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Forest managers need inventory data and information to address sustainability concerns over extended temporal horizons. In situ information is usually derived from field data and computed using appropriate equations. Nonetheless, fieldwork is time-consuming and costly. Thus, new technologies like Light Detection and Ranging (LiDAR) have emerged as an alternative method for forest assessment. In this study, we evaluated the accuracy of geostatistical methods in predicting the Site Index (SI) using LiDAR metrics as auxiliary variables. Since primary variables, which were obtained from forestry inventory data, were used to calculate the SI, secondary variables obtained from LiDAR surveying were considered and multivariate kriging techniques were tested. The ordinary cokriging (CK) method outperformed the simple cokriging (SK) and Inverse Distance Weighted (IDW) methods, which was interpolated using only the primary variable. Aside from having fewer SI sample points, CK was proven to be a trustworthy interpolation method, minimizing interpolation errors due to the highly correlated auxiliary variables, highlighting the significance of the data's spatial structure and autocorrelation in predicting forest stand attributes, such as the SI. CK increased the SI prediction accuracy by 36.6% for eucalyptus, 62% for maritime pine, 72% for pedunculate oak, and 43% for cork oak compared to IDW, outperforming this interpolation approach. Although cokriging modeling is challenging, it is an appealing alternative to non-spatial statistics for improving forest management sustainability since the results are unbiased and trustworthy, making the effort worthwhile when dense secondary variables are available. [ABSTRACT FROM AUTHOR]

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the energy transition. This study explored the feasibility of identifying and quantifying Sb mineralisations through the spectral signature of soils using laboratory reflectance spectroscopy, a non-invasive remote sensing technique, and by employing convolutional neural networks (CNNs). Standard signal pre-processing techniques were applied to the spectral data, and the soils were analysed by inductively coupled plasma mass spectrometry (ICP-MS). Despite achieving high R-squared (0.7) values and an RMSE of 173 ppm for Sb, the study faces a significant challenge of generalisation of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field. [ABSTRACT FROM AUTHOR]

In this work, we present and describe a spectral library (SL) with 15 vascular plant species from Montesinho Natural Park (MNP), a protected area in Northeast Portugal. We selected species from the vascular plants that are characteristic of the habitats in the MNP, based on their prevalence, and also included one invasive species: Alnus glutinosa (L.) Gaertn, Castanea sativa Mill., Cistus ladanifer L., Crataegus monogyna Jacq., Frangula alnus Mill., Fraxinus angustifolia Vahl, Quercus pyrenaica Willd., Quercus rotundifolia Lam., Trifolium repens L., Arbutus unedo L., Dactylis glomerata L., Genista falcata Brot., Cytisus multiflorus (L'Hér.) Sweet, Erica arborea L., and Acacia dealbata Link. We collected spectra (300–2500 nm) from five records per leaf and leaf side, which resulted in 538 spectra compiled in the SL. Additionally, we computed five vegetation indices from spectral data and analysed them to highlight specific characteristics and differences among the sampled species. We detail the data repository information and its organisation for a better understanding of the data and to facilitate its use. The SL structure can add valuable information about the selected plant species in MNP, contributing to conservation purposes. This plant species SL is publicly available in Zenodo platform. [ABSTRACT FROM AUTHOR]

Due to the current energetic transition, new geological exploration technologies are needed to discover mineral deposits containing critical materials such as lithium (Li). The vast majority of European Li deposits are related to Li–Cs–Ta (LCT) pegmatites. A review of the literature indicates that conventional exploration campaigns are dominated by geochemical surveys and related exploration tools. However, other exploration techniques must be evaluated, namely, remote sensing (RS) and geophysics. This work presents the results of the INOVMINERAL4.0 project obtained through alternative approaches to traditional geochemistry that were gathered and integrated into a webGIS application. The specific objectives were to: (i) assess the potential of high-resolution elevation data; (ii) evaluate geophysical methods, particularly radiometry; (iii) establish a methodology for spectral data acquisition and build a spectral library; (iv) compare obtained spectra with Landsat 9 data for pegmatite identification; and (v) implement a user-friendly webGIS platform for data integration and visualization. Radiometric data acquisition using geophysical techniques effectively discriminated pegmatites from host rocks. The developed spectral library provides valuable insights for space-based exploration. Landsat 9 data accurately identified known LCT pegmatite targets compared with Landsat 8. The user-friendly webGIS platform facilitates data integration, visualization, and sharing, supporting potential users in similar exploration approaches. [ABSTRACT FROM AUTHOR]

