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The use of vegetation indices to highlight archaeological features from remote sensing (RS) sensors is increasingly in demand, especially due to the possibility of obtaining high-resolution multispectral images with drones. The objective of this work is divided in two steps: i) to test techniques for the semi-automatic identification of crop marks in a study zone, in Salvada, Portugal, and; ii) to develop a Geographical Information Systems (GIS) open-source application, named ArchMarks, under QGIS software, to enhance the identification of archaeological crop marks using RS data/techniques, integrating the automatic creation of indices and the identification techniques tested before. ArchMarks aims to compute several vegetation and soil indices from multispectral imagery considering four bands (Red, Green and Blue and Near InfraRed (NIR)) for enhancing the identification of archaeological crop marks. In order to define the best approach to implement in the plugin, some tests were performed with two algorithms available in QGIS: ContrastEnhancement and WatershedSegmentation. High-resolution aerial imagery (25-50 cm spatial resolution) was obtained from Web Map Service (WMS) services in addition to Sentinel-2 satellite image, whose spatial resolution is of 10 meters in the Red Green Blue-Near InfraRed bands. The application is free and can be adapted to other region of interest. In the future, the plugin will be improved with a methodology to download remote sensing data (aerial images and satellite data) from WMS sources, band stacking, and machine learning algorithms, such as Support Vector Machine (SVM) algorithm, to automatically classify archaeological traces on the vegetation or bare soil.

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. The authors would like to thank the financial support provided by FCT–Fundação para a Ciência e a Tecnologia, I.P., with the ERA-MIN/0001/2017–LIGHTS project and, also, with the 869274—GREENPEG—H2020-SC5-2018-2019-2020 project. This work was also supported by national funds through the FCT project UIDB/04683/2020–ICT (Institute of Earth Sciences). Joana Cardoso-Fernandes and Filipa Dias were financially supported within the compass of their respective Ph.D. theses, ref. SFRH/BD/136108/2018 and ref. 2020.05534.BD, by national funds from MCTES through FCT and co-financed by the European Social Fund (ESF) through POCH–Programa Operacional Capital Humano and NORTE 2020 regional program. The Spanish Ministerio de Ciencia, Innovacion y Universidades (Project RTI2018-094097-B-100, with ERDF funds) and the University of Basque County (UPV/EHU) (grant GIU18/084) also contributed economically. The French National Research Agency (ANR–10–LABX 21–LABEX RESSOURCES 21) partly supported the Master Student personal grant, and the 776804–NEXT–H2020-SC5-2017 project participated in equipment purchasing.

The role of tourism in the revitalization of rural peripheral areas has been highlighted both in academic and political discourse, with tourism entrepreneurs, particularly local hosts, being recognized as central in the process. However, the role of individual enterprises and entrepreneurs is largely underestimated in the literature and models of destination development. Using the Central Region of Portugal as a case study, this research investigates the impact of pro-development intentions on the behaviour of rural tourism hosts in actually promoting rural development. For this purpose, an empirical model was estimated showing that intentions are important but insufficient to explain behaviour. Other factors such as lodgement location, business success, manager's residence, level of education, and past professional experience, are crucial to explain the adoption of local development practices by local hosts.

Remote sensing has proved to be a powerful resource in geology capable of delineating target exploration areas for several deposit types. Only recently, these methodologies have been used for the detection of lithium (Li)-bearing pegmatites. This happened because of the growing importance and demand of Li for the construction of Li-ion batteries for electric cars. The objective of this study was to develop innovative and effective remote sensing methodologies capable of identifying Li-pegmatites through alteration mapping and through the direct identification of Li-bearing minerals. For that, cloud free Landsat-5, Landsat-8, Sentinel-2 and ASTER images with low vegetation coverage were used. The image processing methods included: RGB (red, green, blue) combinations, band ratios and selective principal component analysis (PCA). The study area of this work is the Fregeneda (Salamanca, Spain)-Almendra (Vila Nova de Foz Coa, Portugal) region, where different known types of Li-pegmatites have been mapped. This study proposes new RGB combinations, band ratios and subsets for selective PCA capable of differentiating the spectral signatures of the Li-bearing pegmatites from the spectral signatures of the host rocks. The potential and limitations of the methodologies proposed are discussed, but overall there is a great potential for the identification of Li-bearing pegmatites using remote sensing. The results obtained could be improved using sensors with a better spatial and spectral resolution.

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.

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} 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.

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.

The São Pedro da Cova waste pile (Porto, Portugal) is composed of coal mining residues that have been self-burning since 2005 and is located close to an inhabited area and social infrastructures, further adding to effects on the environment and human health. Therefore, there is a great interest in the environmental monitoring of this waste pile. This work describes an integrative multi-approach that allows the environmental monitoring of several parameters of the waste pile, applying several technologies. The temperature measurements were obtained by a thermal infrared (TIR) sensor on board an unmanned aerial vehicle (UAV) and supplemented with field measurements. In order to evaluate the altimetric variations, for each flight, a digital elevation model (DEM) was generated considering a multispectral sensor also on board the UAV. The hydrogeochemical characterization was performed through the analysis of groundwater and surface water samples, with and without the influence of mine drainage. The soil monitoring included the analysis of waste material as well as the surface soil in the surrounding area of the waste pile. All the data were analyzed and integrated in a geographical information system (GIS) open-source application. The adopted multi-approach methodology, given its intrinsic interdisciplinary character, has proven to be an effective way of encompassing the complexity of this type of environmental problem.

