Search Results (4,548)

Compound crises of heritage cities in the global south led to the destruction of both physical and intangible assets, as well as the processes and relationships that sustain them; nevertheless, while there is more awareness regarding the importance of intangible heritage, regeneration approaches after the crisis period still focus on tangible losses, while the intangible cultural aspects are often overlooked in planning, especially under conditions of restricted field access. This paper proposes a remote-first CESSOP diagnostic model (CSDM), integrating CES and SOP in four decision-focused products: Crisis Pressure Profile (CPP), Biocultural Asset Map (BAM), CESSOP Vulnerability Matrix (CSVM), and modeling and Action Typology and Priority Pathways (ATPP), rather than adopting an existing framework. The Cultural Ecosystem Services and Sense of Place Diagnostic Model (CESSOP Diagnostic Model). The CSDM is new; it is a theoretically constructed, theoretically grounded, crisis-sensitive diagnostic methodology designed for heritage cities under conditions of data scarcity and restricted field access. This approach includes aspects of document research, open-source data, environmental morphological intelligence, social networks and remote participation inputs, supported with embedded confidence rating. The methodology is illustrated by a study of old Sana’a, Yemen, and it is applied in cases when there are few opportunities for direct observation. Full article

Non-invasive, real-time monitoring of plant water status is critical for precision agriculture and plant physiology. However, existing methods often lack continuous in situ measurement capability or are limited by temporal resolution. This paper proposes a novel non-invasive method based on xylem electrical conductivity, inspired by industrial non-contact fluid measurement. As a ground-based complement to remote sensing, this approach demonstrates the feasibility of online, in situ, and non-invasive monitoring of water stress in grapevine stems under controlled laboratory conditions. The industrial C⁴D sensing system is adaptively modified into a specialized Plant-C⁴D sensor with an array-based design for batch signal acquisition. To validate the electrical response to water loss, a gravimetric natural dehydration experiment was conducted, demonstrating a clear correlation between electrical signals and water content changes in detached stem samples. Full-day dynamic experiments are conducted under three conditions: normal water supply, varying water stress, and plant inactivation. Sensitive characteristic parameters are extracted through signal analysis, and a pattern recognition framework is established to eliminate environmental interference and suppress individual differences. Experimental results on 24 plant samples (Shine Muscat) show that the method accurately discriminates viable from inactivated plants with an accuracy of 91.67% (22/24 correct). Furthermore, the Fuzzy C-Means (FCM) clustering algorithm successfully quantifies the severity of water stress in viable plants, yielding results consistent with actual water supply conditions. While these findings demonstrate the capability of Plant-C⁴D sensor to capture stem water status-related information, the current results do not establish full physiological validation, warranting further exploration with in vivo experiments. Full article

Multi-UAV missions for remote sensing and environmental monitoring under extreme conditions require task allocation and path optimisation to efficiently distribute goals across vehicles. These methods must also be executed reliably inside an autonomous robotics framework. Several methods for the multiple travelling salesman problem (mTSP) show robust offline routeing efficiency. However, system-level validation under realistic operational conditions including waypoint management and inter-UAV separation remains limited. This research transforms the previously proposed Distance Efficient Clustering Kmeans Genetic Algorithm (DECK_GA) from an offline model into a deployment-focused multi-UAV remote sensing framework implemented in ROS2, Aerostack2, and Gazebo. A uniform waypoint management interface integrates planning, Rviz visualisation, and autonomous execution. The system combines Dynamic Centroid Kmeans (DCKmeans) for spatially coherent waypoint allocation with a Distance Efficient Genetic Algorithm (DEGA) for individual UAV route optimisation. The evaluation is conducted in a high-fidelity Antarctic environment where waypoints represent survey desired objectives in moss regions, and altitude is managed using terrain-referenced control involving two to five UAVs and 30 to 120 waypoints. The framework was evaluated against two baselines under identical mission configurations, with 10 trial runs for each: a Traditional GA Divide & Conquer planner and a Classical Kmeans DEGA planner, which utilises the same route optimisation method and differentiates the outcomes of the allocation stage. DECK_GA showed reduced mean planned and executed distances compared to the Traditional GA Divide & Conquer baseline across all configurations, achieving planned distance reductions ranging from 15.99% to 75.36%. Additionally, it produced shorter path than Classical Kmeans DEGA in 14 out of 16 configurations. The average minimum inter-UAV separation was greater than the Traditional GA Divide & Conquer baseline in 15 of the 16 configurations and higher than Classical Kmeans DEGA in 14 of the 16, which demonstrates that the DCKmeans allocation improves spatial separation. This research focuses on the framework for planning to execution instead of the introduction of a new optimisation method, as DECK_GA was proposed in previous research and is now incorporated and tested within an autonomy framework. This evaluation is simulation only. Real world flying, hardware in the loop testing, wind, communication latency, and location error prediction tend to be future developments. Full article

