Search Results (37)

Climate change has increased the frequency of hotter droughts and forest decline processes, highlighting the need for monitoring methodologies capable of detecting loss of vigor on the individual-tree scale. Pinus pseudostrobus is an economically important species in the temperate forests of the indigenous community of Nuevo San Juan Parangaricutiro, Michoacán. The aim of this study was to evaluate the elevational and thermal factors associated with the spatial and temporal variability of P. pseudostrobus vigor by integrating multispectral, LiDAR, and land surface temperature (LST) data. Five multispectral flights were conducted using unmanned aerial vehicles (UAVs) between June 2023 and June 2024, along with one LiDAR flight in April 2024, across an elevational gradient ranging from 2150 to 2920 m. High-resolution orthomosaics, NDVI and LCI indices, a canopy height model, and an object-based classification using Random Forest were generated. LST was obtained from Landsat 8–9 imagery in Google Earth Engine using the Statistical Mono-Window algorithm. Spectral, thermal, and elevation values were extracted at the individual-tree level. The classification achieved an overall accuracy of 69.6% and enabled the identification of 28,585 P. pseudostrobus individuals. NDVI and LCI varied significantly among periods, elevations, and their interaction. The lowest values of vigor were recorded in lower elevation areas, particularly in June 2023, April 2024, and June 2024. In addition, both indices decreased as LST increased, showing strong negative relationships for NDVI (R² = 0.96) and LCI (R² = 0.86). Individuals located at the lower elevational limit showed greater vulnerability to thermal stress. Full article

This study evaluated a cost-effective UAV-based multi-sensor approach for characterizing river corridor conditions during and after moderate floods in two mountain river corridors in Northern Utah, USA: the Logan River and Blacksmith Fork River. These study reaches included urban, rural, and agricultural areas, hydraulic structures and bridges, and fish passage structures. A DJI Matrice 300 UAV was used with two separate payloads: an AgEagle Altum-PT multispectral camera and an R3 Pro V2 two-return LiDAR system. The workflow included UAV flight planning and data collection, post-processing of the multi-spectral and LiDAR sensor data, spatial resolution and accuracy assessment, and interpretation of the resultant data. The multi-spectral post-processing produced pansharpened orthomosaics with a spatial resolution of 0.0432 m, while the UAV LiDAR produced DSM/DTM products at 0.05 m resolution. LiDAR accuracy assessment showed vertical RMSE values of approximately 0.0602 m for the Blacksmith Fork and 0.0782 m for the Logan River. The results showed that multispectral imagery and 2-band LiDAR provided a cost-effective means for detailed remote sensing with each sensor providing complementary information for flood and river corridor assessment. Multispectral imagery supported interpretation of flood extent, vegetation condition, relative turbidity, and thermal patterns, while LiDAR captured terrain and surface features such as banks, levees, floodplain surfaces, channel modifications, and structures. The integrated datasets supported maximum flood extent mapping and flood-level estimation. These datasets can support reach-scale hydraulic modeling, catchment hydrology, river corridor ecology, floodplain conditions, and real-time monitoring of floods, in addition to quantification of flood hazards or post-flood impacts for municipalities and insurers. Full article

Unmanned aerial vehicle (UAV) swarms generate massive multi-modal sensor data streams from onboard payloads such as RGB cameras, LiDAR, and thermal sensors. Large language models (LLMs) can interpret these data for natural language-based swarm coordination. However, deploying LLMs directly on resource-constrained UAV nodes faces a critical bottleneck. Long-context textual sensor logs (e.g., continuous status reports with GPS, altitude, and detection events) lead to high prefill latency. Existing distributed inference frameworks suffer from load imbalance and pipeline bubbles, violating real-time mission requirements. To address these issues, we propose Orion, an edge-only collaborative inference framework for LLM-based sensor data processing in heterogeneous UAV swarms. Orion incorporates three innovations: (1) optimal model partitioning via dynamic programming, (2) adaptive sequence partitioning that balances causal attention load across pipeline stages, and (3) a predictive decoding mechanism that speculatively generates the first token during idle intervals. Experiments on a comprehensive simulation framework ((using Meta’s Llama-2 (Large Language Model Meta AI)) 7B/13B/70B and simulated UAV swarm sensor traces) show that Orion reduces prefill latency by 81% (7B) and 78% (13B) compared to the best cloud–UAV baseline. Among the evaluated frameworks, Orion is the only framework capable of running the full 70B model on memory-constrained UAV nodes, enabling real-time sensor-aware LLM inference. Full article