Prevention quality indicators (PQIs) constitute a set of measures that can be combined with hospital inpatient data to identify the quality of care for ambulatory care sensitive conditions (ACSC). Geographical information system (GIS) web mapping and applications contribute to a better representation of PQI spatial distribution. Unlike many countries in the world, in Portugal, this type of application remains underdeveloped. The main objective of this work was to facilitate the assessment of geographical patterns and trends of health data in Portugal. Therefore, two innovative open source applications were developed. Leaflet Javascript Library, PostGIS, and GeoServer were used to create a web map application prototype. Python language was used to develop the GIS application. The geospatial assessment of geographical patterns of health data in Portugal can be obtained through a GIS application and a web map application. Both tools proposed allowed for an easy and intuitive assessment of geographical patterns and time trends of PQI values in Portugal, alongside other relevant health data, i.e., the location of health care facilities, which, in turn, showed some association between the location of facilities and quality of health care. However, in the future, more research is still required to map other relevant data, for more in-depth analyses.

The existence of diagnostic features in the visible and infrared regions makes it possible to use reflectance spectra not only to identify mineral assemblages but also for calibration and classification of satellite images, considering lithological and/or mineral mapping. For this purpose, a consistent spectral library with the target spectra of minerals and rocks is needed. Currently, there is big market pressure for raw materials including lithium (Li) that has driven new satellite image applications for Li exploration. However, there are no reference spectra for petalite (a Li mineral) in large, open spectral datasets. In this work, a spectral library was built exclusively dedicated to Li minerals and Li pegmatite exploration through satellite remote sensing. The database includes field and laboratory spectra collected in the Fregeneda–Almendra region (Spain–Portugal) from (i) distinct Li minerals (spodumene, petalite, lepidolite); (ii) several Li pegmatites and other outcropping lithologies to allow satellite-based lithological mapping; (iii) areas previously misclassified as Li pegmatites using machine learning algorithms to allow comparisons between these regions and the target areas. Ancillary data include (i) sample location and coordinates, (ii) sample conditions, (iii) sample color, (iv) type of face measured, (v) equipment used, and for the laboratory spectra, (vi) sample photographs, (vii) continuum removed spectra files, and (viii) statistics on the main absorption features automatically extracted. The potential future uses of this spectral library are reinforced by its major advantages: (i) data is provided in a universal file format; (ii) it allows users to compare field and laboratory spectra; (iii) a large number of complementary data allow the comparison of shape, asymmetry, and depth of the absorption features of the distinct Li minerals. The authors are grateful for the financial support provided by FCT– Fundação para a Ciência e a Tecnologia, I.P., through the ERA-MIN/0001/2017–LIGHTS project and also the 869274–GREENPEG–H2020-SC5-2018-2019-2020 project. The work was also supported by National Funds through the FCT project UIDB/04683/2020–ICT (Institute of Earth Sciences). Joana Cardoso-Fernandes and Filipa Dias are financially supported within the compass of their respective Ph.D. theses, ref. SFRH/BD/136108/2018 and ref. 2020.05534.BD, by national funds from MCTES through FCT, and cofinanced by the European Social Fund (ESF) through POCH—Programa Operacional Capital Humano—and NORTE 2020 regional program. The Spanish Ministerio de Ciencia, Innovacion y Universidades (Project RTI2018-094097-B-100, with ERDF funds) and the University of the Basque Country (UPV/EHU) (grant GIU18/084) also contributed economically. The French National Research Agency (ANR–10–LABX 21–LABEX RESSOURCES 21) partly supported Master Student personal grant and the 776804–NEXT– H2020-SC5-2017 project participated to equipment purchase.

Key hydrothermal or supergene alteration minerals are crucial in the remote detection of several mineral deposit types using satellite images. Hydrothermal metasomatic alteration of spodumene and petalite can form eucryptite, albite, K-feldspar and/or micas, and cookeite in more acidic conditions [1, 2]. Moreover, either hydrothermal or supergene alteration of petalite and spodumene lead to the formation of clay minerals like kaolinite, halloysite, pink montmorillonite, and greenish illite-montmorillonite aggregates [1, 3, 4].This study aims at describing for the first time the petalite alteration products from the Bajoca pegmatite (Central Portugal, Fregeneda-Almendra pegmatite field). Field campaigns allowed to identify white to greenish alteration products with increasing alteration degree respectively, but often with a pseudomorphic character preserving the petalite shape and cleavage. Despite being exploited for more than two decades, hitherto such green clayey assemblage was not described. This alteration was not observed at the surface and is restricted to a sector in the base of the open-pit, with intense fracturing.A multidisciplinary study was employed to characterize the alteration products through optical microscopy, XRD, SEM-EDS, and reflectance spectroscopy (350-2500 nm). Petrographic studies show that petalite alteration started along the cleavage, fractures, and crystal borders. Fine white mica and pale brown clays were observed in fractures. Compositional data and spectra obtained with SEM-EDS are compatible with white mica and montmorillonite. Eucryptite was also identified. More heavily altered samples show a complete pseudomorph replacement of petalite, widening of the cleavage and quartz precipitation, the formation of cookeite in close association with white mica, and pseudospherulitic illite filling voids. Locally, a later sericitization is observed superimposed on the previous alteration. The clay agglomerates analyzed with XRD consisted of quartz, illite, montmorillonite/nontronite association with occasional muscovite, albite, kaolinite, and orthoclase. The reflectance spectra show the presence of montmorillonite (ubiquitous), illite and/or white mica, and kaolinite (in two samples).The results seem to indicate at least two stages of petalite alteration: one consistent with the formation of kaolinite in acidic conditions, and another in an alkaline environment that favored illite-montmorillonite [1]. Intense fracturing associated with a known fault-zone was key for fluid circulation. Further investigations are needed to establish the succession of the alteration stages and their relationship with the late-magmatic hydrothermal alteration of petalite to form albite, orthoclase, and eucryptite. Nonetheless, these findings will help to improve satellite detection of lithium-minerals.AcknowledgmentThe work was financial supported by FCT with the ERA-MIN/0001/2017–LIGHTS project, the UIDB/04683/2020–ICT project, and through Ph.D. Thesis, ref. SFRH/BD/136108/2018 and 2020.05534.BD (ESF, NORTE2020).1. London, D. and D.M. Burt, Chemical models for lithium aluminosilicate stabilities in pegmatites and granites. American Mineralogist, 1982. 67(5-6): p. 494-509.2. Charoy, B., F. Noronha, and A. Lima, Spodumene-petalite-eucryptite: mutual relationships and pattern of alteration in Li-rich aplite-pegmatite dykes from northern Portugal. The Canadian Mineralogist, 2001. 39(3): p. 729-746.3. Quensel, P., Minerals of the Varuträsk Pegmatite. Geologiska Föreningen i Stockholm Förhandlingar, 1937. 59(2): p. 150-156.4. Quensel, P., Minerals of the Varuträsk Pegmatite. Geologiska Föreningen i Stockholm Förhandlingar, 1938. 60(2): p. 201-215.

