Abstract
Unmanned aerial vehicles (UAVs) are used nowadays as a standard tool to derive very high-resolution geospatial data. However, UAV payload limitation imposes the use of not such reliable hardware affecting the georeferencing precision. In the literature it is possible to find numerous studies investigating the parameters influencing UAV-based products quality. Even if new photogrammetry methods could, in theory, avoid the use of ground control points (GCPs), they still play a key role to assure quality products. Nevertheless, usually only the number and distribution of GCPs are taking into account, since both change the geometric accuracy of the final products. In order to improve the understanding of the actual influence of GCPs, in this study we evaluate how can different physical characteristics affect GCPs identification in aerial images. The results demonstrate that GCPs' color, material, size and shape, among others, may influence a precise identification in aerial imagery.

Abstract
In this study machine learning methods were applied to RGB data obtained by an unmanned aerial vehicle (UAV) to assess this effectiveness in vineyard classification. The very high-resolution UAV-based imagery was subjected to a photogrammetric processing allowing the generation of different outcomes: orthophoto mosaic, crop surface model and five vegetation indices. The orthophoto mosaic was used in an object-based image analysis approach to group pixels with similar values into objects. Three machine learning techniques-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were applied to classify the data into four classes: grapevine, shadow, soil and other vegetation. The data were divided with 22% (n=240, 60 per class) for training purposes and 78% (n = 850) for testing purposes. The mean value of the objects from each feature were used to create a dataset for prediction. The results demonstrated that both RF and ANN models showed a good performance, yet the RF classifier achieved better results.

Abstract
With the continuous escalation of global warming and consequent water scarcity, techniques to optimize water use of irrigation in agriculture are needed. Thus, deficit irrigation strategies (DI) can be used for a sustainable water usage. However, it is necessary to recursively monitor plant response under DI to ensure their productivity and prevent from severe water stress. The goal of this study is to assesscanopy and soil surface temperatures of olive trees under different irrigation strategies, through thermal infrared images obtained by Unmanned Aerial Vehicle (UAV). The temperatures from the different irrigation strategies were analysed with three approaches using the difference between canopy and air temperatures (Tc-Ta). The use of UAV-based thermal infrared imagery has proven to be extremely useful to the estimation of olive canopy and soil surface temperatures, which allow to discriminate different irrigation treatments.

Abstract
This work focuses on a reservoir with water leaks since its construction, the Beninar reservoir. The purpose of this reservoir was to regulate the Adra River basin, lying between the provinces of Almeria and Granada, and located south of Sierra Nevada Mountains (in the Inner Zones of the Betic Cordilleras, SE Spain). This basin extends over 746 km(2), at an altitude of 2780 m, with a very rough terrain and frequent torrential water flow. Due to the continuous extension of greenhouses in the east and west parts of Almeria, the water demand for agriculture and urban consumption increases day by day. As a consequence, aquifers are being overexploited, causing the current system to not be sustainable for a long time, that is, the storage capacity of the underground media and their possible contributions to an efficient management of resources have not been adequately taken into account. The Beninar dam has always had problems with water leaks. The dam was built even knowing that the land was not the most suitable, due to the frequent earth movements that took place in the town of Beninar, which was submerged beneath the waters of the reservoir. In this work, we process multi-temporal SAR datasets coming from the C-band satellites ERS-1/2, Envisat, and Sentinel-1A/B using MT-InSAR techniques, being able to monitor the deformation behavior of this dam for a long time period of more than 25 years, from 1992 to 2018.

Abstract
Nowadays, many drone models are available, designed for the most diverse applications. However, the various models fall into one of two types of drone: multi-copter or fixed-wing. The first type of drone consumes a lot of energy, since motors have to turn during all the flight. The former type of drone, in general needs a runway to take-off and landing. Moreover, they fly fast and cannot be motionless, which is unsuitable for many applications. In this paper we present a new paramotor drone, conceived and designed to overcome the highlighted limitations and to be a low-cost solution adapted for most applications. The selection of the various components of the presented prototype was based on a very thorough study, considering aerodynamic and efficiency criteria.

Abstract
Digital Marketing is a decisive advantage for the business strategy of the hotel sector. Besides being a powerful tool for implementing tourism marketing actions and policies also enables the monitoring and measurement of results; helping to keep them at a level of effectiveness and success or to improve them. Digital Marketing can also be a strong ally in combating unforeseen economic crises in the hotel sector, as is the case of the current situation that we are experiencing caused by COVID-19. In this article, the Digital Marketing strategy implemented by HSL from the point of view of the digital consumer will be analyzed, through an exploratory analysis of its website, online booking platforms, social networks and how these digital tools have helped HSL throughout the pandemic. COVID-19.