Index Map Research Articles

Constraining millimeter dust emission in nearby galaxies with NIKA2: The case of NGC2146 and NGC2976

This study presents the first millimeter continuum mapping observations of two nearby galaxies, the starburst spiral galaxy NGC2146 and the dwarf galaxy NGC2976, at 1.15 mm and 2 mm using the NIKA2 camera on the IRAM 30m telescope, as part of the Guaranteed Time Large Project IMEGIN. These observations provide robust resolved information about the physical properties of dust in nearby galaxies by constraining their FIR-radio SED in the millimeter domain. After subtracting the contribution from the CO line emission, the SEDs are modeled spatially using a Bayesian approach. Maps of dust mass surface density, temperature, emissivity index, and thermal radio component of the galaxies are presented, allowing for a study of the relations between the dust properties and star formation activity (using observations at 24μm as a tracer). We report that dust temperature is correlated with star formation rate in both galaxies. The effect of star formation activity on dust temperature is stronger in NGC2976, an indication of the thinner interstellar medium of dwarf galaxies. Moreover, an anti-correlation trend is reported between the dust emissivity index and temperature in both galaxies.

Predicting willingness to pay and implement different rooftop strategies to characterize social perception of climate change mitigation and adaptation

With the latest IPCC report, dramatic global climate action must be taken immediately to limit global warming to 1.5 °C, or face more frequent and extreme weather events with catastrophic implications. Cities must invest in climate resilience development; however, government policies are only effective if they are supported by the society in which they serve. As such, this study aims to characterize the social perception of climate resilience development, in particular the implementation of sustainable urban rooftop strategies, to support policy makers and enable individual action. This was accomplished through the analysis of 1,100 answered surveys in Cerdanyola del Vallès (Spain), to assess one’s willingness to pay (WTP) and willingness to implement (WTI) rooftop strategies according to: 1. socio-demographical characteristics; 2. social perceptions and beliefs; and 3. surrounding land use and land cover, and vulnerabilities identified through temperature and normalized difference vegetation index (NDVI) maps. The results of this study found age played a significant role in predictability, with 18–39-year-olds being the most willing to pay and implement the various rooftop scenarios. However, our results uncovered societal inequality as those 85+ were the second group most interested in rooftop agriculture but the most financially restricted. Belief in the viability of rooftop strategies increased respondents WTP and WTI while having access to ones’ rooftop increased willingness to partake in rooftop food cultivation and enhance rooftop greenery. A new finding presented by this study is the quantifiable impact that urban greenery plays on increasing survey respondents WTP and WTI.

%VBur index and steric maps: from predictive catalysis to machine learning.

Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and %VBur are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, e.g. binding energies, falls short in comparison to %VBur. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, %VBur can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods.

Automated mapping process of frontal area and thermal potential indexes: GIS algorithm development and implementation

The impacts of urban areas on microclimate conditions are well-known and highlight the importance of climatological guided urban planning. The growth and evolution of Geographic Information Systems (GIS) have set a backdrop for implementing strategies to apply urban climatological knowledge in planning daily practice. This research brings algorithms to automate the Frontal Area Index (FAI) and Thermal Potential Index (TPI) mapping process. This type of algorithms type facilitates the extraction of detailed spatial information for the decision-making process, making it highly relevant for urban planning and management. Their calculation method is implemented as ESRI © ArcGIS Pro embedded Python Stand Alone Script Tools, using the Python ArcPy library to access the ArcGIS geoprocessing functions. The algorithms are described in detail, allowing their implementation in other GIS platforms. The output maps allow the urban thermal conditions and morphology assessment. The findings from this research may substantially contribute both to the advance in the urban climatology scientific field and to guide urban planners' and managers' practical decision making.