Mapping hydrothermal alteration minerals and structural lineaments using Landsat 8 multispectral imagery provides valuable information for mineral exploration. In northern Portugal, there are several known gold occurrences, but there is the potential to identify new anomalous areas. Gold mineralization occurs in the form of quartz veins and veinlets associated with hydrothermal alteration halos. Fractures are interpreted as conduits for mineralizing fluids, where the interaction between the wall rock and hydrothermal fluids induces compositional variations. Identifying these features is one of the key indicators for targeting new prospective zones of orogenic gold mineralization in the Boticas–Chaves region. Remote sensing image processing methods such as band combinations, band ratios, and principal component analysis (PCA) were implemented to the visible, near-infrared, and shortwave infrared bands of Landsat 8. The results of this investigation demonstrate the capability of the applied imagery enhancement methods in distinguishing different features and identifying hydrothermally altered rocks. Selective PCA proved to be the most effective and reliable method to identify iron oxides and hydroxyl-bearing minerals, compared to other methods, where a simple imagery analysis has a strong influence of noise and is more challenging to interpret. Enhanced imagery allowed the identification of physiographic characteristics and extracted structural features. The combination of mapped hydrothermal alteration minerals and extracted structural features allowed us to predict potential areas for the mineralization occurrence. This investigation proves that remote sensing can be a cost-efficient and time-saving technique for mineral exploration, and its application in new areas can accurately map hydrothermal alteration and outline potential new exploration targets.

Landslides are one of the natural disasters that affect socioeconomic wellbeing. Accordingly, this work aimed to realize a landslide susceptibility map in the coastal district of Mostaganem (Western Algeria). For this purpose, we applied a knowledge-driven approach and the Analytical Hierarchy Process (AHP) in a Geographical Information System (GIS) environment. We combined landslide-controlling parameters, such as lithology, slope, aspect, land use, curvature plan, rainfall, and distance to stream and to fault, using two GIS tools: the Raster calculator and the Weighted Overlay Method (WOM). Locations with elevated landslide susceptibility were close the urban nucleus and to a national road (RN11), in both sites, we registered the presence of strong water streams. The quality of the modeled maps has been verified using the ground truth landslide map and the Area Under Curve (AUC) of the Receiver Operating Characteristic curve (ROC). The study results confirmed the excellent reliability of the produced maps. In this regard, validation based on the ROC indicates an accuracy of 0.686 for the map produced using a knowledge-driven approach. The map produced using the AHP combined with the WOM showed high accuracy (0.753).

Despite the vast evidence on the environmental influence in neurodegenerative diseases, those considering a geospatial approach are scarce. We conducted a systematic review to identify studies concerning environmental atmospheric risk factors for neurodegenerative diseases that have used geospatial analysis/tools. PubMed, Web of Science, and Scopus were searched for all scientific studies that included a neurodegenerative disease, an environmental atmospheric factor, and a geographical analysis. Of the 34 included papers, approximately 60% were related to multiple sclerosis (MS), hence being the most studied neurodegenerative disease in the context of this study. Sun exposure (n = 13) followed by the most common exhaustion gases (n = 10 for nitrogen dioxide (NO2) and n = 5 for carbon monoxide (CO)) were the most studied atmospheric factors. Only one study used a geospatial interpolation model, although 13 studies used remote sensing data to compute atmospheric factors. In 20% of papers, we found an inverse correlation between sun exposure and multiple sclerosis. No consensus was reached in the analysis of nitrogen dioxide and Parkinson’s disease, but it was related to dementia and amyotrophic lateral sclerosis. This systematic review (number CRD42020196188 in PROSPERO’s database) provides an insight into the available evidence regarding the geospatial influence of environmental factors on neurodegenerative diseases.

The Sao 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.

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.

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.

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.

International audience; The acquisition of field data plays an important role in the calibration of the remotely sensed data, especially when combined with reflectance spectroscopy studies. The study area of this work is the Fregeneda-Almendra pegmatite field (spreading from Portugal to Spain) where different lithium (Li)-pegmatites are known. Several image processing techniques were applied to satellite images for Li exploration in the region, including machine learning classifiers. However, these algorithms identified several zones as Li-pegmatites false positives. Taking this into account, the following objectives were delineated: (i) validate the training areas used in previous studies and collect field data for training area refinement; (ii) assess the reason for the false positives previously obtained through field surveys. For that, various outcropping lithologies (Li-pegmatite, metasediments, granite) were sampled for laboratory spectral analysis. The spectral signature of Li-pegmatite was compared with the remaining outcropping lithologies. Also, the spectral signature of the sampled false positive areas was confronted with the spectra of Li-minerals. It was possible to conclude that these two classes present similar water/hydroxide and Al-OH-related features. The sampled granitic and metasedimentary rocks also presented water and/or hydroxide absorption features that can lead to some spectral confusion. However, Li-pegmatites can be discriminated from the remaining lithologies either in the training areas and the false positive areas due to the absence of iron-related absorption features and to the distinct reflectance magnitudes.