The GREENPEG spectral database contains the spectral signature, obtained through reflectance spectroscopy studies, of European pegmatites and minerals, as well as their host rocks. Samples include Nb-Y-F (NYF) and Li-Cs-Ta (LCT)- type pegmatites and host rocks from pegmatite locations in Austria, Ireland, Norway, Portugal, and Spain. The database contains the reflectance spectra (raw and with continuum removed), sample photographs, and main absorption features automatically extracted by a self-proposed Python routine. Whenever possible, spectral mineralogy was interpreted based on the continuum-removed spectra. A detailed description of the database, its content and structure, the measuring instrument, and interoperability with Geographic Information Systems (GIS) is available in this database report. Moreover, examples of how the data can be used and interpreted are also provided. The advantages and added value of the presented dataset reside on its European scale with representative samples from pegmatites with distinct genesis, mineralogy, structure, and host rocks that can be used as a reference for pegmatite exploration at a global scale through satellite image processing, for example. The reported spectral mineral assemblages can also be of interest when considering resource estimation or ore processing. Thus, it is expected that this open dataset, available on the Zenodo platform https://doi.org/10.5281/zenodo.6518319 (Cardoso- Fernandes et al., 2022), will be a reference for distinct types of users ranging from academia to industry. [ABSTRACT FROM AUTHOR]

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 the in situ measurements, 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. Index Termsi--Artificial neural networks (ANNs), coastal zone, image processing, multiple-regression analysis, multispectral satellite data, total suspended matter (TSM) concentration.

With each infectious pandemic or outbreak, the medical community feels the need to revisit basic concepts of immunology to understand and overcome the difficult times brought about by these infections. Regarding viruses, they have historically been responsible for many deaths, and such a peculiarity occurs because they are known to be obligate intracellular parasites that depend upon the host's cell machinery for their replication. Successful infection with the production of essential viral components requires constant viral evolution as a strategy to manipulate the cellular environment, including host internal factors, the host's nonspecific and adaptive immune responses to viruses, the metabolic and energetic state of the infected cell, and changes in the intracellular redox environment during the viral infection cycle. Based on this knowledge, it is fundamental to develop new therapeutic strategies for controlling viral dissemination, by means of antiviral therapies, vaccines, or antioxidants, or by targeting the inhibition or activation of cell signaling pathways or metabolic pathways that are altered during infection. The rapid recovery of altered cellular homeostasis during viral infection is still a major challenge. Here, we review the strategies by which viruses evade the host's immune response and potential tools used to develop more specific antiviral therapies to cure, control, or prevent viral diseases. [ABSTRACT FROM AUTHOR]

Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration. [ABSTRACT FROM AUTHOR]

The automatic or semi-automatic extraction of features from satellite images has always become a challenge for remote sensing researchers. The analysis of satellite imagery of natural scenes presents many unique problems, because they cannot be represented easily by regular rules or grammars. A sand spit does not present a well-defined topographic boundary, because they are influenced by tides, waves and wind. Moreover, the bubbles and foam caused by the breaking waves and the turbidity of the water difficult an accurate extraction of the boundary between land (sand spit) and water. This paper presents different approaches in order to extract sand spits from IKONOS-2 data. A novel semi-automatic approach is proposed, which is based on global thresholding through the Otsu's method, further refined through detected edges (GThE). The performance of GThE is compared with traditional pixel-based and object-based classification algorithms. The dataset is composed by six IKONOS-2 images, acquired between 2001 and 2007, covering a sand spit (Cabedelo) located in Portugal. The performance of the three methods used in the estimation of the sand spit area was evaluated through two sets of reference values of the sand spit area. The proposed GThE method presented better results than the other traditional methods, with a clear advantage of a considerable faster performance, beyond requiring a minimum operator intervention. Finally, the relation of the sand spit area with several hydrodynamic and agitation parameters was investigated, where it was demonstrated that the river discharge was the parameter with higher influence in the Cabedelo area. [ABSTRACT FROM PUBLISHER]

The growing importance and demand of lithium (Li) for industrial applications, in particular rechargeable Li-ion batteries, have led to a significant increase in exploration efforts for Li-bearing minerals. To ensure and expand a stable Li supply to the global economy, extensive research and exploration are necessary. Artificial neural networks (ANNs) provide powerful tools for exploration target identification. They can be cost-effectively applied in various geological settings. This article presents an integrated approach of Li exploration targeting using ANNs for data interpretation. Based on medium resolution geological maps (1:50,000) and stream sediment geochemical data (1 sample per 0.25 km2), the Li potential was calculated for an area of approximately 1200 km2 in the surroundings of Bajoca Mine (Northeast Portugal). Extensive knowledge about geological processes leading to Li mineralisation (such as weathering conditions and diverse Li minerals) proved to be a determining factor in the exploration model. Furthermore, Sentinel-2 satellite imagery was used in a separate ANN model to identify potential Li mine sites exposed on the ground surface by analysing the spectral signature of surface reflectance in well-known Li locations. Finally, the results were combined to design a final map of predicted Li mineralisation occurrences in the study area. The proposed approach reveals how remote sensing data in combination with geological and geochemical data can be used for delineating and ranking exploration targets of almost any deposit type. [ABSTRACT FROM AUTHOR]

