Abstract
Walking robots are well known for being able to walk over rough terrain and adapt to various environments. Hexapod robots are chosen because of their better stability and higher number of different gaits. However, having to hold the whole weight of the body and a large number of actuators makes all walking robots less energetically efficient than wheeled machines. Special methods for energy consumption optimization must be found. In this paper, hexapod robot energy consumption dependence on body elevation and step height is presented. Three main hexapod gaits are used: tripod, tetrapod and wave. Experimental results show that energy consumption does not depend on body elevation or gait. Although, higher steps increases the power consumption. Therefore, when walking over even terrain, lower step heights along with higher body elevation must be selected for tripod or tetrapod gait in order to surpass ground irregularities but still maintain maximum energetic efficiency.

Abstract
In most real multi-robot applications, such as search-and-rescue, cooperative robots have to move to complete their tasks while maintaining communication among themselves without the aid of a communication infrastructure. However, initially deploying and ensuring a mobile ad-hoc network in real and complex environments is an arduous task since the strength of the connection between two nodes (i.e., robots) can change rapidly in time or even disappear. An extension of the Particle Swarm Optimization to multi-robot applications has been previously proposed and denoted as Robotic Darwinian PSO (RDPSO). This paper contributes with a further extension of the RDPSO, thus integrating two research aspects: (i) an autonomous, realistic and fault-tolerant initial deployment strategy denoted as Extended Spiral of Theodorus (EST); and (ii) a fault-tolerant distributed search to prevent communication network splits. The exploring agents, denoted as scouts, are autonomously deployed using supporting agents, denoted as rangers. Experimental results with 15 physical scouts and 3 physical rangers show that the algorithm converges to the optimal solution faster and more accurately using the EST approach over the random deployment strategy. Also, a more fault-tolerant strategy clearly influences the time needed to converge to the final solution, but is less susceptible to robot failures.

Abstract
This paper presents a survey on multi-robot search inspired by swarm intelligence by further classifying and discussing the theoretical advantages and disadvantages of the existing studies. Subsequently, the most attractive techniques are evaluated and compared by highlighting their most relevant features. This is motivated by the gradual growth of swarm robotics solutions in situations where conventional search cannot find a satisfactory solution. For instance, exhaustive multi-robot search techniques, such as sweeping the environment, allow for a better avoidance of local solutions but require too much time to find the optimal one. Moreover, such techniques tend to fail in finding targets within dynamic and unstructured environments. This paper presents experiments conducted to benchmark five state-of-the-art algorithms for cooperative exploration tasks. The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration is useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue). Moreover, the RDPSO is further compared with the best performing algorithms within a population of 14 e-pucks. It is observed that the RDPSO algorithm converges to the optimal solution faster and more accurately than the other approaches without significantly increasing the computational demand, memory and communication complexity.

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.