Anthropic changes resulting from urbanization contribute to the modification of surface materials, originating the suppression of vegetation, waterproofing of the soil and the variation of albedo, which can contribute to the formation of Heat Islands (HI). This phenomenon results from the higher absorption of electromagnetic energy by the buildings, when compared to vegetation, corroborating both the increase of surface temperature and the minimization of the relative humidity of the air. This effect is potentiated at night, when urbanized regions maintain higher average temperatures for a longer time, when compared to vegetation areas. Some impacts of this effect are: (i) thermal discomfort and influence on the local microclimate and; (ii) displacement of water bodies. This work aimed to analyze the relationship between the Land Surface Temperature (LST) and the Vegetation Proportion (Pv), derived from Normalized Difference Vegetation Index (NDVI), in the densely built area of Brasilia (Brazil), in order to evaluate if its characteristics confirm the micro-scale climate change. Several images from Landsat 5 and 8 thermal sensors were processed between 1989 and 2019 and the results obtained point out that the LST is inversely proportional to Pv, i.e., the highest temperatures were observed in classified areas with scarce or no Pv, which corroborates with the hypothesis that anthropic altered regions can influence the local climate. Despite the phenomenon observed and the 50% increase in maximum temperature in 2019 when compared to the 1989 and the decrease in maximum Pv by 36% in the same years, it is mandatory additional studies to confirm these findings.

International audience; Although several lithium (Li) bearing minerals have already been spectrally characterized, there are no current reference spectra for petalite in large and public access spectral libraries. This fact is aggravated by the difficulty in the identification of petalite's diagnostic features. The study area of this work is the Fregeneda (Spain)-Almendra (Portugal) region, where distinct Li bearing minerals occur in several types of enriched pegmatite dikes. Accordingly, the objectives delineated for this work were: (i) improve the existing knowledge on the spectral signatures of Li bearing minerals (lepidolite, spodumene, petalite); (ii) compare the spectra obtained for petalite and spodumene in the study area; (iii) and compare the spectra of the Li bearing minerals from the Fregeneda-Almendra area with the reference spectra from the United States Geological Survey (USGS), the ECOSTRESS and the Geological Survey of Brazil (CPRM) spectral libraries. For that, spectral measurements were conducted in the laboratory using the SR-6500A (Spectral Evolution, Inc.) spectrometer. The results only allowed to discriminate lepidolite, since that, both, petalite and spodumene, present absorption features typical of montmorillonite and illite, or a combination between these two minerals. This is also verified in samples of corresponding minerals in other spectral libraries. No diagnostic features of these two Li bearing minerals were identified, highlighting the difficulty to spectrally discriminate them from each other and from clay minerals.

The Sao Pedro da Cova coal mine, in Gondomar (Portugal), left a substantial ecological footprint in the form of a 28,000 m2 waste pile. This waste pile has been undergoing continuous self-combustion since 2005, leading to the mobilization of pollutants. As a result, it is of vital importance to monitor the condition and evolution of the waste pile, to assess the current and potential risks posed to the inhabitants and to propose adequate mitigation measures. To evaluate and monitor the waste pile surface soil movements, a comparison between different digital elevation models (DEM) was performed. The DEMs were obtained from photogrammetric processing of data collected by different sensors onboard of an unmanned aerial vehicle (UAV). As preliminary results, the waste pile seems to be suffering a generalized topographic recession, verified by the elevation decrease on multiple monitoring points, this effect may be caused by soil erosion or subsidence, potentially caused by the coal fires that have been active in this waste pile. As part of a larger evaluation of the study area within the CoalMine Project, which includes geochemical soil and water analysis, as well as, monitoring of the surface temperatures, the current assessment of soil movement provides crucial insight into the movement and deposit of soil in addition to the potential contaminants present in its composition.

Spatial thinking can be defined as the knowledge about spatial concepts (e.g. location, direction, and scale) applicable on the description, analysis and problems solving in several contexts, such as Geographical Information System (GIS). Students from two Curricular Units (CU) and from 6 different background areas solved a test, named Spatial Thinking Ability Test (STAT), composed by questions about logic, spatial thinking, and geographical reasoning, regarding the GIS context. The aim of this study is to evaluate: i) the differences in knowledge, logical and geographical reasoning of the students and, (ii) the spatial thinking of students in the different areas. The test was applied to 83 students at the beginning and at the end of the semester (that is, respectively before and after students' contact with the concepts taught in the referred CUs). This study presents itself as a support methodology to pedagogical didactics that have been implemented in the CUs where it is intended to provide the domain of computer tools for manipulation and analysis of geographical information. Also, it will contribute to improve the learning of students, and will help teachers in a more effective, interdisciplinary and targeted management of knowledge within the scope of CUs, and to guide the syllabus and didactics for learning that must be guaranteed to all students. The responses were analyzed in the Statistical Package for the Social Sciences (SPSS) software. The analysis of the test results will allow to direct the contents considering the level of students in spatial and geographical reasoning.

