre los parámetros a estimar, así como un tratamiento riguroso de la incertidumbre asociada a las mismas mediante la inclusión de modelos de observación específicamente diseñados. Además, el marco seleccionado abre grandes posibilidades operacionales, puesto que permite la creación de diferentes métodos numéricos adaptados a las necesidades y características específicas de distintos problemas tratados. El sistema de seguimiento 3D con múltiples cámaras propuesto está específicamente diseñado para permitir descripciones esquemáticas de las medidas realizadas individualmente por cada una de las cámaras del sistema: esta elección de diseño, por tanto, no asume ningún algoritmo específico de detección o seguimiento 2D en ninguno de los sensores de la red, y hace que el sistema propuesto sea aplicable a redes reales de vigilancia con capacidades limitadas tanto en términos de procesamiento como de transmision. La combinación robusta de las observaciones capturadas individualmente por las cámaras, ruidosas, incompletas y probablemente contaminadas por falsas detecciones, se basa en un metodo de asociación bayesiana basado en geometría y color: los resultados de dicha asociación permiten el seguimiento 3D de los objetos de la escena mediante el uso de un filtro de partículas. El sistema de fusión de observaciones propuesto tiene, como principales características, una gran precisión en términos de localización 3D de objetos, y una destacable capacidad de recuperación tras eventuales errores debidos a un número insuficiente de datos de entrada. El sistema automático de localización de cámaras se basa en la observación de múltiples objetos móviles y un mapa esquemático de las áreas transitables del entorno monitorizado para inferir la posición absoluta de dicho sensor. Para este propósito, se propone un novedoso marco bayesiano que combina modelos dinámicos inducidos por el mapa en los objetos móviles presentes en la escena con las trayectorias observadas por la cámara, lo que representa un enfoque nunca utilizado en la literatura existente. El sistema de localización se divide en dos sub-tareas diferenciadas, debido a que cada una de estas tareas requiere del diseño de algoritmos específicos de muestreo para explotar en profundidad las características del marco desarrollado: por un lado, análisis de la ambigüedad del caso específicamente tratado y estimación aproximada de la localización de la cámara, y por otro, refinado de la localización de la cámara. El sistema completo, diseñado y probado para el caso específico de localización de cámaras en entornos de tráfico urbano, podría tener aplicación también en otros entornos y sensores de diferentes modalidades tras ciertas adaptaciones. ABSTRACT Mono-camera tracking systems have proved their capabilities for moving object trajectory analysis and scene monitoring, but their robustness and semantic possibilities are strongly limited by their local and monocular nature and are often insufficient for realistic surveillance applications. This thesis is aimed at extending the possibilities of moving object tracking systems to a higher level of scene understanding. The proposed extension comprises two separate directions. The first one is local, since is aimed at enriching the inferred positions of the moving objects within the area of the monitored scene directly covered by the cameras of the system; this task is achieved through the development of a multi-camera system for robust 3D tracking, able to provide 3D tracking information of multiple simultaneous moving objects from the observations reported by a set of calibrated cameras with semi-overlapping fields of view. The second extension is global, as is aimed at providing local observations performed within the field of view of one camera with a global context relating them to a much larger scene; to this end, an automatic camera positioning system relying only on observed object trajectories and a scene map is designed. The two lines of research in this thesis are addressed using Bayesian estimation as a general unifying framework. Its suitability for these two applications is justified by the flexibility and versatility of that stochastic framework, which allows the combination of multiple sources of information about the parameters to estimate in a natural and elegant way, addressing at the same time the uncertainty associated to those sources through the inclusion of models designed to this end. In addition, it opens multiple possibilities for the creation of different numerical methods for achieving satisfactory and efficient practical solutions to each addressed application. The proposed multi-camera 3D tracking method is specifically designed to work on schematic descriptions of the observations performed by each camera of the system: this choice allows the use of unspecific off-the-shelf 2D detection and/or tracking subsystems running independently at each sensor, and makes the proposal suitable for real surveillance networks with moderate computational and transmission capabilities. The robust combination of such noisy, incomplete and possibly unreliable schematic descriptors relies on a Bayesian association method, based on geometry and color, whose results allow the tracking of the targets in the scene with a particle filter. The main features exhibited by the proposal are, first, a remarkable accuracy in terms of target 3D positioning, and second, a great recovery ability after tracking losses due to insufficient input data. The proposed system for visual-based camera self-positioning uses the observations of moving objects and a schematic map of the passable areas of the environment to infer the absolute sensor position. To this end, a new Bayesian framework combining trajectory observations and map-induced dynamic models for moving objects is designed, which represents an approach to camera positioning never addressed before in the literature. This task is divided into two different sub-tasks, setting ambiguity analysis and approximate position estimation, on the one hand, and position refining, on the other, since they require the design of specific sampling algorithms to correctly exploit the discriminative features of the developed framework. This system, designed for camera positioning and demonstrated in urban traffic environments, can also be applied to different environments and sensors of other modalities after certain required adaptations.