Hyperspectral imaging is a well-established remote sensing technique for material detection and classification, relying on hundreds of reflectance bands to exploit the spectral signatures of surface materials. Although hyperspectral imagery has demonstrated excellent capabilities for material identification, its operational implementation may be constrained in some applications due to data volume and processing requirements. Consequently, there is growing interest in evaluating the capability of lower-dimensional multispectral imagery for material detection tasks. In this sense, this article proposes as its contribution the comparative evaluation of machine learning models and neural networks for asbestos–cement detection on VNIR imagery. For the development of this research, the CRISP-DM methodology was adapted into four phases: P1. Business and data understanding; P2. Data preparation; P3. Modelling and evaluation; P4. Model deployment. At the results level, three datasets with different numbers of bands were constructed, which were structured by adding to the original dataset an additional layer with the NDVI and two additional layers with the PCA components of the original image. Across the three datasets, four machine learning models and one neural network model were tuned and evaluated, yielding as a result that in all three datasets the KNN and neural network models achieved the best performance. Likewise, it was found that the detection capability of the models improved with the inclusion of the additional bands. The proposed approach serves as a reference to be extrapolated by research centres and universities for the detection of asbestos and other materials in VNIR images, with a view toward integration into resource-constrained systems and specifically into environmental monitoring systems. Full article

Cyanobacterial blooms occur frequently in lakes worldwide, disrupting aquatic ecosystem balance and directly threatening drinking water safety and fisheries production. Establishing a reliable bloom early-warning system has therefore become an urgent priority for lake water management. This study adopts a structured narrative review approach to synthesize the major early-warning methods, including indicator threshold methods, statistical and empirical models, mechanistic models, machine learning, and remote sensing monitoring. These methods are compared in terms of their fundamental principles, data requirements, predictive capabilities, applicability, interpretability, and computational and maintenance requirements. Emerging trends in multi-source data fusion, multi-model integration, and the development of integrated early-warning systems are also summarized. The findings indicate that each method has distinct strengths and limitations with respect to forecasting lead time, spatial coverage, process interpretation, and operational costs, and that no single method can simultaneously meet the requirements of multiscale bloom monitoring and forecasting. Integrating multi-source data from in situ monitoring, remote sensing observations, and meteorological and hydrological measurements, while coordinating statistical models, mechanistic models, and artificial intelligence algorithms according to specific forecasting objectives, represents an important pathway for improving the robustness and operational applicability of early-warning systems. Given the pronounced seasonal ice cover, substantial hydrological variability, limited monitoring data, and marked regional heterogeneity of some cold and arid region lakes, future research should strengthen high-frequency monitoring during critical periods, promote coordination between remote sensing and in situ observations, and conduct local calibration of early-warning thresholds and model parameters. Season-specific models should also be developed to account for environmental differences among ice-covered, ice-off transition, and open-water periods. Overall, early warning of cyanobacterial blooms in lakes is evolving from the application of individual methods toward the integration of multi-source monitoring, multi-model integration, and decision support, thereby providing a reference for bloom risk prevention and water environment management across different types of lakes. Full article

