Nuno Sidónio Andrade Pereira

Satellite-based remote sensing has become an essential tool for territorial monitoring and environmental analysis, particularly in the context of extreme events. The European Union’s Copernicus Programme provides open-access satellite data that support near-real-time environmental assessment. Among its missions, the Sentinel-2 satellites, equipped with the Multispectral Instrument (MSI), offer high spatial and spectral resolution through 13 spectral bands, enabling the detection and monitoring of water quality parameters in inland and coastal water bodies. In early May 2024, intense rainfall events in Rio Grande do Sul, southern Brazil, triggered one of the most severe flooding episodes recorded in the region over the past 40 years. The objective of this study is to assess the environmental impacts of this event by mapping the flooded areas and analysing changes in water quality using Sentinel-2 imagery. Sentinel-2 Level-1C (Top-of-Atmosphere) products were processed using the Copernicus Browser to evaluate variations in coloured dissolved organic matter (CDOM) and dissolved organic carbon (DOC). Temporal analysis based on true-colour composite images allowed the observation of a significant increase in water extent over a seven-day period. Temporal comparison of Sentinel-2 images acquired before, during, and after the flood consistently showed a progressive expansion of inundated areas, accompanied by spatially coherent increases in satellite-derived CDOM and DOC indicators during the flood peak, followed by a partial decrease after flood recession. Although no simultaneous field measurements were available for quantitative validation, the observed spatial–temporal patterns consistently indicate flood-induced deterioration of water quality. Elevated CDOM and DOC levels are commonly associated with increased organic matter inputs and may have implications for water availability and suitability for human and animal consumption. Given the expected increase in the frequency and intensity of extreme weather events driven by climate change, this study highlights the importance of satellite-based remote sensing for rapid environmental monitoring. The Sentinel satellite constellation demonstrates strong potential for near-real-time assessment, offering timely information and broad spatial coverage to support environmental management and decision-making.

Purpose – Climate change is set to significantly transform the tourism industry, which is particularly sensitive to variations in weather conditions. As global temperatures increase, the attractiveness of certain destinations may shift, potentially affecting their perceived image and competitive positioning. This study presents an exploratory analysis of the relationship between climate change, specifically rising temperatures, and destination image (DI), a critical component of the tourism system. Methodology/Design/Approach – The study focuses on the Mediterranean region, one of the regions most affected by climate change, where four major tourism destinations are found: Portugal, Spain, Italy, and Greece. The Climate Index for Tourism (CIT) is applied to assess the relationship between climate conditions and tourism suitability quantitatively. Datasets of climate projections up to 2100 were obtained from the Copernicus Climate Data Store and post-processed with Python scripts. Findings – Results reveal substantial shifts in the distribution of CIT-rated days (Fair, Good, and Unfavourable) from May to October. These changes likely impact tourists’ perceptions and the image of the studied destinations. Originality of the research – This paper establishes a connection between climate science and tourism marketing by incorporating climate projections within the examination of DI.

Satellite-based remote sensing has become an essential tool for territorial monitoring and environmental analysis, particularly in the context of extreme events. The European Union’s Copernicus Programme provides open-access satellite data that support near-real-time environmental assessment. Among its missions, the Sentinel-2 satellites, equipped with the Multispectral Instrument (MSI), offer high spatial and spectral resolution through 13 spectral bands, enabling the detection and monitoring of water quality parameters in inland and coastal water bodies.In early May 2024, intense rainfall events in Rio Grande do Sul, southern Brazil, triggered one of the most severe flooding episodes recorded in the region over the past 40 years. The objective of this study was to assess the environmental impacts of this event by mapping the flooded areas and analysing changes in water quality using Sentinel-2 imagery. Temporal analysis based on true-colour composite images allowed the observation of a significant increase in water extent over a seven-day period.Sentinel-2 Level-1C (Top-of-Atmosphere) products were processed using the Copernicus Browser to evaluate variations in coloured dissolved organic matter (CDOM) and dissolved organic carbon (DOC). The results revealed a marked increase in the concentrations of both parameters following the flooding event, indicating a degradation of water quality. Elevated CDOM and DOC levels are commonly associated with increased organic matter inputs and may have implications for water availability and suitability for human and animal consumption.Given the expected increase in the frequency and intensity of extreme weather events driven by climate change, this study highlights the importance of satellite-based remote sensing for rapid environmental monitoring. The Sentinel satellite constellation demonstrates strong potential for near-real-time assessment, offering timely information and broad spatial coverage to support environmental management and decision-making.