Islanding detection in Microgrids using Harmonic Signature

In recent years, there has been a growing interest in incorporating microgrids in electrical power networks. This is due to various advantages they present, particularly the possibility of working in either autonomous mode or grid connected, which makes them highly versatile structures for incorporating intermittent generation and energy storage. However, they pose safety issues in being able to support a local island in case of utility disconnection. Thus, in the event of an unintentional island situation, they should be able to detect the loss of mains and disconnect for self-protection and safety reasons. Most of the anti-islanding schemes are implemented within control of single generation devices, such as dc-ac inverters used with solar electric systems being incompatible with the concept of microgrids due to the variety and multiplicity of sources within the microgrid. In this paper, a passive islanding detection method based on the change of the 5th harmonic voltage magnitude at the point of common coupling between grid-connected and islanded modes of operation is presented. Hardware test results from the application of this approach to a laboratory scale microgrid are shown. The experimental results demonstrate the validity of the proposed method, in meeting the requirements of IEEE 1547 standards.

Bayesian scene analysis for multi-camera 3D tracking and camera positioning

Los sistemas de seguimiento mono-cámara han demostrado su notable capacidad para el análisis de trajectorias de objectos móviles y para monitorización de escenas de interés; sin embargo, tanto su robustez como sus posibilidades en cuanto a comprensión semántica de la escena están fuertemente limitadas por su naturaleza local y monocular, lo que los hace insuficientes para aplicaciones realistas de videovigilancia. El objetivo de esta tesis es la extensión de las posibilidades de los sistemas de seguimiento de objetos móviles para lograr un mayor grado de robustez y comprensión de la escena. La extensión propuesta se divide en dos direcciones separadas. La primera puede considerarse local, ya que está orientada a la mejora y enriquecimiento de las posiciones estimadas para los objetos móviles observados directamente por las cámaras del sistema; dicha extensión se logra mediante el desarrollo de un sistema multi-cámara de seguimiento 3D, capaz de proporcionar consistentemente las posiciones 3D de múltiples objetos a partir de las observaciones capturadas por un conjunto de sensores calibrados y con campos de visión solapados. La segunda extensión puede considerarse global, dado que su objetivo consiste en proporcionar un contexto global para relacionar las observaciones locales realizadas por una cámara con una escena de mucho mayor tamaño; para ello se propone un sistema automático de localización de cámaras basado en las trayectorias observadas de varios objetos móviles y en un mapa esquemático de la escena global monitorizada. Ambas líneas de investigación se tratan utilizando, como marco común, técnicas de estimación bayesiana: esta elección está justificada por la versatilidad y flexibilidad proporcionada por dicho marco estadístico, que permite la combinación natural de múltiples fuentes de información sobre los parámetros a estimar, así como un tratamiento riguroso de la incertidumbre asociada a las mismas mediante la inclusión de modelos de observación específicamente diseñados. Además, el marco seleccionado abre grandes posibilidades operacionales, puesto que permite la creación de diferentes métodos numéricos adaptados a las necesidades y características específicas de distintos problemas tratados. El sistema de seguimiento 3D con múltiples cámaras propuesto está específicamente diseñado para permitir descripciones esquemáticas de las medidas realizadas individualmente por cada una de las cámaras del sistema: esta elección de diseño, por tanto, no asume ningún algoritmo específico de detección o seguimiento 2D en ninguno de los sensores de la red, y hace que el sistema propuesto sea aplicable a redes reales de vigilancia con capacidades limitadas tanto en términos de procesamiento como de transmision. La combinación robusta de las observaciones capturadas individualmente por las cámaras, ruidosas, incompletas y probablemente contaminadas por falsas detecciones, se basa en un metodo de asociación bayesiana basado en geometría y color: los resultados de dicha asociación permiten el seguimiento 3D de los objetos de la escena mediante el uso de un filtro de partículas. El sistema de fusión de observaciones propuesto tiene, como principales características, una gran precisión en términos de localización 3D de objetos, y una destacable capacidad de recuperación tras eventuales errores debidos a un número insuficiente de datos de entrada. El sistema automático de localización de cámaras se basa en