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Abstract Following up the previously published systematic review on the same topic and realizing that new studies and evidence had emerged on the matter, we conducted an update on the same research terms. With the objective of updating the information relating environmental risk factors on neurodegenerative diseases and the geographic approaches used to address them, we searched PubMed, Web of Science and Scopus for all scientific studies considering the following three domains: neurodegenerative disease, environmental atmospheric factor and geographical analysis, using the same keywords as in the previously published systematic review. From February 2020 to February 2023, 35 papers were included versus 34 in the previous period, with dementia (including Alzheimer’s disease) being the most focused disease (60.0%) in this update, opposed to multiple sclerosis on the last review (55.9%). Also, environmental pollutants such as PM2.5 and NO2 have gained prominence, being represented in 65.7% and 42.9% of the new studies, opposed to 9.8% and 12.2% in the previous review, compared to environmental factors such as sun exposure (5.7% in the update vs 15.9% in the original). The mostly used geographic approach remained the patient’s residence (82.9% in the update vs 21.2% in the original and 62.3% in total), and remote sensing was used in 45.7% of the new studies versus 19.7% in the original review, with 42.0% of studies using it globally, being the second most common approach, usually to compute the environmental variable. This review has been registered in PROSPERO with the number CRD42020196188.

The land use land cover (LULC) map is extensively employed for different purposes. Machine learning (ML) algorithms applied in remote sensing (RS) data have been proven effective in image classification, object detection, and semantic segmentation. Previous studies have shown that random forest (RF) and support vector machine (SVM) consistently achieve high accuracy for land classification. Considering the important role of Portugal’s Serra da Estrela Natural Park (PNSE) in biodiversity and nature conversation at an international scale, the availability of timely data on the PNSE for emergency evaluation and periodic assessment is crucial. In this study, the application of RF and SVM classifiers, and object-based (OBIA) and pixel-based (PBIA) approaches, with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform for the land cover classification of a burnt area in the PNSE. This aimed to detect the land cover change and closely observe the burnt area and vegetation recovery after the 2022 wildfire. The combination of RF and OBIA achieved the highest accuracy in all evaluation metrics. At the same time, a comparison with the Normalized Difference Vegetation Index (NDVI) map and Conjunctural Land Occupation Map (COSc) of 2023 year indicated that the SVM and PBIA map resembled the maps better.

RESUMO Este trabalho busca argumentar que a filosofia cartesiana é circunscrita invariavelmente pelos limites da razão humana. Para tanto, serão expostos trechos notáveis do corpus cartesiano a respeito da certeza, indicando que, em diferentes momentos de sua filosofia, é sempre possível distinguir que Descartes restringe o escopo de sua doutrina às capacidades do intelecto humano. Posteriormente, será necessário avaliar duas passagens cartesianas em que Richard Popkin se apoia para acusar Descartes de sustentar uma filosofia cética. Por fim, é defendido que essas passagens estão de acordo com o pensamento cartesiano e que, de modo algum, demonstram que o resultado do sistema construído por Descartes é um fracasso diante dos seus objetivos. Em conclusão, baseado na própria nomenclatura oferecida por Popkin, é estabelecido que Descartes sustenta um ceticismo de segundo grau, que é perene em sua filosofia e, como consequência, é evidenciado que Descartes não é um cético malgré lui como Popkin o acusa.