The Andean region has a high cloud density throughout the year. The use of optical remote sensing data in the computation of environmental indices of this region has been hampered by the presence of clouds. To maximize accuracy in the computation of several environmental indices including the normalized difference vegetation index (NDVI), we compared the performance of two algorithms in removing clouds in Landsat-8 Operational Land Imager (OLI) data of a high-elevation area. The study area was Quito, Ecuador, which is a city located close to the equator and in a high-elevation area crossed by the Andes Mountains. The first algorithm was the automatic cloud removal method (ACRM), which employs a linear regression between the different spectral bands and the cirrus band. The second algorithm was independent component analysis (ICA), which considers the noise (clouds) as part of independent components applied over the study area. These methods were evaluated based on several images from different years with different cloud conditions. The results indicate that neither algorithm is effective over this region for the removal of clouds or for NDVI computation. However, after improving ACRM, the NDVI computed using ACRM showed a better correlation than ICA with the MODIS NDVI product.

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. The authors would like to thank the financial support provided by FCT—Fundação para a Ciência e a Tecnologia with the ERA-MIN/0001/2017—LIGHTS project, FEDER through operation POCI-01-0145-FEDER-007690 funded by the Programa Operacional Competitividade e Internacionalização—COMPETE2020 and by National Funds through FCT within the ICT, R and D Unit (reference UIDB/04683/2020). The University of the Basque Country (UPV/EHU) contributed economically with grant GIU18/084. Joana Cardoso-Fernandes is financially supported within the compass of a Ph.D. Thesis, ref. SFRH/BD/136108/2018, by national funds from MCTES through FCT, and co-financed by the European Social Fund (ESF) through POCH—Programa Operacional Capital Humano.

This special issue on Unmanned Aerial Systems (UAS) for Environmental Applications makes three important contributions:(1) It marks the launch of a new section in the International Journal of Remot...

Phenology is one of the most reliable indicators of vegetation dynamics. Assessing and monitoring the dynamics of phenology is relevant to support several decisions in order to improve the efficiency of several farming practices. An open source application – QPhenoMetrics - implemented in QGIS software that estimates vegetation phenology metrics is presented, using Earth Observation Systems (EOS) based time-series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as proxies for phenology. QPhenoMetrics is characterized by freely-usable and updatable code, acceptance of satellite images or text formats, time-series analysis toolbox allowing the selection of region of interest with statistical quality assessment for Vegetation Indices (VI), and estimation of ensemble metrics. The application is structured in three components: (i) input data; (ii) pre-processing of the VI time-series and several fitting methods and (iii) computation of the phenological metrics. QPhenoMetrics produces a plot with the VI time-series and corresponding phenology metrics, and a spreadsheet is created with a list of NDVI or EVI values estimated using the selected fitting method. To evaluate the application, two main Portuguese crops, vineyards and maize, and MOD13 data from MODIS sensor during 2011–2012 were considered. QPhenoMetrics was validated with vineyard phenology observations (2007–2011). A comparative analysis with software products TimeSat and Spirits was also performed. It was concluded that QPhenoMetrics can be very useful for common users to extract phenology information for 16 daily MODIS data in HDF format, text files with NDVI/EVI data and ASCII files, through a simple and intuitive graphic interface. Furthermore, the user can evaluate the quality assessment of VI of the images used. QPhenoMetrics is an effective open source tool that in addition to being free, is readily modifiable by user according to the study requirements.

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico O objetivo deste trabalho é contrapor duas interpretações contemporâneas a respeito de como a filosofia cartesiana se vincula com o ceticismo e, assim, estabelecer se Descartes intencionava responder e eliminar o ceticismo; se sim, indicar como ele teria realizado esta tarefa e, posteriormente, determinar se ele teria sido bem sucedido ou não dentro desta possível empreitada, apontando também para as consequências caso a resposta revele uma tentativa fracassada. Primeiramente, retomamos a leitura inovadora de Richard Popkin acerca da filosofia cartesiana, que a insere dentro do contexto de uma crise pirrônica da modernidade, estabelecendo que a solução dada por Descartes à sua dúvida hiperbólica metodológica não representa só um trabalho epistêmico interno à sua filosofia, mas também aparece como uma reação à crise pirrônica. Popkin entende que a filosofia cartesiana tem como objetivo principal responder os argumentos céticos, mas falha nesta empreitada, o que faria de Descartes um cético malgré lui. Posteriormente, apresentamos a leitura de Thomas Lennon, antagônica à leitura proposta por Popkin, que indica que a filosofia cartesiana não tinha como meta responder ao ceticismo e que, apesar de tê-lo feito, falhar em algo que não está colocado como um objetivo não seria um problema, pelo menos não tão grave como alega Popkin. Desse modo, analisando as fontes apresentadas por estes dois autores e buscando informações contextuais e históricas acerca das passagens do corpus cartesiano que indicam a relação desta filosofia com o ceticismo, procuraremos apontar as forças que emergem das leituras de Popkin e de Lennon, mostrando também onde elas falham. Neste sentido, reconhecemos que não é possível desprezar a importância do ceticismo entre as influências cartesianas, mas também não é possível exagerar ao dizer que refutar o ceticismo era o principal objetivo da filosofia de Descartes. Além disso, constatamos que Descartes não pode ser um cético apesar de suas intenções dogmáticas, como indica Popkin, ainda que a filosofia cartesiana tenha reforçado a dificuldade que o ceticismo colocava após ter os seus fundamentos questionados pelas próprias dúvidas céticas que são o ponto de partida desta filosofia. The aim of this work is to compare two contemporary interpretations of how Cartesian philosophy is linked with skepticism and thus to establish whether Descartes aimed to responding and eliminating skepticism; if so, we will indicate how he would have accomplished this task and then determine whether or not he would have been successful within this possible endeavor, also pointing to the consequences if the answer reveals a failed attempt. First, we return to Richard Popkin's groundbreaking reading of Cartesian philosophy, which places it within the context of a Pyrrhonian crisis of modernity, establishing that Descartes' solution to his own hyperbolic methodological doubt is not merely an epistemic work internal to his philosophy, but it also appears as a reaction to the Pyrrhonian crisis. Popkin understands that Cartesian philosophy has as its main goal to answer the skeptical arguments, but fails in this endeavor, which would make Descartes a skeptic malgré lui. Subsequently, we present Thomas Lennon's reading, antagonistic to Popkin’s, which claims that Cartesian philosophy was not meant to respond to skepticism and that, despite having done so, failing to do something that was not set as a goal would not be a problem, at least not as major as Popkin says. Thus, by analyzing the sources presented by these two authors and seeking contextual and historical information about the passages of the Cartesian corpus that indicate the relation of this philosophy with skepticism, we point out the forces that emerge from the readings of Popkin and Lennon, also showing where they fail. In this sense, we recognize that it is not possible to overlook the importance of skepticism among Cartesian’s influences, but neither it is possible to exaggerate by saying that refuting skepticism was the main purpose of Descartes’ philosophy. Furthermore, we find that Descartes cannot be a skeptic despite his dogmatic intentions, as Popkin indicates, even though Cartesian philosophy has reinforced the difficulty that skepticism posed after having its foundations questioned by the very skeptical doubts that are the starting point of this philosophy.

