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Soil erosion is a severe and complex issue in the agriculture area. The main objective of this study was to assess the soil loss in two regions, testing different methodologies and combining different factors of the Revised Universal Soil Loss Equation (RUSLE) based on Geographical Information Systems (GIS). To provide the methodologies to other users, a GIS open-source application was developed. The RUSLE equation was applied with the variation of some factors that compose it, namely the slope length and slope steepness (LS) factor and practices factor (P), but also with the use of different sources of information. Eight different erosion models (M1 to M8) were applied to the two regions with different ecological conditions: Montalegre (rainy-mountainous) and Alentejo (dry-flat), both in Portugal, to compare them and to evaluate the soil loss for 3 potential erosion levels: 0–25, 25–50 and >50 ton/ha·year. Regarding the methodologies, in both regions the behavior is similar, indicating that the M5 and M6 methodologies can be more conservative than the others (M1, M2, M3, M4 and M8), which present very consistent values in all classes of soil loss and for both regions. All methodologies were implemented in a GIS application, which is free and available under QGIS software.
Lia Duarte; Mário Cunha; Ana Teodoro. Comparing Hydric Erosion Soil Loss Models in Rainy Mountainous and Dry Flat Regions in Portugal. Land 2021, 10, 554 .
AMA StyleLia Duarte, Mário Cunha, Ana Teodoro. Comparing Hydric Erosion Soil Loss Models in Rainy Mountainous and Dry Flat Regions in Portugal. Land. 2021; 10 (6):554.
Chicago/Turabian StyleLia Duarte; Mário Cunha; Ana Teodoro. 2021. "Comparing Hydric Erosion Soil Loss Models in Rainy Mountainous and Dry Flat Regions in Portugal." Land 10, no. 6: 554.
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.
Ana Teodoro; Patrícia Santos; Jorge Espinha Marques; Joana Ribeiro; Catarina Mansilha; Armindo Melo; Lia Duarte; Cátia Rodrigues de Almeida; Deolinda Flores. An Integrated Multi-Approach to Environmental Monitoring of a Self-Burning Coal Waste Pile: The São Pedro da Cova Mine (Porto, Portugal) Study Case. Environments 2021, 8, 48 .
AMA StyleAna Teodoro, Patrícia Santos, Jorge Espinha Marques, Joana Ribeiro, Catarina Mansilha, Armindo Melo, Lia Duarte, Cátia Rodrigues de Almeida, Deolinda Flores. An Integrated Multi-Approach to Environmental Monitoring of a Self-Burning Coal Waste Pile: The São Pedro da Cova Mine (Porto, Portugal) Study Case. Environments. 2021; 8 (6):48.
Chicago/Turabian StyleAna Teodoro; Patrícia Santos; Jorge Espinha Marques; Joana Ribeiro; Catarina Mansilha; Armindo Melo; Lia Duarte; Cátia Rodrigues de Almeida; Deolinda Flores. 2021. "An Integrated Multi-Approach to Environmental Monitoring of a Self-Burning Coal Waste Pile: The São Pedro da Cova Mine (Porto, Portugal) Study Case." Environments 8, no. 6: 48.
In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.
Lia Duarte; Ana Teodoro; Joaquim Sousa; Luís Pádua. QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps. Agronomy 2021, 11, 952 .
AMA StyleLia Duarte, Ana Teodoro, Joaquim Sousa, Luís Pádua. QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps. Agronomy. 2021; 11 (5):952.
Chicago/Turabian StyleLia Duarte; Ana Teodoro; Joaquim Sousa; Luís Pádua. 2021. "QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps." Agronomy 11, no. 5: 952.
