Audiencia tradicional frente a audiencia social: un análisis comparativo en el prime-time televisivo

La interacción de redes sociales y productos televisivos ha dado pie al nacimiento de la televisión social en la que el espectador participa activamente en el desarrollo de los espacios. Este fenómeno emergente está siendo objeto de múltiples investigaciones en el campo de las audiencias por las posibilidades y el potencial que supone para el medio a la hora de conocer e interactuar con los espectadores. El objetivo de este trabajo es realizar una comparación entre la audiencia real y la audiencia social (o impacto social) de los programas emitidos en prime-time durante varias semanas de los meses de abril y mayo de 2013. Esta investigación se centra en Twitter por ser la red social que concentra gran parte de los debates sobre televisión (Gallego, 2013). Para ello se plantean las siguientes hipótesis de partida: 1- No existe paralelismo entre los cinco programas más vistos en televisión con aquellos que se sitúan entre los cinco con mayor audiencia social del mismo día. 2- El éxito de un programa en audiencia social no depende exclusivamente de su formato. Para alcanzar los objetivos de la investigación se estudian los datos de audiencia real procedentes de Kantar Media, así como los de impacto social facilitados Tuitele y Global-In-Media.

Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.