Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

Generating reference models for structurally complex data: application to the stabilometry medical domain

We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of singlevalued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc.

Detecting and characterising ramp events in wind power time series

In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

Multiple Hashing Integration for Real-Time Large Scale Part-to-Part Video Matching

A real-time large scale part-to-part video matching algorithm, based on the cross correlation of the intensity of motion curves, is proposed with a view to originality recognition, video database cleansing, copyright enforcement, video tagging or video result re-ranking. Moreover, it is suggested how the most representative hashes and distance functions - strada, discrete cosine transformation, Marr-Hildreth and radial - should be integrated in order for the matching algorithm to be invariant against blur, compression and rotation distortions: (R; _) 2 [1; 20]_[1; 8], from 512_512 to 32_32pixels2 and from 10 to 180_. The DCT hash is invariant against blur and compression up to 64x64 pixels2. Nevertheless, although its performance against rotation is the best, with a success up to 70%, it should be combined with the Marr-Hildreth distance function. With the latter, the image selected by the DCT hash should be at a distance lower than 1.15 times the Marr-Hildreth minimum distance.

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

Modelo multivariante de evolución en el tiempo de la calidad percibida en la educación superior

Las instituciones de educación superior deben gestionar eficaz y eficientemente sus procesos de captación de nuevos estudiantes, y con este objetivo necesitan mejorar su comprensión sobre los antecedentes que inciden en la intención de recomendarlas. Por lo que esta Tesis Doctoral se centra en el estudio y análisis de las componentes de la calidad del servicio de la educación superior, como antecedentes de la intención de recomendación de una institución universitaria. El enfoque que se adopta en esta investigación integra las dimensiones de calidad docente y de calidad de servicio e incorpora en el análisis la valoración global de la carrera. Paralelamente se contempla la moderación de la experiencia y el desempeño académico del estudiante. En esta Tesis Doctoral se hace uso de la Encuesta de Calidad de la Universidad ORT Uruguay cedida a los autores para su explotación con fines de investigación. Los estudiantes cumplimentan la encuesta semestralmente con carácter obligatorio en una plataforma en línea autoadministrada, que permite identificar las valoraciones realizadas por los estudiantes a lo largo de su paso por la universidad. Por lo que la base de datos es un panel no balanceado que consta de 195.058 registros obtenidos, a partir de 7.077 estudiantes en 17 semestres (marzo de 2003 a 2011). La metodología se concreta en los Modelos de Ecuaciones Estructurales, que proporciona una serie de ventajas con respecto a otras aproximaciones. Una de las más importantes es que permite al investigador introducir información a priori y valorar su inclusión, además de reformular las modelizaciones propuestas desde una perspectiva multi-muestra. En esta investigación se estiman los modelos con MPLUS 7. Entre las principales conclusiones de esta Tesis Doctoral cabe señalar que las percepciones de calidad, servicio, docencia y carrera, inciden positivamente en la intención de recomendar la universidad, y que la variable experiencia del estudiante modera dichas relaciones. Los resultados señalan, en general, que a medida que los estudiantes avanzan en su carrera, los efectos totales de la percepción de la calidad del servicio en la calidad global de la carrera y en la intención de recomendar la universidad son mayores que los efectos que tiene la percepción de calidad de la docencia. Estos hallazgos señalan la necesidad que tienen estas instituciones de educación superior de incorporar en su planificación estratégica la segmentación de los estudiantes según su experiencia. ABSTRACT For institutions of higher education to effectively and efficiently manage their processes for attracting new students, they need to understand the influences that activate student intentions to recommend a program and/or college. This Thesis describes research identifying the quality components of a university that serve as antecedents of student intentions to recommend. The research design integrates teaching and service dimensions of higher education, as well as measures of student perceptions of the overall quality of a program. And introduces the student quality and student experience during the program as moderators of these relationships. This Thesis makes use of the Quality Survey of the Universidad ORT Uruguay ceded to the authors for their exploitation for research purposes. The students complete the survey each semester in a self-administered online platform, which allows to identify the assessments conducted by the students throughout its passage by the university. So that the database is an unbalanced panel consisting of 195.058 records obtained from 7.077 students in 17 semesters (march 2003 to 2011). The methodology of analysis incorporated Simultaneous Equation Models, which provides a number of advantages with respect to other approaches. One of the most important is that it allows the researcher to introduce a priori information and assess its inclusion, in addition to reformulate the modellings proposals with a multi-sample approach. In this research the models are estimated with MPLUS 7. Based on the findings, student perceptions of quality, service, teaching and program, impact positively the intent to recommend the university, but the student’s experience during the program moderates these relationships. Overall, the results indicate that as students advance in the program, the full effects of the perception of service quality in the overall quality of the program and in the intention to recommend the university, outweigh the effects of the perceived teaching quality. The results indicate the need for institutions of higher education to incorporate in its strategic planning the segmentation of the students according to their experience during the program.