The advent of Geographical Information Systems (GIS) has changed the way people think and interact with the world. The main objectives of this paper are: (i) to provide an overview of 10 years (2010–2020) regarding the creation/development of GIS open-source applications; and (ii) to evaluate the GIS open-source plugins for environmental science. In the first objective, we evaluate the publications regarding the development of GIS open-source geospatial software in the last 10 years, considering desktop, web GIS and mobile applications, so that we can analyze the impact of this type of application for different research areas. In the second objective, we analyze the development of GIS open-source applications in the field of environmental sciences (with more focus on QGIS plugins) in the last 10 years and discuss the applicability and usability of these GIS solutions in different environmental domains. A bibliometric analysis was performed using Web of Science database and VOSViewer software. We concluded that, in general, the development of GIS open-source applications has increased in the last 10 years, especially GIS mobile applications, since the big data and Internet of Things (IoT) era, which was expected given the new advanced technologies available in every area, especially in GIS.
Lia Duarte; Ana Teodoro. GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature. Geomatics 2021, 1, 206 -245.
AMA StyleLia Duarte, Ana Teodoro. GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature. Geomatics. 2021; 1 (2):206-245.
Chicago/Turabian StyleLia Duarte; Ana Teodoro. 2021. "GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature." Geomatics 1, no. 2: 206-245.
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.
Lia Duarte; Ana Teodoro; Mariana Lobo; João Viana; Vera Pinheiro; Alberto Freitas. An Open Source GIS Application for Spatial Assessment of Health Care Quality Indicators. ISPRS International Journal of Geo-Information 2021, 10, 264 .
AMA StyleLia Duarte, Ana Teodoro, Mariana Lobo, João Viana, Vera Pinheiro, Alberto Freitas. An Open Source GIS Application for Spatial Assessment of Health Care Quality Indicators. ISPRS International Journal of Geo-Information. 2021; 10 (4):264.
Chicago/Turabian StyleLia Duarte; Ana Teodoro; Mariana Lobo; João Viana; Vera Pinheiro; Alberto Freitas. 2021. "An Open Source GIS Application for Spatial Assessment of Health Care Quality Indicators." ISPRS International Journal of Geo-Information 10, no. 4: 264.
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.
Joana Cardoso-Fernandes; João Silva; Filipa Dias; Alexandre Lima; Ana Teodoro; Odile Barrès; Jean Cauzid; Mônica Perrotta; Encarnación Roda-Robles; Maria Ribeiro. Tools for Remote Exploration: A Lithium (Li) Dedicated Spectral Library of the Fregeneda–Almendra Aplite–Pegmatite Field. Data 2021, 6, 33 .
AMA StyleJoana Cardoso-Fernandes, João Silva, Filipa Dias, Alexandre Lima, Ana Teodoro, Odile Barrès, Jean Cauzid, Mônica Perrotta, Encarnación Roda-Robles, Maria Ribeiro. Tools for Remote Exploration: A Lithium (Li) Dedicated Spectral Library of the Fregeneda–Almendra Aplite–Pegmatite Field. Data. 2021; 6 (3):33.
Chicago/Turabian StyleJoana Cardoso-Fernandes; João Silva; Filipa Dias; Alexandre Lima; Ana Teodoro; Odile Barrès; Jean Cauzid; Mônica Perrotta; Encarnación Roda-Robles; Maria Ribeiro. 2021. "Tools for Remote Exploration: A Lithium (Li) Dedicated Spectral Library of the Fregeneda–Almendra Aplite–Pegmatite Field." Data 6, no. 3: 33.
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.
Rui Frutuoso; Alexandre Lima; Ana Cláudia Teodoro. Application of remote sensing data in gold exploration: targeting hydrothermal alteration using Landsat 8 imagery in northern Portugal. Arabian Journal of Geosciences 2021, 14, 1 -18.
AMA StyleRui Frutuoso, Alexandre Lima, Ana Cláudia Teodoro. Application of remote sensing data in gold exploration: targeting hydrothermal alteration using Landsat 8 imagery in northern Portugal. Arabian Journal of Geosciences. 2021; 14 (6):1-18.
Chicago/Turabian StyleRui Frutuoso; Alexandre Lima; Ana Cláudia Teodoro. 2021. "Application of remote sensing data in gold exploration: targeting hydrothermal alteration using Landsat 8 imagery in northern Portugal." Arabian Journal of Geosciences 14, no. 6: 1-18.
