The viability of urban life requires a series of changes in Land Use Land Cover (LULC), incorporating anthropic elements at the expense of vegetated areas, changing the land cover, which affects surface albedo. One of the impacts could be the formation of an Urban Heat Island (UHI), where temperatures are higher in cities compared to the vegetated surrounding areas, especially after sunset. Remote Sensing (RS) data, such as Land Surface Temperature (LST), image classification algorithms, and vegetation indices, can be used to understand this dynamic. This research aimed to assess the sensitivity of RS products to LULC changes and evaluate the surface thermal behaviour of (Portugal) between 2016 and 2023, during summer and winter. RS was able to identify some changes in LULC and ascertain that LST, in general, was higher in anthropic areas, especially during the summer.

This study explores dimensionality reduction techniques, namely, PCA (Principal Component Analysis) and ICA (Independent Component Analysis), to condense Earth Observation (EO) data obtained from Landsat 9 and PRISMA satellites to detect alteration zones related to Cobalt (Co) mineralization in the Áramo mine, situated in Asturias, Spain, by employing Support Vector Machine (SVM) Machine Learning (ML) algorithm. The ICA-based models exhibit slightly better performance than PCA-based ones, particularly in delineating alteration zones in the Landsat 9 image, showing promising results in distinguishing alteration zones from host rocks, demonstrating the viability of these techniques applied to mineral exploration. However, the results show the need for refined field data collection methodologies to enhance prediction accuracy for more robust results, in the scope of the HORIZON Europe S34I project (https://s34i.eu/).

The Horizon Europe S34I project aims to enhance exploration methods to secure critical raw materials (CRM) and location management through innovative methods to process Earth Observation data. Placer detection using Copernicus optical data was recently assessed on the Iberian Peninsula Atlantic coast, but radar data potential is still unknown. This search evaluates the contributions from Sentinel-1 data for placer exploration through textural analysis and unsupervised learning with K-means. Different numbers of clusters and iterations were tested, and different attempts were created using several input features for unsupervised classification. RGB compositions were tested to further explore the contribution of the textural indices. The results show the potential of Sentinel-1 data and unsupervised learning in identifying distinct classes in the foreshore and intertidal zones. The Homogeneity textural index allowed for the discrimination of different classes within the Spanish rias while onshore it highlighted geological contacts and fault zones. This study showcases radar data's potential for placer exploration, paving the path for new future applications.

This study briefly overviews the methodologies employed at the mining pilot site in Austria, to demonstrate continuous monitoring of raw material extraction (progress, stability, waste dumps) under the scope of S34I project. Several unmanned aerial vehicle (UAV) flights were performed, and tri-stereo digital elevation models (DEMs) were computed and compared to estimate the volume of the material extracted and deposited. The low-cost global navigation satellite system (GNSS)-based monitoring system was installed in April 2023 and provided near real-time data. In addition, state-of-the-art Interferometric synthetic aperture radar (InSAR) processing provided dense displacement times series in 3D to create the deformation model.

This study addresses the challenge of subpixel occurrence in identifying Lithium Cesium Tantalum (LCT) pegmatites, crucial sources of lithium for electric batteries. Previous spectral unmixing methods have been applied in brownfield sites with pre-existing large mines, facilitating pegmatite endmember acquisition. However, this work focuses on a method for greenfield exploration, presenting a knowledge transfer approach to transfer pre-extracted pegmatite endmembers to new areas using a spectral unmixing based method. Two study areas in Minas Gerais, Brazil, were chosen: Area 1 (A1) as a brownfield site for deriving a pegmatite endmember, and Area 2 (A2) simulates a greenfield area to test the efficacy of the pre-extracted endmember. The Mixture Tuned Matched Filtering (MTMF) classification method was employed, showcasing potential value in scenarios where no pegmatite occurrences are known.

Several methodologies can be employed in the prospection of Lithium (Li) in hard-rock (pegmatites). Spectrometry analysis, a Remote Sensing (RS) technique, can be applied to understand the surface spectral response of a sample, both to identify the rock-forming or alteration minerals in its composition and to validate the data collected in situ (by sensors onboard satellites/drones, for example). This paper aims to make available for public use the information acquired on the spectral composition of rock samples from the Barroso pegmatite field in Portugal, within the scope of the INOVMINERAL4.0 project. As a result, a spectral library was created with 47 spectra, collected from 11 different samples. All data is made available in a universal format, thus contributing to open science, corroborating the validation of local spectral data, and stimulating the creation of other databases, in other locations.

Remote Sensing has been successfully applied in the identification of pegmatitic targets. However, the spatial resolution of open data satellites, which is often much larger than the outcrop size of the target mineral or rock, has been a recurrent challenge in this scientific field. This restricts remote sensing methods that are dependent on large outcrop sizes for successful identification. This work applied spectral unmixing approach on WorldView-3 satellite imagery, to evaluate the potential for high spatial resolution imagery on the Tysfjord Niobium-Yttrium-Fluorine (NYF) pegmatite field (Norway). The preliminary results of this research are encouraging and make a strong contribution to the scientific field of mineral exploration.

PRISMA data is still underexploited in what concerns mineral exploration, while the high demand for battery components such as lithium (Li) instigates exploration of other low-grade deposits such as St. Austell (Cornwall, UK). This study assesses the potential of PRISMA data to target such Li deposits through the detection of topaz as a proxy to the mineralization using band math and partial unmixing techniques. The topaz distribution maps obtained are coherent between each other and with the known geology of the area, highlighting the PRISMA potential, although there could be some shortcomings related to its spatial resolution. Comparison with Sentinel-2 or Worldview-3 data shows the limitations of multispectral products, despite some potential to use Worldview-3 that need to be further investigated. In the future, new approaches to directly detect Li-micas and field validation of the results must be conducted.

Lithium-Cesium-Tantalum (LCT) pegmatites are enriched in several raw materials. However, their small size and the limited penetration depth of the sensors, limits remote sensing approaches. This study evaluates the usefulness of hyperspectral data to identify geochemical halos related to LCT pegmatites by exploiting the information acquired in European projects. It was possible to identify key minerals and related mineralogical changes that can be due to hydrothermal alteration. Partial Least Squares Regression (PLSR) was used to model the abundance of Li, Rb and Cs. The most reliable results were obtained for Cs, with results being influenced by lithological and weathering factors. New outcomes are expected, namely mineral chemistry studies that will complement the hyperspectral results.

Remote sensors transported on an Unmanned Aerial Vehicle (UAV) give us new possibilities to collect data from a specific area. One of the last uses is the estimation of parameters on natural resources as water parameters at geothermal pools. The main idea to use them is to avoid high costs in the maintenance in comparison with other techniques as water sampling. Thus, this work aims to establish models to monitor phosphate levels on geothermal pools using thermal remote sensors above an UAV in Ecuador. The model results indicate an acceptable correlation between the surface temperature; acquired from a thermal sensor; and the phosphate levels get from the water samples. Consequently, we have an alternative to monitor the phosphate and the possibility to replicate the methodology with other water parameters at pools.

Several algorithms were developed to map lithium (Li)-pegmatites in recent years. This preliminary study attempts to validate the same algorithms in the Trancoso region (Portugal), an area with unknown economic potential. The application of RGB combinations, Band Ratios (BR), and Selective Principal Component Analysis (PCA) to Sentinel-2 and Landsat-8 images allowed to identify four target areas for Li-exploration. Some targets correspond to an inert exploitation of the former mine's waste, confirming the potential of this methodology. Moreover, outcropping metric Li-dykes were missed with this approach, highlighting the need to refine the existing algorithms. Reflectance spectroscopy studies of Li-dykes and host rocks support these conclusions and will be crucial to improve the methodology. The collected spectra increased the knowledge on this greenfield area and its economic potential and provided information on new mineralization proxies to be used in future satellite detection studies.

This work aims to integrate Remote Sensing (RS) and cadastral data in QGIS software to perform the spatiotemporal mapping of Wine Yield (WY) cluster zones in the Douro region. Spatiotemporal modelling approach for prediction of wine yield was based on Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and topographic data. The results showed that 74%$(\mathrm{R}^{2}=0.744,\ \mathrm{n}=128,\ \mathrm{p} < 0.000)$WY interannual variability at administrative division could be explained by the developed model. This information allows establishing wine production region pattern which can improve the agronomic and economic efficiency of vineyard and winery operations.

