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Keywords = road surface area extraction

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16 pages, 11784 KiB  
Article
Application of Unmanned Aerial Vehicle and Airborne Light Detection and Ranging Technologies to Identifying Terrain Obstacles and Designing Access Solutions for the Interior Parts of Forest Stands
by Petr Hrůza, Tomáš Mikita and Nikola Žižlavská
Forests 2025, 16(5), 729; https://doi.org/10.3390/f16050729 - 24 Apr 2025
Viewed by 525
Abstract
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. [...] Read more.
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 article belongs to the Section Forest Operations and Engineering)
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31 pages, 3248 KiB  
Article
Assessment of Heavy Metal Contamination of Seawater and Sediments Along the Romanian Black Sea Coast: Spatial Distribution and Environmental Implications
by Elena Ristea, Oana Cristina Pârvulescu, Vasile Lavric and Andra Oros
Sustainability 2025, 17(6), 2586; https://doi.org/10.3390/su17062586 - 14 Mar 2025
Cited by 4 | Viewed by 1597
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Environmental Protection and Sustainable Ecological Engineering)
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16 pages, 7510 KiB  
Article
Identifying the Key Controlling Factors of Icings in Permafrost Regions: A Case Study of Eruu, Sakha Republic, Russia
by Ruotong Li, Miao Yu, Minghui Jia, Zijun Wang, Hao Yao and Yunhu Shang
Water 2025, 17(5), 607; https://doi.org/10.3390/w17050607 - 20 Feb 2025
Viewed by 742
Abstract
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. [...] Read more.
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
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23 pages, 4583 KiB  
Article
Research on Fine-Scale Terrain Construction in High Vegetation Coverage Areas Based on Implicit Neural Representations
by Yi Zhang, Peipei He, Haihang Jing, Bin He, Weibo Yin, Junzhen Meng, Yuntian Ma, Haifeng Zhang, Bo Zhang and Haoxiang Shen
Sustainability 2025, 17(3), 1320; https://doi.org/10.3390/su17031320 - 6 Feb 2025
Viewed by 852
Abstract
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 [...] Read more.
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
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20 pages, 9760 KiB  
Article
Evaluating Surface Stability for Sustainable Development Following Cessation of Mining Exploitation
by Rafał Misa, Anton Sroka and Dawid Mrocheń
Sustainability 2025, 17(3), 878; https://doi.org/10.3390/su17030878 - 22 Jan 2025
Cited by 2 | Viewed by 967
Abstract
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 [...] Read more.
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
(This article belongs to the Section Energy Sustainability)
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24 pages, 13644 KiB  
Article
Deformation Slope Extraction and Influencing Factor Analysis Using LT-1 Satellite Data: A Case Study of Chongqing and Surrounding Areas, China
by Jielin Liu, Chong Xu, Binbin Zhao, Zhi Yang, Yi Liu, Sihang Zhang, Xiaoang Kong, Qiongqiong Lan, Wenbin Xu and Wenwen Qi
Remote Sens. 2025, 17(1), 156; https://doi.org/10.3390/rs17010156 - 5 Jan 2025
Cited by 2 | Viewed by 1531
Abstract
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 [...] Read more.
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
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18 pages, 8484 KiB  
Article
Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information
by Hong Zhu, Jian Meng, Jiaqi Yao and Nan Xu
ISPRS Int. J. Geo-Inf. 2024, 13(10), 369; https://doi.org/10.3390/ijgi13100369 - 18 Oct 2024
Viewed by 1728
Abstract
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 [...] Read more.
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
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37 pages, 6394 KiB  
Article
Insights into the Effects of Tile Size and Tile Overlap Levels on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography
by Calimanut-Ionut Cira, Miguel-Ángel Manso-Callejo, Ramon Alcarria, Teresa Iturrioz and José-Juan Arranz-Justel
Remote Sens. 2024, 16(16), 2954; https://doi.org/10.3390/rs16162954 - 12 Aug 2024
Cited by 1 | Viewed by 2705
Abstract
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 [...] Read more.
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
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19 pages, 11953 KiB  
Article
Investigation of Bus Shelters and Their Thermal Environment in Hot–Humid Areas—A Case Study in Guangzhou
by Yan Pan, Shan Li and Xiaoxiang Tang
Buildings 2024, 14(8), 2377; https://doi.org/10.3390/buildings14082377 - 1 Aug 2024
Cited by 3 | Viewed by 2047
Abstract
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 [...] Read more.