Studying changes in temperature is fundamental for understanding its interactions with the environment and biodiversity. However, studies in mountainous areas are few, due to their complex formation and the difficulty of obtaining local data. We analysed changes in temperature over time in Montesinho Natural Park (MNP) (Bragança, Portugal), an important conservation area due to its high level of biodiversity. Specifically, we aimed to analyse: i) whether temperature increased in MNP over time, ii) what environmental factors influence the Land Surface Temperature (LST), and iii) whether vegetation is related to changes in temperature. We used annual summer and winter mean data acquired from the Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets/products (e.g. LST, gathered at four different times: 11am, 1pm, 10pm and 2am, Enhance vegetation index - EVI, and Evapotranspiration - ET), available on the cloud-based platform Google Earth Engine between 2003 and 2021). We analysed the dynamics of the temporal trend patterns between the LST and local thermal data (from a weather station) by correlations; the trends in LST over time with the Mann-Kendall trend test; and the stability of hot spots and cold spots of LST with Local Statistics of Spatial Association (LISA) tests. The temporal trend patterns between LST and Air Temperature (Tair) data were very similar (ρ > 0.7). The temperature in the MNP remained stable over time during summer but increased during winter nights. The biophysical indices were strongly correlated with the summer LST at 11am and 1pm. The LISA results identified hot and cold zones that remained stable over time. The remote-sensed data proved to be efficient in measuring changes in temperature over time.

Vitoria-Gasteiz has taken several urban greening actions such as the introduction of a ring of parks that connect the city’s surroundings, a sustainable mobility plan, and urban green structure strategies. Previous studies establish a connection to the importance of greening to mitigate the surface urban heat island (SUHI) and evaluate the effectiveness of these measures on urban climate. In this study, land surface temperature (LST), a remote sensing (RS) parameter, recorded by Landsat satellites (5, 7, and 8) was used to evaluate the effect of SUHI in Vitoria-Gasteiz between 1985–2021. The aim was to evaluate whether the urban greening actions influenced the local thermal conditions and, consequently, helped minimize the SUHI. Thirty sampling locations were identified, corresponding to different local climate zones (LCZ), at which LST data were extracted. A total of 218 images were processed and separated into summer and winter. Four of the 30 locations had, since 2003, on-site meteorological stations with regular air temperature (Tair) measurements which were used to validate the LST data. The results showed that Spearman’s correlation between Tair and LST was higher than 0.88 in all locations. An amount of 21 points maintained the same LCZ classification throughout the analysed period and nine underwent a LCZ transformation. The highest average temperature was identified in the city centre (urbanized area), and the lowest average was in a forest on the outskirts of the city. SUHI was more intense during the summer. A significant increase in SUHI intensity was identified in areas transformed from natural to urban LCZs. However, SUHI during satellite data acquisition periods has shown a minimal change in areas where sustainable practices have been implemented. RS was valuable for analysing the thermal behaviour of the LCZs, despite the limitation inherent in the satellite’s time of passage, in which the SUHI effect is not as evident.

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.

Mining activities promote resulting wastes, so coal mines are prone to release contaminants to the environment, namely to the soil and water. Therefore, the analysis of this type of risk is crucial in waste pile management. The São Pedro da Cova (Porto, Portugal) coal waste pile has been studied in recent years, with several data acquired from 2019–2021 under a research project using distinct methodologies. These results are now combined in a multi-approach method to estimate the environmental impacts of the waste pile and identify the contamination. With the integration of all the data in a Geographical Information System (GIS) environment, and to fulfill a scientific gap, this study aims: (i) to create a susceptibility map of contamination in the areas surrounding the self-burning coal waste pile in São Pedro da Cova, using Analytical Hierarchy Process (AHP) and Fuzzy AHP approaches; and (ii) to develop a webGIS application incorporating all the information acquired that can be useful for the residents of São Pedro da Cova and also to the decision-making public entities and researchers. The results obtained show that the contamination susceptibility is higher surrounding the abandoned mine, particularly along the waste piles and the corresponding runoff areas, which can be especially sensitive.

