Abstract
For Precise Agriculture purposes, several steps of a maize crop-system were recorded by the use of a GPS receiver with EGNOS and RTK capabilities. The field is about 35 km far from two GNSS CORS, one from RENEP, operated by IGS, and the other from SERVIR, operated by IGEoE. Both networks disseminate real-time GNSS data streams over the Internet using the NTRIP protocol. The GNSS data streams from RENEP reference stations (including validated station coordinates) provide the user with a real-time access to the ETRS89 and, those same streams from IGEoE, a military institution, are in ITRS, allowing large scale scientific applications. The validation of the EGNOS and the RTK solutions, obtained in the two TRS systems, was achieved by the results from post-processed measurements. RTK solutions, when compared to the post-processed values in the same TRS, show sub-decimeter accuracy what is enough for many of the Precision Agriculture studies. However, the two RTK solutions have a translation with a magnitude of the order of 0.5 m that can be explained by the independence of the ETRS89 on the continental drift. Indeed, at the zone where the field is located, while the ETRFyy Cartesian coordinates have velocities less than 1 mm/year, the ITRFyy Cartesian coordinates have velocities greater than 1 cm/year, what give rise to a point position variation with a magnitude of 2.5 cm/year. In order to correlate the tractor velocity, during a pre-emergence herbicide application, to the terrain slope, the field orthometric heights were obtained by the use of GRS80 ondulations, on a 1.5' x 1.5'grid, in the local Portuguese geoid model GeodPT08. The global precision of this model is estimated in 4 cm, which is within the error for the real time solutions obtained.

Abstract
Water and energy balance interactions with vegetation in mountainous terrain are influenced by topographic effects, spatial variation in vegetation type and density, and water availability. This is the case for the mountainous areas of northern Portugal, where ancestral irrigated meadows (lameiros) are a main component of a complex vegetation mosaic. The widely used surface energy balance model METRIC was applied to four Landsat images to determine the spatial and temporal distribution of the energy balance terms in the identified land cover types (LCT). A discussion on the variability of evapotranspiration (ET) through the various vegetation types was supported by a comparison between the respective crop coefficients and those available in the literature corresponding to the LC, which has shown the appropriateness of METRIC estimates of ET. METRIC products derived from images of May and June - NDVI, surface temperature, net radiation, soil heat flux, sensible heat flux, and ET - were used to characterize the LCTs, through application of principal component analysis. Three principal components explained the variance of observed variables and their varimax rotated loadings allowed a good explanation of the behaviour of the explanatory variables in association with the LCTs. Information gained contributes to improve the characterization of the study area and may further support conservation and management of these mountain landscapes.

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In this paper, a new fast and computationally light weight methodology is proposed to pinpoint a robot in a structured scenario. The localisation algorithm performs a tracking routine to pinpoint the robot's position as it moves in a known map. To perform such tracking routine, it is necessary to know the initial position of the vehicle. This paper briefly describes the tracking routine and presents a solution to pinpoint that initial position in an autonomous way. Experimental results on the performance of the proposed methodology are presented in this paper in two different scenarios: 1) in the Middle Size Soccer Robotic League (MSL), with artificial vision data from an omni directional robot, and 2) in an indoor environment with a Laser Range Finder data from a differential traction robot (called Robot Vigil). © 2012 IEEE.

Abstract
Video segmentation is one of the most important parts of a vision system which allows partitioning each frame into homogeneous regions that share a common property. This work proposes a new methodology that aggregates three different techniques: background subtraction, region growing and a pseudo Fuzzy colour model to define colour subspaces that characterize each class. In addition, the pseudo Fuzzy colour model allows a given colour to belong to more than one class and enables the expansion ofthe classes through a dynamic model based on belonging and persistence information. In case of shared colours among classes, regional features are searched in order to determine the object's class. Tests with test and real videos of sports footages show promising results.