Search Results (119)

As a key tropospheric photochemical pollutant, ground-level ozone (O₃) poses significant threats to ecosystems through its strong oxidative capacity. With China’s rapid industrialization and urbanization, worsening O₃ pollution has emerged as a critical environmental concern. This study examines O₃’s impacts on forest ecosystems in Southwestern China (Yunnan, Guizhou, Sichuan, and Chongqing), which harbors crucial forest resources. We analyzed high-resolution monitoring data from over 200 stations (2019–2023), employing spatial interpolation to derive the regional maximum daily 8 h average O₃ (MDA8-O₃, ppb) and accumulated O₃ exposure over 40 ppb (AOT40) metrics. Through AOT40-based exposure–response modeling, we quantified the forest relative yield losses (RYL), economic losses (ECL) and ECL/GDP (GDP: gross domestic product) ratios in this region. Our findings reveal alarming O₃ increases across the region, with a mean annual MDA8-O₃ anomaly trend of 2.4% year⁻¹ (p < 0.05). Provincial MDA8-O₃ anomaly trends varied from 1.4% year⁻¹ (Yunnan, p = 0.059) to 4.3% year⁻¹ (Guizhou, p < 0.001). Strong correlations (r > 0.85) between annual RYL and annual MDA8-O₃ anomalies demonstrate the detrimental effects of O₃ on forest biomass. The RYL trajectory showed an initial decline during 2019–2020 and accelerated losses during 2020–2023, peaking at 13.8 ± 6.4% in 2023. Provincial variations showed a 5-year averaged RYL ranging from 7.10% (Chongqing) to 15.85% (Yunnan). O₃ exposure caused annual ECL/GDP averaging 4.44% for Southwestern China, with Yunnan suffering the most severe consequences (ECL/GDP averaging 8.20%, ECL averaging CNY 29.8 billion). These results suggest that O₃-driven forest degradation may intensify, potentially undermining the regional carbon sequestration capacity, highlighting the urgent need for policy interventions. We recommend enhanced monitoring networks and stricter control methods to address these challenges. Full article

This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article

Artificial intelligence (AI) has emerged as a powerful tool for advancing the interpretation of ground penetrating radar (GPR) data, offering solutions to long-standing challenges in manual analysis, such as subjectivity, inefficiency, and limited scalability. This review investigates recent developments in AI-driven techniques for GPR interpretation, with a focus on machine learning, deep learning, and hybrid approaches that incorporate physical modeling or multimodal data fusion. We systematically analyze the application of these techniques across various domains, including utility detection, infrastructure monitoring, archeology, and environmental studies. Key findings highlight the success of convolutional neural networks in hyperbola detection, the use of segmentation models for stratigraphic analysis, and the integration of AI with robotic and real-time systems. However, challenges remain with generalization, data scarcity, model interpretability, and operational deployment. We identify promising directions, such as domain adaptation, explainable AI, and edge-compatible solutions for practical implementation. By synthesizing current progress and limitations, this review aims to bridge the gap between theoretical advancements in AI and the practical needs of GPR practitioners, guiding future research towards more reliable, transparent, and field-ready systems. Full article

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

China’s accelerating industrialization has led to worsening air pollution, characterized by recurrent haze episodes. The accurate quantification of PM_2.5 distribution is crucial for air quality assessment and public health management. Although traditional prediction models can effectively identify PM_2.5 concentration fluctuations with moderate accuracy, their dependence relies heavily on extensive ground-based monitoring station data, limiting their applicability in areas with sparse monitoring coverage. To address this limitation, this study proposes a novel algorithm for high-precision PM_2.5 concentration prediction, termed VDMS (Vision Transformer with DLSTM Multi-Head Self-Attention and Self-supervision). Based on the traditional Vision Transformer (ViT) architecture, VDMS incorporates a Double-Layered Long Short-Term Memory (DLSTM) network and a Multi-Head Self-Attention mechanism to enhance the model’s capacity to capture temporal sequence features and global dependencies. These enhancements contribute to greater stability and robustness in feature representation, ultimately improving prediction performance. Cross-validation experimental results show that the VDMS model outperforms benchmark models in PM_2.5 concentration prediction tasks, achieving a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 4.05 μg/m³, and a mean absolute error (MAE) of 3.23 μg/m³. Furthermore, experiments conducted in areas with sparse ground monitoring stations demonstrate that the model maintains high predictive accuracy, further validating its applicability and generalization capability in data-limited scenarios. Moreover, the VDMS model adopts a modular design, offering strong scalability that allows its architecture to be adjusted according to specific requirements. This adaptability renders it suitable for monitoring various atmospheric pollutants, providing essential technical support for precise environmental management and air quality forecasting. Full article