QGIS is a free and open-source software that allows viewing, editing, and analyzing georeferenced data. It is a Geographic Information System (GIS) software composed by tools that allow to manipulate geographic information and consequently to create maps which help to get a better understanding and organization of geospatial data. Unfortunately, maps created directly in the GIS desktop software are not automatically transferred to a website. This research aimed to compare publishing capabilities in different QGIS plugins to create Web Maps. This study analyzes four QGIS plugins (QGIS2Web, QGIS Cloud, GIS Cloud Publisher and Mappia Publisher), performing a comparison between them, considering their advantages and disadvantages, the free and subscription plans, the tools offered by each plugin and other generic aspects. The four plugins were tested in a specific case study to automatically obtain different Web Maps. This study could help users to choose the most adequate tools to publish Web Maps under QGIS software.

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area.

As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease its import dependence on non-European countries for these raw materials. It is in this context that the GREENPEG project was established, an EU project whose main objective is to identify new deposits of pegmatites in Europe in an environmentally friendly way. Remote sensing is a non-contact exploration tool that allows for identifying areas of interest for exploration at the early stage of exploration campaigns. Several RS methods have been developed to identify Li-Cs-Ta (LCT) pegmatites, but in this study, a new methodology was developed to detect Nb-Y-F (NYF) pegmatites in the Tysfjord area in Norway. This methodology is based on spectral analysis to select bands of the Sentinel 2 satellite and adapt RS methods, such as Band Ratios and Principal Component Analysis (PCA), to be used as input in the Random Forest (RF) and other tree-based ensemble algorithms to improve the classification accuracy. The results obtained are encouraging, and the algorithm was able to successfully identify the pegmatite areas already known and new locations of interest for exploration were also defined.

(1) Background: spatial thinking is indirectly applied in numerous daily activities (e.g., when defining the route when going to school/work) or in scientific areas (e.g., predicting the spatial–temporal spread of contagious diseases), and its ability might be improved using geographical information systems (GIS). The main objective of this study was to perform an analysis of the spatial thinking of students in two curricular units (CUs) that had come from different background areas; (2) Methods: to that end, the Spatial Thinking Ability Test (STAT), composed of 15 multiple choice questions to measure spatial thinking, was given to 83 students before and after exposure to GIS concepts and software. Students’ answers were analyzed question-by-question and as total scores. The statistical analysis was performed using the paired samples t-test, the independent samples t-test or the Mann–Whitney statistical test and the nonparametric Kruskal–Wallis test; (3) Results: an overall significant improvement was observed from the pre- to the post-test. Additionally, total scores were not significantly different between students of different CUs, courses, or genders; (4) Conclusions: this exploratory study can be considered as a support methodology for pedagogical didactics that have been implemented in the CUs and may be readily applied in other academic environments, namely with students from different backgrounds, countries, and teaching strategies, thus promoting the discussion of all such experiences and consequent improvement in geographical education.

Risk mapping is a crucial part of spatial planning, as it optimizes the allocation of resources in its management. It is, therefore, of great interest to build tools that enhance its production. This work focuses on the implementation of a susceptibility model for different types of spatially distributed risk in a geographic information systems (GIS) Python plugin. As an example, the susceptibility model was applied to study the occurrence of wildfires in the municipality of Vila Nova de Foz Côa, Portugal. The plugin was developed to simplify the production and evaluation of susceptibility maps regarding the available geographical information. Regarding our case study, the data used corresponds to three training areas, ten years of burned areas and nine environmental variables. The model is applied to different combinations of these factors. The validation, performed with receiver operating characteristic (ROC) curves, resulted in an area under the curve (AUC) of 74% for a fire susceptibility model, calculated with the same environmental factors used in official Portuguese cartography (land use and slope) and with the optimal training area, years of information on burned area and level of land use classification. After experimenting with four variable combinations, a maximum AUC of 77% was achieved. This study confirms the suitability of the variables chosen for the production of official fire susceptibility models but leaves out the comparison between the official methodology and the methodology proposed in this work.

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.

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.

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.