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).
Rachida Senouci; Nasr-Eddine Taibi; Ana Teodoro; Lia Duarte; Hamidi Mansour; Rabia Meddah. GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria. Sustainability 2021, 13, 630 .
AMA StyleRachida Senouci, Nasr-Eddine Taibi, Ana Teodoro, Lia Duarte, Hamidi Mansour, Rabia Meddah. GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria. Sustainability. 2021; 13 (2):630.
Chicago/Turabian StyleRachida Senouci; Nasr-Eddine Taibi; Ana Teodoro; Lia Duarte; Hamidi Mansour; Rabia Meddah. 2021. "GIS-Based Expert Knowledge for Landslide Susceptibility Mapping (LSM): Case of Mostaganem Coast District, West of Algeria." Sustainability 13, no. 2: 630.
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.
Mariana Oliveira; André Padrão; André Ramalho; Mariana Lobo; Ana Cláudia Teodoro; Hernâni Gonçalves; Alberto Freitas. Geospatial Analysis of Environmental Atmospheric Risk Factors in Neurodegenerative Diseases: A Systematic Review. International Journal of Environmental Research and Public Health 2020, 17, 8414 .
AMA StyleMariana Oliveira, André Padrão, André Ramalho, Mariana Lobo, Ana Cláudia Teodoro, Hernâni Gonçalves, Alberto Freitas. Geospatial Analysis of Environmental Atmospheric Risk Factors in Neurodegenerative Diseases: A Systematic Review. International Journal of Environmental Research and Public Health. 2020; 17 (22):8414.
Chicago/Turabian StyleMariana Oliveira; André Padrão; André Ramalho; Mariana Lobo; Ana Cláudia Teodoro; Hernâni Gonçalves; Alberto Freitas. 2020. "Geospatial Analysis of Environmental Atmospheric Risk Factors in Neurodegenerative Diseases: A Systematic Review." International Journal of Environmental Research and Public Health 17, no. 22: 8414.
Home ranges in animals can be estimated by different methods like minimum convex polygons, characteristic hulls or kernels while correlative ecological niche models (ENMs) are commonly employed for forecasting species' ranges. However, ENMs can also model the distribution of individuals if environmental very high spatial resolution data are available. Indeed, remote sensing (RS) can provide images with pixel sizes of few centimetres. Here, we modelled the distribution of individual lizards (Podarcis bocagei) combining aerial‐like photographs recorded with a compact camera and a matrix of temperature/humidity data‐loggers to obtain several environmental layers with very high spatial resolution. We recorded lizards’ positions in a 20 × 20 m mesocosm with a high precision GPS device (~10 cm of error), multiple times per day throughout the whole period of daily activity. We built an orthophoto map (pixels of 20 cm2) from camera pictures, a digital surface model, and a land‐cover supervised classification map. We recreated climate‐like variables by combining data‐logger data. For each individual, we calculated the distance to males and females, excluding the focal lizard. We computed individual realized niche models with Bioclim, GAM, GLM, Maxent and random forest. Models attained a very high evaluation score in most cases. The most contributing variables were related to microclimate (isothermality, minimum temperature and humidity) and distance to conspecifics. Our very high spatial resolution models provided robust information on how space is used by each lizard. Correlative models can identify the most suitable areas inside the home range, similar to core areas estimated from kernel algorithms, but allowed better statistical inference. Overall, RS tools generated high‐quality environmental data, and when combined with ENMs, improved the robustness of the predictions on spatial patterns of small terrestrial animals.
N. Sillero; R. Dos Santos; A. C. Teodoro; M. A. Carretero. Ecological niche models improve home range estimations. Journal of Zoology 2020, 313, 145 -157.
AMA StyleN. Sillero, R. Dos Santos, A. C. Teodoro, M. A. Carretero. Ecological niche models improve home range estimations. Journal of Zoology. 2020; 313 (2):145-157.