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
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23 pages, 22346 KiB  
Article
Correlation between Soil Moisture Change and Geological Disasters in E’bian Area (Sichuan, China)
by Hongyi Guo and Antonio Miguel Martínez-Graña
Appl. Sci. 2024, 14(15), 6685; https://doi.org/10.3390/app14156685 - 31 Jul 2024
Viewed by 1429
Abstract
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, [...] Read more.
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
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21 pages, 27708 KiB  
Article
Spatiotemporal Variations of Vegetation and Its Response to Climate Change and Human Activities in Arid Areas—A Case Study of the Shule River Basin, Northwestern China
by Xiaorui He, Luqing Zhang, Yuehan Lu and Linghuan Chai
Forests 2024, 15(7), 1147; https://doi.org/10.3390/f15071147 - 1 Jul 2024
Cited by 5 | Viewed by 1840
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Application of Remote Sensing in Vegetation Dynamic and Ecology)
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17 pages, 5534 KiB  
Article
The Heterogeneous Effects of Microscale-Built Environments on Land Surface Temperature Based on Machine Learning and Street View Images
by Tianlin Zhang, Zhao Lin, Lei Wang, Wenzheng Zhang, Yazhuo Zhang and Yike Hu
Atmosphere 2024, 15(5), 549; https://doi.org/10.3390/atmos15050549 - 29 Apr 2024
Cited by 4 | Viewed by 1924
Abstract
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 [...] Read more.
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
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16 pages, 9479 KiB  
Article
Spatial Gradient Differences in the Cooling Island Effect and Influencing Factors of Urban Park Green Spaces in Beijing
by Chao Xu, Wenjing Wang and He Zhu
Buildings 2024, 14(5), 1206; https://doi.org/10.3390/buildings14051206 - 24 Apr 2024
Cited by 9 | Viewed by 1656
Abstract
Urban park green spaces exhibit significant cool island effects, which can effectively mitigate the urban heat environment. Clarifying the characteristics and differences in the cool island effects of urban parks across different spatial gradients within cities is instrumental in identifying potential issues and [...] Read more.
Urban park green spaces exhibit significant cool island effects, which can effectively mitigate the urban heat environment. Clarifying the characteristics and differences in the cool island effects of urban parks across different spatial gradients within cities is instrumental in identifying potential issues and optimizing the structure and resource allocation of park green spaces in a scientifically rational manner. This study focuses on parks within the central urban area of Beijing, utilizing remote sensing image interpretation and land surface temperature (LST) inversion to extract relevant characteristics of park green spaces and the park cool island intensity (PCI) index. Various mathematical and statistical methods including correlation analysis, regression analysis, and cluster analysis are employed to conduct comparative studies across three gradients: within the 3rd ring road, between the 3rd and 5th ring roads, and outside the 5th ring road. The analysis reveals that both park green space characteristics and urban heat island effects exhibit spatial gradient differences, collectively influencing the cool island effects of urban park green spaces. PCI gradually decreases across the three spatial gradients. Cluster analysis identifies four distinct types of parks with different cool island effect characteristics, highlighting the need for optimization and improvement in over half of the parks. Various indicators of park green space characteristics show different correlations with PCI, with variations in correlation strength and thresholds across gradients. The fitting effects of regression equations for each characteristic indicator and PCI gradually worsen from within the 3rd ring road to outside the 5th ring road, with different factors playing important roles across gradients. This study enhances our understanding of the cool island effects of urban park green spaces and facilitates the proposition of differentiated optimization management strategies for urban park green space planning and system construction in different regions. Full article
(This article belongs to the Special Issue Advanced Studies in Urban and Regional Planning)
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16 pages, 3335 KiB  
Article
Intelligent Tire Prototype in Longitudinal Slip Operating Conditions
by Jennifer Bastiaan, Abhishek Chawan, Wookjin Eum, Khalil Alipour, Foroogh Rouhollahi, Mohammad Behroozi and Javad Baqersad
Sensors 2024, 24(9), 2681; https://doi.org/10.3390/s24092681 - 23 Apr 2024
Cited by 5 | Viewed by 1932
Abstract
With the recent advances in autonomous vehicles, there is an increasing need for sensors that can help monitor tire–road conditions and the forces that are applied to the tire. The footprint area of a tire that makes direct contact with the road surface, [...] Read more.