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. The authors would like to thank the financial support provided by FCT—Fundação para a Ciência e a Tecnologia, I.P., with the ERA-MIN/0001/2017—LIGHTS project. The work was also supported by National Funds through the FCT project UIDB/04683/2020—ICT (Institute of Earth Sciences). Joana Cardoso-Fernandes is financially supported within the compass of a Ph.D. Thesis, ref. SFRH/BD/136108/2018, by national funds from MCTES through FCT, and co-financed by the European Social Fund (ESF) through POCH—Programa Operacional Capital Humano. The Spanish Ministerio de Ciencia, Innovacion y Universidades (Project RTI2018-094097-B-100, with ERDF funds) and the University of the Basque Country (UPV/EHU) (grant GIU18/084) also contributed economically.

Thirty-three years ago, an accident at the Chernobyl Nuclear Power Plant released huge amounts of radioactive materials like cesium, strontium and plutonium into the atmosphere, which spread over a vast area. It is known that its effects can cause damage do the DNA of living beings, leading to death or altered fitness of the biota, but the effects of land abandonment under such conditions are not, as the vicinities of the power plant were permanently evacuated. Thus, the objective of this study was to analyze the state of primary production of the vegetation in the Chernobyl Exclusion Zone after the accident, comparing with its previous state. Through Normalized Difference Vegetation Index (NDVI) and radiation measurements, the relationship between these variables was assessed using Analysis of Variance (ANOVA) and Generalized Additive Models (GAM), to understand how the vegetation responded to the radiation exposure. The data considered was Landsat satellite imagery and sampled areas registered on the field. The results show that the NDVI increased over the years after the accident and that it is independent of the current radiation measured. This suggests that to some level of radiation exposure, the positive effects of land abandonment and/or the negative effects of radiation on abundance of herbivore species surpass the long term negative effects of radiation exposure on the vegetation. Nonetheless, above some radiation threshold level, primary production is expected to be negatively affected.

Machine learning algorithms (MLAs) have gained great importance in remote sensing-based applications, and also in mineral prospectivity mapping. Studies show that MLAs can outperform classical classification techniques. So, MLAs can be useful in the exploration of strategical raw materials like lithium (Li), which is used in consumer electronics and in the green-power industry. The study area of this work is the Fregeneda-Almendra region (between Spain and Portugal), where Li occurs in pegmatites. However, their smaller exposition can be regarded as a problem to the application of remote sensing methods. To overcome this, Support Vector Machine (SVM) and Random Forest (RF) algorithms were applied to. This study aims at: (i) comparing the performance accuracy in lithological mapping achieved by SVM and by RF; (ii) evaluating the sensitivity of both classifiers to class imbalance and; (iii) compare the results achieved with previously obtained results. For these, the same Level 1-C Sentinel-2 images (October 2017) were used. SVM showed slightly better accuracy, but RF was able to correctly classify a larger number of mapped Li-bearing pegmatites. The performance of the models was not equal for all classes, having all underperformed in some classes. Also, RF was affected by class imbalanced, while SVM prove to be more insensitive. The potential of this kind of approach in Li-exploration was confirmed since both algorithms correctly identified the presence of Li-bearing pegmatites in the three open-pit mines where they outcrop as well in areas where Li-pegmatites were mapped. Also, some of the areas classified as Li-bearing pegmatites are corroborated by the interest areas delimited in previous studies.

Several scientific studies with different concept on the mapping of pegmatites have been done in Muiane and Naipa (Mozambique) region. However, none of the studies compare different satellite data and different remote sensing classification algorithms. This study aims to compare the land cover/use classification maps and their accuracies considered sentinel-2, aster, and Landsat OLI imagery. The algorithms employed to evaluate the pegmatites location at Naipa and muiane in alto ligonha pegmatite district were minimum distance (MinD), spectral angle mapper (SAM), and maximum likelihood (ML). The identified features of landscape characteristics selected includes 8 class (kaolinite; montmorillonite; water; built up; bare soil; grasslands; shrubs; isolated bush). The results showed that SAM and MinD algorithms are appropriate for mineralogical mapping validated with ground truth data and geological maps. A kappa index of 0.85 and an overall accuracy (OA) of 80% was obtained for SAM algorithm, and a kappa of 0,80 and OA of 90% for the MinD algorithm. The classification of the images using SAM and mind showed better results for the clays (kaolinite, montmorillonite) visible in both classifications, has also been tested unsupervised classifications or criteria determined by the geologist using an input training dataset in the case of supervised classifications.

