Search Results (6)

The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, CubeSats, and terrestrial infrastructures that comprise the framework of Space–Aerial–Ground Integrated Networks (SAGINs) as vital enablers of the International Mobile Telecommunications (IMT)-2030 standards. This paper examines the role of UAVs in providing flexible and quickly deployable airborne connectivity. It also discusses how CubeSats enhance global coverage through low-latency relaying and resilient backhaul links from low Earth orbit (LEO). Additionally, the paper highlights how terrestrial systems contribute high-capacity, densely concentrated communication layers that support various end-user applications. By examining their interoperability and coordinated resource allocation, the paper underscores that the seamless interaction of SAGIN nodes is essential for achieving the ultra-reliable, intelligent, and pervasive communication capabilities envisioned by IMT-2030. As 6G aims for ultra-low latency, high reliability, and massive connectivity, UAVs and CubeSats emerge as key enablers for extending coverage and capacity, particularly in remote and dense urban regions. Furthermore, the role of large language models (LLMs) is explored for intelligent network management and real-time data optimization, while quantum communication is analyzed for ensuring security and minimizing latency. The integration of LLMs into quantum-enhanced edge intelligence for SAGINs represents an emerging research frontier for adaptive, high-throughput, and context-aware decision-making. By exploiting quantum-assisted parallelism and entanglement-based optimization, LLMs enhance the processing efficiency of multimodal data across space, aerial, and terrestrial nodes. This paper further investigates distributed quantum inference and multimodal sensor data fusion to enable resilient, self-optimizing communication systems comprising a high volume of data traffic, which is a critical bottleneck in the global connectivity transition. LLMs are envisioned as cognitive control centers capable of generating semantic representations for mission-critical communications that enhance energy efficiency, reliability, and adaptive learning at the edge. The findings of the survey reveal that quantum-enhanced LLMs overcome challenges pertaining to bandwidth allocation, dynamic routing, and interoperability in existing classical communication systems. Overall, quantum-empowered LLMs significantly assist intelligent, autonomous, and immersive communications in SAGIN, while enabling secure, privacy-preserving communication. Full article

The Ningaloo Reef, Australia’s longest fringing reef, is uniquely positioned in the NW region of the continent, with clear, oligotrophic waters, relatively low human impacts, and a high level of protection through the World Heritage Site and its marine park status. Non-invasive optical sensors, which seamlessly derive bathymetry and bottom reflectance, are ideally suited for mapping and monitoring shallow reefs such as Ningaloo. Using an existing airborne hyperspectral survey, we developed a new, geomorphic layer for the reef for depths down to 20 m, through an object-oriented classification that combines topography and benthic cover. We demonstrate the classification approach using three focus areas in the northern region of the Muiron Islands, the central part around Point Maud, and Gnaraloo Bay in the south. Topographic mapping combined aspect, slope, and depth into 18 classes and, unsurprisingly, allocated much of the area into shallow, flat lagoons, and highlighted narrow, deeper channels that facilitate water circulation. There were five distinct geomorphic classes of coral-algal mosaics in different topographic settings. Our classifications provide a useful baseline for stratifying ecological field surveys, designing monitoring programmes, and assessing reef resilience from current and future threats. Full article

Mission planning is the guidance for a UAV team to perform missions, which plays the most critical role in military and civil applications. For complex tasks, it requires heterogeneous cooperative multi-UAVs to satisfy several mission requirements. Meanwhile, airborne sensor allocation and path planning are the critical components of heterogeneous multi-UAVs system mission planning problems, which affect the mission profit to a large extent. This paper establishes the mathematical model for the integrated sensor allocation and path planning problem to maximize the total task profit and minimize travel costs, simultaneously. We present an integrated mission planning framework based on a two-level adaptive variable neighborhood search algorithm to address the coupled problem. The first-level is devoted to planning a reasonable airborne sensor allocation plan, and the second-level aims to optimize the path of the heterogeneous multi-UAVs system. To improve the mission planning framework’s efficiency, an adaptive mechanism is presented to guide the search direction intelligently during the iterative process. Simulation results show that the effectiveness of the proposed framework. Compared to the conventional methods, the better performance of planning results is achieved. Full article

Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band’s performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities. Full article

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa $<0.996$ ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants. Full article

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges. Full article