Chicago/Turabian StyleN. Sillero; R. Dos Santos; A. C. Teodoro; M. A. Carretero. 2020. "Ecological niche models improve home range estimations." Journal of Zoology 313, no. 2: 145-157.
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.
P. Lourenço; A.C. Teodoro; J.A. Gonçalves; J.P. Honrado; M. Cunha; N. Sillero. Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data. International Journal of Applied Earth Observation and Geoinformation 2020, 95, 102263 .
AMA StyleP. Lourenço, A.C. Teodoro, J.A. Gonçalves, J.P. Honrado, M. Cunha, N. Sillero. Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data. International Journal of Applied Earth Observation and Geoinformation. 2020; 95 ():102263.
Chicago/Turabian StyleP. Lourenço; A.C. Teodoro; J.A. Gonçalves; J.P. Honrado; M. Cunha; N. Sillero. 2020. "Assessing the performance of different OBIA software approaches for mapping invasive alien plants along roads with remote sensing data." International Journal of Applied Earth Observation and Geoinformation 95, no. : 102263.
Spotted fever group Rickettsia (SFGR) is one among the aetiologies that cause fever of unknown origin in Angola. Despite their occurrence, there is little information about its magnitude in this country either because it is misdiagnosed or due to the lack of diagnostic resources. For this purpose, eighty-seven selected malaria- and yellow fever-negative serum specimens collected between February 2016 and March 2017 as part of the National Laboratory of Febrile Syndromes, from patients with fever (≥37.5°C) for at least 4 days and of unknown origin, were screened for Rickettsia antibodies through an immunofluorescence assay (IFA). Serological results were interpreted according to the 2017 guidelines for the detection of Rickettsia spp. Three seroreactive patients had detectable IgM antibodies to Rickettsia with an endpoint titre of 32 and IgG antibodies with endpoint titres of 128 and 256. These findings supported a diagnosis of Rickettsia exposure amongst these patients and highlight that rickettsioses may be among the cause of unknown febrile syndromes in Angola. Therefore, physicians must be aware of this reality and must include this vector-borne disease as part of aetiologies that should be considered and systematically tested in order to delineate appropriate strategies of diagnostic and control of Rickettsia in Angola.
P. F. Barradas; Z. Neto; T. L. Mateus; A. C. Teodoro; L. Duarte; H. Gonçalves; P. Ferreira; F. Gärtner; R. Sousa; I. Amorim. Serological Evidence of Rickettsia Exposure among Patients with Unknown Fever Origin in Angola, 2016-2017. Interdisciplinary Perspectives on Infectious Diseases 2020, 2020, 1 -5.
AMA StyleP. F. Barradas, Z. Neto, T. L. Mateus, A. C. Teodoro, L. Duarte, H. Gonçalves, P. Ferreira, F. Gärtner, R. Sousa, I. Amorim. Serological Evidence of Rickettsia Exposure among Patients with Unknown Fever Origin in Angola, 2016-2017. Interdisciplinary Perspectives on Infectious Diseases. 2020; 2020 ():1-5.
Chicago/Turabian StyleP. F. Barradas; Z. Neto; T. L. Mateus; A. C. Teodoro; L. Duarte; H. Gonçalves; P. Ferreira; F. Gärtner; R. Sousa; I. Amorim. 2020. "Serological Evidence of Rickettsia Exposure among Patients with Unknown Fever Origin in Angola, 2016-2017." Interdisciplinary Perspectives on Infectious Diseases 2020, no. : 1-5.
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.
Cesar I. Alvarez-Mendoza; Ana Teodoro; Alberto Freitas; Joao Fonseca. Spatial estimation of chronic respiratory diseases based on machine learning procedures—an approach using remote sensing data and environmental variables in quito, Ecuador. Applied Geography 2020, 123, 102273 .
AMA StyleCesar I. Alvarez-Mendoza, Ana Teodoro, Alberto Freitas, Joao Fonseca. Spatial estimation of chronic respiratory diseases based on machine learning procedures—an approach using remote sensing data and environmental variables in quito, Ecuador. Applied Geography. 2020; 123 ():102273.