la observación de múltiples objetos móviles y un mapa esquemático de las áreas transitables del entorno monitorizado para inferir la posición absoluta de dicho sensor. Para este propósito, se propone un novedoso marco bayesiano que combina modelos dinámicos inducidos por el mapa en los objetos móviles presentes en la escena con las trayectorias observadas por la cámara, lo que representa un enfoque nunca utilizado en la literatura existente. El sistema de localización se divide en dos sub-tareas diferenciadas, debido a que cada una de estas tareas requiere del diseño de algoritmos específicos de muestreo para explotar en profundidad las características del marco desarrollado: por un lado, análisis de la ambigüedad del caso específicamente tratado y estimación aproximada de la localización de la cámara, y por otro, refinado de la localización de la cámara. El sistema completo, diseñado y probado para el caso específico de localización de cámaras en entornos de tráfico urbano, podría tener aplicación también en otros entornos y sensores de diferentes modalidades tras ciertas adaptaciones. ABSTRACT Mono-camera tracking systems have proved their capabilities for moving object trajectory analysis and scene monitoring, but their robustness and semantic possibilities are strongly limited by their local and monocular nature and are often insufficient for realistic surveillance applications. This thesis is aimed at extending the possibilities of moving object tracking systems to a higher level of scene understanding. The proposed extension comprises two separate directions. The first one is local, since is aimed at enriching the inferred positions of the moving objects within the area of the monitored scene directly covered by the cameras of the system; this task is achieved through the development of a multi-camera system for robust 3D tracking, able to provide 3D tracking information of multiple simultaneous moving objects from the observations reported by a set of calibrated cameras with semi-overlapping fields of view. The second extension is global, as is aimed at providing local observations performed within the field of view of one camera with a global context relating them to a much larger scene; to this end, an automatic camera positioning system relying only on observed object trajectories and a scene map is designed. The two lines of research in this thesis are addressed using Bayesian estimation as a general unifying framework. Its suitability for these two applications is justified by the flexibility and versatility of that stochastic framework, which allows the combination of multiple sources of information about the parameters to estimate in a natural and elegant way, addressing at the same time the uncertainty associated to those sources through the inclusion of models designed to this end. In addition, it opens multiple possibilities for the creation of different numerical methods for achieving satisfactory and efficient practical solutions to each addressed application. The proposed multi-camera 3D tracking method is specifically designed to work on schematic descriptions of the observations performed by each camera of the system: this choice allows the use of unspecific off-the-shelf 2D detection and/or tracking subsystems running independently at each sensor, and makes the proposal suitable for real surveillance networks with moderate computational and transmission capabilities. The robust combination of such noisy, incomplete and possibly unreliable schematic descriptors relies on a Bayesian association method, based on geometry and color, whose results allow the tracking of the targets in the scene with a particle filter. The main features exhibited by the proposal are, first, a remarkable accuracy in terms of target 3D positioning, and second, a great recovery ability after tracking losses due to insufficient input data. The proposed system for visual-based camera self-positioning uses the observations of moving objects and a schematic map of the passable areas of the environment to infer the absolute sensor position. To this end, a new Bayesian framework combining trajectory observations and map-induced dynamic models for moving objects is designed, which represents an approach to camera positioning never addressed before in the literature. This task is divided into two different sub-tasks, setting ambiguity analysis and approximate position estimation, on the one hand, and position refining, on the other, since they require the design of specific sampling algorithms to correctly exploit the discriminative features of the developed framework. This system, designed for camera positioning and demonstrated in urban traffic environments, can also be applied to different environments and sensors of other modalities after certain required adaptations.

Study of the effects of moving targets in SAR images for their detection.

