Abstract
Tourism is an information-intensive sector and today's tourist is hungry for information about everything that surrounds him and is increasingly demanding about the mechanisms that are made available for access and interaction with information. This new reality requires rethinking many of the existing solutions. In this context, the Internet of Things (IoT) is revolutionizing the way we think, design and implement Information and Communication Technologies (ICT) solutions for the tourism sector, opening up unprecedented opportunities in terms of how we can provide information and services. This new reality is enabling reengineering the interaction-process between tourists and its surrounding space. For heritage spaces, typically visited by countless tourists, there is an opportunity to rethink the entire process of supporting the interpretation and fruition of heritage, carried out by tourists. In order to understand how this reengineering can be carried out, a review of the state of the art is carried out with regard to how the IoT has been applied in the context of tourism. Then, the methodology that governed the creation of a conceptual model based on IoT is clearly defined, capable of transforming the way physical spaces of tourist interest can be interpreted and how their fruition can be improved. Particular importance is given to the contextualization of the experience, since the information provided must be adjusted to the visitor, according to their profile, which may necessarily reflect different types of interest or prior knowledge about the space. Finally, this article presents a conceptual model where its components are described and where it is discussed how the model can transform the experience of visiting touristic spaces and how tourists can access information and services that entities promoters of these spaces wish to make available. In the dissertation carried out, important aspects of the model and the gains it may generate for the revitalization and promotion of heritage are discussed.

Abstract
To identify the most frequently developed topics in the area of Big Data and Digital Marketing, a quantitative analysis was developed in December 2020. This analysis was focused on 750 publications and later on 67 publications on Big Data and Digital Marketing from the Scopus database. A bibliometric analysis was developed using the VOSviewer software and the technique of term co-occurrence and author co-authorship. Clusters were found for each of the analyzed situations.

Abstract
In this work, X-ray fluorescence (XRF) and Laser-induced breakdown spectroscopy (LIBS) analyses were applied to samples of quartz, montebrasite, and turquoise hydrothermal veins in the Argemela Tin Mine (Central Portugal). Montebrasite (LiAl(PO4)(OH,F)) is potentially the main ore mineral; with its alteration, lithium (Li) can disseminate into other minerals. A hand sample was cut and analyzed by XRF and LIBS for several elements of interest including Cu, P, Al, Si, and Li. Although XRF cannot measure Li, results from its analysis are effective for distinguishing turquoise from montebrasite. LIBS analysis complemented this study, making it possible to conclude that turquoise does not contain any significant Li in its structure. The difference in spot size between the techniques (5 mm vs. 300 mu m for XRF and LIBS, respectively) resulted in a poorer performance by XRF in accurately identifying mixed minerals. A thin section was petrographically characterized and mapped using LIBS. The mapping results demonstrate the possibility of the successful identification of minerals and their alterations on a thin section. The results of XRF analysis and LIBS mapping in petrographic sections demonstrate the efficacy of these methods as tools for element and mineral identification, which can be important in exploration and mining phases, complementing more traditional techniques.

Abstract
Predawn leaf water potential (Psi(pd)) is widely used to assess plant water status. Also, pigments concentration work as proxy of canopy's water status. Spectral data methods have been applied to monitor and assess crop's biophysical variables. This work developed two models to estimate Psi(pd) using a hand-held spectroradiometer (400-1010 nm) to obtain canopy and foliar reflectance in four dates of 2018 and a pressure chamber to measure Psi(pd). Two modelling approaches, combining spectral data and several machine learning algorithms (MLA), were used to estimate Psi(pd) in a commercial vineyard in the Douro Wine Region. The first approach estimated Psi(pd) through vine's canopy reflectance; several vegetation indices (VIs) were computed and selected, namely the SPVi(opt)(1_)(950;596;521;) SPVIopt2_896;880;901; PRI_CI2(opt_539;560,573;716 )and NPCIopt_983;972, as well as a time-dynamic variable based on Psi(pd) (Psi(pd)_(0)). The second modelling approach is based on pigments' concentrations; several VIs were optimized for non-correlated pigments of vine's leaves, assessed by its hyperspectral reflectance. The following variables for Psi(pd) estimation were selected through stepwise forward method: Psi(pd)_(0); NRIgreen_LUT520;532; NRIgreen_LWC540;551. The B-MARS algorithm performed the best results for both modelling approaches, presenting a RRMSE in both validation modelling approaches between 13-14%.

Abstract
Roads and roadsides provide dispersal channels for non-native invasive alien plants (IAP), many of which hold devastating impacts in the economy, human health, biodiversity and ecosystem functionality. Remote sensing is an essential tool for efficiently assessing and monitoring the dynamics of IAP along roads. In this study, we explore the potentialities of object based image analysis (OBIA) approach to map several invasive plant species along roads using very high spatial resolution imagery. We compared the performance of OBIA approaches implemented in one open source software (OTB/Monteverdi) against those available in two proprietary pro-grams (eCognition and ArcGIS). We analysed the images by two sequential processes. First, we obtained a land-cover map for 15 study sites by segmenting the images with the algorithms Mean Shift Segmentation (MSS) and Multiresolution Segmentation (MRS), and by classifying the segmented images with the algorithms Support Vector Machine (SVM), Nearest Neighbour Classifier (NNC) and Maximum Likelihood Classifier (MLC). We created a mask using the polygons classified as non-vegetation to crop the images of the 15 study sites. Second, we repeated the previous segmentation and classification steps over the 15 masked images of vegetated areas using the same algorithms. OTB/Monteverdi, with MSS and SVM algorithms, showed to be a good software for land-cover mapping (OA = 87.0%), as well as ArcGIS, with MSS and MLC algorithms (OA = 84.3%). However, these two programs, using the same segmentation algorithms, did not achieve good accuracy results when mapping IAP species (OA(OTB/Monteverdi) = 63.3%; OAA(cos = 45.7%). eCognition, with MRS and NNC algorithms, reached better classification results in both land-cover and IAP maps (OA(Land-cover )= 95.7%; OA(Invasive-plant )= 92.8%). 'Bare soil' and 'Road', and 'A. donax' were the classes with best and worst overall accuracy, respectively, when mapping land-cover classes in the three programs. 'Other trees' was the class with the most accurate and significant differences in the three programs when mapping IAP species. The separation of each invasive species should be improved with a phenology-based design of field surveys. This study demonstrates the effectiveness of sequential segmentation and classification of RS data for mapping and monitoring plant invasions along linear infrastructures, which allows to reduce the time, cost and hazard of extensive field campaigns along roadsides.

Abstract
Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen's |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity.