ecotrends is an R package that provides a comprehensive framework for assessing species vulnerability by analysing temporal trends in habitat suitability. The framework consists of calculating ecological niche models successively on the same species occurrence data over time, using a time series of environmental variables, for any period and periodicity. Then, a trend analysis of habitat suitability is performed with Sen's slope, based on non-parametric Kendall's rank correlation. ecotrends includes functions for automatically gathering a yearly time series of environmental data from TerraClimate, calculating and evaluating Maxent models, assessing temporal trends in habitat suitability, and estimating variable importance. Although the built-in functions facilitate the use of a specific environmental database and modelling method, ecotrends works as well with any variables and models the user may provide. This adaptable framework can be applied to any taxon or guild, study area, spatial scale, or temporal resolution, as long as the required data are available. ecotrends offers a valuable tool for estimating species vulnerability over time and for supporting the evaluation of conservation measures. ecotrends allows the analysis of habitat suitability trends over time, requiring only species occurrence data and a time series of environmental data.

BackgroundNeurodegenerative diseases are an increasing concern for the aging population worldwide. In Portugal, as in many other developed countries, the population is aging rapidly. Understanding temporal and spatial patterns is of utmost relevance to help manage the burden these diseases place on the healthcare system.MethodsIn this retrospective study, we analyzed over 500,000 hospitalizations discharged between 2000 and 2016. We used the empirical Bayes method to compute the smoothed age-standardized hospitalization rates for each neurodegenerative disease, for all hospitalizations per year, and across administrative divisions (districts and municipalities). We then searched for data clusters using both global and local spatial autocorrelation methods based on the Moran index.ResultsA steady increase in age-standardized hospitalization rates was observed throughout the study period. Statistically significant global spatial autocorrelation was found when considering all diseases per municipality (Moran’s I = 0.010, p-value < 0.001). In addition, when considering the districts, only Alzheimer’s disease, dementia, and basal ganglia disorders did not show significant spatial autocorrelation. When considering municipalities, all diseases showed significant positive global spatial autocorrelation.ConclusionTemporal analysis showed increasing age-standardized hospitalization rates over time, likely reflecting the aging population in Portugal. The spatial analysis showed significant clustering, which may reflect geographic differences in hospitalization practices, access to care, population structure, or other contextual factors. Through this study, we hope to enlighten future research by providing insights into the anticipated spatio-temporal patterns.

The exploration of coastal placer deposits, often enriched in critical raw materials demanded by industry, is significantly challenged by the dynamic marine environment and by the limited research devoted to developing dedicated exploration methodologies. This study presents the first systematic integration of multi-source geospatial data in the R & iacute;as Baixas for placer mineral prediction in the initial exploratory stage of these deposits. The primary objective is to investigate the presence of Titanium (ilmenite, and rutile), Zirconium (zircon), and Rare Earth Element (REE)-bearing minerals (monazite, xenotime, allanite, and garnets) in R & iacute;as Baixas (NW Spain). The methodology includes a lithological reclassification and the generalization of coastal types. These features are then integrated with watershed, coastline dynamics, and mineral occurrence data. Validation includes existing semi-quantitative and qualitative mineral identification data, and new field observations of heavy mineral accumulations. This integration allowed us to identify nine potential and ten predictive areas with a high probability of hosting coastal placers. The validation process showed a 79% spatial correlation, confirming a significant heavy mineral accumulation in 15 areas. This work underscores the efficacy of integrated cartography in prioritizing potential and predictive areas during the crucial first stage of mineral exploration. The methodology can be further enhanced by incorporating additional data, such as stream sediment geochemistry and the application of remote sensing techniques.

In the Iberian Pyrite Belt (IPB), long-term persistence of mine waste piles poses environmental challenges. The present work studies the Trimpancho Mining Complex in northern IPB with exposed mine waste and acidic waters in the proximity to the Chan & ccedil;a River, a tributary of the Guadiana international river. A multidisciplinary approach is proposed, using hyperspectral reflectance spectroscopy, portable X-ray fluorescence (pXRF), multispectral Unmanned Aerial Vehicle (UAV) and Sentinel-2 images. Spectroscopic, geochemical and remote sensing methods were applied to characterise the mining area. Comparison of hyperspectral data with spectral libraries were used to validate mineralogy. Multispectral UAV data is used for custom band-ratios and adapted to Sentinel-2 images. Results grouped the samples into four groups. Spectroscopy is indicative of clays (white mica and smectite group), hematite/goethite, jarosite, and arsenopyrite and pyrite (exclusive to the Group 2); iron-rich samples reach maximum reflectance earlier than iron-poor samples. Geochemical studies show an increase in content of heavy metal such as As, Cu, Fe, Pb, and Zn from Group 1 < Group 3 approximate to Group 4 < Group 2, but Group 4 showed elevated Pb and Zn. Custom false colour composition highlighted the groups in UAV and satellite, thus constituting cost-effective tools for finding contamination sources.

Changes in land use and land cover (LULC) are among the leading contributors to global environmental transformation. Analyzing these dynamics is essential for understanding historical land utilization patterns and identifying the key drivers behind such shifts. This research focuses on LULC changes in the Kunar region of eastern Afghanistan. To classify the LULC types, the study area was divided into nine major classes using the Support Vector Machine (SVM) algorithm, based on Landsat 07 Enhanced Thematic Mapper Plus (ETM+) data for 2004 and Landsat 8 Operational Land Imager (OLI) data for 2014 and 2024. Past and present changes were evaluated using ArcGIS 10.8, while future scenarios for 2034 and 2044 were simulated using the Land Change Modeler (LCM) embedded in the TerrSet platform, combined with the Cellular Automata–Markov Chain (CA-MC) model with 90% kappa agreement validation value. From 2004 to 2024, grassland expanded significantly from 68.93% (3406 km2) to 73.94% (3654 km2). Built-up areas grew from 0.59% (29.10 km2) in 2014 to 1.02% (50.39 km2) in 2024. Conversely, dense forest cover declined from 27.50% (1358.90 km2) to 22.96% (1134.75 km2), a decrease of 224.15 km2. Barren land, after a temporary increase, also showed a net decline. Projections for 2034 and 2044 suggest a further reduction in forested areas to 1077 km2, while grasslands and urbanized zones are expected to increase to 3690 km2 and 60.63 km2, respectively. These trends emphasize a swift transition in land use patterns, primarily driven by the conversion of forested and barren landscapes into settlements and grasslands. The findings underline the urgent need for implementing sustainable land management strategies to curb environmental degradation and ensure balanced land resource utilization in the future.

The increasing demand for high-quality geospatial visualizations in scientific publications has highlighted the need for accessible and standardized tools that support reproducible research. Researchers from various disciplines—often without expertise in Geographic Information Systems (GIS)—frequently require a map figure to locate their study area. This paper presents the Study Area Map Generator, a web-based application developed using Shiny for Python, designed to automate the creation of country- and city-level study area maps. The tool integrates geospatial data processing, cartographic rendering, and user-friendly customization features within a browser-based interface. It enables users—regardless of GIS proficiency—to generate publication-ready maps with customizable titles, basemaps, and inset views. A usability survey involving 92 participants from diverse professional and geographic-based backgrounds revealed high levels of satisfaction, ease of use, and perceived usefulness, with no significant differences across GIS experience levels. The application has already been adopted in academic and policy contexts, particularly in low-resource settings, demonstrating its potential to democratize access to cartographic tools. By aligning with open science principles and supporting reproducible workflows, the Study Area Map Generator contributes to more equitable and efficient scientific communication. The application is freely available online. Future developments include support for subnational units, thematic overlays, multilingual interfaces, and enhanced export options.

This contribution is a new multimethod toolset to explore for buried, small-scale (0.01-5 million m3) rare metal and high-purity quartz pegmatites, which was developed as part of the four-and-a-half-year European Union H2020 GREENPEG project. It is underpinned by a complementary suite of existing, revised, and new methodologies, the use of three GREENPEG-developed geophysical exploration devices (EASA-certified, helicoptercompatible nose stinger magnetometer, piezoelectric seismograph, and drone-borne hyperspectral system), and two new databases (spectral library and petrophysical database for pegmatite ores). The toolset is based on the latest understanding of how pegmatites form and become enriched in ore minerals. In this regard, the theoretical component of the toolset resembles that of a comprehensive review article. The toolset has been tested in four active pegmatite exploration areas in a representative range of European surface environments-from coastal Arctic to temperate forest, alpine, and Mediterranean settings. Individual tools or tool combinations can be used to vector toward buried pegmatite-related mineralization, such as for Li, high-purity quartz for silica and metallic Si, ceramic feldspar, rare earth elements, Ta, Be, and Cs, to maximize the success of subsequent more costly exploration such as drilling in ways that optimize environmental, social, and governance outcomes. The tools are optimized for the small size, variable surface environment, depth, geologic setting, mineralogy, chemistry, and often highly variable physicochemical properties of pegmatite ore deposits. They can be used at province, district, and/or prospect scale. This guide is for those who have exploration knowledge and/or experience but who may be new or need updating in the state of the art of pegmatite exploration.

Human activities are impacting biodiversity worldwide. Biodiversity monitoring is essential to assess and support conservation status and trends. Remote sensing has played a crucial role in supporting biodiversity monitoring, but more intuitive and fast-processing tools are still required to improve biodiversity conservation. Herein, we present a Google Earth Engine (GEE) App called Montreds, which implements a biodiversity monitoring tool to measure trends in species habitat suitability over time by calculating ecological niche models (ENMs) with a time series of satellite products. The application is specific to Montesinho Natural Park/Nogueira Special Conservation Area, a protected area located in northeastern Portugal. The application calculates ENMs over time with MaxEnt for five taxa (vascular plants, amphibians, reptiles, birds, and mammals), using a time series of six Moderate-Resolution Imaging Spectroradiometer (MODIS) products between 2001 and 2023. Habitat suitability trends are estimated using the Mann-Kendall test. The Montrends' main output is a map for each modelled species with positive, negative, or null trends over time. If habitat suitability decreases monotonically over time, the trend is identified as negative. The application allows the users to select the species to be modelled, the temporal period, the number of model replicates, and the proportion of training and test records. The application runs the analyses intuitively in about a minute. Several results are displayed: the mean MaxEnt model over time and the Mann-Kendall trends for the whole study area, the species presences, the pixels with significant trends, and the species' occurrences in significant pixels. The application also provides the main MaxEnt outputs, including Area Under the Curve (AUC) values and variable contributions, plots of the global contributions of predictor variables over time, average trend values, and information on MaxEnt parameters. Decision-makers and conservation planners can use this application as a complementary tool for biodiversity monitoring and conservation.

Semi-Mediterranean (SM) and semi-arid (SA) regions, exemplified by the Kurdo-Zagrosian forests in western Iran and northern Iraq, have experienced frequent wildfires in recent years. This study proposes a modified Non-Negative Matrix Factorization (NMF) method for detecting fire-prone areas using satellite-derived data in SM and SA forests. The performance of the proposed method was then compared with three other already proposed NMF methods: principal component analysis (PCA), K-means, and IsoData. NMF is a factorization method renowned for performing dimensionality reduction and feature extraction. It imposes non-negativity constraints on factor matrices, enhancing interpretability and suitability for analyzing real-world datasets. Sentinel-2 imagery, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Zagros Grass Index (ZGI) from 2020 were employed as inputs and validated against a post-2020 burned area derived from the Normalized Burned Ratio (NBR) index. The results demonstrate NMF’s effectiveness in identifying fire-prone areas across large geographic extents typical of SM and SA regions. The results also revealed that when the elevation was included, NMF_L1/2-Sparsity offered the best outcome among the used NMF methods. In contrast, the proposed NMF method provided the best results when only Sentinel-2 bands and ZGI were used.

Remote sensing applications for marine placer deposit exploration remain limited due to the mineralogical complexity and dynamic coastal processes. This study presents the first medium- to high-level detailed multi-scale remote sensing analysis of placer deposits in the Rías Baixas, NW Spain, focusing on five beaches within the Vigo Estuary. Ten beach samples were analyzed for their heavy mineral (HM) content and spectral signatures, using bromoform separation and FieldSpec 4 spectroradiometer equipment, respectively. The spectral signatures of beach samples with a high HM content were characterized and resampled for the Sentinel-2 application, employing the Spectral Angle Mapper (SAM) algorithm. Field validation and an unmanned aerial vehicle (UAV) survey confirmed surface placer occurrences and the SAM’s results. Santa Marta Beach exhibited significant placer anomalies (up to 30% HM), correlating with low SAM values (minimum value–0.10), indicating high spectral similarity. The SAM-derived anomaly patches aligned with the field observations, demonstrating Sentinel-2’s potential for placer deposit mapping. This work highlights the application of Sentinel-2 in the exploration of placer deposits and the use of a specific spectral range of these deposits in coastal environments. These tools are non-invasive, more environmentally friendly, and sustainable, and can be extrapolated to other regions of the world with similar characteristics.

The increasing demand for critical raw materials, such as antimony—a semimetal with strategic relevance in fire-retardant applications, electronic components, and national security—has made the identification of European sources essential for the European Union’s strategic autonomy. Remote sensing offers a valuable tool for detecting alteration minerals associated with subsurface gold and antimony deposits that reach the surface. However, the coarse spatial resolution of the most freely available satellite data remains a limiting factor. The PlanetScope satellite constellation presents a promising low-cost alternative for the academic community, providing 3 m spatial resolution and eight spectral bands. In this study, we evaluated PlanetScope’s capacity to detect Fe3+-bearing iron oxides—key indicators of hydrothermal alteration—by applying targeted band ratios (BRs) in northern Portugal. A comparative analysis was conducted to validate its performance using established BRs from Sentinel-2, ASTER, and Landsat 9. The results were assessed through relative comparison methods, enabling both quantitative and qualitative evaluation of the spectral similarity among sensors. Spatial patterns were analyzed, and points of interest were identified and subsequently validated through fieldwork. Our findings demonstrate that PlanetScope is a viable option for mineral exploration applications, capable of detecting iron oxide anomalies associated with alteration zones while offering finer spatial detail than most freely accessible satellites.

Remote Sensing data are used across various fields, and their selection must consider the study's objectives, sensor capabilities, and different resolutions. In urban climate investigations, such as the Urban Heat Island (UHI), thermal sensors estimate Land Surface Temperature (LST), but freely available products have low spatial resolution. This study proposes a methodology to resample MODIS LST from 1 km to 10 m (MODIS_LST_1km and MODIS_LST_10m, respectively), between 2018 and 2023, in different seasons, along the border between Portugal and Spain. We used Google Earth Engine to calculate MODIS_LST_1km and the Normalized Difference Vegetation Index (NDVI) from Sentinel-2. We resampled the NDVI to 1 km and calculated a regression equation for each data when images from both products were available. We applied the resulting equation using NDVI with a 10 m (original resolution) to obtain MODIS_LST_10m. We validated the results with Landsat-8 LST (Landsat_LST) data through Spearman correlation in R software. Additionally, we analyzed the correlation between NDVI and the Enhanced Vegetation Index (EVI) from Landsat-8 with MODIS_LST_10m to assess whether higher temperatures corresponded to areas with low vegetation. The model showed good explainability, especially in summer, and validation with Landsat-8 was also more significant in summer (ρ between 0.736 and 0.895). The correlations between MODIS_LST_10m with EVI and NDVI were negative on most dates, indicating higher temperatures in less vegetated areas. For future studies, we plan to test other sensors/satellites, such as Sentinel-3, to reinforce the robustness of this methodology.

Active remote sensing technologies such as Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar ( SAR) have been established as tools for geological investigations since the 1980s. These sensors, deployable on Unmanned Aerial Vehicles (UAVs), aircrafts and satellites, emit their own electromagnetic signals to map Earth's surface features. LiDAR provides high-resolution topographic data, enabling detailed analysis of geomorphology, tectonics, and landslide identification. However, its spatial resolution and application scope depend on the platform: UAV-mounted sensors yield denser point clouds (hundreds/thousands of points per m(2)) suited for local-scale studies, whereas airborne or satellite systems offer broader coverage at reduced point densities, limiting fine-scale geological interpretation. SAR sensors capture amplitude and phase data from the backscattered signal, enabling advanced geological and engineering applications such as interferometric deformation monitoring and subsurface imaging. The utility of SAR further depends on wavelength selection (e.g., X-, L-, or C-band), where longer wavelengths (e.g., L-band) exhibit greater penetration through vegetation and surface layers, enabling soil moisture mapping or sediment thickness estimation, while shorter wavelengths (e.g., X-band) enhance surface displacement measurements. This study synthesizes recent advancements in the application of SAR and LiDAR technologies for geological analyses, performing a systematic review of peer-reviewed literature indexed in the Scopus database, focusing on the latest publications from 2015 to 2025. The key terms used for the techniques employed were SAR and LiDAR, which were combined with geological domain terms - mineral, geology, tectonics, lithology, structural geology, and geological exploration. The bibliometric analysis was structured in three stages: search, refinement, and screening. The study relied solely on abstract analysis to prioritize scalability, with no full-text review performed. The results provide insights into the evolution of the application of active remote sensors in geological studies.