Synthetic Aperture Radar’s (SAR) are systems designed in the early 50’s that are capable of obtaining images of the ground using electromagnetic signals. Thus, its activity is not interrupted by adverse meteorological conditions or during the night, as it occurs in optical systems. The name of the system comes from the creation of a synthetic aperture, larger than the real one, by moving the platform that carries the radar (typically a plane or a satellite). It provides the same resolution as a static radar equipped with a larger antenna. As it moves, the radar keeps emitting pulses every 1/PRF seconds —the PRF is the pulse repetition frequency—, whose echoes are stored and processed to obtain the image of the ground. To carry out this process, the algorithm needs to make the assumption that the targets in the illuminated scene are not moving. If that is the case, the algorithm is able to extract a focused image from the signal. However, if the targets are moving, they get unfocused and/or shifted from their position in the final image. There are applications in which it is especially useful to have information about moving targets (military, rescue tasks,studyoftheflowsofwater,surveillanceofmaritimeroutes...).Thisfeatureiscalled Ground Moving Target Indicator (GMTI). That is why the study and the development of techniques capable of detecting these targets and placing them correctly in the scene is convenient. In this document, some of the principal GMTI algorithms used in SAR systems are detailed. A simulator has been created to test the features of each implemented algorithm on a general situation with moving targets. Finally Monte Carlo tests have been performed, allowing us to extract conclusions and statistics of each algorithm.

Impacts of global change on iberian forest ecosystems inferred from the spanish national forest inventory = Impactos del cambio global en ecosistemas forestales ibéricos a partir del inventario forestal nacional

Los efectos del cambio global sobre los bosques son una de las grandes preocupaciones de la sociedad del siglo XXI. Algunas de sus posibles consecuencias como son los efectos en la producción, la sostenibilidad, la pérdida de biodiversidad o cambios en la distribución y ensamblaje de especies forestales pueden tener grandes repercusiones sociales, ecológicas y económicas. La detección y seguimiento de estos efectos constituyen uno de los retos a los que se enfrentan en la actualidad científicos y gestores forestales. En base a la comparación de series históricas del Inventario Forestal Nacional Español (IFN), esta tesis trata de arrojar luz sobre algunos de los impactos que los cambios socioeconómicos y ambientales de las últimas décadas han generado sobre nuestros bosques. En primer lugar, esta tesis presenta una innovadora metodología con base geoestadística que permite la comparación de diferentes ciclos de inventario sin importar los diferentes métodos de muestreo empleados en cada uno de ellos (Capítulo 3). Esta metodología permite analizar cambios en la dinámica y distribución espacial de especies forestales en diferentes gradientes geográficos. Mediante su aplicación, se constatarán y cuantificarán algunas de las primeras evidencias de cambio en la distribución altitudinal y latitudinal de diferentes especies forestales ibéricas, que junto al estudio de su dinámica poblacional y tasas demográficas, ayudarán a testar algunas hipótesis biogeográficas en un escenario de cambio global en zonas de especial vulnerabilidad (Capítulos 3, 4 y 5). Por último, mediante la comparación de ciclos de parcelas permanentes del IFN se ahondará en el conocimiento de la evolución en las últimas décadas de especies invasoras en los ecosistemas forestales del cuadrante noroccidental ibérico, uno de los más afectados por la invasión de esta flora (Capítulo 6). ABSTRACT The effects of global change on forests are one of the major concerns of the XXI century. Some of the potential impacts of global change on forest growth, productivity, biodiversity or changes in species assembly and spatial distribution may have great ecological and economic consequences. The detection and monitoring of these effects are some of the major challenges that scientists and forest managers face nowadays. Based on the comparison of historical series of the Spanish National Forest Inventory (NFI), this thesis tries to shed some light on some of the impacts driven by recent socio-economic and environmental changes on our forest ecosystems. Firstly, this thesis presents an innovative methodology based on geostatistical techniques that allows the comparison of different NFI cycles regardless of the different sampling methods used in each of them (Chapter 3). This methodology, in conjunction with other statistical techniques, allows to analyze changes in the spatial distribution and population dynamics of forest species along different geographic gradients. By its application, this thesis presents some of the first evidences of changes in species distribution along different geographical gradients in the Iberian Peninsula. The analysis of these findings, of species population dynamics and demographic rates will help to test some biogeographical hypothesis on forests under climate change scenarios in areas of particular vulnerability (Chapters 3, 4 and 5). Finally, by comparing NFI cycles with permanent plots, this thesis increases our knowledge about the patterns and processes associated with the recent evolution of invasive species in the forest ecosystems of North-western Iberia, one of the areas most affected by the invasion of allien species at national scale (Chapter 6).