Search Results (79)

Road dust contains a variety of heavy metals and is a widely used sustainability indicator for monitoring pollution and assessing environmental and health risks in sustainable development. Heavy metals in road dust mainly originate from worn-off particles from vehicles, such as tires, brake pads, road dust, and emissions from exhaust pipes. These heavy metal particles could remain on the road surface for a long period and cause environmental pollution. In this preliminary study, road dust was collected from 8 representative areas in Macao. The heavy metal content from road dust in Macao was extracted from each of the collected samples for an assessment of the heavy metal pollution and its potential threat to human health. The results show that heavy metals primarily originate from human activities, including transportation emissions (Mn: 67.37%, Zn: 57.01%, Sb: 54.1%) and industrial activities (Al: 84.70%, Fe: 76.71%, Pb: 65.32%). The metal-specific non-carcinogenic risk ranges from 1.17 × 10⁻⁷ to 2.65 × 10⁻⁵ and the total carcinogenic risk is 6.91 × 10⁻¹⁰, showing minimum health effects from heavy metals in road dust. Furthermore, there is a significant correlation between the total vehicle counts and the heavy metal contents such as Al, Si, As, V, and Fe (r = 0.50 to 0.82). This work represents the first characterization of heavy metal contents and risks of urban road dust in Macao. Full article

Using Shanghai as a case study, this paper develops a multi-source fusion and interpretable machine learning framework. Sentiment indices were extracted from Weibo check-ins with ERNIE 3.0, street-view elements were identified using Mask2Former, and urban indicators like the Normalized Difference Vegetation Index, floor area ratio, and road network density were integrated. The coupling between residents’ sentiments and streetscape features during heatwaves was analyzed with Extreme Gradient Boosting, SHapley Additive exPlanations, and GeoSHAPLEY. Results show that (1) the average sentiment index is 0.583, indicating a generally positive tendency, with sentiments clustered spatially, and negative patches in central areas, while positive sentiments are concentrated in waterfronts and green zones. (2) SHapley Additive exPlanations analysis identifies NDVI (0.024), visual entropy (0.022), FAR (0.021), road network density (0.020), and aquatic rate (0.020) as key factors. Partial dependence results show that NDVI enhances sentiment at low-to-medium ranges but declines at higher levels; aquatic rate improves sentiment at 0.08–0.10; openness above 0.32 improves sentiment; and both visual entropy and color complexity show a U-shaped relationship. (3) GeoSHAPLEY shows pronounced spatial heterogeneity: waterfronts and the southwestern corridor have positive effects from water–green resources; high FAR and paved surfaces in the urban area exert negative influences; and orderly interfaces in the vitality corridor generate positive impacts. Overall, moderate greenery, visible water, openness, medium-density road networks, and orderly visual patterns mitigate negative sentiments during heatwaves, while excessive density and hard surfaces intensify stress. Based on these findings, this study proposes strategies: reducing density and impervious surfaces in the urban area, enhancing greenery and quality in waterfront and peripheral areas, and optimizing urban–rural interfaces. These insights support heat-adaptive and sustainable street design and spatial governance. Full article

In the São Brás de Alportel municipality, water scarcity poses a significant constraint on agricultural activities. This study utilises Remote Sensing (RS) and Geographical Information Systems (GISs) to identify existing irrigated areas, delineate catchment basins, and select the most suitable sites for the installation of new surface water reservoirs. First, the principal territorial components were characterised, including physical elements (climate, geology, soils, and hydrography) and anthropogenic infrastructure (road network and high-voltage power lines). Summer Sentinel-2 satellite imagery was then analysed to calculate the Normalised Difference Vegetation Index (NDVI), enabling the identification and classification of irrigated agricultural parcels. Flow directions and accumulations derived from Digital Elevation Models (DEMs) facilitated the characterisation of 38 micro-catchments and the extraction of 758 km of the drainage network. The siting criteria required a minimum setback of 100 m from roads and high-voltage lines, excluded farmland currently in use, and favoured mountainous areas with low permeability. Only 18.65% (2854 ha) of the municipality is agricultural land, of which just 4% (112 ha) currently benefits from irrigation. The NDVI-based classification achieved a Kappa coefficient of 0.88, indicating high reliability. Three sites demonstrated adequate storage capacity, with embankments measuring 8 m, 10 m, and 12 m in height. At one of these sites, two reservoirs arranged in a cascade were selected as an alternative to a single structure exceeding 12 m in height, thereby reducing environmental and landscape impact. The reservoirs fill between October and November in an average rainfall year and between October and January in a dry year, maintaining a positive annual water balance and allowing downstream plots to be irrigated by gravity. The methodology proved to be objective, replicable, and essential for the sustainable expansion of irrigation within the municipality. Full article

