Abstract:Abstract: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 env… Show more
“…The standardized maps are classified in the range of bytes (from 0 to 255), representing different fuzzy membership decision sets ( Figure 6 and Table 2). In this analysis, the suitable fuzzy functions and related control points shown in Table 2 were selected on the basis of a review of the literature in conformity with WMPCL and Oita Prefecture legislation [6,14,15,[19][20][21][23][24][25][26][27][28][29][30][31][32][33][34][41][42][43][44]. Water bodies in our study denote streams, rivers, and other surface water sources.…”
Section: Factor Standardization Using Fuzzy Functionmentioning
Recently, the popularity of anaerobic digestion (AD) has been increasing, and selecting a site for AD is a complex process for both individuals and decision-makers. This process requires the selected site to fulfill all regulation requirements, simultaneously reducing environmental, sociocultural, technical, political, economic, and public health costs. A geographic-information-system-based multi-criteria decision analysis (GIS-MCDA) is presented in this study to evaluate and examine the suitability of the study area in order to propose a suitable site for an AD facility in Oita City, Japan. Multi-criteria evaluation (MCE) was used to standardize the map layers into fuzzy membership functions. The proximity variables were classified into seven factors and three constraint groups, specifically, environmental, sociocultural, and technical/economic criteria. The proximity and criteria maps were combined using weighted linear combination (WLC) techniques, and the resulting suitability map was evaluated on a grading scale of 0-255 B. The analytical hierarchy process (AHP) ranking analysis indicated that an area of about 13.36 km 2 from the entire study area is the most suitable and that the remaining two options are still suitable for the intended purpose, but the entire decision will be entrusted to the decision-makers' judgment.
“…The standardized maps are classified in the range of bytes (from 0 to 255), representing different fuzzy membership decision sets ( Figure 6 and Table 2). In this analysis, the suitable fuzzy functions and related control points shown in Table 2 were selected on the basis of a review of the literature in conformity with WMPCL and Oita Prefecture legislation [6,14,15,[19][20][21][23][24][25][26][27][28][29][30][31][32][33][34][41][42][43][44]. Water bodies in our study denote streams, rivers, and other surface water sources.…”
Section: Factor Standardization Using Fuzzy Functionmentioning
Recently, the popularity of anaerobic digestion (AD) has been increasing, and selecting a site for AD is a complex process for both individuals and decision-makers. This process requires the selected site to fulfill all regulation requirements, simultaneously reducing environmental, sociocultural, technical, political, economic, and public health costs. A geographic-information-system-based multi-criteria decision analysis (GIS-MCDA) is presented in this study to evaluate and examine the suitability of the study area in order to propose a suitable site for an AD facility in Oita City, Japan. Multi-criteria evaluation (MCE) was used to standardize the map layers into fuzzy membership functions. The proximity variables were classified into seven factors and three constraint groups, specifically, environmental, sociocultural, and technical/economic criteria. The proximity and criteria maps were combined using weighted linear combination (WLC) techniques, and the resulting suitability map was evaluated on a grading scale of 0-255 B. The analytical hierarchy process (AHP) ranking analysis indicated that an area of about 13.36 km 2 from the entire study area is the most suitable and that the remaining two options are still suitable for the intended purpose, but the entire decision will be entrusted to the decision-makers' judgment.
“…MATLAB can be used in supporting optical and astrophysical research activities, especially in analyzing and visualizing mathematical equations of complex optical and astrophysical phenomena [16]. The implementation of MATLAB in optics and astrophysics courses can support simple visual constructs of every optical and astrophysical equation [17]. The implementation of MATLAB in optics and astrophysics courses also has a positive impact on students' abilities.…”
This study aims to simply examine the relationship between optics and astrophysics by visualizing the refractive index and reflectance of sodium metal based on Drude's theory using MATLAB. Sodium metal is used in this study because it is an alkaline metal found on Earth which is a life-supporting element. MATLAB is used to visualize the refractive index and reflectance of sodium metal based on Drude's theory because it is one of the software that is often used by students in supporting optics and astrophysics courses. This research presents a simple way for students to visualize the refractive index and reflectance of sodium metal based on Drude's theory with the help of MATLAB. Visualization of the refractive index and reflectance of sodium metal based on Drude's theory begins by describing the equations of the refractive index and reflectance based on Drude's theory. We realized the visualization of these equations in graphical form with the help of MATLAB. In this study, students were able to visualize the refractive index and reflectance of sodium metal based on Drude's theory using MATLAB by varying the frequency of sodium plasma and sodium attenuation frequency.
“…The construction of PV power plants in various landscapes, such as desert, mountain, coast, lake [20][21][22], has also led to the differences in their environmental effects. Researchers need to urgently evaluate these effects and issues with the rapidly growing PV power plants [23]. However, datasets on the distribution of PV power plants are still scarce in many regions.…”
Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.
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