Chicago/Turabian StyleCesar I. Alvarez-Mendoza; Ana Teodoro; Alberto Freitas; Joao Fonseca. 2020. "Spatial estimation of chronic respiratory diseases based on machine learning procedures—an approach using remote sensing data and environmental variables in quito, Ecuador." Applied Geography 123, no. : 102273.
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.
Joana Cardoso-Fernandes; Ana Teodoro; Alexandre Lima; Encarnación Roda-Robles. Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites. Remote Sensing 2020, 12, 2319 .
AMA StyleJoana Cardoso-Fernandes, Ana Teodoro, Alexandre Lima, Encarnación Roda-Robles. Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites. Remote Sensing. 2020; 12 (14):2319.
Chicago/Turabian StyleJoana Cardoso-Fernandes; Ana Teodoro; Alexandre Lima; Encarnación Roda-Robles. 2020. "Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites." Remote Sensing 12, no. 14: 2319.
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.
Joana Cardoso-Fernandes; Ana C. Teodoro; Alexandre Lima; Mônica Perrotta; Encarnación Roda-Robles. Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives. Applied Sciences 2020, 10, 1785 .
AMA StyleJoana Cardoso-Fernandes, Ana C. Teodoro, Alexandre Lima, Mônica Perrotta, Encarnación Roda-Robles. Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives. Applied Sciences. 2020; 10 (5):1785.
Chicago/Turabian StyleJoana Cardoso-Fernandes; Ana C. Teodoro; Alexandre Lima; Mônica Perrotta; Encarnación Roda-Robles. 2020. "Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives." Applied Sciences 10, no. 5: 1785.
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.
Ubaldo Gemusse; Alexandre Lima; Ana Teodoro. Comparing different techniques of satellite imagery classification to mineral mapping pegmatite of Muiane and Naipa: Mozambique). Earth Resources and Environmental Remote Sensing/GIS Applications X 2019, 11156, 111561E .
AMA StyleUbaldo Gemusse, Alexandre Lima, Ana Teodoro. Comparing different techniques of satellite imagery classification to mineral mapping pegmatite of Muiane and Naipa: Mozambique). Earth Resources and Environmental Remote Sensing/GIS Applications X. 2019; 11156 ():111561E.
Chicago/Turabian StyleUbaldo Gemusse; Alexandre Lima; Ana Teodoro. 2019. "Comparing different techniques of satellite imagery classification to mineral mapping pegmatite of Muiane and Naipa: Mozambique)." Earth Resources and Environmental Remote Sensing/GIS Applications X 11156, no. : 111561E.
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 São João 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 km2 in the pegmatite district of São João Del Rei and the zone B with approximately 90 km2 in the pegmatite district of Araçuaí. 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.
Douglas Barbosa dos Santos; Ana Teodoro; Alexandre Lima; Joana Cardoso-Fernandes. Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil. Earth Resources and Environmental Remote Sensing/GIS Applications X 2019, 11156, 111561F .
AMA StyleDouglas Barbosa dos Santos, Ana Teodoro, Alexandre Lima, Joana Cardoso-Fernandes. Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil. Earth Resources and Environmental Remote Sensing/GIS Applications X. 2019; 11156 ():111561F.
Chicago/Turabian StyleDouglas Barbosa dos Santos; Ana Teodoro; Alexandre Lima; Joana Cardoso-Fernandes. 2019. "Remote sensing techniques to detect areas with potential for lithium exploration in Minas Gerais, Brazil." Earth Resources and Environmental Remote Sensing/GIS Applications X 11156, no. : 111561F.