Remote sensing data proved to be a valuable resource in a variety of earth science applications. Using high-dimensional data with advanced methods such as machine learning algorithms (MLAs), a sub-domain of artificial intelligence, enhances lithological mapping by spectral classification. Support vector machines (SVM) are one of the most popular MLAs with the ability to define non-linear decision boundaries in high-dimensional feature space by solving a quadratic optimization problem. This paper describes a supervised classification method considering SVM for lithological mapping in the region of Souk Arbaa Sahel belonging to the Sidi Ifni inlier, located in southern Morocco (Western Anti-Atlas). The aims of this study were (1) to refine the existing lithological map of this region, and (2) to evaluate and study the performance of the SVM approach by using combined spectral features of Landsat 8 OLI with digital elevation model (DEM) geomorphometric attributes of ALOS/PALSAR data. We performed an SVM classification method to allow the joint use of geomorphometric features and multispectral data of Landsat 8 OLI. The results indicated an overall classification accuracy of 85%. From the results obtained, we can conclude that the classification approach produced an image containing lithological units which easily identified formations such as silt, alluvium, limestone, dolomite, conglomerate, sandstone, rhyolite, andesite, granodiorite, quartzite, lutite, and ignimbrite, coinciding with those already existing on the published geological map. This result confirms the ability of SVM as a supervised learning algorithm for lithological mapping purposes.

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 Belém, 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.

Education, together with science and technology, is the main driver of the progress and transformations of a country. The use of new technologies of learning can be applied to the classroom. Computer learning supports meaningful and long-term learning. Therefore, in the era of digital society and environmental issues, a relevant role is provided by open source software and free data that promote universality of knowledge. Earth observation (EO) data and remote sensing technologies are increasingly used to address the sustainable development goals. An important step for a full exploitation of this technology is to guarantee open software supporting a more universal use. The development of image processing plugins, which are able to be incorporated in Geographical Information System (GIS) software, is one of the strategies used on that front. The necessity of an intuitive and simple application, which allows the students to learn remote sensing, leads us to develop a GIS open source tool, which is integrated in an open source GIS software (QGIS), in order to automatically process and classify remote sensing images from a set of satellite input data. The application was tested in Vila Nova de Gaia municipality (Porto, Portugal) and Aveiro district (Portugal) considering Landsat 8 Operational Land Imager (OLI) data.

Unmanned Aerial Vehicle (UAV) imagery allows for a new way of obtaining geographic information. In this work, a Geographical Information System (GIS) open source application was developed in QGIS software that estimates several parameters and metrics on tree crown through image analysis techniques (image segmentation and image classification) and fractal analysis. The metrics that have been estimated were: area, perimeter, number of trees, distance between trees, and a missing tree check. This methodology was tested on three different plantations: olive, eucalyptus, and vineyard. The application developed is free, open source and takes advantage of QGIS integration with external software. Several tools available from Orfeo Toolbox and Geographic Resources Analysis Support System (GRASS) GIS were employed to generate a classified raster image which allows calculating the metrics referred before. The application was developed in the Python 2.7 language. Also, some functions, modules, and classes from the QGIS Application Programming Interface (API) and PyQt4 API were used. This new plugin is a valuable tool, which allowed for automatizing several parameters and metrics on tree crown using GIS analysis tools, while considering data acquired by UAV.

A mixed methods bibliometric analysis was performed to ascertain the characteristic of scientific literature published in a 10-year period (2007–2016) regarding the application of remote sensing data in human health. A search was performed on the Scopus database, followed by manual revision using synthesis studies’ techniques, requiring the authors to sort through more than 8000 medical concepts to create the query, and to manually select relevant papers from over 2000 documents. From the initial 2752 papers identified, 520 articles were selected for analysis, showing that the United States ranked first, with a total of 250 (48.1% of the total) documents, followed by France and the United Kingdom, with 67 (12.9% of the total) and 54 (10.4% of the total) documents, respectively. When considering authorship, the top three authors were Vounatsou P (22 articles), Utzinger J (19 articles), and Vignolles C (13 articles). Regarding disease-specific keywords, malaria, dengue, and schistosomiasis were the most frequent keywords, occurring 142, 34, and 24 times, respectively. For some infectious diseases and other highly pathogenic or emerging infectious diseases, remote sensing has become a very powerful instrument. Also, several studies relate different environmental factors retrieved by remote sensing data with other diseases, such as asthma exacerbations. Health-related remote sensing publications are increasing and this paper highlights the importance of these related technologies toward better information and, ideally, better provision of healthcare. On the other hand, this paper provides an overall picture of the state of the research regarding the application of remote sensing data in human health and identifies the most active stakeholders e.g., authors and institutions in the field, informing possible new collaboration research groups.