The non-invasive nutritional analysis of feed through images captured by hyperspectral cameras represents an innovative and promising approach in the field of biotechnology. With this technology, it is possible to capture images at multiple wavelengths and identify unique spectral patterns associated with different molecular components, such as total fat and moisture. The technique is particularly valuable in biological environments, enabling a more detailed and comprehensive analysis of the presence and distribution of nutrients. The objective of this work was to perform a nutritional pre-characterization of Hermetia illucens (Black Soldier Fly-BSF) larvae meal, using hyperspectral images in the range of 400–1000 nm, with a spectral resolution of 7 nm and a spatial sampling of 512 pixels, and correlate them to traditional chemical analysis methods. The results were also compared to wheat flour samples. The chemical results of BSF meal indicated 7.2% ± 0.05% (w/w) and 28.15% ± 0.15% (w/w) in moisture and total fat content, respectively. When compared to wheat flour, BSF meal contains less total fat and this difference was clearly identified in the spectra derived from the hyperspectral images, which underscores the efficiency and utility of multispectral cameras to conduct real-time and non-destructive analyses. Furthermore, this analysis can be extended to other components (e.g., protein content), increasing the potential of the technique for feed characterization.

Hyperspectral imaging (HSI) systems collect electromagnetic radiation, emitted or reflected by the scene under observation at, typically, hundreds of contiguous and regularly spaced narrow spectral bands, from the visible to the infra-red region of the spectrum. The result is a 3D data cube with two spatial dimensions and a wavelength dimension, composed of hundreds of co-registered images containing information on the spectral signature of the materials present in the field of view of the sensor at the time of capture. Retrieving this information in an efficient way, requires compute systems capable of dealing with a huge amount of data and complex algorithms. The recent development of single board computers (SBC) with enough compute power to deal with HSI processing using machine learning (ML) algorithms opens new opportunities for embedded HSI systems for Earth Observation remote sensing, laboratory setups and industrial applications. In this work, we evaluate the performance of the ODROID-XU4, a heterogeneous computing device, running ML algorithms for HSI classification. The experimental results show the potential of SBC as alternative hardware platforms for applications requiring low power and low cost devices, such as onboard processing units and edge computing.

Hyperspectral imaging (HSI) is a competitive remote sensing technique in several fields, from Earth observation to health, robotic vision, and quality control. Each HSI scene contains hundreds of (narrow) contiguous spectral bands. The amount of data generated by HSI devices is often both a solution and a problem for a given application. Extracting information from HSI data cubes is a complex and computationally demanding problem. To tackle this challenge, convolutional neural networks (CNNs) have been widely applied to HSI classification. Despite their success, CNNs are computationally demanding algorithms with high memory requirements due to their large number of internal parameters. The recent interest in using HSI devices in mobile and embedded systems for air and spaceborne platforms turned the attention to computationally lightweight CNN architectures with good classification accuracy. In this article, we present a contribution in that direction. The proposed method combines the ghost-module architecture with a CNN-based HSI classifier to reduce the computational cost and, simultaneously, achieves an efficient classification method with high performance. Our new method is evaluated against nine standard HSI classifiers, and five improved deep-CNN architectures, over five commonly used HSI data sets for algorithm benchmarking. Conducted experiments show that the proposed method exhibits similar or better performance than the other classifiers, achieving top values in the considered performance metrics—even for very limited training sets—and, most importantly, with a fraction of the computational cost. Our novel approach for HSI classification is a strong candidate for implementation on systems with limited computational resources.

In this work we use the Arduino UNO R3 open source hardware platform to assemble an experimental apparatus for the measurement of the time constant of an RC circuit. With adequate programming, the Arduino is used as a signal generator, a data acquisition system and a basic signal visualisation tool. Theoretical calculations are compared with direct observations from an analogue oscilloscope. Data processing and curve fitting is performed on a spreadsheet. The results obtained for the six RC test circuits are within the expected interval of values defined by the tolerance of the components. The hardware and software prove to be adequate to the proposed measurements and therefore adaptable to a laboratorial teaching and learning context.

Probabilistic Key Management Practical Concerns in Wireless Sensor Networks

The subject of Key Management in Wireless Sensor Networks has gained increased attention from the security community around the world in the last years. Several proposals were made concerning the peculiarities of resource constrains inherent to sensor devices. One of the most accepted of proposals is based on random distribution of keys among the sensor nodes, which was followed by some variants in order to increase its security. In this paper we present some drawbacks of this class of proposals based on probabilistic key management, concerning its applicability to present real world applications where the technological constrains strictly compromise the mathematical theoretical models.