The semantic segmentation of remote sensing images is vital for accurate surface monitoring and environmental assessment. Multimodal remote sensing images (RSIs) provide a more comprehensive dimension of information, enabling faster and more scientific decision-making. However, existing methods primarily focus on modality and spectral channels when utilizing spectral features, with limited consideration of their association to ground object types. This association, commonly referred to as the spectral characteristics of ground objects (SCGO), results in distinct spectral responses across different modalities and holds significant potential for improving the segmentation accuracy of multimodal RSIs. Meanwhile, the inclusion of redundant features in the fusion process can also interfere with model performance. To address these problems, a ground object spectral feature awareness network (SFANet) specifically designed for RSIs that effectively leverages spectral features by incorporating the SCGO is proposed. SFANet includes two innovative modules: (1) the Spectral Aware Feature Fusion module, which integrates multimodal features in the encoder based on SCGO, and (2) the Adaptive Spectral Enhancement module, which reduces the confusion from redundant information in the decoder. SFANet significantly improves the mIoU by 5.66% and 4.76% compared to the baseline on two datasets, outperforming existing multimodal RSIs segmentation networks by adaptively enhanced spectral feature awareness. SFANet demonstrates significant advancements over other multimodal RSIs segmentation networks and provides new perspectives for RSI-specific network design by incorporating spectral characteristics. This work offers new perspectives for the design of segmentation networks for RSIs. Full article

The increasing frequency and severity of agricultural fires pose significant threats to food security, economic stability, and environmental sustainability. Traditional fire-detection methods, relying on satellite imagery and ground-based sensors, often suffer from delayed response times and high false-positive rates, limiting their effectiveness in mitigating fire-related damages. In this study, we propose an advanced deep learning-based fire-detection framework that integrates the Single-Shot MultiBox Detector (SSD) with the computationally efficient MobileNetV2 architecture. This integration enhances real-time fire- and smoke-detection capabilities while maintaining a lightweight and deployable model suitable for Unmanned Aerial Vehicle (UAV)-based agricultural monitoring. The proposed model was trained and evaluated on a custom dataset comprising diverse fire scenarios, including various environmental conditions and fire intensities. Comprehensive experiments and comparative analyses against state-of-the-art object-detection models, such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and SSD-based variants, demonstrated the superior performance of our model. The results indicate that our approach achieves a mean Average Precision (mAP) of 97.7%, significantly surpassing conventional models while maintaining a detection speed of 45 frames per second (fps) and requiring only 5.0 GFLOPs of computational power. These characteristics make it particularly suitable for deployment in edge-computing environments, such as UAVs and remote agricultural monitoring systems. Full article

Last-mile delivery (LMD) is an important aspect of contemporary logistics that directly affects operational cost, efficiency, and customer satisfaction. In this paper, we provide a review of the optimization techniques of LMD, focusing on Artificial Intelligence (AI) driven decision-making, IoT-supported real-time monitoring, and hybrid delivery networks. The combination of AI and IoT improves predictive analytics, dynamic routing, and fleet management, but scalability and regulatory issues are still major concerns. Hybrid frameworks that integrate drones or Unmanned Aerial Vehicles (UAVs), ground robots, and conventional vehicles reduce energy expenditure and increase delivery range, especially in urban contexts. Furthermore, sustainable logistics approaches, including electric vehicle fleets and shared delivery infrastructures, provide promise for minimizing environmental impact. However, economic viability, legal frameworks, and infrastructure readiness still influence the feasibility of large-scale adoption. This review offers a perspective on the changing patterns in LMD, calling for regulatory evolution, technological advancement, as well as interdisciplinary approaches toward cost-effective, durable, and environmentally friendly logistics systems. Full article