With the recent advances in autonomous vehicles, there is an increasing need for sensors that can help monitor tire–road conditions and the forces that are applied to the tire. The footprint area of a tire that makes direct contact with the road surface, known as the contact patch, is a key parameter for determining a vehicle’s effectiveness in accelerating, braking, and steering at various velocities. Road unevenness from features such as potholes and cracks results in large fluctuations in the contact patch surface area. Such conditions can eventually require the driver to perform driving maneuvers unorthodox to normal traffic patterns, such as excessive pedal depressions or large steering inputs, which can escalate to hazards such as the loss of control or impact. The integration of sensors into the inner liner of a tire has proven to be a promising method for extracting real-time tire-to-road contact patch interface data. In this research, a tire model is developed using Abaqus/CAE and analyzed using Abaqus/Explicit to study the nonlinear behavior of a rolling tire. Strain variations are investigated at the contact patch in three major longitudinal slip driving scenarios, including acceleration, braking, and free-rolling. Multiple vertical loading conditions on the tire are applied and studied. An intelligent tire prototype called KU-iTire is developed and tested to validate the strain results obtained from the simulations. Similar operating and loading conditions are applied to the physical prototype and the simulation model such that valid comparisons can be made. The experimental investigation focuses on the effectiveness of providing usable and reliable tire-to-road contact patch strain variation data under several longitudinal slip operating conditions. In this research, a correlation between FEA and experimental testing was observed between strain shape for free-rolling, acceleration, and braking conditions. A relationship between peak longitudinal strain and vertical load in free-rolling driving conditions was also observed and a correlation was observed between FEA and physical testing. Full article
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21 pages, 26821 KiB  
Article
Long-Term Volumetric Change Estimation of Red Ash Quarry Sites in the Afro-Alpine Ecosystem of Bale Mountains National Park in Ethiopia
by Mohammed Ahmed Muhammed, Abubeker Mohammed Hassen, Temesgen Alemayehu Abera, Luise Wraase, Behailu Legese Ejigu, Binyam Tesfaw Hailu, Georg Miehe and Dirk Zeuss
Remote Sens. 2024, 16(7), 1226; https://doi.org/10.3390/rs16071226 - 30 Mar 2024
Cited by 3 | Viewed by 2900
Abstract
The Bale Mountains National Park (BMNP) in Ethiopia comprises the largest fraction of the Afro-Alpine ecosystem in Africa, which provides vital mountain ecosystem services at local, regional, and global levels. However, the BMNP has been severely threatened by natural and anthropogenic disturbances in [...] Read more.
The Bale Mountains National Park (BMNP) in Ethiopia comprises the largest fraction of the Afro-Alpine ecosystem in Africa, which provides vital mountain ecosystem services at local, regional, and global levels. However, the BMNP has been severely threatened by natural and anthropogenic disturbances in recent decades. In particular, landscape alteration due to human activities such as red ash quarrying has become a common practice in the BMNP, which poses a major environmental challenge by severely degrading the Afro-Alpine ecosystem. This study aims to quantify the long-term volumetric changes of two red ash quarry sites in the BMNP using historical aerial photographs and in situ data, and to assess their impact on the Afro-Alpine ecosystem. The Structure-from-Motion multi-view stereo photogrammetry algorithm was used to reconstruct the three-dimensional landscape for the year 1967 and 1984 while spatial interpolation techniques were applied to generate the current digital elevation models for 2023. To quantify the volumetric changes and landscape alteration of the quarry sites, differences in digital elevation models were computed. The result showed that the volume of resources extracted from the BMNP quarry sites increased significantly over the study period from 1984 to 2023 compared with the period from 1967 to 1984. In general, between 1967 and 2023, the total net surface volume of the quarry sites decreased by 503,721 ± 27,970 m3 and 368,523 ± 30,003 m3, respectively. The extent of the excavated area increased by 53,147 m2 and 45,297 m2 for Site 1 and 2, respectively. In terms of habitat loss, major gravel road construction inside the BMNP resulted in the reduction of Afro-Alpine vegetation by 476,860 m2, ericaceous vegetation by 403,806 m2 and Afromontane forest by 493,222 m2 with associated decline in species diversity and density. The excavation and gravel road construction have contributed to the degradation of the Afro-Alpine ecosystem, especially the endemic Lobelia rhynchopetalum on the quarry sites and roads. If excavation continues at the same rate as in the last half century, it can threaten the whole mountain ecosystem of the National Park and beyond, highlighting the importance of preventing these anthropogenic changes and conserving the remaining Afro-Alpine ecosystem. Full article
(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems II)
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