Landslide risk assessment integrating susceptibility, hazard, and vulnerability analysis in Northern Pakistan

The purpose of this study is to assess the landslide risk for Hunza–Nagar Valley (Northern Pakistan). In this study, different conditioning factors, e.g., topographical, geomorphological, climatic, and geological factors were considered. Two machine learning approaches, i.e., logistic regression and artificial neural network were used to develop landslide susceptibility maps. The accuracy test was carried out using the receiving operative characteristic (ROC) curve. Which showed that the success and prediction rates of LR model is 82.60 and 81.60%, while 77.90 and 75.40%, for the ANN model. Due to the physiographic condition of the area, the rainfall density was considered as the primary triggering factor and landslide index map was generated. Moreover, using the Aster data the land cover (LC) map was developed. The settlements were extracted from the LC map and used as the elements at risk and hence, the vulnerability index was developed. Finally, the landslide risk map (LRM) for the Hunza–Nagar valley was developed. The LRM indicated that 37.25 (20.21 km2) and 47.64% (25.84 km2) of the total settlements lie in low and very high-risk zones. This landslide risk map can help decision-makers for potential land development and landslide countermeasures.

Index and Sustainability Status of Agricultural Mechanization for Paddy Field in Indonesia

The use of agricultural machinery is one of the important factors in increasing agricultural production. A quantitative assessment of the agricultural mechanization index and its impact on agricultural production is needed in planning the development of agricultural mechanization nationally. This study aims to determine the value of the agricultural mechanization index which consists of the degree, level, and capacity of agricultural mechanization for tools and agricultural machinery for pre-harvest rice (2-wheel tractor, 4-wheel tractor, water pump, and rice transplanter). This research was conducted in several stages, namely collecting data, observing the working capacity of 3 units of machinery for each type of machinery, processing data and making agricultural mechanization index maps. Based on the results of the study, it was found that the average value of the degree of agricultural mechanization for 2-wheel tractors, 4-wheel tractors, water pumps and rice transplanters was 56.68%, 55.13%, 10.55 % and 13.94%. The average value of mechanization level in Indonesia is 0.45 hp/ha and the average value of mechanization capacity for rice pre-harvest activities in Indonesia is 1.28 kW hour/ha. In addition, based on the results of the classification of mechanization level, cropping index and rice productivity, it can be seen that the provinces of East Java, Central Java, West Java, Banten, DI Yogyakarta and Aceh are provinces that are recommended to increase the number of agricultural machinery, especially 2-wheel tractors, 4-wheel tractors. water pump and rice transplanter.

Challenges in real-time generation of scintillation index maps

Challenges in real-time generation of scintillation index maps

The impacts of green spaces on mitigating the urban hot island effect in the city of Tashkent

This article explores the factors contributing to the formation of urban heat islands in Tashkent, their impacts, and the significance of urban green spaces in reducing the intensity of urban heat islands. The study identifies two types of urban heat islands in Tashkent: air and surface hot islands. The cooling effect of green spaces in Tashkent has been quantified using the Invest software, and a heat mitigation index map of Tashkent has been developed. The findings suggest that urban green spaces play a crucial role in mitigating the intensity of urban heat islands in Tashkent.

Environmental Sensitivity Index Mapping for Environmental Sustainable Cleanup along NAOC Pipeline, Asemoku, Delta State, Nigeria

Environmental Sensitivity Index Mapping for Environmental Sustainable Cleanup along NAOC Pipeline, Asemoku, Delta State, Nigeria

An Evaluation of Radiometric Normalization Methods for Gaofen-2 Vegetation Index Mapping

An Evaluation of Radiometric Normalization Methods for Gaofen-2 Vegetation Index Mapping