We applied UAV (Unmanned Aerial Vehicle) and ALS (Airborne Laser Scanning) remote sensing methods to identify terrain obstacles encountered during timber extraction in the skidding process with the aim of proposing accessibility solutions to the inner parts of forest stands using skidding trails. At the Vítovický žleb site, located east of Brno in the South Moravian Region of the Czech Republic, we analysed the accuracy of digital terrain models (DTMs) created from UAV LiDAR (Light Detection and Ranging), RGB (Red–Green–Blue) UAV, ALS data taken on site and publicly available LiDAR data DMR 5G (Digital Model of Relief of the Czech Republic, 5th Generation, based on airborne laser scanning, providing pre-classified ground points with an average density of 1 point/m²). UAV data were obtained using two types of drones: a DJI Mavic 2 mounted with an RGB photogrammetric camera and a GeoSLAM Horizon laser scanner on a DJI M600 Pro hexacopter. We achieved the best accuracy with UAV technologies, with an average deviation of 0.06 m, compared to 0.20 m and 0.71 m for ALS and DMR 5G, respectively. The RMSE (Root Mean Square Error) values further confirm the differences in accuracy, with UAV-based models reaching as low as 0.71 m compared to over 1.0 m for ALS and DMR 5G. The results demonstrated that UAVs are well-suited for detailed analysis of rugged terrain morphology and obstacle identification during timber extraction, potentially replacing physical terrain surveys for timber extraction planning. Meanwhile, ALS and DMR 5G data showed significant potential for use in planning the placement of skidding trails and determining the direction and length of timber extraction from logging sites to forest roads, primarily due to their ability to cover large areas effectively. Differences in the analysis results obtained using GIS (Geographic Information System) cost surface solutions applied to ALS and DMR 5G data DTMs were evident on logging sites with terrain obstacles, where the site-specific ALS data proved to be more precise. While DMR 5G is based on ALS data, its generalised nature results in lower accuracy, making site-specific ALS data preferable for analysing rugged terrain and planning timber extractions. However, DMR 5G remains suitable for use in more uniform terrain without obstacles. Thus, we recommend combining UAV and ALS technologies for terrain with obstacles, as we found this approach optimal for efficiently planning the logging-transport process. Full article

This study assesses the spatial distribution and contamination levels of some heavy metals (HMs), i.e., cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), and lead (Pb), in seawater and surface sediments along the Romanian Black Sea coast (RBSC). Sampling was conducted at 40 stations across 12 transects during May–June 2021, and the measured levels of HM concentrations were compared with Environmental Quality Standards (EQS), i.e., maximum allowable concentration (MAC) values, for seawater and effects range-low (ERL) thresholds for sediments. HM concentrations were measured using high-resolution continuum source atomic absorption spectrometry (HR-CS AAS). In seawater, the levels of Cd, Cu, and Pb concentrations exceeded the MAC values at three stations located in areas influenced by the Danube River or anthropogenic activities. In sediments, exceedances of ERL thresholds were found for Ni at 11 stations, for Cu at three stations, and for Pb at one station. HM contamination of sediment samples collected from these stations can be caused by both natural and anthropogenic sources, e.g., the Danube River, rock/soil weathering and erosion, agricultural runoff, port and construction activities, maritime and road transport, coastal tourism, petrochemical industry, wastewater discharges, offshore oil and gas extraction. Principal Component Analysis (PCA) provided valuable information about the relationships between relevant variables, including water depth and HM concentrations in seawater and sediments, and potential sources of contamination. The results highlight the influence of fluvial inputs and localized human activities on HM contamination. While the overall chemical status of Romanian Black Sea waters and sediments remains favorable, targeted management strategies are needed to address localized pollution hotspots and mitigate potential ecological risks. These findings provide valuable insights for environmental monitoring and sustainable coastal management. Full article

