Search Results (2,080)

In the coastal zone, two types of habitats—linear and areal—are distinguished. The main differences between both types are their shape and structure and the hydro- and litho-dynamic, salinity, and ecological gradients. Studying linear littoral habitats is essential for interpreting the ’coastal squeeze’ effect. The study’s main objective was to assess short-term behavior of soft cliffs as littoral linear habitats during calm season storm events in the example of the Olandų Kepurė cliff, located on a peri-urban protected seashore (Baltic Sea, Lithuania). The approach combined the surveillance of the cliff using unmanned aerial vehicles (UAVs) with the data analysis using an ArcGIS algorithm specially adjusted for linear habitats. The authors discerned two short-term behavior forms—cliff base cavities and scarp slumps. The scarp slumps are more widely spread. It is particularly noticeable at the beginning of the spring–summer period when the difference between the occurrence of both forms is 3.5 times. In contrast, cliff base cavities proliferate in spring. This phenomenon might be related to a seasonal Baltic Sea level rise. The main conclusion is that 55 m long cliff cells are optimal for analyzing short-term cliff behavior using UAV and GIS. Full article

This paper explores the feasibility of floating urban development in Italy, given its extensive coastline and inland hydrographic network. The key drivers for floating urban development, as an adaptive approach in low-lying waterfront areas, include the increasing threats posed by rising sea levels and flooding and the shortage of land for urban expansion. However, as not all waterfront areas are suitable for floating urban development, a geographical analysis based on a thorough evaluation of multiple factors, including urban–economic parameters and climate-related variables, led to the identification of a specific area of the Lazio coast, the river Tiber Delta. A comprehensive urban mapping process provided a multifaceted geo-referenced information layer, including several climatic, urban, anthropic, and environmental parameters. Within the GIS environment, it is possible to extract and perform statistical analyses crucial for assessing the impact of flood and sea-level rise hazards, particularly regarding buildings and land cover. This process provides a robust framework for understanding the spatial dimensions of flood and sea-level rise impacts and supporting informed design-making. A research-by-design phase follows the simulation research and mapping process. Several design scenarios are developed aimed at regenerating this vulnerable area. These scenarios seek to transform its susceptibility to flooding into a resilient, adaptive, urban identity, offering climate-resilient housing solutions for a population currently residing in unauthorized, substandard housing within high flood-risk zones. This paper proposes a comprehensive analytical methodology for supporting the design process of floating urban development, given the highly determinant role of site-specificity in such a challenging and new urban development approach. Full article

Since manure sources are widely dispersed and the disposal of manure in landfills or its direct application onto soil is often restricted by laws in many countries, selecting suitable sites for manure management facilities is an important step for sustainable livestock farming. The main purpose of this study is to explore suitable locations for situating large-scale biogas plants from livestock manure in Bangladesh using spatial modeling. This study analyzed land suitability based on several geographical, topographical, environmental, and socio-economic criteria, which were also optimized by reflecting optimum transportation distances from manure sources to the chosen sites using GIS (Geographic Information System) network analysis. Then, the environmental benefits of selected biogas plants were estimated through mathematical equations. It was found that 475, 15, and 68 large-scale biogas plants were spatially possible from large-animal, small-animal, and poultry manure, respectively, to produce a total electricity of 7682.72 GWh (gigawatt) in 2023. By implementing the proposed scenarios, renewable energy production will be increased in Bangladesh by at least 8.69%, and GHG (greenhouse gas) emissions will be reduced by approximately 6636.09 gigagram CO₂eq by disposing of 90.14 million tons of manure each year. Hence, the potential selection of biogas plant locations and benefit analysis of different scenarios will guide the establishment of a local decision for the utilization of regional bioenergy from livestock manure in Bangladesh. Full article

Landscape, as an important component of environmental quality, is increasingly valued by scholars for its visual dimension. Unlike evaluating landscape visual quality through on-site observation or using digital photos, the landscape visualization modeling method supported by unmanned aerial vehicle (UAV) aerial photography, geographic information System (GIS), and PixScape has the advantage of systematically scanning landscape geographic space. The data acquisition is convenient and fast, and the resolution is high, providing a new attempt for landscape visualization analysis. In order to explore the application of visibility modeling based on high-resolution UAV remote sensing images in landscape visual evaluation, this study takes campus landscape as an example and uses high-resolution campus UAV remote sensing images as the basic data source to analyze the differences between the planar method and tangent method provided by PixScape 1.2 software in visual modeling. Six evaluation factors, including Naturalness (N), Normalized Shannon Diversity Index (S), Contagion (CONTAG), Shannon depth (SD), Depth Line (DL), and Skyline (SL), are selected to evaluate the landscape vision of four viewpoints in the campus based on analytic hierarchy process (AHP) method. The results indicate that the tangent method considers the visual impact of the vertical amplitude and the distance between landscape and viewpoints, which is more in line with the real visual perception of the human eyes. In addition, objective quantitative evaluation metrics based on visibility modeling can reflect the visual differences of landscapes from different viewpoints and have good applicability in campus landscape visual evaluation. It is expected that this research can enrich the method system of landscape visual evaluation and provide technical references for it. Full article