A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption. However, existing recognition algorithms generally suffer from problems such as the inability to recognize long-distance moving targets and adapt to new environments, as well as low recognition accuracy. Here, we demonstrate that applying transfer learning to recognition algorithms can adapt to new environments and improve accuracy. We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. We used transfer learning to make the algorithm more adaptable to environmental changes. Our results show that the proposed moving ground-target recognition algorithm can identify target types, improve accuracy, and adapt to a new environment with good performance. We anticipate that our algorithm will be the starting point for more complex recognition algorithms. For example, target recognition algorithms based on multi-modal fusion and transfer learning can better meet actual needs. Full article

Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images. Here we present a dual-channel deep learning model, integrating surveillance images and multi-source numerical data for air quality forecasting. Our model, which combines a single-channel hybrid network consisting of VGG16 and LSTM (named VGG16-LSTM) with a single-channel Long Short-Term Memory (LSTM) network, efficiently captures detailed spatiotemporal features from surveillance image sequences and temporal features from atmospheric, meteorological, and temporal data, enabling accurate time-series forecasting of PM_2.5 and PM₁₀ concentrations. Experiments conducted on the 2021 Shanghai dataset demonstrate that the proposed model significantly outperforms traditional machine learning methods in terms of accuracy and robustness for time-series forecasting, achieving R² values of 0.9459 and 0.9045 and RMSE values of 4.79 μg/m³ and 11.51 μg/m³ for PM_2.5 and PM₁₀, respectively. Furthermore, validation results on the datasets from two stations in Kaohsiung, Taiwan, with average R² values of 0.9728 and 0.9365 and average RMSE values of 1.89 μg/m³ and 5.69 μg/m³ for PM_2.5 and PM₁₀ using a pretrain–finetune training strategy, confirm the model’s adaptability across diverse geographical contexts. These findings highlight the potential of integrating surveillance images to enhance air quality prediction, offering an effective supplement to ground-level environmental monitoring. Future work will focus on expanding datasets and optimizing network architectures to further improve forecasting accuracy and computational efficiency, enhancing the model’s scalability for broader regional air quality management. Full article

Coastal litter is a severe environmental issue impacting marine ecosystems and coastal communities in Thailand, with plastic pollution posing one of the most urgent challenges. Every month, millions of tons of plastic waste enter the ocean, where items such as bottles, cans, and other plastics can take hundreds of years to degrade, threatening marine life through ingestion, entanglement, and habitat destruction. To address this issue, we deploy drones equipped with high-resolution cameras and sensors to capture detailed coastal imagery for assessing litter distribution. This study presents the development of an AI-driven coastal litter detection system using edge computing and 5G communication networks. The AI edge server utilizes YOLOv8 and a recurrent neural network (RNN) to enable the drone to detect and classify various types of litter, such as bottles, cans, and plastics, in real-time. High-speed 5G communication supports seamless data transmission, allowing efficient monitoring. We evaluated drone performance under optimal flying heights above ground of 5 m, 7 m, and 10 m, analyzing accuracy, precision, recall, and F1-score. Results indicate that the system achieves optimal detection at an altitude of 5 m with a ground sampling distance (GSD) of 0.98 cm/pixel, yielding an F1-score of 98% for cans, 96% for plastics, and 95% for bottles. This approach facilitates real-time monitoring of coastal areas, contributing to marine ecosystem conservation and environmental sustainability. Full article

In volcanic regions, the analysis of Thermal InfraRed (TIR) satellite imagery for Land Surface Temperature (LST) retrieval is a valid technique to detect ground thermal anomalies. This allows us to achieve rapid characterization of the shallow thermal field, supporting ground surveillance networks in monitoring volcanic activity. However, surface temperature can be influenced by processes of different natures, which interact and mutually interfere, making it challenging to interpret the spatio-temporal variations in the LST parameter. In this paper, we use a workflow to detect the main thermal patterns in active volcanic areas by analyzing the Independent Component Analysis (ICA) results applied to satellite nighttime TIR imagery time series. We employed the proposed approach to study the surface temperature distribution at the Campi Flegrei caldera volcanic site (Southern Italy, Naples) during the 2013–2022 time interval. The results revealed the contribution of four main distinctive thermal patterns, which reflect the endogenous processes occurring at the Solfatara crater, the environmental processes affecting the Agnano plain, the unique microclimate of the Astroni crater, and the morphoclimatic aspects of the entire volcanic area. Full article