Icings, a significant hydrogeological phenomenon in permafrost regions, form when groundwater flows to the surface or through river crevices and freezes under low temperatures. These formations pose serious threats to infrastructure, including roads, railways, and bridges, while also serving as vital freshwater resources. Despite their importance, the mechanisms governing icing formation and the quantitative relationships between groundwater-controlling factors—such as freeze–thaw processes and precipitation—and icing distribution remain poorly understood. This knowledge gap hinders disaster prevention efforts and the sustainable utilization of water resources in cold regions. This study investigates the development patterns and influencing factors of icings in Eruu, a high-latitude permafrost region, using Landsat 4–5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI imagery with a 30 m resolution (2005–2024) and meteorological and geothermal data. By combining NDSI and MDII, the differentiation accuracy of water bodies was improved, and the K-Means clustering algorithm was applied to extract the icing region. The results revealed that the annual icing surface area ranged from 208,800 to 459,000 m², with a minimum in 2009 and a maximum in 2011. The average annual increase was approximately 4304.5 m² (p = 0.0255). Icings began freezing in October, radiating outward from the center, and melted by late May or early June. The Pearson correlation analysis showed (1) a strong negative correlation between snowfall and icing area (r = −0.544); (2) a positive correlation between freezing duration and icing area (r = 0.471); and (3) over the study period, annual average temperature and total precipitation exhibited no obvious change trend, with weak positive correlations between icing area and total precipitation (r = 0.290) and annual average temperature (r = 0.248). The observations of icing areas will be further applied to disaster prevention efforts. Additionally, the source of icings is clean and can be extracted for drinking purposes. Therefore, these findings enhance the understanding of icing mechanisms, support the prediction of icing development, and inform disaster prevention and resource management in permafrost regions. Full article

Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous areas. This article proposes a method for constructing fine terrain in high vegetation coverage areas based on implicit neural representation. This method consists of data preprocessing, multi-scale and multi-feature high-difference point cloud initial filtering, and an upsampling module based on implicit neural representation. Firstly, preprocess the regional point cloud data is preprocessed; then, K-dimensional trees (K-d trees) are used to construct spatial indexes, and spherical neighborhood methods are applied to capture the geometric and physical information of point clouds for multi-feature fusion, enhancing the distinction between terrain and non-terrain elements. Subsequently, a differential model is constructed based on DSM (Digital Surface Model) at different scales, and the elevation variation coefficient is calculated to determine the threshold for extracting the initial set of ground points. Finally, the upsampling module using implicit neural representation is used to finely process the initial ground point set, providing a complete and uniformly dense ground point set for the subsequent construction of fine terrain. To validate the performance of the proposed method, three sets of point cloud data from mountainous terrain with different features are selected as the experimental area. The experimental results indicate that, from a qualitative perspective, the proposed method significantly improves the classification of vegetation, buildings, and roads, with clear boundaries between different types of terrain. From a quantitative perspective, the Type I errors of the three selected regions are 4.3445%, 5.0623%, and 5.9436%, respectively. The Type II errors are 5.7827%, 6.8516%, and 7.3478%, respectively. The overall errors are 5.3361%, 6.4882%, and 6.7168%, respectively. The Kappa coefficients of the measurement areas all exceed 80%, indicating that the proposed method performs well in complex mountainous environments. Provide point cloud data support for the construction of wind and photovoltaic bases in China, reduce potential damage to the ecological environment caused by construction activities, and contribute to the sustainable development of ecology and energy. Full article

While the cessation of underground mining operations reduces immediate risks to surface structures, it does not fully eliminate long-term surface hazards, which can hinder the sustainable development of post-mining communities. This study presents a combination of analytical and practical methods to quantitatively assess these persistent hazards, focusing on three critical areas: the risk of surface instability from discontinuous phenomena at shallow road headings, the progression of subsidence after mining has ceased, and surface uplift due to rising mine water levels. By providing practical examples, this research highlights the importance of ongoing monitoring and hazard assessment to support sustainable land use in former mining regions. These findings contribute to a broader understanding of post-mining environmental impacts, offering valuable insights into mitigating surface risks that can influence local sustainability efforts. This study supports the global drive toward sustainable development by addressing the long-term effects of resource extraction on land stability and community resilience. Full article