Satellite remote sensing has become a vital data source for monitoring power infrastructure. As critical infrastructure supporting power line operations, the monitoring and maintenance of power towers have become core elements in ensuring grid reliability. However, power tower detection in satellite remote sensing imagery remains challenging because of the substantial scale differences between distribution and transmission towers, the weak feature representation of small objects, and interference from complex backgrounds. To address these challenges, this paper proposes MS-YOLO, a power tower detection algorithm for satellite remote sensing imagery based on multi-scale feature extraction and small object enhancement. First, the poly kernel inception bottleneck (PKI_Bottleneck) module is introduced into the YOLOv9 backbone, enhancing the extraction of scale-diverse features and contextual cues while limiting interference from complex backgrounds. Second, the dual-branch semantic-spatial synergy attention (DSSA) module is introduced. By decoupling deep semantic and shallow spatial information, it effectively preserves small object features while suppressing environmental noise, enhancing the perception capability for small tower objects. Finally, a dynamic focal-weighted intersection over union (DFW-IoU) loss function is introduced to optimize the balance between easy and difficult samples, compelling the model to prioritize small objects and challenging samples during gradient updates. Experimental results demonstrate that MS-YOLO achieves mAP50 values of 79.1% and 95.7% on the two datasets used in this paper, representing improvements of 4.1% and 3.4% over baseline model. These results validate the effectiveness of the improved model for power tower detection in complex remote sensing scenarios. Full article

Green and Blue Infrastructure (GBI) is increasingly addressed within climate adaptation and mitigation policies as a strategic operational measure for reducing climate-related impacts in urban environments. However, GBI effectiveness relies strictly on the biophysical condition of natural assets and on the capacity to monitor and interpret ecosystem processes over time, specifically vegetation quality and its physiological response to climatic stressors within complex urban fabrics. This variability emphasizes the need to integrate ecosystem performance into decision-making through digital frameworks capable of quantifying and accounting for ecological resources across temporal scales. In this context, Remote Sensing (RS) technologies provide a structured informational basis for assessing vegetation health and surface thermal patterns in relation to climatic stress thresholds and human exposure. This paper presents a policy-aligned geospatial evidence framework to bridge the gap between environmental monitoring and urban climate action. By integrating high-resolution multispectral remote sensing with heterogeneous spatial datasets and climate models, the framework enables the multitemporal assessment of ecological conditions to inform where GBI measures can support SECAP implementation, project refinement, and monitoring activities. Developed within the Horizon Europe KNOWING project and applied to the Naples East district, Italy, the framework was operationalized within the city’s Sustainable Energy and Climate Action Plan (SECAP). The application produces scenario-oriented outputs for interpreting the potential contribution of GBI and NbS measures to outdoor heat-stress reduction under SECAP conditions. Its practical value lies in translating biophysical data into reusable GIS/WMS layers that connect ecological performance, climate exposure, socio-energetic vulnerability, and planned urban transformations, thereby supporting SECAP implementation, project refinement, and monitoring. Full article

Monitoring the spatial distribution of oyster reefs in an accurate, efficient, and flexible manner is crucial for assessing changes in nearshore fishery habitat environments. However, traditional optical remote sensing methods are often affected by illumination variation, tidal fluctuation, and spectral confusion in complex intertidal environments. An automated extraction framework based on the SAM (Segment Anything Model) using UAV (Unmanned Aerial Vehicle) DOM (Digital Orthophoto Map) imagery is proposed to achieve high-precision oyster reef identification and area estimation. Multi-resolution UAV imagery was processed, and key SAM parameters were systematically optimized under different illumination conditions. The results show that spatial resolution significantly influences segmentation accuracy, and appropriate resolution selection improves both stability and reliability. Based on UAV data acquired in 2025, the total oyster reef area in the study region was estimated to be 2.24 ha. After parameter optimization, segmentation accuracy improved from 93.22% to 97.61% in illuminated areas and from 93.30% to 96.67% in shaded areas. The proposed method demonstrates strong robustness under varying environmental conditions and effectively enhances boundary detection accuracy. A scalable and reliable approach is provided for coastal habitat monitoring and offers new insights into automated object extraction in complex remote sensing imagery. Full article

