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21 pages, 8908 KiB  
Article
Spatiotemporal Heterogeneity and Zonal Adaptation Strategies for Agricultural Risks of Compound Dry and Hot Events in China’s Middle Yangtze River Basin
by Yonggang Wang, Jiaxin Wang, Daohong Gong, Mingjun Ding, Wentao Zhong, Muping Deng, Qi Kang, Yibo Ding, Yanyi Liu and Jianhua Zhang
Remote Sens. 2025, 17(16), 2892; https://doi.org/10.3390/rs17162892 - 20 Aug 2025
Abstract
Compound dry and hot events or extremes (CDHEs) have emerged as major climatic threats to agricultural production and food security in the middle reaches of the Yangtze River Basin (MRYRB), a critical grain-producing region in China. However, agricultural risks associated with CDHEs, incorporating [...] Read more.
Compound dry and hot events or extremes (CDHEs) have emerged as major climatic threats to agricultural production and food security in the middle reaches of the Yangtze River Basin (MRYRB), a critical grain-producing region in China. However, agricultural risks associated with CDHEs, incorporating both natural and socio-economic factors, remain poorly understood in this area. Using a Hazard-Exposure-Vulnerability (HEV) framework integrated with a weighting quantification method and supported by remote sensing technology and integrated geographic data, we systematically assessed the spatiotemporal dynamics of agricultural CDHE risks and corresponding crop responses in the MRYRB from 2000 to 2019. Results indicated an increasing trend in agricultural risks across the region, particularly in the Poyang Lake Plain (by 21.9%) and Jianghan Plain (by 9.9%), whereas a decreasing trend was observed in the Dongting Lake Plain (by 15.2%). Spatial autocorrelation analysis further demonstrated a significant negative relationship between gross primary production (GPP) and high agricultural risks of CDHEs, with a spatial concordance rate of 52.6%. These findings underscore the importance of incorporating CDHE risk assessments into agricultural management. To mitigate future risks, we suggest targeted adaptation strategies, including strengthening water resource management and developing multi-source irrigation systems in the Poyang Lake Plain, Dongting Lake, and the Jianghan Plain, improving hydraulic infrastructure and water source conservation capacity in northern and southwestern Hunan Province, and prioritizing regional risk-based adaptive planning to reduce agricultural losses. Our findings rectify the longstanding assumption that hydrological abundance inherently confers robust resistance to compound drought and heatwave stresses in lacustrine plains. Full article
(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)
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11 pages, 707 KiB  
Article
Genomic Investigation of Bacterial Co-Infection in Southern Pudu (Pudu puda) with Fatal Outcome: Application of Forensic Microbiology in Wildlife Impacted by Anthropogenic Disasters
by Valentina Aravena-Ramírez, Edhnita Inostroza-Muñoz, Fredy Riquelme, César Mellado, Nilton Lincopan, Paula Aravena and Danny Fuentes-Castillo
Animals 2025, 15(16), 2435; https://doi.org/10.3390/ani15162435 - 20 Aug 2025
Abstract
The southern pudu (Pudu puda) faces significant threats from anthropogenic activities and infectious diseases. Using whole-genome sequencing (WGS) and forensic microbiology research, we describe a triple bacterial co-infection in a southern pudu impacted by wildfire disasters. The deer presented infected burn [...] Read more.