Reflectance spectroscopy has been used to identify several deposit types. However, applications concerning lithium (Li)-pegmatites are still scarce. Reflectance spectroscopic studies complemented by microscopic and geochemical studies were employed in the Fregeneda–Almendra (Spain–Portugal) pegmatite field to analyze the spectral behavior of Li-minerals and field lithologies. The spectral similarity of the target class (Li-pegmatites) with other elements was also evaluated. Lepidolite was discriminated from other white micas and the remaining Li-minerals. No diagnostic feature of petalite and spodumene was identified, since their spectral curves are dominated by clays. Their presence was corroborated (by complementary techniques) in petalite relics and completely replaced crystals, although the clay-related absorption depths decrease with Li content. This implies that clays can be used as pathfinders only in areas where argillic alteration is not prevalent. All sampled lithologies present similar water and/or hydroxide features. The overall mineral assemblage is very distinct, with lepidolite, cookeite, and orthoclase exclusively identified in Li-pegmatite (being these minerals crucial targets for Li-pegmatite discrimination in real-life applications), while chlorite and biotite can occur in the remaining lithologies. Satellite data can be used to discriminate Li-pegmatites due to distinct reflectance magnitude and mineral assemblages, higher absorptions depths, and distinct Al–OH wavelength position. The potential use of multi- and hyperspectral data was evaluated; the main limitations and advantages were discussed. These new insights on the spectral behavior of Li-minerals and pegmatites may aid in new Li-pegmatite discoveries around the world. [ABSTRACT FROM AUTHOR]

Coal-mining subsidence causes ground fissures and destroys surface structures, which may lead to severe casualties and economic losses. Time series interferometric synthetic aperture radar (TS-InSAR) plays an important role in surface deformation detection and monitoring without the restriction of weather and sunlight conditions. In addition, the probability integral method (PIM) is a surface movement model that is widely used in the field of mining subsidence. In recent years, the integration of TS-InSAR and the PIM has been extensively studied. In this paper, we propose a new method to estimate mining subsidence with the PIM based on TS-InSAR results. This study focuses on the improvement of a boundary constraint and dynamic parameter estimation in the PIM through the inversion of the line-of-sight (LOS) time series deformation derived by TS-InSAR. In addition, 45 Sentinel-1A images from 17 June 2015 to 27 December 2017 of a coal mine in Jiaozuo are utilized to acquire the surface displacement. We apply a time series deformation analysis using small baseline subsets (SBAS) and place the results into an improved PIM to estimate the mining parameters. The simulated mining subsidence is highly consistent with the leveling data, exhibiting an RMSE of 0.0025 m. Compared with the conventional method, the proposed method is more accurate in discovering displacement in mining areas. In the final section of this paper, some sources of error that affect the experiment are discussed. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms have shown great performance in geological remote sensing applications. The study area of this work was the Fregeneda–Almendra region (Spain–Portugal) where the support vector machine (SVM) was employed. Lithium (Li)-pegmatite exploration using satellite data presents some challenges since pegmatites are, by nature, small, narrow bodies. Consequently, the following objectives were defined: (i) train several SVM's on Sentinel-2 images with different parameters to find the optimal model; (ii) assess the impact of imbalanced data; (iii) develop a successful methodological approach to delineate target areas for Li-exploration. Parameter optimization and model evaluation was accomplished by a two-staged grid-search with cross-validation. Several new methodological advances were proposed, including a region of interest (ROI)-based splitting strategy to create the training and test subsets, a semi-automatization of the classification process, and the application of a more innovative and adequate metric score to choose the best model. The proposed methodology obtained good results, identifying known Li-pegmatite occurrences as well as other target areas for Li-exploration. Also, the results showed that the class imbalance had a negative impact on the SVM performance since known Li-pegmatite occurrences were not identified. The potentials and limitations of the methodology proposed are highlighted and its applicability to other case studies is discussed. [ABSTRACT FROM AUTHOR]