Unmanned aerial vehicles (UAVs) are increasingly being used as diagnostic platforms in electricity transmission and distribution, enabling safer and faster inspections compared to manual climbing operations or manned aerial support. This article presents an implementation-oriented inspection process that integrates RGB imaging, infrared (IR) thermography and (optionally) LiDAR documentation for critical energy infrastructure and photovoltaic (PV) installations. The survey consists of two stages: a preliminary stage under controlled conditions and an operational stage in a real-world environment, limited only by UAV flight restrictions. Thermal measurements are recorded in radiometric formats and analyzed using polygon- and profile-based tools to identify temperature anomalies (hot spots) and support maintenance escalation decisions. This manuscript presents standardized sample templates for mission logs, QA/QC activities, and anomaly lists, intended to support reproducible data collection in future studies. The proposed process supports predictive maintenance by enabling repeatable inspections, archive-based trend analysis, and integration with asset management processes, while minimizing operational risk and avoiding power outages when technically feasible. Full article

Low-level jets (LLJs) are common strong wind structures in the atmospheric boundary layer. They have important impacts on aviation safety, wind energy utilization and pollutant dispersion. However, the formation mechanisms of LLJs are complex. Traditional parameterization schemes and numerical models still show limitations in forecasting LLJ occurrence and resolving their structures. In this study, wind lidar, near-surface turbulence and gradient meteorological observations from the Semi-Arid Climate and Environment Observatory of Lanzhou University are combined to construct a multi-source low-level dataset. Four processing modules are designed, including multi-source data fusion, turbulence preprocessing, turbulence intermittency metrics and LLJ identification, to overcome the constraints of single-platform observations. Six commonly used machine learning algorithms (LightGBM, XGBoost, CatBoost, K-nearest neighbors, Balanced Random Forest, and ExtraTrees) are compared. A two-stage classification–regression framework is then adopted. LightGBM is used for LLJ occurrence, and CatBoost is used for LLJ height and intensity, to build an LLJ-2Stage prediction system. The system performs automatic LLJ identification and predicts jet intensity and core height. For LLJ occurrence, the harmonic-mean F1-score of precision and recall reaches 0.820. The coefficient of determination R² is 0.643 for height prediction and 0.794 for intensity prediction. Both the classification and regression parts show good accuracy and stability. The SHAP method is further applied to assess model interpretability and to identify key predictors that control LLJ occurrence, height and intensity. Results indicate that thermal variables, such as net radiation (Rn) and sensible heat flux (H), dominate LLJ occurrence and structural changes. The strength of turbulence intermittency provides valuable supplementary information for locating the LLJ core height. Two representative nocturnal LLJ cases further show a consistent near-surface evolution during the LLJ period, with enhanced TKE and reduced H, followed by a gradual recovery after decay, while Rn remains persistently low, consistent with the SHAP-indicated effects. The proposed framework predicts LLJ occurrence and structural evolution and is of significance for improving understanding of boundary layer processes, air-pollution control, wind energy utilization and low-level aviation safety. Full article

Remediated landfills require long-term monitoring due to ongoing processes such as settlement, water infiltration, leachate migration, and biogas emissions, which may lead to cover degradation and environmental risks. Traditional ground-based inspections are often time-consuming, costly, and limited in terms of spatial coverage. This study presents the application of Unmanned Aerial Vehicle (UAV)-based remote sensing methods for the structural assessment of a remediated landfill. A multi-sensor approach was employed, combining geometric data (Light Detection and Ranging (LiDAR) and photogrammetry), hydrological modeling (surface water accumulation and runoff), multispectral imaging, and thermal data. The results showed that subsidence-induced depressions modified surface drainage, leading to water accumulation, concentrated runoff, and vegetation stress. Multispectral imaging successfully identified zones of persistent instability, while UAV thermal imaging detected a distinct leachate-related anomaly that was not visible in red–green–blue (RGB) or multispectral data. By integrating geometric, hydrological, spectral, and thermal information, this paper demonstrates practical applications of remote sensing data in detecting cover degradation on remediated landfills. Compared to traditional methods, UAV-based monitoring is a low-cost and repeatable approach that can cover large areas with high spatial and temporal resolution. The proposed approach provides an effective tool for post-closure landfill management and can be applied to other engineered earth structures. Full article

Dynamic social and legal transformations drive technological innovation and the transition of energy and heating sectors toward renewable sources and higher efficiency. Ensuring the reliable operation of these systems requires regular inspections, fault detection, and infrastructure maintenance. Unmanned Aerial Vehicles (UAVs) are increasingly being used for monitoring and diagnostics of photovoltaic and wind farms, power transmission lines, and urban heating networks. Based on literature from 2015 to 2025 (Scopus database), this review compares UAV platforms, sensors, and inspection methods, including thermal, RGB/multispectral, LiDAR, and acoustic, highlighting current challenges. The analysis of legal regulations and resulting operational limitations for UAVs, based on the frameworks of the EU, the US, and China, is also presented. UAVs offer high-resolution data, rapid coverage, and cost reduction compared to conventional approaches. However, they face limitations related to flight endurance, weather sensitivity, regulatory restrictions, and data processing. Key trends include multi-sensor integration, coordinated multi-UAV missions, on-board edge-AI analytics, digital twin integration, and predictive maintenance. The study highlights the need to develop standardised data models, interoperable sensor systems, and legal frameworks that enable autonomous operations to advance UAV implementation in energy and heating infrastructure management. Full article