The southern pudu (Pudu puda) faces significant threats from anthropogenic activities and infectious diseases. Using whole-genome sequencing (WGS) and forensic microbiology research, we describe a triple bacterial co-infection in a southern pudu impacted by wildfire disasters. The deer presented infected burn wounds on the extremities and dog bite wounds in the lumbosacral region, from which a multidrug-resistant CTX-M-1-producing Escherichia coli sequence type (ST) ST224 and a Klebsiella oxytoca ST145 were isolated, respectively. The patient died 13 days after admission in a wildlife rehabilitation center. During the necropsy, a sample from intracardiac blood was collected, and WGS analyses confirmed systemic dissemination of an E. coli ST224 clone. The broad virulome (adhesins, invasins, toxins, and immune evasion genes) and resistome against beta-lactams (blaCTX-M-1), aminoglycosides [aac(3)-IId, aph(3′)-Ia, aph(3″)-Ib, aph(6)-Id], macrolides [mph(A)], sulfonamides (sul2), trimethoprim (dfrA17), and fluoroquinolones (gyrA and parC mutations) of E. coli ST224 contributed to the treatment failure and death of the wild animal. Additionally, an oval nodule was identified in the abdominal cavity caused by Acinetobacter baumannii ST1365, the first WGS-confirmed report in wildlife. This study highlights the value of applying forensic microbiology and WGS to investigate and understand One Health pathogens threatening wildlife impacted by natural and anthropogenic disasters. Full article
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26 pages, 1769 KiB  
Article
Identification of Boundaries of Measurements for City Environmental Quality
by Hasni Gayathma Gunasekara, Kamani Sylva and Sardhanee Dias
Urban Sci. 2025, 9(8), 328; https://doi.org/10.3390/urbansci9080328 - 19 Aug 2025
Abstract
Cities have become the largest consumers of resources and contributors to pollution due to urbanization. Therefore, measuring quality and maintaining standards have become crucial, as the boundaries of measurements for a city’s environmental quality are vague. This research study followed a qualitative approach [...] Read more.
Cities have become the largest consumers of resources and contributors to pollution due to urbanization. Therefore, measuring quality and maintaining standards have become crucial, as the boundaries of measurements for a city’s environmental quality are vague. This research study followed a qualitative approach to verify the factors affecting city environmental quality and to identify the boundaries of measurements using Sri Lankan cities as a case study. Data analysis was conducted using a thematic analysis approach, which adhered to the qualitative nature of the research. Findings revealed that seven main factors—energy consumption, water consumption, material and resource consumption, land utilization, disaster resilience, education, and governance—play a significant role in maintaining a city’s environmental quality. It was revealed that measuring boundaries can vary according to individual units (such as household, industrial, or commercial buildings) or city boundaries, in order to maintain quality standards. The findings revealed significant considerations for environmental quality performance, highlighting the influence of urban planning, governance, and public awareness on environmental sustainability outcomes in cities. Notably, this study contributes to a deeper understanding of how environmental quality intersects with social well-being in urban planning, affecting the quality of life and equitable access to urban resources. Full article
(This article belongs to the Special Issue Sustainable Urbanization, Regional Planning and Development)
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32 pages, 15059 KiB  
Article
Impacts of Land Use Patterns on Flood Risk in the Chang-Zhu-Tan Urban Agglomeration, China
by Ting Zhang, Kai Wu, Xiulian Wang, Xinai Li, Long Li and Longqian Chen
Remote Sens. 2025, 17(16), 2889; https://doi.org/10.3390/rs17162889 - 19 Aug 2025
Abstract
Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan [...] Read more.