MAPEAMENTO DE ÍNDICE DE RISCO DE INUNDAÇÃO DE ÁREA A JUSANTE DE UMA BARRAGEM EM CASO DE ROMPIMENTO

We aimed to evaluate the flood risk index due to the hypothetical break of the São Bento dam (SC, Brazil). The risk index was obtained by combining the hazard and vulnerability indices. The hazard index was obtained by multiplying the velocity by the depth of the water, simulated with HEC-RAS 2D. The simulated flood extent was 64.8 km², with a maximum flow of 3586.60 m³·s-1, a depth of up to 2.0 m, and a velocity of up to 2.0 m·s-1 in most of the area. The formation of the breach occurred in 2.85 h, and the peak flow occurred at 1.67 h. The volume that overflowed with the break was 44.75 hm³. The flood wave reached 22.0 km in length, where the municipality of Forquilhinha is located. The risk index map showed that the risk is high up to 6 km from the dam since most people were at least 60 years old and had low incomes. In this location, the people were not safe inside buildings because they had a high probability of being destroyed. Thus, risk mapping may be adopted as a preventive measure to assist in preparing an emergency action plan and serve for environmental education and territorial planning. Keywords: Dam Break; HEC-RAS 2D; Risk Mapping.

Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping

This study aims to build a web-based interactive visualization dashboard from granular flood vulnerability index estimation maps using data from satellite imagery. The approach used to build this visualization dashboard is a two-dimensional (2D) approach created with the qgis2web python plugin facilitated with a JavaScript leaflet. Raw data from satellite imagery consisting of indicators of the causes of flooding are extracted in comma-separated value (CSV) format. Furthermore, the data is integrated based on its spatial attributes and stored in Geographic JavaScript Object Notation (GeoJSON) format to produce a visualization of the flood vulnerability index map. In web views, dashboards are built by utilizing hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). This interactive dashboard has several useful features in helping the process of monitoring the flood vulnerability of an area such as zoom, "show me where I am", measure distance, search, legend, and change year. Thus, the flood vulnerability estimation map dashboard is expected to assist the government in monitoring areas with extreme flood vulnerability and support the decision-making process related to mitigation of areas that have high flood vulnerability.

A Geovisualization Dashboard of Granular Food Security Index Map using GIS for Monitoring the Provincial Level Food Security Status

This study aims to build a web-based interactive geovisualization dashboard from a granular food security index map using satellite imagery and other geospatial big data. The map dashboard is built using a two-dimensional (2D) data visualization approach. Making a two-dimensional map using QuantumGIS (QGIS) tools, displayed in the form of WebGIS with the plugin used "Qgis2web" based on javascript leaflets. Once included in WebGIS, interactive visualizations are displayed on websites with interfaces based on hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). The dashboard map is equipped with interactive features such as legend, click grid, zoom, show me where I am, measure distance, and search. Therefore, the dashboard map can be used to monitor the food security index, search for food security index areas, as well as geographical identification of food security index areas which are useful for supporting the analysis of decision-making or policies by the government regarding food security strategies.

A Framework to Identify Rock Glaciers and Model Mountain Permafrost in the Jhelum Basin, Kashmir Himalaya, India

AbstractDeglaciation has led to the transformation of glaciers into rock glaciers in various mountainous regions worldwide. However, the science of permafrost and rock glaciers remains under‐researched in the Himalayan region. This study presents a detailed inventory, dynamics, and permafrost distribution map for the Jhelum basin in the Kashmir Himalaya. The Permafrost Zonation Map (PZM) is created using a Logistic Regression Model based on topographic and climatic variables. High‐resolution satellite images are used to identify rock glaciers as visual indicators. Active and relic rock glaciers are classified based on surface topography and geomorphology. Results reveal 207 rock glaciers in the Jhelum basin, covering ∼50 km2, with over 100 falling into the active category. The PZM aligns well with the global permafrost zonation index map. Slope, aspect, and elevation of rock glaciers are computed using the ASTER Digital Elevation Model. The average elevations range from 3,700 m to 4,550 m, and the average surface slope ranges from 12° to 26°, with maximum slopes from 25° to 65°. Most rock glaciers are oriented toward the south or southeast. Field investigations confirm that these rock glaciers occur in highly elevated regions with steep slopes. This study provides valuable information on the high areal abundance of permafrost in the Himalayan region and suggests increased risks of thawing permafrost due to climate warming in the future. The findings contribute to the understanding of permafrost and rock glaciers, filling knowledge gaps in the Himalayan context.