We present a tunable diode-laser absorption spectroscopy (TDLAS) system operating at 1.5711 µm for CO₂ gas concentration measurements. The system can operate in either a traditional direct-mode (dTDLAS) sawtooth wavelength scan or a recently demonstrated wavelength-toggled single laser differential-absorption lidar (WTSL-DIAL) mode using precompensated current pulses. The use of such precompensated pulses offsets the slow thermal constants of the diode laser, leading to fast toggling between ON and OFF-resonance wavelengths. A short measurement time is indeed pivotal for atmospheric sensing, where ambient factors, such as turbulence or mechanical vibrations, would otherwise deteriorate sensitivity, precision and accuracy. Having a system able to operate in both modes allows us to benchmark the novel experimental procedure against the well-established dTDLAS method. The theory behind the new WTSL-DIAL method is also expanded to include the periodicity of the current modulation, fundamental for the calculation of the OFF-resonance wavelength. A two-detector scheme is chosen to suppress the influence of laser intensity fluctuations in time (1/f noise), and its performance is eventually benchmarked against a one-detector approach. The main difference between dTDLAS and WTSL-DIAL, in terms of signal processing, lies in the fact that while the former requires time-consuming data processing, which limits the maximum update rate of the instrument, the latter allows for computationally simpler and faster concentration readings. To compare other performance metrics, the update rate was kept at 2 kHz for both methods. To analyze the dTDLAS data, a four-parameter Lorentzian fit was performed, where the fitting function comprised the six main neighboring absorption lines centered around 1.5711 µm. Similarly, the spectral overlap between the same lines was considered when analyzing the WTSL-DIAL data in real time. Our investigation shows that, for the studied time intervals, the WTSL-DIAL approach is 3.65 ± 0.04 times more precise; however, the dTDLAS-derived CO₂ concentration measurements are less subject to systematic errors, in particular pressure-induced ones. The experimental results are accompanied by a thorough explanation and discussion of the models used, as well as their advantages and limitations. Full article

This article examines the modeling of sustainable development in transport logistics, focusing on the impact of climate factors, changing weather conditions, and digitalization processes. The study analyzes the complex influence of adverse weather phenomena, such as fog, rain, snow, extreme temperatures, and strong winds, whose frequency and intensity are increasing due to climate change, on the efficiency, safety, and reliability of transport systems across all modes except pipelines. Special attention is paid to the integration of weather-resilient sensor technologies, including LiDAR, thermal imaging, and advanced monitoring systems, to strengthen infrastructure resilience and ensure uninterrupted transport operations under environmental stress. The methodological framework combines comparative analytical methods with economic–mathematical modeling, particularly Leontief’s input–output model, to evaluate the mutual influence between the transport sector and sustainable economic growth within an interconnected ecosystem of economic and technological factors. The findings confirm that data-driven management strategies, the digital transformation of logistics, and the strengthening of centralized hubs contribute significantly to increasing the resilience and flexibility of transport systems, mitigating the negative economic impacts of climate risks, and promoting long-term sustainable development. Practical recommendations are proposed to optimize freight flows, adapt infrastructure to changing weather risks, and support the integration of innovative digital technologies as part of an evolving ecosystem. Full article

With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion of shallow proven reserves and the increasing issues of mixed surrounding rocks with shallow ore bodies make it increasingly important to build intelligent mines and implement green and sustainable development strategies. However, previous mineralization predictions for the Yanshan Iron Mine largely relied on traditional geological data (such as blasting rock powder, borehole profiles, etc.) exploration reports or three-dimensional explicit ore body models, which lacked precision and were insufficient to meet the requirements for intelligent mine construction. Therefore, this study, based on artificial intelligence technology, focuses on geoscience big data mining and quantitative prediction, with the goal of achieving multi-scale, multi-dimensional, and multi-modal precise positioning of the Yanshan Iron Mine and establishing its intelligent mine technology system. The specific research contents and results are as follows: (1) This study collected and organized multi-source geoscience data for the Yanshan Iron Mine, including geological, geophysical, and remote sensing data, such as mine drilling data, centimeter-level drone image data, and high-spectral data of rocks and minerals, establishing a rich mine big data set. (2) SOM clustering analysis was performed on the elemental data of rock and mineral samples, identifying key elements positively correlated with iron as Mg, Al, Si, S, K, Ca, and Mn. TSG was used to interpret shortwave and thermal infrared hyperspectral data of the samples, identifying the main alteration mineral types in the mining area. Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. This model was then applied to centimeter-level drone images, achieving high-precision intelligent identification of magnetite in the mining area. Combined with LiDAR image elevation data, a real-time three-dimensional surface mineral monitoring model for the mining area was built. (4) The Bagged Positive Label Unlabeled Learning (BPUL) method was adopted to integrate five evidence maps—carbonate alteration, chloritization, mixed rockization, fault zones, and magnetic anomalies—to conduct three-dimensional mineralization prediction analysis for the mining area. The locations of key target areas were delineated. The SHAP index and three-dimensional explicit geological models were used to conduct an in-depth analysis of the contributions of different feature variables in the mineralization process of the Yanshan Iron Mine. In conclusion, this study successfully constructed the technical framework for intelligent mine construction at the Yanshan Iron Mine, providing important theoretical and practical support for mineralization prediction and intelligent exploration in the mining area. Full article