Flood risk assessment is an effective tool for disaster prevention and mitigation. As land use is a key factor influencing flood disasters, studying the impact of different land use patterns on flood risk is crucial. This study evaluates flood risk in the Chang-Zhu-Tan (CZT) urban agglomeration by selecting 17 socioeconomic and natural environmental factors within a risk assessment framework encompassing hazard, exposure, vulnerability, and resilience. Additionally, the Patch-Generating Land Use Simulation (PLUS) and multilayer perceptron (MLP)/Bayesian network (BN) models were coupled to predict flood risks under three future land use scenarios: natural development, urban construction, and ecological protection. This integrated modeling framework combines MLP’s high-precision nonlinear fitting with BN’s probabilistic inference, effectively mitigating prediction uncertainty in traditional single-model approaches while preserving predictive accuracy and enhancing causal interpretability. The results indicate that high-risk flood zones are predominantly concentrated along the Xiang River, while medium-high- and medium-risk areas are mainly distributed on the periphery of high-risk zones, exhibiting a gradient decline. Low-risk areas are scattered in mountainous regions far from socioeconomic activities. Simulating future land use using the PLUS model with a Kappa coefficient of 0.78 and an overall accuracy of 0.87. Under all future scenarios, cropland decreases while construction land increases. Forestland decreases in all scenarios except for ecological protection, where it expands. In future risk predictions, the MLP model achieved a high accuracy of 97.83%, while the BN model reached 87.14%. Both models consistently indicated that the flood risk was minimized under the ecological protection scenario and maximized under the urban construction scenario. Therefore, adopting ecological protection measures can effectively mitigate flood risks, offering valuable guidance for future disaster prevention and mitigation strategies. Full article
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25 pages, 5199 KiB  
Article
Analysis and Proposal of Strategies for the Management of Drone Swarms Through Wi-Fi Technologies
by Guido Betcher-Sbrolla, Elena Lopez-Aguilera and Eduard Garcia-Villegas
Drones 2025, 9(8), 584; https://doi.org/10.3390/drones9080584 - 18 Aug 2025
Abstract
The main purpose of this paper is to explore the benefits of combining two radio interfaces onboard an unmanned aerial vehicle (UAV) to communicate with a ground control station (GCS) and other UAVs inside a swarm. The goals are to use the IEEE [...] Read more.
The main purpose of this paper is to explore the benefits of combining two radio interfaces onboard an unmanned aerial vehicle (UAV) to communicate with a ground control station (GCS) and other UAVs inside a swarm. The goals are to use the IEEE 802.11ah standard (Wi-Fi HaLow) combined with the IEEE 802.11ax specification (Wi-Fi 6/6E) to enable real-time video transmission from UAVs to the GCS. While airport runway inspection serves as the proof-of-concept use case, the proposed multi-hop architectures apply to other medium-range UAV operations (i.e., a few kilometers) requiring real-time video transmission, such as natural disaster relief and agricultural monitoring. Several scenarios in which a UAV swarm performs infrastructure inspection are emulated. During the missions, UAVs have to send real-time video to the GCS through a multi-hop network when some damage in the infrastructure is found. The different scenarios are studied by means of emulation. Emulated scenarios are defined using different network architectures and radio technologies. Once the emulations finish, different performance metrics related to time, energy and the multi-hop video transmission network are analyzed. The capacity of a multi-hop network is a limiting factor for the transmission of high-quality video. As a first contribution, an expression to find this capacity from distances between UAVs in the emulated scenario is found using the NS-3 simulator. Then, this expression is applied in the algorithms in charge of composing the multi-hop network to offer on-demand quality video. However, the main contribution of this work lies in the development of efficient mechanisms for exchanging control information between UAVs and the GCS, and for forming a multi-hop network to transmit video. Full article
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15 pages, 3219 KiB  
Article
Dynamic Risk Assessment of Collapse Geological Hazards on Highway Slopes in Basalt Regions During Rainy Seasons
by Lihui Qian, Peng Zhao and Zhongshui Li
Atmosphere 2025, 16(8), 978; https://doi.org/10.3390/atmos16080978 - 17 Aug 2025
Viewed by 137
Abstract
Anchored in the four-factor theory of natural hazard risk, this study presents a dynamic risk assessment of collapse geological hazards (CGHs) using the S3K highway slope in Changbai Korean Autonomous County, China, as a case study. Building on previous research, the methodological framework [...] Read more.