The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (Glycine max L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. The peak leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) from PROBA-V/Sentinel-3 data were calculated according to ground-truth soybean agricultural parcels in continental Croatia during 2015–2021. Four machine learning regression algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), as well as their combination, were evaluated for predicting the peak LAI and FAPAR on the entire agricultural land in the study area, with RF producing the highest prediction accuracy with an R² in the range of 0.250–0.590. The translation from K-means classes to the FAO land suitability standard was performed using a relative-based approach, ranking five resulting classes based on their relative mean sums of LAI and FAPAR values. The results of the proposed approach indicate that it is viable for major crops, while cropland suitability prediction for minor crops would require higher spatial resolution, such as vegetation indices from Sentinel-2 imagery. Full article

Traffic flow prediction is one of the most important and attractive topics in geographical information science (GIS), traffic management, and logistics. Traffic flows exhibit significant complexity and dynamics, requiring a thorough understanding of their spatiotemporal evolution patterns for accurate prediction and analysis. Existing studies utilizing deep learning for traffic flow prediction often suffer from distribution shift issues, leading to poor generalization capabilities when dealing with data that has different spatiotemporal distributions. Based on this, we propose a traffic flow prediction model based on prompt learning, leveraging graph convolutional networks to focus on the spatiotemporal dependencies of traffic flows. The model utilizes spatiotemporal context learning capabilities to capture the periodic states of traffic flows, enhancing the extraction of spatiotemporal features by integrating spatiotemporal information. Experimental results show that the spatiotemporal traffic flow prediction model equipped with a spatiotemporal prompt learning module outperforms several mainstream benchmark models in terms of predictive performance. The model presents efficient learning performance that reaches optimal state in a short period of time, reduces the impact of distribution shifts, and can be adapted to spatiotemporal traffic flow data under varying spatiotemporal contexts. Full article

Floods have catastrophic effects worldwide, particularly in monsoonal Asia. This systematic review investigates the literature from the past two decades, focusing on the use of remote sensing (RS), Geographic Information Systems (GISs), and technologies for flood disaster management in South Asia, and addresses the urgent need for effective strategies in the face of escalating flood disasters. This study emphasizes the importance of tailored GIS- and RS-based flood disaster studies inspired by diverse research, particularly in India, Pakistan, Bangladesh, Sri Lanka, Nepal, Bhutan, Afghanistan, and the Maldives. Our dataset comprises 94 research articles from Google Scholar, Scopus, and ScienceDirect. The analysis revealed an upward trend after 2014, with a peak in 2023 for publications on flood-related topics, primarily within the scope of RS and GIS, flood-risk monitoring, and flood-risk assessment. Keyword analysis using VOSviewer revealed that out of 6402, the most used keyword was “climate change”, with 360 occurrences. Bibliometric analysis shows that 1104 authors from 52 countries meet the five minimum document requirements. Indian and Pakistani researchers published the most number of papers, whereas Elsevier, Springer, and MDPI were the three largest publishers. Thematic analysis has identified several major research areas, including flood risk assessment, flood monitoring, early flood warning, RS and GIS, hydrological modeling, and urban planning. RS and GIS technologies have been shown to have transformative effects on early detection, accurate mapping, vulnerability assessment, decision support, community engagement, and cross-border collaboration. Future research directions include integrating advanced technologies, fine-tuning spatial resolution, multisensor data fusion, social–environmental integration, climate change adaptation strategies, community-centric early warning systems, policy integration, ethics and privacy protocols, and capacity-building initiatives. This systematic review provides extensive knowledge and offers valuable insights to help researchers, policymakers, practitioners, and communities address the intricate problems of flood management in the dynamic landscapes of South Asia. Full article

