Abstract

Abstract
Animal experiments have gained importance in human diseases studies, namely neurological diseases and its biomechanical and physiological aspects. As a model of human disease, the rat offers many advantages over other organisms. For the biomechanical aspects of locomotion these studies are based on the analysis of animals' kinetic parameters, accessed through a locomotion measurement system. However, these systems are not yet thoroughly developed, are still scarce and are also very expensive when developed for studies using small rodents. In this paper, a system capable of measuring contact forces of small rodents is presented. The platform hardware is based on a 5 x 3 matrix of ultra-sensitive force sensors that produce a set of signals acquired in a LabVIEW (TM) environment, used for data acquisition and processing. The post processing steps include the removal of null data, curve normalization related to the rat's weight and expressed as percentage of passage, resulting in a gait pattern. The proposed cost-effective system has achieved excellent results regarding the locomotion profile of healthy animals.

Abstract
Traditional imageryprovided, for example, by RGB and/or NIR sensorshas proven to be useful in many agroforestry applications. However, it lacks the spectral range and precision to profile materials and organisms that only hyperspectral sensors can provide. This kind of high-resolution spectroscopy was firstly used in satellites and later in manned aircraft, which are significantly expensive platforms and extremely restrictive due to availability limitations and/or complex logistics. More recently, UAS have emerged as a very popular and cost-effective remote sensing technology, composed of aerial platforms capable of carrying small-sized and lightweight sensors. Meanwhile, hyperspectral technology developments have been consistently resulting in smaller and lighter sensors that can currently be integrated in UAS for either scientific or commercial purposes. The hyperspectral sensors' ability for measuring hundreds of bands raises complexity when considering the sheer quantity of acquired data, whose usefulness depends on both calibration and corrective tasks occurring in pre- and post-flight stages. Further steps regarding hyperspectral data processing must be performed towards the retrieval of relevant information, which provides the true benefits for assertive interventions in agricultural crops and forested areas. Considering the aforementioned topics and the goal of providing a global view focused on hyperspectral-based remote sensing supported by UAV platforms, a survey including hyperspectral sensors, inherent data processing and applications focusing both on agriculture and forestrywherein the combination of UAV and hyperspectral sensors plays a center roleis presented in this paper. Firstly, the advantages of hyperspectral data over RGB imagery and multispectral data are highlighted. Then, hyperspectral acquisition devices are addressed, including sensor types, acquisition modes and UAV-compatible sensors that can be used for both research and commercial purposes. Pre-flight operations and post-flight pre-processing are pointed out as necessary to ensure the usefulness of hyperspectral data for further processing towards the retrieval of conclusive information. With the goal of simplifying hyperspectral data processingby isolating the common user from the processes' mathematical complexityseveral available toolboxes that allow a direct access to level-one hyperspectral data are presented. Moreover, research works focusing the symbiosis between UAV-hyperspectral for agriculture and forestry applications are reviewed, just before the paper's conclusions.

Abstract
The usage of small-sized unmanned aerial systems (UAS) has increased in the last years, in many different areas, being agriculture and forestry those who benefit the most from this relatively new remote sensing platform. Leaf area index, canopy and plant volume are among the parameters that can be determined using the very high resolution aerial data obtained by sensors coupled in unmanned aerial vehicles (UAV). This remote sensing technology affords the possibility of monitoring the vegetative development, identifying different types of issues, enabling the application of the most appropriated treatments in the affected areas. In this paper, a methodology allowing to perform multi-temporal UAS-based data analysis obtained by different sensors is proposed. A case study in vineyards and chestnuts is used to prove the benefits of continuous crop monitoring in its management and productivity of agroforestry parcels/activities. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
In the last decade, SAR interferometry techniques, especially those that use time series analysis experienced a strong development in both, methodologies and applications, becoming an operational tool for deformation monitoring. The emergence of a growing number of SAR dedicated missions combined with the increasing interest from academics, but also private research groups, reflected in the number of available software packages developed with interferometric analysis purposes, were the major responsible for the InSAR/MTI achievements occurred over the past few years. Many free-of-charge (freeware or open-source) and commercial software packages exist. Due to its proven reliability and freeware distribution among the scientific community, Stanford Method for Persistent Scatterers/Multi-Temporal Interferometry (StaMPS/MTI) implementation, is widely used for ground deformation monitoring. This paper presents viStaMPS v1.2, a collaborative scientific project that appeared with three major purposes: (1) facilitate the usage by users nonfamiliar with the specificities of the programming language that supports StaMPS; (2) implement several visualization tasks not available in the StaMPS standard approach requiring that each user to develop its own code for visualization and interpretation purposes and (3) create a collaborative research project, continually under development counting on the dynamism of its users to improve and/or add new features. (C) 2014 Published by Elsevier Ltd.

Abstract
Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery. Simultaneously, there has been an exponential increase in the development of sensors and instruments that can be installed in UAV platforms. By combining the aforementioned factors, unmanned aerial system (UAS) setups composed of UAVs, sensors, and ground control stations, have been increasingly used for remote sensing applications, with growing potential and abilities. This paper's overall goal is to identify advantages and challenges related to the use of UAVs for aerial imagery acquisition in forestry and coastal environments for preservation/prevention contexts. Moreover, the importance of monitoring these environments over time will be demonstrated. To achieve these goals, two case studies using UASs were conducted. The first focuses on phytosanitary problem detection and monitoring of chestnut tree health (Padrela region, Valpacos, Portugal). The acquired high-resolution imagery allowed for the identification of tree canopy cover decline by means of multi-temporal analysis. The second case study enabled the rigorous and non-evasive registry process of topographic changes that occurred in the sandspit of Cabedelo (Douro estuary, Porto, Portugal) in different time periods. The obtained results allow us to conclude that the UAS constitutes a low-cost, rigorous, and fairly autonomous form of remote sensing technology, capable of covering large geographical areas and acquiring high precision data to aid decision support systems in forestry preservation and coastal monitoring applications. Its swift evolution makes it a potential big player in remote sensing technologies today and in the near future.