The combustion of coal wastes resulting from mining is of particular environmental concern, and the importance of proper management involving real-time assessment of their status and identification of probable evolution scenarios is recognized. Continuous monitoring of the combustion temperature and emission levels of certain gases allows for the possibility of planning corrective actions to minimize their negative impact on the surroundings. Optical fiber technology is well suited to this purpose and here we describe the main attributes and results obtained from a fiber optic sensing system projected to gather data on distributed temperature and gas emissions in these harsh environments.

The studies of forest ecosystems from remotely-sensed data are of great interest to researchers because of ecosystem services provided by this ecosystem, including protection of soils and vegetation, climate stabilization, and regulation of the hydrological cycle. In this context, our study focused on the use of a spectral angle mapper (SAM) classification method for mapping species in the Azrou Forest, Central Middle Atlas, Morocco. A Sentinel-2A image combined with ground reference data were used in this research. Four classes (holm oak, cedar forest, bare soil, and others-unclassified) were selected; they represent, respectively, 27, 11, 24, and 38% of the study area. The overall accuracy of classification was estimated to be around 99.72%. This work explored the potential of the SAM classification combined with Sentinel-2A data for mapping land cover in the Azrou Forest ecosystem.

Geographical Information Systems (GIS) are often used to assess and monitor the environmental impacts caused by mining activities. The aim of this work was to develop a new application to produce dynamic maps for monitoring the temperature variations in a self-burning coal waste pile, under a GIS open source environment—GIS-ECOAL (freely available). The performance of the application was evaluated with distributed temperature measurements gathered in the S. Pedro da Cova (Portugal) coal waste pile. In order to obtain the temperature data, an optical fiber cable was disposed over the affected area of the pile, with 42 location stakes acting as precisely-located control points for the temperature measurement. A monthly data set from July (15 min of interval) was fed into the application and a video composed by several layouts with temperature measurements was created allowing for recognizing two main areas with higher temperatures. The field observations also allow the identification of these zones; however, the identification of an area with higher temperatures in the top of the studied area was only possible through the visualization of the images created by this application. The generated videos make possible the dynamic and continuous visualization of the combustion process in the monitored area.

In the framework of Portuguese radio astronomical capacitation towards participation in the Square Kilometer Array (SKA) project, a site was selected for radio astronomical testing purposes and the development of a radio astronomical infrastructure. The site is within Herdade da Contenda (HC), a large national forest perimeter, located in Alentejo (Portugal). In order to minimize the impacts in the ecosystem and landscape, an application based on the Geographic Information System (GIS) open source environment was created, the HC Environmental Integrated Management System. This application combines several functionalities and menus with different characterization methods allowing the creation of multiple maps regarding the HC characteristics, such as Digital Elevation Model (DEM), Land Use Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), groundwater vulnerability, erosion risk, flood risk and forest fire risk. Other geographical information can be added if necessary (human heritage visualization and fauna and flora). A decision making support tool was also developed. It incorporates an algorithm running through a series of assigned weights and eliminatory factors to find the locations best suited for the infrastructure with minimal impact to the local ecosystem. In order to test the application and the decision making tool, several maps were used as input in order to decide which sites are more adequate. The application developed can be adopted for other protected or natural areas.