Empirical studies are valuable for assessing soil and water pollution, as they can reduce costs and save time. The present study discusses previous research results using a questionnaire to gather experts’ judgments on technical issues and potential pollution related to the vulnerability of Wastewater Treatment Plants (WWTPs) in Greece. The questionnaire included 44 closed-type questions based on the Likert Scale. It was distributed to a representative sample of 116 operators over seven (7) months (April–November 2021). Geographical Information Systems (GISs) were employed to visualize the spatial distribution of the seismic vulnerability of WWTPs. The study outputs include eight (8) maps depicting the spatial distribution of seismic vulnerability, both with and considering soil–water pollution, by calculating the existence of seismic hazards and identifying potentially affected regions. Additionally, eight (8) tables support this analysis. The survey findings highlight the most vulnerable regions and WWTPs in the country. The results suggest that after excluding Zone III, the WWTPs of Zone II of the national Seismic Hazard Map (SHM) are estimated to be the most vulnerable. This study spatially visualizes the indicator of seismic vulnerability (ISV) and the seismic vulnerability index concerning potential soil–water pollution (ISV-REF), according to the SHM and regions. Most WWTPs have low ISV-REF, while maps illustrate the exceedance of that parameter, identifying the safest units and indicating that Zone I has the safest units according to the exceedance percentages. Integrating data on regions, ISV, ISV-REF, and their exceedance in GIS could lead to authorities’ and technicians’ decisions to implement quick measures. Researchers should also focus their studies more precisely, mitigating the seismic vulnerability of critical infrastructure, such as WWTPs. Full article

The goal of this study is to analyze the land use and land cover change (LUCC) in the Northeast Sandy Land from 1990 to 2023 and reveal the characteristics of the changes in the value of ecosystem services in the area, aiming to provide scientific references for the ecological sustainable development of the Northeast Sandy Land. Using Landsat series remote sensing images from 1990 to 2023, we endeavored to obtain the information mapping of LUCC in the study area in four periods (1990, 2000, 2010, and 2023), applied GIS spatial analysis and numerical statistical analysis methods to study the LUCC in the area, and calculated the resulting changes in ecosystem service value (ESV) based on the table of ESV coefficients. The results show that: (1) in the past 33 years, LUCC in the Northeast Sandy Land has changed significantly, with an increase in the area of farmland, sandy land, and other lands, and a decrease in the area of forest, grassland, and water; (2) the value of ecosystem services has increased from CNY 1,624,557.77 billion in 1990 to CNY 173,999.99 billion in 2023, the average annual growth rate is 0.24%/a; (3) in the single ESV, the ESV of biodiversity increase is the greatest, with an increase of 0.72%/a; (4) LUCC is obvious in the Northeast Sandy Land, and LUCC is the main reason affecting the change in regional ESV. Full article

Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents’ actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization. Full article

Urbanization growth and climate change have increased the frequency and severity of floods in urban areas. One of the effective methods for reducing stormwater volume and managing urban floods is the low-impact development best management practice (LID-BMP). This study aims to mitigate flood volume and peak discharge caused by land use changes in the Darabad basin located in Tehran, Iran, using LID-BMPs. For this purpose, land use maps were extracted for a period of 23 years from 2000 to 2022 using Landsat satellite images. Then, by using a combination of geographic information system-based multi-criteria decision analysis (GIS-MCDA) method and spatial criteria, four types of LID-BMPs, including bioretention basin, green roof, grass swale, and porous pavement, were located in the study area. Next, rainfall–runoff modeling was applied to calculate the changes in the mentioned criteria due to land use changes and the application of LID-BMPs in the area using soil conservation service curve number (SCS-CN) method. The simulation results showed that the rise in built-up land use from 43.49 to 56.51 percent between the period has increased the flood volume and peak discharge of 25-year return period by approximately 60 percent. The simulation results also indicated that the combined use of the four selected types of LID-BMPs will lead to a greater decrease in stormwater volume and peak discharge. According to the results, LID-BMPs perform better in shorter return periods in a way that the average percentage of flood volume and peak discharge reduction in a 2-year return period were 36.75 and 34.96 percent, while they were 31.37 and 26.5 percent in a 100-year return period. Full article

Identifying suitable sites for urban, industrial, and tourist development is important, especially in areas with increasing population and limited land availability. Kharga Oasis, Egypt, stands out as a promising area for such development, which can help reduce overcrowding in the Nile Valley and Delta. However, soil and various environmental factors can affect the suitability of civil engineering projects. This study used Geographic Information Systems (GISs) and a multi-criteria decision-making approach to assess the suitability of Kharga Oasis for construction activities. Geotechnical parameters were obtained from seismic velocity data, including Poisson’s ratio, stress ratio, concentration index, material index, N-value, and foundation-bearing capacity. A comprehensive analysis of in situ and laboratory-based geological and geotechnical data from 24 boreholes examined soil plasticity, water content, unconfined compressive strength, and consolidation parameters. By integrating geotechnical, geomorphological, geological, environmental, and field data, a detailed site suitability map was created using the analytic hierarchy process to develop a weighted GIS model that accounts for the numerous elements influencing civil project design and construction. The results highlight suitable sites within the study area, with high and very high suitability classes covering 56.87% of the land, moderate areas representing 27.61%, and unsuitable areas covering 15.53%. It should be noted that many settlements exist in highly vulnerable areas, emphasizing the importance of this study. This model identifies areas vulnerable to geotechnical and geoenvironmental hazards, allowing for early decision-making at the beginning of the planning process and reducing the waste of effort. The applied model does not only highlight suitable sites in the Kharga Oasis, Egypt, but, additionally, it provides a reproducible method for efficiently assessing land use suitability in other regions with similar geological and environmental conditions around the world. Full article