MONITORING OF WATER BODIES AND RECLAIMED LANDS AFFECTED BY WARFARE USING SATELLITE DATA

The paper presents the results of monitoring the state of water bodies and reclaimed lands affected by warfare using remote sensing methods and in-situ surveys. On the example of the flooding of the floodplain of the Irpin river by the waters of the Kyiv reservoir as a result of the destruction of the culvert structure, as well as the flooding of the floodplain of the Dnipro river near village Otradokamyanka after the explosion of the Kakhovska hydro power plant (HPP), the change in the area of inundation was calculated based on spectral index maps and surface classification using Sentinel-2 L2A imagery. On the base of Sentinel-2 L2A images, maps of SAVI and NDWI indices were obtained. They revealed the places of soil cover by sediments and the increase of the area of water bodies. Monitoring of reclaimed lands affected by warfare was carried out in the floodplain of the Irpin river on the Irpin Drainage and Moistening System during three time intervals: before the war in 2019; during the fighting in the spring of 2022 and a year after the end of military actions. Studies have shown a significant prevalence of waterlogged areas in the present time, which confirms the destruction of drainage network.The calculated values of the NDVI index turned out to be too high for agricultural crops, which indicates that the lands is overgrown with shrubs. Based on the results of the conducted monitoring, the territory was classified according to the degree of its damage by shell craters. Based on the results of the studies, it is recommended to monitor water bodies and reclaimed lands that have been affected by the war using images of different spatial resolution, a complex of spectral indices, a combination of image bands and in-situ surveys.

Indexing and segment-level mapping of soil quality in a spatially complex watershed in northern Iran.

Soil quality (SQ) modeling and mapping is a leading research field aiming to provide reproducible and cost-effective yet accurate SQ predictions at the landscape level. This endeavor was conducted in a complex watershed in northern Iran. We classified the region into spectrally and topographically homogenous land units (average area of 48 ± 23ha) using object-based segmentation analysis. Following the physicochemical analysis of soil samples from 98 stations, the Nemoro soil quality index (SQIn) was produced using the minimum dataset procedure and a non-linear sigmoid scoring function. SQIn values averaged 0.21 ± 0.06 and differed statistically between major land uses. To predict and map SQIn for each land unit, the best-performing regression model (F(3, 84) = 45.57, p = 0.00, R2 = 0.62) was built based on the positive contribution of the mean Landsat 8-OLI band 5, and negative influence of land surface temperature retrieved from Landsat 8-OLI band 10 and surface slope (t-test p-values < 0.01). Results showed that dense-canopy woodlands located in low-slope land units exhibit higher SQIn while regions characterized by either low-vegetation or steep-sloped land units had SQ deficits. This study provides insights into SQ prediction and mapping across spatially complex large-scale landscapes.

Temporal and spatial variability of physical and chemical properties in reclaimed soil after sulphur borehole mining

Mining contributes to the creation of degraded areas that are difficult to rehabilitate because they are subject to various transformations. Specifically, sulphur mining using the Frasch borehole method causes multidirectional changes of soil properties and thus presents challenges for post-mining rehabilitation. The aim of the current study was to evaluate the effectiveness of agricultural (meadow-type) reclamation and to understand how the soil recovers over time by assessing the spatio-temporal variability of the reclaimed soil after borehole sulphur mining. Selected soil properties and spectral indices based on satellite data were analysed: before reclamation, 3 years after reclamation and 15 years after reclamation. Texture, bulk density, porosity, water capacity, total organic carbon (TOC) and pH were measured and distribution maps of these parameters were generated using kriging. Additionally, maps of spectral indices (Green Normalized Difference Vegetation Index, Normalized Difference Soil Moisture Index, Soil Adjusted Vegetation Index, Enhanced Vegetation Index and Red–Green Index) based on Landsat imagery were created to support the field survey data. The soil revealed high temporal and spatial variability, as evidenced by the distributions of measured parameters and their coefficients of variation at different reclamation stages. Multidirectional remodelling of the soil structure occurred, resulting in changes in the differential porosity, field water capacity, texture, reaction and TOC. Statistically confirmed differences in soil parameters between sampling points were observed. The variability of the physical and chemical parameters at the individual sampling point also differed between the reclamation stages. We observed a general trend towards homogenisation of the spatial distributions of the soil parameters. This was also confirmed by the maps of spectral indices, which showed establishment and development of a continuous vegetation cover and the subsequent improvement of its condition.