The use of remote sensing considering Unmanned Aerial Vehicles (UAV) is a recent alternative to collect data from different fields. One of the applications is related to soil parameter estimation considering different multispectral vegetation indexes. The typical use is to establish a relationship between the plant health and a vegetation index. However, this is a simple estimation where is not considered any field (soil) validation or real parameter estimation. Thus, this work aims to establish a model to estimate the percentage of nitrogen (N) in the soil, using vegetation indexes obtained from UAV and soil field measurements under balsa trees in Ecuador. The results show a high correlation between the Green Normalized Difference Vegetation Index (GNDVI) and the soil-nitrogen in comparison with other vegetation indexes, as the Normalized Vegetation Index (NDVI). Thus, this methodology allows to estimate the percentage of N to optimize the balsa production.

Satellite-based mineral exploration will be increasingly relevant in the future to achieve more conscious exploration models. Therefore, new applications to high-demand mineral commodities such as lithium (Li) are emerging. Previous applications to the study area of Fregeneda (Spain) - Almendra (Portugal) showed a high number of false positives despite their ability to identify Li pegmatites. So, the objective of the present work is to improve the classification results using Artificial Neural Networks (ANNs). For that, the same Sentinel-2A images were used and a three-layer feedforward network was computed using the backpropagation method. The ANN created was able to identify all the open-pit mines exploiting Li pegmatites in the area. However, the high number of Li false positives persisted. Future applications may include Convolutional Neural Networks (CNNs) to improve these results.

Raw-materials like lithium (Li) are crucial to the current global decarbonization, but Li-exploration presents some technical challenges. Therefore, new solutions for Li-exploration are needed. Consequently, the aim of this study is to present a unique multi-scale remote sensing approach for Li-pegmatite exploration integrated within the LIGHTS project, considering as study area the Bajoca mine (Portugal). Satellite data allowed the identification of the spectral signatures of Li-pegmatites at a district scale, while drone-borne hyperspectral measurements provided data at the target scale. Handheld spectroscopy and in situ hyperspectral scans of the mine walls were carried out to validate the satellite and drone data. Hyperspectral field and laboratory scans also aim to collect information at the mineral scale, to distinguish different lithological materials, and to identify the Li-rich areas. In the future, machine learning algorithms will deliver an automated integration of all acquired data.

The landscape structure and composition of fauna and flora in the Mediterranean basin is shaped by fire. Thus, many plants and animals are resilient to fire. This is the case of most Mediterranean reptiles, which are adapted to open and heterogeneously complex landscapes as those derived from wildfires. However, socioeconomic and climate-change factors are modifying the Mediterranean fire regime, increasing the vulnerability of specific reptiles to intense and frequent fires. The Mediterranean terrestrial tortoises of the genus Testudo show high individual mortality by flames, and population declining in burnt landscapes. We predict an increasing negative impact of fire on the remaining tortoise populations across the Mediterranean Basin. In this work, the fire hazard map in the Mediterranean Basin was created based on remote sensing data and Geographical Information Systems (GIS) and it was overlapped to the location of the remaining tortoise wild populations. Our results showed that tortoise populations were mostly identified in medium fire hazard. Given the negative response of terrestrial tortoises to fire, we predict a high extinction risk of Testudo species derived from fire activity in synergy to other environmental stressors.

The São Pedro da Cova (Portugal) coal mine produced a significant volume of waste deposited in different piles that have been on self-burning, continuously, for the past 15 years, increasing regional environmental concerns. Attempting to study and monitor the combustion phenomenon in these waste deposits, periodic measurements have been made using multiple sensors on-board of an unmanned aerial vehicle (UAV) and also in situ measurements. Assessments from RGB camera, thermal infrared and multispectral sensors, in addition to the geostatistical analysis of the in situ data, confirmed that the self-burning process is still active. Furthermore, the UAV methodology applied seems adequate for steep settings with difficult accessibility, as well as allowing for a better understanding and monitoring of the overall progression and state of the waste piles' self-burning process.

A Vegetation Index (VI) is easily accessible and is associated with a wide spatial and temporal availability. However, are these datasets really useful? One of the main difficulties related to their use, such as MODIS time-series, is the potentially high number of pixels with low radiometric quality. In this work, a Quality Assurance analysis was performed using four MODIS VI scenes from 2011, based on the flags Pixel Reliability and Quality Detailed. The considered dataset covers two different areas of Portugal and two distinct species. Two VI products were analyzed: Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The EVI presented generally better results than the NDVI. Additionally, both NDVI and EVI indexes presented a seasonal dependence with better quality in the dry season, and a geographical/land use influence supported by better quality observed in the south area. In conclusion, quality indexes should be considered in VI applications.