The use of satellite imagery for surface deformation monitoring has been steadily increasing. However, the study of extracting deformation slopes from deformation data requires further advancement. This limitation not only poses challenges for subsequent studies but also restricts the potential for deeper exploration and utilization of deformation data. The LT-1 satellite, China’s largest L-band synthetic aperture radar satellite, offers a new perspective for monitoring. In this study, we extracted deformation slopes in Chongqing and its surrounding areas of China based on deformation data generated by LT-1. Twelve factors were selected to analyze their influence on slope deformation, including elevation, topographic position, slope, landcover, soil, lithology, relief, average rainfall intensity, and distances to rivers, roads, railways, and active faults. A total of 5863 deformation slopes were identified, covering an area of 140 km², mainly concentrated in the central part of the study area, with the highest area density reaching 0.22%. Among these factors, average rainfall intensity was found to have the greatest impact on deformation slope. These findings provide valuable information for geological disaster early warning and management in Chongqing and surrounding areas, while also demonstrating the practical value of the LT-1 satellite in deformation monitoring. Full article

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems. Full article

Studies addressing the supervised extraction of geospatial elements from aerial imagery with semantic segmentation operations (including road surface areas) commonly feature tile sizes varying from 256 × 256 pixels to 1024 × 1024 pixels with no overlap. Relevant geo-computing works in the field often comment on prediction errors that could be attributed to the effect of tile size (number of pixels or the amount of information in the processed image) or to the overlap levels between adjacent image tiles (caused by the absence of continuity information near the borders). This study provides further insights into the impact of tile overlaps and tile sizes on the performance of deep learning (DL) models trained for road extraction. In this work, three semantic segmentation architectures were trained on data from the SROADEX dataset (orthoimages and their binary road masks) that contains approximately 700 million pixels of the positive “Road” class for the road surface area extraction task. First, a statistical analysis is conducted on the performance metrics achieved on unseen testing data featuring around 18 million pixels of the positive class. The goal of this analysis was to study the difference in mean performance and the main and interaction effects of the fixed factors on the dependent variables. The statistical tests proved that the impact on performance was significant for the main effects and for the two-way interaction between tile size and tile overlap and between tile size and DL architecture, at a level of significance of 0.05. We provide further insights and trends in the predictions of the extensive qualitative analysis carried out with the predictions of the best models at each tile size. The results indicate that training the DL models on larger tile sizes with a small percentage of overlap delivers better road representations and that testing different combinations of model and tile sizes can help achieve a better extraction performance. Full article

The acceleration of urbanization intensifies the urban heat island, outdoor activities (especially the road travel) are seriously affected by the overheating environment, and the comfort and safety of the bus shelter as an accessory facility of road travel are crucial to the passenger’s experience. This study investigated the basic information (e.g., distribution, orientation) of 373 bus shelters in Guangzhou and extracted the typical style by classifying the characteristics of these bus shelters. Additionally, we also measured the thermal environment of some bus shelters in summer and investigated the cooling behavior of passengers in such an environment. The results show that the typical style of bus shelters in the core area of Guangzhou is north–south orientation, with only one station board at the end of the bus, two backboards, two roofs (opaque green), and the underlying surface is made of red permeable brick. The air temperature and relative humidity under different bus shelters, tree shading areas, and open space in summer are 34–37 °C and 49–56%, respectively. For the bus shelters with heavy traffic loads, the air temperature is basically above 35.5 °C, and the thermal environment is not comfortable. During the hot summer, when there is no bus shelter or trees to shade the sun, the waiting people adjust their position with the sun’s height, azimuth angles, and direct solar radiation intensity to reduce the received radiation as much as possible, which brings great inconvenience to them. When only bus shelters provide shade, people tend to gather in the shaded space, and cooling measures such as umbrellas, hats, and small fans are still needed to alleviate thermal discomfort. However, the aforementioned various spontaneous cooling behaviors still cannot effectively alleviate overheating, and it is very important to increase auxiliary cooling facilities in bus shelters. Full article