Remote sensing analysis of desert sensitive areas using MEDALUS model and GIS in the Niger River Basin

Environmental degradation is a dynamic issue that requires ongoing monitoring to ensure ecological repair and environmental management for sustainable development. This study employed remote sensing techniques to ascertain the desertification sensitivity areas in the Niger River Basin (NRB) using the MEDALUS Model. The indicators for the model were divided into three categories: vegetation quality,climate quality, and soil quality index. The net primary productivity, estimated by the Carnegie Ames Stanford Approach model was substituted in the vegetation quality index. Each quality index was computed by calculating the geometric mean of selected sub-indices, while the geometric mean of the three quality indicators was utilized to determine the Environmental sensitivity to desertification in the study area. A total of 26.10% of the study area had low soil quality, while 26.16% and 17.41% of the study area had low vegetation and climate indicators, respectively. The study revealed that about 36.56% of the study area is highly sensitive while 27.65% and 35.78% are moderately and less sensitive respectively, to desertification in the NRB. The desertification-sensitivity index map shows that the northern part of the study area is highly susceptible to desertification and is encroaching southwards. The findings reveal that climate variables are major influencers in the study area. Our research is of importance to the Niger Basin Authority, planners, and other government agencies in charge of protecting the environment, to ascertain the sensitive areas to desertification, and to help cushion the effect of land degradation in the NRB.

A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using kNN and SVM regression models in northeastern Greece

Wind energy presents a high growth potential in the EU as an emission reduction strategy and to achieve the climate neutrality goal by 2050. Wind farms suitability analysis is one of the primary goals in the spatial planning of wind energy developments. This research paper introduces a hybrid spatial multicriteria GIS-based framework that combines Analytic Hierarchy Process (AHP), PROMETHEE II and Machine Learning algorithms to determine and predict the most efficient onshore wind farm locations by generating suitability index mappings. The methodology allows to overcome PROMETHEE II limitations in raster driven suitability analysis, utilizing machine learning regression methods as the k Nearest Neighbor and Support Vector Machines to predict a graduating mapping of suitability index for wind farm locations in northeastern Greece. The best configured models presented a RMSE of 0.0344 and 0.0154 respectively, indicating a quite high predictive performance. Suitability results indicate that 56.10 % of the feasible locations in the Thrace area present a positive outranking character for the kNN model and 56.79 % for the SVR model. The proposed framework, enriched by PROMETHEE II capabilities, assists energy and spatial planners in identifying suitable sites for wind farm siting and enables rational decision making that enhances efficient wind energy investments.

High Inflammation and Coronary Calcification in an Acute Coronary Syndrome Successfully Treated with Cutting Balloon

Abstract Complex coronary atherosclerosis may exhibit different phenotypes of coronary plaques, from non-calcified highly vulnerable atheroma to heavily calcified ones. Computed coronary tomography angiography (CCTA) may identify these different phenotypes and the recently introduced CCTA-based techniques for mapping coronary inflammation along the coronary arteries may provide useful additional information on cardiovascular risk. Here we present the case of a 68-year-old male patient with acute coronary syndrome in whom invasive coronary angiography and CCTA revealed a severe three-vessel disease with a heavily calcified lesion. Mapping of the CT fat attenuation index along the coronary arteries identified a high level of coronary inflammation, especially associated with the non-calcified lesions. All lesions were successfully revascularized by implantation of drug-eluting stents. A cutting balloon was used for the lesion identified by CCTA as heavily calcified, followed by stent implantation, with good results. In conclusion, CCTA, in association with novel techniques for mapping coronary inflammation, may represent an extremely useful tool for preparing complex interventions in multivessel diseases, helping preprocedural planning in high-risk patients.