Nowadays, Earth Observation (EO) sensors with different technical characteristics installed on various platforms can provide data for multiple applications. Integrating, combining and processing data for various mining-related applications is required to assist decision-making and process adaptation procedures. Moreover, jointly using different data sets or products created from various sources allows for increasing precision and overcoming inherent measurement uncertainties, thereby enhancing the reliability of the results. Our research shows the potential of an integrated multi-sensor/multi-wavelength SAR data monitoring system that implements an innovative model-aided Phase Unwrapping (PhU) approach for generating ground displacement maps and time series in critical open pit mining areas. This allows us to mitigate the risk associated with rock falls and instabilities that can lead to severe damage to workers and neighbourhoods. Furthermore, the study also points out that jointly using Unmanned Aerial Vehicle (UAV) and spaceborne optical data is valuable to remotely estimate stockpile volume changes with enhanced accuracy and precision, supporting the mining companies’ management.

The demand for Critical Raw Materials (CRM) is increasing due to the need to decarbonize economies and transition to a sustainable low-carbon future achieving climate goals. To address this, the European Union is investing in the discovery of new mineral deposits within its territory. The S34I project (Secure and Sustainable Supply of Raw Materials for EU Industry) is developing Earth observation (EO) methods to support this goal. This study compares the performance of two satellites, Sentinel-2 and Landsat-9, for mineral exploration in two geologically distinct areas in northern Spain. The first area, Ria de Vigo, contains marine placer deposits of heavy minerals, while the second, Aramo, hosts Co-Ni epithermal deposits. These sites provide exceptional case studies to improve EO-based methods for CRM exploration onshore and coastal regions, focusing on deposits often overlooked in remote sensing studies. Standard remote sensing methods such as RGB combinations, Principal Component Analysis (PCA), and band ratios were adapted and compared for both satellites. The results showed similar performance in the Ria de Vigo area, but Sentinel-2 performed better in Aramo, identifying a higher number of zones of mineral alterations. The study highlights the advantages of Sentinel-2’s higher spatial resolution, especially for mapping smaller or more scattered mineral deposits. These findings suggest that Sentinel-2 could play a larger role in mineral exploration. This research provides valuable insights into using EO data for diverse CRM deposits.

Political and territorial conflicts shape land use and environmental risks in border regions. Integrating Remote Sensing (RS) data/techniques with Geographical Information Systems (GIS) has significantly enhanced the study and practical management of political and administrative boundaries, offering new insights into spatial governance, border dynamics, and territorial organization. This study examines how contested borders influence forest fire severity, focusing on landuse/land cover changes driven by political and geographical interactions. Combining RS and GIS analysis, this research analyzes how deforestation, fire-affected areas, and land conversion in a hand, and disputed borders on the other hand, interact. Sentinel-2, and Google Earth imagery were used to track changes over time, with Normalized Burn Ratio (NBR) derived from Sentinel 2 images, and data from official reports used to assess fire incidence distribution and severity. Additionally, statistical analysis (spatial overlap) examines the relationship between fire occurrence, severity, and proximity to the border. By analyzing fire frequency and intensity in relation to distance from the border of Iran and Iraq, this study identifies spatial trends in fire activity and their connection to territorial dynamics. The results highlight how territorial, geopolitical, and geographical constraints shape land-use transitions, influencing fire patterns in border areas, providing insight into the link between contested borders, land-use change, and fire risk, with environmental management and policy implications.