Forest fires are widely recognized as one of the most critical events in global change. Successful fire management depends on effective fire prevention, detection, and presuppression, having an adequate fire suppression capability, and consideration of fire ecology relationships. Geographical information systems (GIS) provide tools to create, transform, and combine georeferenced variables. In Portugal, as in many other countries, it is mandatory that all the municipalities produce forest fire risk maps on an annual basis, following the rules of the Portuguese Forest Authority, a governmental association. This article presents the results of a research project aimed at producing forest fire risk maps in a GIS open source environment in Portugal. The requirements of an open source application are better quality, higher reliability, more flexibility, lower cost, and an end to predatory vendor lock-in. Three different open source desktop GIS software projects were evaluated: Quantum GIS (QGIS), generalitat valenciana, Sistema d'Informacio Geografica, and Kosmo. Taking into account the skills and experience of the authors, the main advantage of QGIS relies on the easiness and quickness in developing new plug-ins, using Python language. Therefore, this project was developed in QGIS platform and the interface was created in Python. This application incorporates seven procedures under a single toolbar. The production of the forest fire risk map comprises several steps and the production of several maps: probability, susceptibility, hazard, vulnerability, economic value, potential loss, and finally the forest fire risk map. The forest fire risk map comprises five classes: very low risk (dark green), low risk (green), medium risk (yellow), high risk (orange), and very high risk (red). This application was tested in three different municipal governments of the Norwest zone of Portugal. This application has the advantages of grouping in a unique toolbar all the procedures needed to produce forest fire risk maps and is free for the institution/user. Beyond being an open source application, this application may be faster and easier when compared with the GIS proprietary solutions that usually comprise several steps and the use of different software extensions. This work presents several contributions for the area of the GIS open source applications to forest fire risk management.

Spatial and temporal variation of river plumes can be studied by remote sensing. The main objectives of this letter were to model the Douro River Plume (DRP) size based on image segmentation techniques and to relate it to different parameters such as water volume, last available plume, tide height, and wind speed. Twenty-one MEdium Resolution Imaging Spectrometer (MERIS) scenes of the study area were considered, covering 20 months from 2003 to 2005. Two different segmentation techniques were applied (watershed and region based) to the MERIS scenes in order to estimate the DRP size. Two models were developed in order to relate the DRP size with the water volume, last available plume, tide height, and wind speed. In the first model, the DRP size was modeled using the discharged water volume as input. In the second model, the DRP size was retrieved as a function of the water volume, last available plume, tide height, and wind speed. The last available plume-a factor of crucial importance-was weighted using a forgetting factor, function of the time difference between two consecutive MERIS scenes. A significant correlation was found between DRP dimension and water volume (r = 0.66) during wet season (i.e., excluding dry summer period), because the plume derived from MERIS imagery in the region under study represents river Douro plume only when the river flow exceeds a certain threshold. Despite some particular points, the second model was able to model the plume size obtained through image segmentation, with a mean percentage variation of 34.8% for region-based segmentation method. Segmentation of MERIS data have been shown to be a valid method for modeling the DRP size. Furthermore, DRP size was found to be proportional to water volume, excluding the summer period.

In this paper, we present different methodologies to estimate the total suspended matter (TSM) concentration in a particular area of the Portuguese coast, from remotely sensed multispectral data, based on single-band models, multiple regression, and artificial neural networks (ANNs). Simulations on different beaches of the study area were performed to determine a relationship between the TSM concentration and the spectral response of the seawater. Based on thein situmeasurements, empirical models were established in order to relate the seawater reflectance with the TSM concentration for TERRA/ASTER, SPOT HRVIR, and Landsat/TM. Seven images of these three sensors were calibrated and atmospherically and geometrically corrected. Single-band models, multiple regression, and ANNs were applied to the visible and near-infrared (NIR) bands of these sensors in order to estimate the TSM concentration. Statistical analysis using correlation coefficients and error estimation was employed, aiming to evaluate the most accurate methodology. The chosen methodology was further applied to the seven processed images. The analysis of the root-mean-square errors achieved by both the linear and nonlinear models supports the hypothesis that the relationship between the seawater reflectance and TSM concentration is clearly nonlinear. The ANNs have been shown to be useful in estimating the TSM concentration from reflectance of visible and NIR bands of ASTER, HRVIR, and TM sensors, with better results for ASTER and HRVIR sensors. Maps of TSM concentration were produced for all satellite images processed.