Abstract
METRIC (TM) is a satellite-based surface energy balance model aimed at estimating and mapping crop evapotranspiration (ET). It has been applied to a large range of vegetation types, mostly annual crops. When applied to anisotropic woody canopies, such as olive orchards, extensions are required to algorithms for estimating the leaf area index (LAI), surface temperature, and momentum roughness length (Z(om)). The computation of the radiometric surface temperature needs to consider a three-source condition, thus differentiating the temperature of the canopy (T-c), of the shaded ground surface (T-shadow), and of the sunlit ground surface (T-sunlit). The estimation of the Z(om) for tall and incomplete cover is based upon the LAI and crop height using the Perrier equation. The LAI, Zorn, and temperature derived from METRIC after these adjustments were tested against field collected data with good results. The application of METRIC to a two year set of Landsat images to estimate ET of a super-intensive olive orchard in Southern Portugal produced good ET estimates that compared well with ground-based ET. The analysis of METRIC performance showed a quantitative improvement of ET estimates when applying the three-source condition for temperature estimation, as well as the Z(om) computation with the Perrier equation. Results show that METRIC can be used operationally to estimate and mapping ET of super-intensive olive orchards aiming at improving irrigation water use and management.

Abstract
The estimation of crop evapotranspiration (ETc) from the reference evapotranspiration (ETo) and a standard crop coefficient (K-c) in olive orchards requires that the latter be adjusted to planting density and height. The use of the dual K-c approach may be the best solution because the basal crop coefficient K-cb represents plant transpiration and the evaporation coefficient reproduces the soil coverage conditions and the frequency of wettings. To support related computations for a super intensive olive orchard, the model SIMDualKc was adopted because it uses the dual K-c approach. Alternatively, to consider the physical characteristics of the vegetation, the satellite-based surface energy balance model METRIC (TM) - Mapping EvapoTranspiration at high Resolution using Internalized Calibration - was used to estimate ETc and to derive crop coefficients. Both approaches were compared in this study. SIMDualKc model was calibrated and validated using sap-flow measurements of the transpiration for 2011 and 2012. In addition, eddy covariance estimation of ETc was also used. In the current study, METRIC (TM), was applied to Landsat images from 2011 to 2012. Adaptations for incomplete cover woody crops were required to parameterize METRIC. It was observed that ETc obtained from both approaches was similar and that crop coefficients derived from both models showed similar patterns throughout the year. Although the two models use distinct approaches, their results are comparable and they are complementary in spatial and temporal scales.

Abstract
This paper analyzes the impact of climate dynamics on vegetation growth for a rural mountainous region in northeastern Portugal. As a measure of vegetation growth, we use the normalized difference vegetation index (NDVI), which is based on the ten-day synthesis data set (S10) from Satellite Pour l'Observation de la Terre (SPOT-VEGETATION) imagery from 1998 to 2011. We test whether the dynamic growth pattern of the NDVI has changed due to climate variability, and we test the relationship of NDVI with temperature and available soil water (ASW). In order to do so, we use a time-frequency approach based on Kalman filter regressions in the time domain. The advantage of our approach is that it can be used even in the case where the sample size is relatively small. By estimating the important relationships in the time domain first and transferring them into the frequency domain, we are still able to derive a complete spectrum over all frequencies. In our example, we find a change of the cyclical pattern for the spring season and different changes if we take into account all seasons. In other words, we can distinguish between deterministic changes of the vegetation cycles and stochastic changes that only occur randomly. Deterministic changes imply that the data-generating process has changed (such as climate), whereas stochastic changes imply only temporary changes. We find that individual seasons undergo cyclical changes that are different from other seasons. Moreover, our analysis shows that temperature and ASW are the main drivers of vegetation growth. We can also recognize a shift of the relative importance away from temperature to soil water.

Abstract
This paper proposes a new, fast and computationally light weight methodology to pinpoint a robot in a structured scenario. The localisation algorithm performs a tracking routine to pinpoint the robot's pose as it moves in a known map, without the need for preparing the environment, with artificial landmarks or beacons. To perform such tracking routine, it is necessary to know the initial position of the vehicle. This paper describes the tracking routine and presents a solution to pinpoint that initial position in an autonomous way, using a multi-hypotheses strategy. This paper presents experimental results on the performance of the proposed method applied in two different scenarios: (1) in the Middle Size Soccer Robotic League (MSL), using artificial vision data from an omnidirectional robot and (2) in indoor environments using 3D data from a tilting Laser Range Finder of a differential drive robot (called RobVigil). This paper presents results comparing the proposed methodology and an Industrial Positioning System (the Sick NAV350), commonly used to locate Autonomous Guided Vehicles (AGVs) with a high degree of accuracy in industrial environments.

Abstract
The sports community needs technological aid to extract accurate statistics and performance data from both practice sessions and games. To obtain such information, players must be tracked over time and their movements processed so that individual actions and team plays are simultaneously analyzed. In order to perform this analysis in an automated, formal and accurate way, the authors developed a cost conscientious processing system fed by two overhead cameras (roughly one video stream for each half-field). Players are detected by vest colors, and Fuzzy Logic is used to allow for a given color to be shared by different teams. Color models for the background and the teams are dynamic over time to make up for changes in natural lighting conditions and consequent color changes. Player tracking is further enhanced using Kalman Filtering. Some examples of the analysis, made possible by the proposed system, are shown. Results are based on videos collected during the Portuguese Handball SuperCup competition for the year 2011.

Abstract
The adoption of NAO humanoid robots in the RoboCup Standard Platform League (SPL) broguht a new set of challenges on this league on the computer vision area. This paper presents a new color indexing mode and a study of the impact of the reduction of the color spectrum, to be processed on the classification, segmentation and object detection, in a NAO robot, playing on the SPL league. The experiments were performed in the context of the Portuguese Team concluding that a 21 bit look-up table may replace the current 24 bit table used.