Lithium (Li) is defined as an alkaline metal which does not exist in nature in its free form. Moreover, it has properties that make it possible to be applied in several manners, such as industrial use, especially in the ceramic and glass industries, as well as the battery industry which has had an increase in this element consumption. It is crucial to use less expensive and faster techniques, compared to classical and intrusive methods, to identify new Li deposits. Remote sensing as proved to be a powerful tool to identify areas with potential for Li exploration. The objective of this work is to apply several images processing techniques, such as RGB band combinations, Band Ratios and Principal Components Analysis (PCA) to identify potential areas for Li prospection in the pegmatite district of Sao Joao Del Rei, located in the south of the state of Minas Gerais, Brazil. In these areas, two study zones were defined: the zone A, with approximately 323 km(2) in the pegmatite district of Sao Joao Del Rei and the zone B with approximately 90 km(2) in the pegmatite district of Aracuai. The results of the techniques applied in this study are very promising since, in addition to ease and low cost, these techniques can be applied to several locations. This approach is highly valuable for the Li mining industry.

Several studies have demonstrated that air quality and weather changes have influence in the prevalence of chronic respiratory diseases. Considering this context, the spatial risk modeling along the cities can help public health programs in finding solutions to reduce the frequency of respiratory diseases. With the aim to have a regional coverage and not only data in specific (point) locations, an effective alternative is the use of remote sensing data combined with field air quality data and meteorological data. During the last years, the use of remote sensing data allowed the construction of models to determine air quality data with satisfactory results. Some models using remote sensing based air quality data presented good levels of correlation (R2 > 0.5), proving that it is possible to establish a relationship between remote sensing data and air quality data. In order to establish a spatial health respiratory risk model for Quito, Ecuador, an empirical model was computed considering data between 2013 and 2017, using the median data values in each parish of the city. The variables are: i) 46 Landsat-8 satellite images with less than 10% of cloud cover and some indexes (normalized difference vegetation index NDVI, Soil-adjusted Vegetation Index SAVI, etc.); ii) air quality data (nitrogen dioxide - NO2, Ozone - O3, particulate matter less than 2.5μ - PM2.5 and sulfur dioxide - SO2) obtained from local air quality network stations and; iii) the hospital discharge rates from chronic respiratory diseases (CRD). In order to establish a probability model to get a CRD, a logistic regression was used. The empirical model is expressed as the probability of occurrence during the studied time. All the procedures were implemented in R Studio. The methodology proposed in this work can be used by health and governmental entities to access the risk of getting a respiratory disease, considering an application of remote sensing in the environmental and health management programs.

The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 &micro, m diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index&mdash, NDVI, normalized difference soil index&mdash, NDSI, soil-adjusted vegetation index&mdash, SAVI, normalized difference water index&mdash, NDWI, and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.

Groundwater is a crucial natural resource for regular socio-economic function. Groundwater vulnerability to pollution can be assessed through Geographical Information System (GIS)-based qualitative methods. GIS-based tools, dedicated to the assessment of groundwater vulnerability, usually present several limitations, such as high cost, unavailable code, and a lack of functionality concerning the flexible application of vulnerability indices and vulnerability map comparison. The objective of this work was to create a new GIS-based open source application for groundwater vulnerability assessment, GVTool, developed using QGIS software, with the capability of creating and comparing groundwater vulnerability maps considering four different methods: DRASTIC, GOD, SINTACS, and Susceptibility Index (SI). This application incorporates features from a previous tool, DRASTIC Model, and new functionalities were included, namely three additional vulnerability assessment methods, map comparative analysis, map statistics, and index interval reclassification and symbology definition. The GVTool functionalities and capabilities are illustrated through a groundwater vulnerability assessment in Serra da Estrela mountain (Central Portugal). GVTool is mostly useful in integrated assessments, helping to verify if the groundwater vulnerability maps are accurate and to decide which is the most suitable method or the combination of methods to express groundwater vulnerability to pollution in a specific area.

In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Bel&eacute, m, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.

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 programs (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 (OAOTB/Monteverdi = 63.3%; OAArcGIS = 45.7%). eCognition, with MRS and NNC algorithms, reached better classification results in both land-cover and IAP maps (OALand-cover = 95.7%; OAInvasive-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.

Remote-sensing applications using the remotely piloted aerial system RPAS are becoming more frequent. RPAS is used in different contexts and in several areas, such as environmental studies, cultural heritage, civil engineering, forestry, and cartography. To process the images resulting from the RPAS, different types of image-based 3D modelling software proprietary or open source are used. MicMac is an open-source software which allows generating georeferenced information which can be manipulated or visualized under a geographical information system GIS environment. So, the integration between the MicMac procedures within a GIS software could be very useful. The main objective of this work was to create an open-source GIS application based on MicMac photogrammetric tools to obtain the orthophotographs, point clouds, and digital surface models. To test the application developed, two distinct areas were considered: one in a more natural environment Aguda beach near Porto city, Portugal and another in an urban environment in the city of Coimbra, Portugal. High-resolution data sets were obtained with a ground sampling distance GSD of approximately 4.5 cm. Shaded relief image and dense point cloud were generated. This open-source application can be automated and can create all the files required to run the functionalities from MicMac to obtain the georeferenced information, within a GIS software, bringing photogrammetric data generation to a wider user community. Moreover, integrating this application with the GIS software has several advantages like generating more georeferenced information, such as vegetation indices, or even creating the land use land cover map. Creation of shapefiles with the projection centre of the camera, the area covered by each photograph, and taking account of the number of images that appear in each location are also useful in performing certain tasks.