This review evaluates the use of unmanned aerial vehicles (UAVs) in detecting and managing water stress in specialty crops through thermal, multispectral, and hyperspectral imaging. Based on 104 scholarly articles from 2012 to 2024, the review highlights the advantages, limitations, and evolution of these imaging systems. Vineyards are the most studied crops for precision irrigation compared to other crops. The paper traces the shift from standalone imaging to multi-sensor fusion approaches, integrating vegetation indices and machine learning models for improved accuracy, resolution, and real-time stress assessment. It also addresses knowledge gaps such as scalability, payload constraints, and computational demands. Issues like flight altitude, sensor angle, and lighting conditions can lead to data inconsistencies, affecting water stress detection and decision-making. Emerging technologies like LiDAR, AI, and machine learning are proposed to enhance UAV data processing and stress detection. Future research should focus on developing automated data correction, multi-sensor fusion, and AI-driven real-time analysis to address sensor calibration and environmental factors. The review also advocates for integrating UAV data with satellite and ground sensors into smart irrigation systems to create a multi-scale monitoring framework, thereby advancing precision agriculture and water resource management. Full article

The architecture, engineering, construction, and operations industry faces an urgent need to enhance construction and demolition waste management in urban areas, driven by increasing demolition and construction activities and a desire to align with sustainable practices and the circular economy principles. To address this need, a systematic literature review on the building information modelling methodology was conducted, employing a structured protocol and specific tools for the analysis of academic studies, based on PRISMA guidelines and StArt software (version 3.4 BETA). Ninety relevant studies published between 1998 and 2024, were analysed and selected from the Web of Science, Scopus, and Engineering Village databases. Findings indicate that China leads in publications with 34%, followed by Brazil (8%) and the United Kingdom (7%). The analysis emphasises the use of drones and LiDAR scanners for precise spatial data, processed by 3D reconstruction tools like Pix4D and FARO As-Built. Revit excels in 3D modelling, providing a robust platform for visualisation and analysis. Visual programming tools such as Dynamo automate processes and optimise material reuse. The study presents a conceptual framework that integrates these technologies with the principles of the circular economy, clarifying the interactions and practical applications that promote the sustainable management of demolition waste from urban buildings and process efficiency. Although the approach promotes material reuse and sustainability, it still faces barriers such as the need for waste segregation at the source, the adaptation of innovative technologies, like the iPhone 15 Pro LiDAR and thermal cameras, as well as associated costs. These factors may limit its adoption in larger-scale projects, particularly due to the increased complexity of buildings. Full article

The atmospheric boundary layer is a crucial transitional region connecting the surface with the free atmosphere, playing a bridging role in land-sea-air interactions and the interactions between different atmospheric layers. This study utilizes rotary-wing UAVs, high-resolution lidar, and WRF simulation data to analyze the vertical distribution characteristics of temperature, humidity, wind speed, and wind direction boundary layer over the Mount Si’e region in 4–6 April 2024. The results indicate that the boundary layer temperature decreases with increasing altitude, reaching up to 18°C, while humidity decreases with height, dropping to as low as 35%. Daytime wind speeds range from 4 to 8 m/s, decreasing to 2 to 4 m/s at night. The boundary layer height can reach up to 900 m during the day and drops to 100–200 m at night, showing distinct diurnal variation characteristics. UAV observations are in good agreement with lidar and WRF simulation results, highlighting the application value of UAVs in high temporal and spatial resolution boundary layer studies. The study also reveals the significant impact of complex terrain on boundary layer characteristics, providing scientific insights into the dynamic and thermal processes of the boundary layer and offering reference value for improving regional weather forecasting and numerical simulations. Full article

Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem. Full article

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields. Full article