Spatial distribution is a critical factor influencing the success or failure of hotel management. This study examines the spatial distribution patterns of foreign star-rated hotels in China from 2000 to 2015 based on 27 typical city cases, using global and local spatial autocorrelation methods within GIS spatial analysis. The research explores the evolution of these patterns, analyzes key characteristics, and combines these insights with a stepwise regression method. Pearson correlation analysis is used to identify factors that influence the evolution of the spatial pattern. This study reveals that, first, the Z-value of global spatial autocorrelation of foreign star-rated hotels in China decreases from 2.38 to 1.63, indicating that the spatial distribution of foreign star-rated hotels in China has shifted from imbalanced to balanced, transitioning from economically developed regions such as areas with overseas Chinese populations, provincial capitals, and municipalities directly under central government control, toward tourist cities. Second, star-rated hotels hold a critical position within the spatial pattern, highlighting their central role in shaping the hospitality landscape. Third, the spatial distribution of foreign star-rated hotels is primarily influenced by the number of inbound tourists, followed by the presence of scenic spots rated 4A and above. The influence of other factors is found to be less significant. Fourth, the correlation coefficient between tourism demand and foreign star-rated hotels increased by 0.004, whereas the correlation coefficient between tourism supply and foreign star-rated hotels decreased by 0.036, indicating that market factors are playing an increasingly important role in shaping the evolution of foreign star-rated hotels in China, reflecting broader market dynamics. This study provides practical guidance for local Chinese hotels facing competition from foreign-funded establishments and offers theoretical insight into the strategic implementation of transnational operations. It points out the expansion direction of local Chinese hotels across different developmental stages. Full article

This work presents the results of research aimed at evaluating the performance of the photovoltaic system connected to the electrical grid at the University of Brasília (UnB), Brazil. Following the guidelines established by the Brazilian Standard for Performance Monitoring and Analysis of Grid-connected Photovoltaic Systems, it was possible to evaluate the system’s performance by determining the Performance Ratio (PR) indicator. The operating temperatures were estimated using measured values of the ambient temperature and solar irradiation. These data were collected by a nearby solarimetric station. Next, the theoretical energy injected into the electrical grid was determined based on calculations of the Direct Current (DC) power at the inverter input and the Alternating Current (AC) power at the inverter output. To this end, the coefficients of the inverter efficiency curve were considered as well as a loss scenario, as recommended. With these results, as well as the information about the total photovoltaics (PV) system AC production obtained from the inverter supervisory system, it was possible to determine the average annual PR achieved and compare the theoretical and practical results obtained. The main contribution of this paper is the performance evaluation of a 125 kWp grid-connected photovoltaic system at the University of Brasília (UnB), assessed using Brazilian Standards for performance monitoring and analysis. The system, installed on the rooftop of the UED building, consists of 298 Canadian Solar HiKu CS3W-420P modules with a 15-degree north pitch angle facing geographic north. It interfaces with the grid through two three-phase inverters, model CSI-75K-T400 (74.76 kWp) and a CSI-50KTL-GI (50.4 kWp). The results showed that the system with a 50kW inverter had an average PR of 78%, while the system with a 75 kW inverter showed a PR variation from 56% to 93%. The information obtained in this work will be used to develop computational tools capable of monitoring and evaluating, in real time, the performance of photovoltaic systems and ensuring that the expected financial return is achieved through the use of preventive and corrective maintenance actions in a timely manner. Full article

As the global shift from fossil fuels to the Paris Agreement has accelerated, wind energy has become a key alternative to hydroelectric power. However, existing research often needs to improve in integrating diverse environmental, economic, and climate-related variables when modeling wind energy potential, particularly under future climate change scenarios. Addressing these gaps, this study employs the maximum entropy (MaxEnt) method, a robust and innovative tool for spatial modeling, to identify optimal wind farm sites in Türkiye. This research advances site selection methodologies and enhances predictive accuracy by leveraging a comprehensive dataset and incorporating climate change scenarios. The results indicate that 89% of the current licensed projects will maintain compliance in the future, while 8% will see a decrease in compliance. Furthermore, the wind energy potential in Türkiye is expected to increase because of climate change. These results confirm the suitability of existing project locations and identify new high-potential areas for sustainable wind energy development. This study provides policymakers, investors, and developers actionable insights to optimize wind energy integration into the national energy portfolio, supporting global climate goals by accelerating the adoption of renewable energy sources. Full article