This study assesses the performance of the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset in estimating ground-level concentrations (GLCs) of aerosols and criteria pollutants across the Himawari-8 observational area, covering China, Indonesia, and Australia, from 2016 to 2023. Ground-based monitoring networks in these regions are limited in scope, making it necessary to rely on satellite-derived aerosol optical depth (AOD) as a proxy for GLCs. While AOD offers broad coverage, it presents challenges, particularly in capturing surface-level pollution accurately during episodic events. CAMS, which integrates satellite data with atmospheric models, is evaluated here to determine its effectiveness in addressing these issues. The study employs square root transformation to normalize pollutant concentration data and calculates monthly–hourly long-term averages to isolate pollution anomalies. Geographically weighted regression (GWR) and Jacobian matrix (dY/dX) methods are applied to assess the spatial variability of pollutant concentrations and their relationship with meteorological factors. Results show that while CAMS captures large-scale pollution episodes, such as the 2019/2020 Australian wildfires, discrepancies in representing GLCs are apparent, especially when vertical aerosol stratification occurs during short-term pollution events. The study emphasizes the need for integrating CAMS data with higher-resolution satellite observations, like Himawari-8, to improve the accuracy of real-time air quality monitoring. The findings highlight important implications for public health interventions and environmental policy-making, particularly in regions with insufficient ground-based data. Full article

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) have rapidly evolved, becoming integral to various applications such as environmental monitoring, disaster response, and precision agriculture. This paper provides a comprehensive review of the advancements and the challenges in UAV-UGV collaboration and its potential applications. These systems offer enhanced situational awareness and operational efficiency, enabling complex tasks that are beyond the capabilities of individual systems by leveraging the complementary strengths of UAVs and UGVs. Key areas explored in this review include multi-UAV and multi-UGV systems, collaborative aerial and ground operations, and the communication and coordination mechanisms that support these collaborative efforts. Furthermore, this paper discusses potential limitations, challenges and future research directions, and considers issues such as computational constraints, communication network instability, and environmental adaptability. The review also provides a detailed analysis of how these issues impact the effectiveness of UAV-UGV collaboration. Full article

The application of the Multispectral Instrument (MSI) aboard Sentinel-2A/B constellation for assessing water quality in Chilean lakes represents an emerging area of research, particularly for the environmental monitoring of optically complex water bodies. Similarly, atmospheric correction processors applied to aquatic environments, such as the Case 2 Networks (C2RCC-Nets), are notably underrepresented. This study evaluates the capability of C2RCC-Nets using different neural networks—Case-2 Regional/Coast Color (C2RCC), C2X-Extreme (C2X), and C2X-Complex (C2XC)—to estimate Secchi depth in Lake Lanalhue (eutrophic), Lake Villarrica (oligo-mesotrophic), and Lake Panguipulli (oligotrophic). The evaluation used different statistical methods such as Spearman’s correlation and normalized error metrics (nRMSE, nMAE, and nbias) to assess the agreement between satellite-derived data and in situ measurements. C2XC demonstrated the best fit for Lake Lanalhue, with an nRMSE = 33.13%, nMAE = 23.51%, and nbias = 8.57%, in relation to the median ground truth values. In Lake Villarrica, the C2XC neural network displayed a moderate correlation (r_s = 0.618) and error metrics, with an nRMSE of 24.67% and nMAE of 20.67%, with an nbias of 4.21%. In the oligotrophic Lake Panguipulli, no relationship was observed between estimated and measured values, which could be related to the fact that the selected neural networks were developed for very case 2 waters. These findings highlight the need for methodological advancements in processing satellite-derived water quality products for Chile’s optical water types, particularly for very clear waters. Nonetheless, this study underscores the need for model-specific calibration of C2RCC-Nets, as lakes with different optical water types and trophic states may require tailored training ranges for inherent optical properties. Full article