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Background: Demographic changes in the western world entail new clinical approaches and challenges in older persons. Low skeletal muscle mass and low physical performance in older persons are both predisposing conditions for disability and obtaining knowledge in this cohort is essential. Aim: The primary aim of the study was to analyze a broader spectrum of gait characteristics within this specific population and differentiate them across different test conditions. Methods: Two centers participating at the SPRINTT project with hi-tech gait analysis available conducted a cross-sectional descriptive study on N = 115 community-dwelling older persons with low muscle mass and physical performance. Reference values of 13 gait parameters were collected across different conditions: usual gait speed, fast gait speed, and usual gait speed while simultaneously naming animals. Results and discussion: This study shows the first spatio-temporal reference values in a community-dwelling older population composed of individuals with low skeletal muscle mass and low physical performance. In comparison to the normative spatio-temporal gait parameters in older persons reported in the literature, this population showed some differences. The mean gait speed was lower than 1 m/s, considered as a cutoff for vulnerable community-dwelling individuals, which corresponds to a greater risk of falls, hospitalization, and mortality. The stride length variability was higher, exposing to a greater risk of falling, and was also associated with a higher risk of developing cognitive decline. Conclusion: This study represents the first step in the development of quantitative reference values in community-dwelling older persons with low physical performance and low skeletal muscle mass.

This study provides insight into the assessment of canopy biophysical parameter retrieval using passive sensors and specifically into the quantification of tree height in a discontinuous canopy using a low-cost camera on board an unmanned aerial vehicle (UAV). The UAV was a 2-m wingspan fixed-wing platform with 5.8kg take-off weight and 63km/h ground speed. It carried a consumer-grade RGB camera modified for color-infrared detection (CIR) and synchronized with a GPS unit. In this study, the configuration of the electric UAV carrying the camera payload enabled the acquisition of 158ha in one single flight. The camera system made it possible to acquire very high resolution (VHR) imagery (5cmpixel-1) to generate ortho-mosaics and digital surface models (DSMs) through automatic 3D reconstruction methods. The UAV followed pre-designed flight plans over each study site to ensure the acquisition of the imagery with large across- and along-track overlaps (i.e. >80%) using a grid of parallel and perpendicular flight lines. The validation method consisted of taking field measurements of the height of a total of 152 trees in two different study areas using a GPS in real-time kinematic (RTK) mode. The results of the validation assessment conducted to estimate tree height from the VHR DSMs yielded R2=0.83, an overall root mean square error (RMSE) of 35cm, and a relative root mean square error (R-RMSE) of 11.5% for trees with heights ranging between 1.16 and 4.38m. An assessment conducted on the effects of the spatial resolution of the input images acquired by the UAV on the photo-reconstruction method and DSM generation demonstrated stable relationships for pixel resolutions between 5 and 30cm that rapidly degraded for input images with pixel resolutions lower than 35cm. RMSE and R-RMSE values obtained as a function of input pixel resolution showed errors in tree quantification below 15% when 30cmpixel-1 resolution imagery was used to generate the DSMs. The study conducted in tw

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. © 2014 Elsevier Ltd.