Anchored in the four-factor theory of natural hazard risk, this study presents a dynamic risk assessment of collapse geological hazards (CGHs) using the S3K highway slope in Changbai Korean Autonomous County, China, as a case study. Building on previous research, the methodological framework consists of three sequential stages: (1) critical indicators for CGHs in basalt regions are identified, with iron-staining anomalies—a hallmark of such terrains—innovatively integrated as a slope stability metric; (2) a system dynamics (SD) model is developed in Vensim to quantify dynamic feedback mechanisms, focusing on the “rock weathering–rainfall triggering–slope instability” nexus, and time-varying parameters are introduced to enable monthly-scale risk prediction; and (3) a 500 m × 500 m grid system is established using ArcGIS 10.4, and a computer program is developed to achieve SD-GIS coupling and calculate grid parameters. The information value method is then employed to determine risk thresholds, thereby completing CGH risk assessment and prediction. The results indicate that over the next five years, high-risk areas will exhibit spatial agglomeration when monthly rainfall exceeds approximately 130 mm (July and August). Conversely, when monthly rainfall is below around 60 mm, the entire region will display low or no risk. Model simulations reveal that risks during the rainy season over the next five years will exhibit insignificant variability, prompting simplification of the resultant cartography. Field validation corroborates the robustness of the model. This research overcomes the primary limitations of conventional static assessment models by improving the dynamic predictability and the applicability to basalt terrains. The integrated SD-GIS framework presents a novel methodological paradigm for dynamic CGH risk analysis and offers support for the formulation of targeted disaster mitigation strategies. Full article
(This article belongs to the Section Climatology)
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20 pages, 18751 KiB  
Article
Identifying Slope Hazard Zones in Central Taiwan Using Emerging Hot Spot Analysis and NDVI
by Kieu Anh Nguyen, Yi-Jia Jiang and Walter Chen
Sustainability 2025, 17(16), 7428; https://doi.org/10.3390/su17167428 - 17 Aug 2025
Viewed by 119
Abstract
Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential [...] Read more.
Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article
(This article belongs to the Special Issue Landslide Hazards and Soil Erosion)
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17 pages, 2002 KiB  
Article
Identification of Critical Transmission Sections Considering N-K Contingencies Under Extreme Events
by Xiongguang Zhao, Xu Ling, Mingyu Yan, Yi Dong, Mingtao He and Yirui Zhao
Energies 2025, 18(16), 4342; https://doi.org/10.3390/en18164342 - 14 Aug 2025
Viewed by 180
Abstract
Monitoring critical transmission sections is essential for ensuring the operational security of power grids. This paper proposes a systematic method to identify critical transmission sections using the maximum flow–minimum cut theorem. The approach begins by representing the power grid as an undirected graph [...] Read more.
Monitoring critical transmission sections is essential for ensuring the operational security of power grids. This paper proposes a systematic method to identify critical transmission sections using the maximum flow–minimum cut theorem. The approach begins by representing the power grid as an undirected graph and identifying its hanging nodes. The network is then partitioned into several undirected subgraphs based on identified cut points. Each subgraph is transformed into a flow network according to actual power flow data. An efficient minimum cut set search algorithm is developed to locate potential transmission sections. To assess the risk under extreme conditions, a mixed-integer optimization model is formulated to select sections that are vulnerable to overload-induced tripping during N-K line outages caused by natural disasters. Simulation results on the IEEE RTS 24-bus and IEEE 39-bus systems validate the effectiveness and applicability of the proposed method. Full article
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15 pages, 852 KiB  
Article
Ecological Emotions and Environmental Education: Voices of Youth in a Mediterranean Region
by Irida Tsevreni, Anna Maria Kali and Fotini Bonoti
Societies 2025, 15(8), 225; https://doi.org/10.3390/soc15080225 - 14 Aug 2025
Viewed by 141
Abstract
This study examines climate anxiety and perceptions regarding the future among Generation Z youth living in a Mediterranean region vulnerable to climate-related natural disasters. It also explores their perceptions of the content and effectiveness of environmental education. A quantitative survey based on an [...] Read more.