The objective of this work was to analyze the behavior of the Funchal Port (Madeira Island, Portugal) breakwater using physical and numerical models in order to analyze the evolution of the underwater layers, combining information between the two models as well as simulating storm conditions and atypical docking/undocking impacts of cruise ships. Regarding the physical model using a wave generation system, the data collected and the test conditions were addressed considering different scenarios (return periods of 20, 50 and 100 years). It was found that for return periods of 20 years, the behavior of the infrastructure as a whole was stable. For return periods of 50 years, wave overtopping became more frequent and intense, and began to impact the superstructure. For the 100-year return period the behavior of the infrastructure was found to be unstable, with persistent wave overtopping. The morphology of the submerged layers also changed. The main objectives of the numerical model approach were to estimate the natural frequencies of vibration (when materials suffer a considerable modification, a change in their frequencies of vibration can be detected), and to calculate the expected displacements corresponding to cruise ship docking processes (agitation in a protected area) and the associated maximum maritime (agitation in an exposed area) forces. The maximum displacements obtained for the two cases were about 0.03 m, which is a high value. Considering the results obtained from this work, the Port authorities decided to maintain the monitoring program and perform an underwater video inspection.

Over the last few years, the use of remote sensing data in different applications such as estimation of air pollution concentration and health applications has become very popular and new. Thus, some studies have established a possible relationship between environmental variables and respiratory health parameters. This study proposes to estimate the prevalence of Chronic Respiratory Diseases, where there is a relationship between remote sensing data (Landsat 8) and environmental variables (air pollution and meteorological data) to determine the number of hospital discharges of patients with chronic respiratory diseases in Quito, Ecuador, between 2013 and 2017. The main objective of this study is to establish and evaluate an alternative LUR model that is capable of estimate the prevalence of chronic respiratory diseases, in contrast with traditional LUR models, which typically assess air pollutants. Moreover, this study also evaluates different analytic techniques (multiple linear regression, multilayer perceptron, support vector regression, and random forest regression) that often form the basis of spatial models. The results show that machine learning techniques, such as support vector machine, are the most effective in computing such models, presenting the lowest root-mean-square error (RMSE). Additionally, in this study, we show that the most significant remote sensing predictors are the blue and infrared bands. Our proposed model is a spatial modeling approach that is capable of determining the prevalence of chronic respiratory diseases in the city of Quito, which can serve as a useful tool for health authorities in policy- and decision-making.

Surface ozone is problematic to air pollution. It influences respiratory health. The air quality monitoring stations measure pollutants as surface ozone, but they are sometimes insufficient or do not have an adequate distribution for understanding the spatial distribution of pollutants in an urban area. In recent years, some projects have found a connection between remote sensing, air quality and health data. In this study, we apply an empirical land use regression (LUR) model to retrieve surface ozone in Quito. The model considers remote sensing data, air pollution measurements and meteorological variables. The objective is to use all available Landsat 8 images from 2014 and the air quality monitoring station data during the same dates of image acquisition. Nineteen input variables were considered, selecting by a stepwise regression and modelling with a partial least square (PLS) regression to avoid multicollinearity. The final surface ozone model includes ten independent variables and presents a coefficient of determination (R2) of 0.768. The model proposed help to understand the spatial concentration of surface ozone in Quito with a better spatial resolution.

The Tafilalet plain in Morocco is a very rich ecosystem. It presents enormous ecological and natural values, such as groundwater and agricultural soil; however, it is undergoing rapid changes due to natural and anthropogenic factors, where soil salinity constitutes one of the major problems. In this context, remote sensing data/techniques used to map and to model soil salinity is a valuable tool in management activities and in decision-making. This paper focuses on modeling and mapping soil salinity in Tafilalet plain, Morocco, based on Landsat 8 OLI satellite data in combination with ground field data. Our results indicated that the coefficient of determination (R2) varies from 0.53 to 0.75 and the Root Mean Square Error (RMSE) ranges between 0.62 and 0.80 dS/m. Based on the results, we can conclude that this approach is an effective and valid methodology for modeling and spatial mapping soil salinity in this area and this method could also be applied for other regions with similar characteristics.

Capturing spatial and temporal environmental dynamics in heterogeneous tropical landscapes through remote sensing data has become an important challenge in environmental monitoring studies due to cloud contamination. In Brazilian Amazon, there is a great need of data able to provide information for a wide range of decision makers. Alongside this, a wide variety of free remotely sensed data products have been made available. The combination of information from more than one sensor could maximise the number of cloud-free images as well as their temporal and spatial resolution. Based on this framework, this study investigates the potential and quality of a set of fused images obtained from the Sentinel-2 Multispectral Imager Instrument (MSI) and Landsat OLI satellites sensors over the region of Brazilian Amazon, comparing their performance according to different land cover/land use areas. In this context, the objectives of this study were: (1) assess qualitatively the two main group of image fusion approaches, namely component substitution (CS) and multiresolution analysis (MRA), as well as their suitability for the fusion of Sentinel-2 MSI and Landsat-8 OLI image pair from selected areas in Brazilian Amazon; (2) compare three different image fusion methods for measuring efficacy and performance to determine the best spatio-temporal information: the Intensity Hue Saturation (IHS) method, the Brovey transformation (BT) and the Gram-Schmidt (GS) method; (3) assess quantitatively the methods employed through a set of fusion quality metrics in order to identify the more accurate results based on different reference images.