Short-Wave Infrared (SWIR) remote sensing plays a significant role in environmental monitoring, agricultural analysis, and nanosatellite-based Earth observation applications. Existing remote sensing frameworks suffer from limitations such as inefficient spectral band selection, high computational complexity, and lack of intelligent optimization techniques for compact nanosatellite payload systems. This study aims to develop an intelligent filter selection system for a four-band SWIR optical payload using multispectral satellite imagery and deep learning (DL)-based optimization techniques. The proposed framework focuses on improving spectral feature extraction, environmental condition classification, and nanosatellite payload efficiency. A multispectral field image dataset was utilized for experimental analysis, where preprocessing techniques, including atmospheric correction and Z-score normalization, were applied. Mutual Information (MI)-based optimal band selection and SWIR filter mapping were performed to identify the significant spectral bands B05, B08, B11, and B12. Feature extraction was conducted using raw SWIR band values and spectral band ratios. A Vision Transformer (ViT) model was employed for environmental condition classification while the Whale Optimization Algorithm (WOA) was integrated to optimize model parameters and improve convergence performance. The proposed ViT–WOA framework achieved superior classification performance with 96.93% accuracy, 97.19% precision, 96.93% recall, 96.92% F1-score. and a Kappa coefficient of 0.9540. The framework effectively classified cloudy, rainy, and sunny environmental conditions with reduced classification loss and improved spectral feature learning efficiency. The proposed system demonstrated reliable SWIR spectral analysis and intelligent payload optimization for nanosatellite remote sensing applications. The integration of transformer-based learning and metaheuristic optimization provided an effective solution for efficient Earth observation and environmental monitoring systems. Full article

Offshore wind farms are rapidly expanding into deeper and more remote ocean regions. Their structural safety and operational reliability in harsh marine environments have garnered widespread global attention. Sensing technologies capture structural and environmental conditions and are indispensable to structural monitoring. Accordingly, this review examines the applications of environmental monitoring, supervisory control and data acquisition, condition monitoring, and structural health monitoring systems covering both the horizontal-axis and vertical-axis types of fixed and floating offshore wind turbines. It also summarizes key technologies for data transmission and optimal sensor placement. However, uncertainty in sensing data can significantly affect monitoring results, yet existing studies lack an adequate summary and in-depth discussion. We therefore focus on sources of sensing uncertainty, including the marine environment, the host platform, variations in environmental and operational conditions, and sparse sensing. By analyzing their effects on monitoring data, we explore key methods for overcoming data uncertainties and improving sensing accuracy. This paper also evaluates the application potential of cutting-edge artificial intelligence and digital twin technologies. Furthermore, the study points out that fusing multi-source signal data to establish a highly reliable intelligent decision-making and early warning framework is likely to become an important development direction for offshore wind power monitoring. This review aims to provide valuable support for the safe development of offshore wind farms towards deep-sea regions over the coming decades. Full article

In deep-space exploration and remote sensing, characterizing radiative transfer in complex planetary atmospheres is fundamental for robust target detection and optical navigation. On the Martian surface, intense scattering and attenuation by dust aerosols pose severe environmental interference, challenging star sensors used for high-precision navigation. To address this, this study develops a spectral radiative transfer model based on the Null Collision Monte Carlo Method to characterize the optical background of the dusty Martian atmosphere. Mie scattering theory is employed for dust particles, while gas molecular absorption is modeled via line-by-line integration. The simulated sky radiance is validated against Mars rover Navcam observations, yielding an average relative error of 7.83% between the modeled and observed radiance values across scattering angles greater than 5°. Building on this, an imaging link model evaluates surface-based detection performance, including signal-to-noise ratio, detection success probability, and star count. Optical parameters—aperture, field of view, and integration time—are optimized for nighttime and dawn-dusk modes. Spatio-temporal assessments are conducted globally across Martian years, focusing on the Zhurong landing site and Tianwen-3 candidates. Finally, an Earth-environment equivalence experiment using a 60% transmittance filter verifies the design’s robustness. This work confirms the feasibility of star-sensor-based attitude determination on Mars. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