Soil erosion constitute a major threat to human lives and assets worldwide, as well as a major environmental disturbance. The Revised Universal Soil Loss Equation (RUSLE) integrated with Geographical Information System (GIS) has been the most widely used model in predicting and mapping soil erosion loss. Remote sensing has particular utility for soil loss model applications, providing observations on several key aspects of Land use and Land cover (LULC) linked to the cover-management factor C of the RUSLE, over wide areas and in consistent and repeatable measurements. A free and open source GIS application coupled with remote sensing data was developed under QGIS software allowing to improve the C factor functionality: (i) automatically download satellite images; (ii) clip with the study case and; (ii) perform a supervised or unsupervised classification, in order to obtain the land cover classification and produce the final C map. One of the most efficient supervised classification algorithms is the Support Vector Machine (SVM). Random Forest (RF) is also an easy-to-use machine learning algorithm for supervised classification. The automation of this functionality was based in the R and SAGA software, both integrated in QGIS. To perform the supervised classification, SVM and RF methods were incorporated. The overall accuracy and Kappa values are also automatically obtained by the R script and GRASS algorithms, which allows to evaluate the result obtained. To perform the unsupervised classification K-means algorithm from SAGA was used. This updating in RUSLE application improve the results obtained for C factor and help us to obtain a most accurate estimation of RUSLE erosion risk map. The application was tested using Sentinel 2A images in two different periods, after and before the forest fire event in Coimbra region, Portugal. In the end, the three resulted maps from SVM, RF and K-means classification were compared.
Lia Duarte; Ana Teodoro; Mario Cunha. A semi-automatic approach to derive land cover classification in soil loss models. Earth Resources and Environmental Remote Sensing/GIS Applications X 2019, 11156, 111560B .
AMA StyleLia Duarte, Ana Teodoro, Mario Cunha. A semi-automatic approach to derive land cover classification in soil loss models. Earth Resources and Environmental Remote Sensing/GIS Applications X. 2019; 11156 ():111560B.
Chicago/Turabian StyleLia Duarte; Ana Teodoro; Mario Cunha. 2019. "A semi-automatic approach to derive land cover classification in soil loss models." Earth Resources and Environmental Remote Sensing/GIS Applications X 11156, no. : 111560B.
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.
Joana Cardoso-Fernandes; Ana Teodoro; Alexandre Lima; E. Roda-Robles. Evaluating the performance of support vector machines (SVMs) and random forest (RF) in Li-pegmatite mapping: preliminary results. Earth Resources and Environmental Remote Sensing/GIS Applications X 2019, 11156, 111560Q .
AMA StyleJoana Cardoso-Fernandes, Ana Teodoro, Alexandre Lima, E. Roda-Robles. Evaluating the performance of support vector machines (SVMs) and random forest (RF) in Li-pegmatite mapping: preliminary results. Earth Resources and Environmental Remote Sensing/GIS Applications X. 2019; 11156 ():111560Q.
Chicago/Turabian StyleJoana Cardoso-Fernandes; Ana Teodoro; Alexandre Lima; E. Roda-Robles. 2019. "Evaluating the performance of support vector machines (SVMs) and random forest (RF) in Li-pegmatite mapping: preliminary results." Earth Resources and Environmental Remote Sensing/GIS Applications X 11156, no. : 111560Q.
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.
Cesar I. Alvarez-Mendoza; Ana Teodoro; Juan Ordoñez; Andres Benitez; Alberto Freitas; Joao Fonseca. Modeling the prevalence of respiratory chronic diseases risk using satellite images and environmental data. Remote Sensing Technologies and Applications in Urban Environments IV 2019, 11157, 1115705 .
AMA StyleCesar I. Alvarez-Mendoza, Ana Teodoro, Juan Ordoñez, Andres Benitez, Alberto Freitas, Joao Fonseca. Modeling the prevalence of respiratory chronic diseases risk using satellite images and environmental data. Remote Sensing Technologies and Applications in Urban Environments IV. 2019; 11157 ():1115705.
Chicago/Turabian StyleCesar I. Alvarez-Mendoza; Ana Teodoro; Juan Ordoñez; Andres Benitez; Alberto Freitas; Joao Fonseca. 2019. "Modeling the prevalence of respiratory chronic diseases risk using satellite images and environmental data." Remote Sensing Technologies and Applications in Urban Environments IV 11157, no. : 1115705.