This study examines climate anxiety and perceptions regarding the future among Generation Z youth living in a Mediterranean region vulnerable to climate-related natural disasters. It also explores their perceptions of the content and effectiveness of environmental education. A quantitative survey based on an online questionnaire was conducted with 93 undergraduate students and future environmental education teachers in Greece. We investigated their ecological emotions, thoughts about the future, and their ideas on the orientation of environmental education content. The results reveal (a) a high level of climate anxiety among participants, (b) pessimistic ideas about the future, and (c) the need for a holistic pedagogical approach to environmental education theory and praxis. Full article
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29 pages, 9110 KiB  
Article
Wind Field Retrieval from Fengyun-3E Radar Based on a Backpropagation Neural Network
by Zhengxuan Zhao, Fang Pang, George P. Petropoulos, Yansong Bao, Qing Xiao, Yuanyuan Wang, Shiqi Li, Wanyue Gao and Tianhao Wang
Remote Sens. 2025, 17(16), 2813; https://doi.org/10.3390/rs17162813 - 14 Aug 2025
Viewed by 181
Abstract
Ocean surface wind fields are crucial for marine environmental research and applications in weather forecasting, ocean disaster monitoring, and climate change studies. However, traditional wind retrieval methods often struggle with modeling complexity and ambiguity due to the nonlinear nature of geophysical model functions [...] Read more.
Ocean surface wind fields are crucial for marine environmental research and applications in weather forecasting, ocean disaster monitoring, and climate change studies. However, traditional wind retrieval methods often struggle with modeling complexity and ambiguity due to the nonlinear nature of geophysical model functions (GMFs), leading to increased computational costs and reduced accuracy. To tackle these challenges, this study establishes a sea surface wind field retrieval model employing a backpropagation (BP) neural network, which integrates multi-angular observations from the Wind Radar (WindRAD) sensor aboard the Fengyun-3E (FY-3E) satellite. Experimental results show that the proposed model achieves high precision in retrieving both wind speed and direction. The wind speed model achieves a root-mean-square error (RMSE) of 1.20 m/s for the training set and 1.00 m/s for the selected test set when using ERA5 data as the reference, outperforming the official WindRAD products. For wind direction, the model attains an RMSE of 23.99° on the training set and 24.58° on the test set. Independent validation using Tropical Atmosphere Ocean (TAO) buoy observations further confirms the model’s effectiveness, yielding an RMSE of 1.29 m/s for wind speed and 24.37° for wind direction, also surpassing official WindRAD products. The BP neural network effectively captures the nonlinear relationship between wind parameters and radar backscatter signals, showing significant advantages over traditional methods and maintaining good performance across different wind speeds, particularly in the moderate range (4–10 m/s). In summary, the method proposed herein significantly enhances wind field retrieval accuracy from space; it has the potential to optimize satellite wind field products and improve global wind monitoring and meteorological forecasting. Full article
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27 pages, 4588 KiB  
Article
Remote Sensing as a Sentinel for Safeguarding European Critical Infrastructure in the Face of Natural Disasters
by Miguel A. Belenguer-Plomer, Omar Barrilero, Paula Saameño, Inês Mendes, Michele Lazzarini, Sergio Albani, Naji El Beyrouthy, Mario Al Sayah, Nathan Rueche, Abla Mimi Edjossan-Sossou, Tommaso Monopoli, Edoardo Arnaudo and Gianfranco Caputo
Appl. Sci. 2025, 15(16), 8908; https://doi.org/10.3390/app15168908 - 13 Aug 2025
Viewed by 248
Abstract
Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how [...] Read more.