Land-use land-cover (LULC) change contributes to major ecological impacts, particularly in areas undergoing land abandonment, inducing modifications on habitat structure and species distributions. Alternative land-use policies are potential solutions to alleviate the negative impacts of contemporary tendencies of LULC change on biodiversity. This work analyzes these tendencies in the Montesinho Natural Park (Portugal), an area representative of European abandoned mountain rural areas. We built ecological niche models for 226 species of vertebrates (amphibians, reptiles, birds, and mammals) and vascular plants, using a consensus modelling approach available in the R package 'biomod2'. We projected the models to contemporary (2018) and future (2050) LULC scenarios, under four scenarios aiming to secure relevant ecosystem services and biodiversity conservation for 2050: an afforestation and a rewilding scenario, focused on climate-smart management strategies, and a farmland and an agroforestry recovery scenario, based on re-establishing human traditional activities. We quantified the influences of these scenarios on biodiversity through species habitat suitability changes for 2018–2050. We analyzed how these management strategies could influence indices of functional diversity (functional richness, functional evenness and functional dispersion) within the park. Habitat suitability changes revealed complementary patterns among scenarios. Afforestation and rewilding scenarios benefited more species adapted to habitats with low human influence, such as forests and open woodlands. The highest functional richness and dispersion was predicted for rewilding scenarios, which could improve landscape restoration and provide opportunities for the expansion and recolonization of forest areas by native species. The recovery of traditional farming and agroforestry activities results in the lowest values of functional richness, but these strategies contribute to complex landscape matrices with diversified habitats and resources. Moreover, this strategy could offer opportunities for fire suppression and increase landscape fire resistance. An integrative approach reconciling rewilding initiatives with the recovery of extensive agricultural and agroforestry activities is potentially an harmonious strategy for supporting the provision of ecosystem services while securing biodiversity conservation and functional diversity within the natural park. • We simulated 4 future LULC scenarios to secure ecosystem services and biodiversity. • Afforestation and rewilding benefited species adapted to low human influence. • The highest functional richness and dispersion were predicted for rewilding. • Traditional farming and agroforestry provided the lowest functional richness. • An integrative approach (rewilding/agroforestry) is a potential harmonious strategy. [ABSTRACT FROM AUTHOR]

Background: The special area of conservation Montesinho/Nogueira (SAC-MN) is a key area for biodiversity conservation in the Iberian Peninsula. Covering an area of approximately 1081 km² in the northeast of Portugal mainland, the SAC-MN is home to a wide range of species, including several endemic and endangered species and priority habitats. Despite its ecological significance and importance for conservation, there is a lack of publicly available biodiversity data, which urges the need to create a comprehensive and up-to-date biodiversity dataset for the SAC-MN., New Information: To bridge the knowledge gap on biodiversity in SAC-MN, we undertook a thorough data collection process, including species occurrence records and conservation status information at regional (Portugal) and European levels, from multiple sources. We collected and compiled this information for five major taxonomic groups (amphibians, birds, flora - vascular plants, mammals and reptiles) in SAC-MN, resulting in a total of 31,871 records with 1,312 documented species. In addition, we developed an easy-to-navigate web-based geographic information system (Web GIS). In this article, we present an in-depth report on the process of compiling and preparing data, as well as the development and design of our Web GIS to increase awareness and enhance understanding of the importance of preserving biodiversity in SAC-MN., (Nuno Garcia, João C. Campos, Daniel Silva, João Alírio, Lia B. Duarte, Salvador Arenas-Castro, Isabel Pôças, Armando Loureiro, Ana C. Teodoro, Neftalí Sillero.)

Cancer development is closely related to chronic inflammation, which is associated with identifiable markers of tumor progression, such as uncontrolled cell proliferation, angiogenesis, genomic instability, chemotherapeutic resistance, and metastases. Redox processes mediated by reactive oxygen species (ROS) and nitric oxide (NO) within the inflammatory tumor microenvironment play an essential role in directly influencing intercellular and intracellular signaling. These reactive species originating in the cancer cell or its microenvironment, mediate the epithelial-mesenchymal transition (EMT) and the mesenchymal-epithelial transition (MET). However, intracellular interactions between NO and ROS must be controlled to prevent cell death. Melanoma, breast, and colon cancer cells have developed a mechanism to survive and adapt to oxidative and nitrosative stress. The mechanism involves a spatial-temporal fine adjustment of the intracellular concentrations of NO and ROS, thereby guaranteeing the successful development of cancer cells. Physiological concentrations of NO and supra physiological concentrations of ROS are prevalent in cancer cells at the primary site. The situation reverses in cancer cells undergoing the EMT prior to being released into the blood stream. Intracellular supra physiological concentrations of NO found in circulating cancer cells endow them with anoikis resistance. When the anoikis-resistant cancer cells arrive at a metastatic site they undergo the MET. Endogenous supra physiological concentrations of ROS and physiological NO concentrations are prevalent in these cells. Understanding tumor progression from the perspective of redox signaling permits the characterization of new markers and approaches to therapy. The synthesis and use of compounds with the capacity of modifying intracellular concentrations of NO and ROS may prove effective in disrupting a redox homeostasis operative in cancer cells., (Copyright © 2019 Elsevier Inc. All rights reserved.)