Wildfires pose increasing environmental and socio-economic risks, particularly in climate-sensitive and tropical regions, necessitating reliable satellite-based monitoring and predictive frameworks. This study presents a comprehensive survey of satellite-derived wildfire indicators and spatiotemporal modeling approaches, covering their historical development, current methodologies, and emerging research directions. We review major active fire and hotspot datasets derived from MODIS, VIIRS, and related platforms, along with key environmental drivers such as vegetation indices, meteorological variables, and land-surface with a specific case study for the Indonesian region. Modeling approaches are synthesized from classical statistical regression and time-series analysis to contemporary machine learning and deep learning architectures, including convolutional neural networks, recurrent neural networks, and transformer-based models. The analysis highlights the transition toward multi-source data integration and spatiotemporal deep learning frameworks capable of capturing complex wildfire dynamics. Finally, we identify future research challenges, including hybrid physical–AI modeling, uncertainty quantification, and scalable real-time wildfire intelligence systems. This survey provides a structured reference for researchers and practitioners seeking to advance satellite-based wildfire monitoring and prediction. Full article

UAV-based remote sensing excels in rapid response, high timeliness, simple operation, and high degrees of automation, and has been widely applied for geological hazard monitoring. Deep learning methods based on unitemporal UAV images can only analyze the static appearance of a scene, while bitemporal change detection can capture the dynamic evolution of hazards; however, due to diverse geological landforms and topography, environmental noises such as vegetation cover, and dynamic weather conditions, change detection of geological hazards from UAV images based on traditional deep learning technology is not always effective. Therefore, there is an urgent need to utilize large vision-language models (LVLMs) to further improve the accuracy and robustness of the change detection model. Motivated by this, this paper proposes a novel remote sensing model for geological hazard monitoring, referred to as CLIFF (CLIP-BIT-EfficientNet), based on the multi-modal LVLM Contrastive Language–Image Pre-training (CLIP), the change detection network Bitemporal Image Transformer (BIT), and the classification network EfficientNet, along with corresponding datasets and model fine-tuning strategies. The proposed transfer fusion module bridges the CLIFF and BIT networks by aligning their feature distributions and dimensions, allowing the general knowledge of the LVLM and the task-specific knowledge of the learnable branch to reinforce each other. Furthermore, this integrated pipeline addresses the scarcity of labeled hazard data by allowing the BIT to train on larger public datasets, while fine-tuning EfficientNet on smaller hazard-classification datasets within the change area, making the approach more efficient and reliable than direct classification methods. Experimental results show that the proposed CLIFF algorithm outperforms state-of-the-art deep learning algorithms such as LightCDNet and ChangeFormer, with an IoU of 75.74% and an F1 score of 0.8689 for change detection. Meanwhile, CLIFF has an overall accuracy rate of 86.89% in identifying geological hazards along gas pipelines, such as crude oil spills, collapses, landslides, and floods, with per-class accuracies of 87.32% and 86.17% for crude oil spills and landslides, respectively. Full article

High-frequency (HF) RFID systems operating at 13.56 MHz are widely used in applications such as near-field communication, contactless identification, and smart sensing. Their performance strongly depends on the inductive coupling between the reader and tag antennas, which is influenced by antenna geometry, relative position, orientation, and environmental conditions. This work investigates the antenna component of passive HF RFID tags, represented by planar spiral inductors, without integrating an RFID microchip, allowing the electromagnetic coupling to be analyzed independently of chip-specific effects. A low-cost automated measurement platform was developed to experimentally evaluate the influence of antenna geometry, distance, orientation, and temperature on inductively coupled HF RFID antennas. The platform combined an automated positioning system with a mobile application for remote operation, minimizing the influence of the operator during measurements. A second experimental setup was designed to investigate the effect of temperature on antenna performance. Experimental results show that rectangular spiral antennas generally provided stronger inductive coupling than the other geometries investigated. Furthermore, varying the receiving antenna orientation improved the coupling between rectangular and octagonal antennas under specific configurations. Temperature variations within the investigated range had only a minor influence on antenna performance. The proposed platform provides a low-cost, portable, and reproducible solution for the experimental characterization of HF RFID antennas operating at 13.56 MHz. Full article