Critical infrastructure, such as transport networks, energy facilities, and urban installations, is increasingly vulnerable to natural hazards and climate change. Remote sensing technologies, namely satellite imagery, offer solutions for monitoring, evaluating, and enhancing the resilience of these vital assets. This paper explores how applications based on synthetic aperture radar (SAR) and optical satellite imagery contribute to the protection of critical infrastructure by enabling near real-time monitoring and early detection of natural hazards for actionable insights across various European critical infrastructure sectors. Case studies demonstrate the integration of remote sensing data into geographic information systems (GISs) for promoting situational awareness, risk assessment, and predictive modeling of natural disasters. These include floods, landslides, wildfires, and earthquakes. Accordingly, this study underlines the role of remote sensing in supporting long-term infrastructure planning and climate adaptation strategies. The presented work supports the goals of the European Union (EU-HORIZON)-sponsored ATLANTIS project, which focuses on strengthening the resilience of critical EU infrastructures by providing authorities and civil protection services with effective tools for managing natural hazards. Full article
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9 pages, 1214 KiB  
Proceeding Paper
Multi-Criteria Evaluation Model for Campus Disaster Resilience Under Extreme Climate Conditions
by Yue Sun, Xiaohe Bai and Yifei Ouyang
Eng. Proc. 2025, 103(1), 12; https://doi.org/10.3390/engproc2025103012 - 12 Aug 2025
Viewed by 146
Abstract
As global climate disasters become frequent, colleges and universities in disaster-prone areas are facing problems in disaster response and post-disaster recovery. Based on the theory of urban resilience, we case-studied nine universities in Conghua District, Guangzhou City, China, using the Delphi method and [...] Read more.
As global climate disasters become frequent, colleges and universities in disaster-prone areas are facing problems in disaster response and post-disaster recovery. Based on the theory of urban resilience, we case-studied nine universities in Conghua District, Guangzhou City, China, using the Delphi method and the analytic hierarchy process (AHP). We constructed a multi-criteria evaluation model for campus disaster prevention resilience under extreme climate conditions. By identifying 4 facets and 16 criteria, 9 colleges were ranked. The distance of the college from the city center, the terrain and natural environment of the college, the level of the college, and the ownership of the college affected their ranking The results of this study help campus managers and planners integrate campus resilience plans into campus planning, institutional regulations, campus site selection, and campus construction in the future. Full article
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34 pages, 1262 KiB  
Review
Deep Learning-Based Fusion of Optical, Radar, and LiDAR Data for Advancing Land Monitoring
by Yizhe Li and Xinqing Xiao
Sensors 2025, 25(16), 4991; https://doi.org/10.3390/s25164991 - 12 Aug 2025
Viewed by 281
Abstract
Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or [...] Read more.
Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or limited spectral information (LiDAR) often hinder comprehensive and robust characterization of land surfaces. Recent advancements in synergistic harmonization technology for land monitoring, along with enhanced signal processing techniques and the integration of machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize the comprehensive applications of synergistic harmonization technology for geosciences, with a particular focus on recent advancements. Most of the existing review papers focus on the application of a single technology in a specific area, highlighting the need for a comprehensive review that integrates synergistic harmonization technology. This review provides a comprehensive review of advancements in land monitoring achieved through the synergistic harmonization of optical, radar, and LiDAR satellite technologies. It details the unique strengths and weaknesses of each sensor type, highlighting how their integration overcomes individual limitations by leveraging complementary information. This review analyzes current data harmonization and preprocessing techniques, various data fusion levels, and the transformative role of machine learning and deep learning algorithms, including emerging foundation models. Key applications across diverse domains such as land cover/land use mapping, change detection, forest monitoring, urban monitoring, agricultural monitoring, and natural hazard assessment are discussed, demonstrating enhanced accuracy and scope. Finally, this review identifies persistent challenges such as technical complexities in data integration, issues with data availability and accessibility, validation hurdles, and the need for standardization. It proposes future research directions focusing on advanced AI, novel fusion techniques, improved data infrastructure, integrated “space–air–ground” systems, and interdisciplinary collaboration to realize the full potential of multi-sensor satellite data for robust and timely land surface monitoring. Supported by deep learning, this synergy will improve our ability to monitor land surface conditions more accurately and reliably. Full article
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29 pages, 12645 KiB  
Article
The IoRT-in-Hand: Tele-Robotic Echography and Digital Twins on Mobile Devices
by Juan Bravo-Arrabal, Zhuoqi Cheng, J. J. Fernández-Lozano, Jose Antonio Gomez-Ruiz, Christian Schlette, Thiusius Rajeeth Savarimuthu, Anthony Mandow and Alfonso García-Cerezo
Sensors 2025, 25(16), 4972; https://doi.org/10.3390/s25164972 - 11 Aug 2025
Viewed by 466
Abstract
The integration of robotics and mobile networks (5G/6G) through the Internet of Robotic Things (IoRT) is revolutionizing telemedicine, enabling remote physician participation in scenarios where specialists are scarce, where there is a high risk to them, such as in conflicts or natural disasters, [...] Read more.