E’bian Yi Autonomous County is a mineral-rich area located in a complex geological structure zone. The region experiences frequent geological disasters due to concentrated rainfall, steep terrain, and uneven vegetation cover. In particular, during the rainy season, large amounts of rainwater rapidly accumulate, increasing soil moisture and slope pressure, making landslides and debris flows more likely. Additionally, human activities such as mining, road construction, and building can alter the original geological structure, exacerbating the risk of geological disasters. According to publicly available data from the Leshan government, various types of geological disasters occurred in 2019, 2020, 2022, and 2023, resulting in economic losses and casualties. Although some studies have focused on geological disaster issues in E’bian, these studies are often limited to specific areas or types of disasters and lack comprehensive spatial and temporal analysis. Furthermore, due to constraints in technology, funding, and manpower, geophysical exploration, field geological exploration, and environmental ecological investigations have been challenging to carry out comprehensively, leading to insufficient and unsystematic data collection. To provide data support and monitoring for regional territorial spatial planning and geological disaster prevention and control, this paper proposes a new method to study the correlation between soil moisture changes and geological disasters. Six high-resolution Landsat remote sensing images were used as the main data sources to process the image band data, and terrain factors were extracted and classified using a digital elevation model (DEM). Meanwhile, a Normalized Difference Vegetation Index–Land Surface Temperature (NDVI-LST) feature space was constructed. The Temperature Vegetation Drought Index (TVDI) was calculated to analyze the variation trend and influencing factors of soil moisture in the study area. The research results showed that the variation in soil moisture in the study area was relatively stable, and the overall soil moisture content was high (0.18 < TVDI < 0.33). However, due to the large variation in topographic relief, it could provide power and be a source basis for geological disasters such as landslide and collapse, so the inversion value of TVDI was small. The minimum and maximum values of the correlation coefficient (R²) were 0.60 and 0.72, respectively, indicating that the surface water content was relatively large, which was in good agreement with the calculated results of vegetation coverage and conducive to the restoration of ecological stability. In general, based on the characteristics of remote sensing technology and the division of soil moisture critical values, the promoting and hindering effects of soil moisture on geological hazards can be accurately described, and the research results can provide effective guidance for the prevention and control of geological hazards in this region. Full article

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10⁻³ a^–1. The area within 1 km of roads experienced a higher increase of 3 × 10⁻³ a^–1, while the roadless areas experienced a lower increase of 1.1 × 10⁻³ a^–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of “impervious surfaces”, “cropland”, and “grassland”. Climate changes, such as increased rainfall, have resulted in larger areas of “wetlands” and “sparse vegetation”. These results provide valuable information for ecosystem restoration and environmental protection in the SRB. Full article

Global climate change has exacerbated alterations in urban thermal environments, significantly impacting the daily lives and health of city residents. Measuring and understanding urban land surface temperatures (LST) and their influencing factors is important in addressing global climate change and enhancing the well-being of residents. However, due to limitations in data precision and analytical methods, existing studies often overlook the microscale examination closely related to residents’ daily lives, and lack a deep exploration of the spatial heterogeneity of the influencing factors. This leads to these results being ineffective in guiding the planning and construction of cities. Taking Shenzhen as a case study, our study investigates the effects of various microscale build environment characteristics of LST using street view images and machine learning. A convolutional neural network model adopting the SegNet architecture is used to perform semantic segmentation on street view images, extracting features of the microscale urban-built environment. The LST is inverted through the Google Earth Engine (GEE) platform. By using Multiscale Geographically Weighted Regression (MGWR) models, our study reveals the comprehensive impact of the urban-built environment on LST and its significant spatial heterogeneity. The findings indicate that the proportions of sky, roads, and buildings are positively correlated with LST, while trees have a significant cooling effect. Although earth and water can reduce LST, their overall contribution is minimal due to limitations in their area and distribution patterns. This study not only reveals the key factors affecting urban LST at the microscale but also emphasizes the necessity of considering the spatial heterogeneity of these factors’ impacts. This suggests the need for targeted strategies for different areas to effectively improve the urban thermal environment and achieve sustainable urban development. Full article