The integration of robotics and mobile networks (5G/6G) through the Internet of Robotic Things (IoRT) is revolutionizing telemedicine, enabling remote physician participation in scenarios where specialists are scarce, where there is a high risk to them, such as in conflicts or natural disasters, or where access to a medical facility is not possible. Nevertheless, touching a human safely with a robotic arm in non-engineered or even out-of-hospital environments presents substantial challenges. This article presents a novel IoRT approach for healthcare in or from remote areas, enabling interaction between a specialist’s hand and a robotic hand. We introduce the IoRT-in-hand: a smart, lightweight end-effector that extends the specialist’s hand, integrating a medical instrument, an RGB camera with servos, a force/torque sensor, and a mini-PC with Internet connectivity. Additionally, we propose an open-source Android app combining MQTT and ROS for real-time remote manipulation, alongside an Edge–Cloud architecture that links the physical robot with its Digital Twin (DT), enabling precise control and 3D visual feedback of the robot’s environment. A proof of concept is presented for the proposed tele-robotic system, using a 6-DOF manipulator with the IoRT-in-hand to perform an ultrasound scan. Teleoperation was conducted over 2300 km via a 5G NSA network on the operator side and a wired network in a laboratory on the robot side. Performance was assessed through human subject feedback, sensory data, and latency measurements, demonstrating the system’s potential for remote healthcare and emergency applications. The source code and CAD models of the IoRT-in-hand prototype are publicly available in an open-access repository to encourage reproducibility and facilitate further developments in robotic telemedicine. Full article
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26 pages, 717 KiB  
Article
Exploring the Interplay Between Individual and Organisational Resilience in the Construction Sector: A Comprehensive Analysis
by M. Mitansha and Regan Potangaroa
Sustainability 2025, 17(16), 7229; https://doi.org/10.3390/su17167229 - 10 Aug 2025
Viewed by 308
Abstract
Environmental complexities and continuously evolving scenarios like natural disaster, political instabilities, pandemics have become a major challenge for construction organisations. Since no system or organisation can be designed to anticipate all possible risks, resilience has become a fundamental necessity. To achieve resilience at [...] Read more.
Environmental complexities and continuously evolving scenarios like natural disaster, political instabilities, pandemics have become a major challenge for construction organisations. Since no system or organisation can be designed to anticipate all possible risks, resilience has become a fundamental necessity. To achieve resilience at the organisational level, it is vital to consider, assess and utilise individual resilience of employees as they constitute the core of the organisational system. Though the concept of individual resilience has been extensively applied across a plethora of academic fields, there is a lack of unified understanding of the relationship between individual resilience and organisational resilience. While prior research has acknowledged both constructs independently, their interplay within high-risk sectors such as construction remains underexplored. Thus, the current study employs qualitative research methods, including case studies and semi-structured interviews with 20 construction professionals from various construction organisations of New Zealand. The collected data were analysed through NVivo to identify crucial factors and mechanisms involved between resilient individuals and resilient organisations. The results include mediating factors and a relevant model that can help in establishing the link between individual resilience and organisational resilience of the New Zealand construction industry. The study contributes theoretically by re-conceptualising resilience as a dynamic-mediated construct, and practically by offering targeted strategies for resilience-building within project-based environments. Future studies may explore the gap between resourcefulness and resilience to formulate robust plans and policies to support organisations, government, and other stakeholders during setbacks. Full article
(This article belongs to the Special Issue Analysis on Real-Estate Marketing and Sustainable Civil Engineering)
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