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In order to improve the positioning accuracy of the traditional master-slave unmanned aerial vehicle (UAV) cooperative navigation algorithm based on range and angle measurement information, given the angle and range measurement errors of low-cost slave UAV measuring equipment, the error model of the cooperative navigation system of master-slave UAV is reconstructed. The navigation and measurement errors of the slave UAV are estimated and compensated. The state equations and measurement equations are derived, then the algorithm is implemented using Kalman filter. Simulation results show that for a slave UAV's inertial navigation system based on a low accuracy micro electromechanical system (MEMS) with a gyro drift level of 10 (°) /h, the root mean square (RMS) of the east and the north velocity errors within 500 s are 0.25 m/s and 0.74 m/s, respectively, and the RMS of the latitude and the longitude errors are 17.10 m and 9.10 m, respectively, under single master UAV combined navigation. The speed accuracy is 3-10 times higher than that of the traditional algorithm, and the position accuracy is about 20 times higher. The positioning accuracy is close to the level of master UAV under double mater UAVs' measurement references. The estimation accuracy of range measurement errors of the slave UAV is high, while the estimation accuracy of angle measurement errors is affected by the heading accuracy of the slave UAV itself. The estimation accuracy of angle measurement error can be further improved if heading references such as magnetic heading exist. [ABSTRACT FROM AUTHOR]

This paper studies the problem of plane search of unmanned aerial vehicles (UAVs) using dynamic sensors. A gradient-type nominal search strategy is studied. Since this strategy leads to local optimum which causes the suspension of the task, it does not guarantee to meet the goal of covering the search domain such that each point is surveyed for a certain level. A new strategy is consequently developed based on the nominal search strategy. Furthermore, a novel search strategy, taking advantage of area division, is proposed for the demand that the excessive search level and time cost are both as low as possible. Simulation results of a single UAV and multi-UAVs demonstrate the feasibility of the search strategies. [ABSTRACT FROM AUTHOR]

Aiming at the noise, cost and safety problems of current unmanned aerial vehicle (UAV) operation within cities, this paper divides the airspace into layers by altitudes and proposes an operation cost and crash risk model based on noise protection zones under aerodynamic and other constraints. This model satisfies the standard operation conditions of UAVs, and reduces the noise impact on the environment and the ground population, as well as operation cost and crash risk. Thus, based on the noise protection zone, the improved Dubins path planning method is used. It combines the Dubins path planning idea with the tangent line of the geometric circle, and adds node processing to enrich the optional paths for UAV operation and optimize the paths. As the Dijkstra algorithm is used as the the best path searching algorithm, and the lowest total cost is set as the goal, a UAV optimal operation path is searched and compared with the A* algorithm. Simulation experiments verify the effectiveness of the proposed model and the improved method. They reduce the noise impact of UAV operation and the operation cost, and improve the safety and efficiency of the operation. The results indicate that the optimal operation altitude of the example UAV, a biplane medium-sized UAV with a mass of 15 kg and a paddle disk area of about 1.313 m², is 40 m, and its operation minimum total cost is 5.42; compared with the results in the other altitude layers, the total operation cost is reduced by 37.56% at most and 5.91% at least. [ABSTRACT FROM AUTHOR]

The threat power transmission and distribution projects pose to the ecological environment has been widely discussed by researchers. The scarcity of early environmental monitoring and supervision technologies, particularly the lack of effective real-time monitoring mechanisms and feedback systems, has hindered the timely quantitative identification of potential early-stage environmental risks. This study aims to comprehensively review the literature and analyze the research context and shortcomings of the advance warning technologies of power transmission and distribution projects construction period using the integrated space–sky–ground system approach. The key contributions of this research include (1) listing ten environmental risks and categorizing the environmental risks associated with the construction cycle of power transmission and distribution projects; (2) categorizing the monitoring data into one-dimensional, two-dimensional, and three-dimensional frameworks; and (3) constructing the potential environmental risk knowledge system by employing the knowledge graph technology and visualizing it. This review study provides a panoramic view of knowledge in a certain field and reveals the issues that have not been fully explored in the research field of monitoring technologies for potential environmental damage caused by power transmission and transformation projects. [ABSTRACT FROM AUTHOR]

Accurate delineation of spring phenology (e.g., start of growing season, SOS) of deciduous forests is essential for understanding its responses to environmental changes. To date, SOS dates from analyses of satellite images and vegetation index (VI) −based phenological models have notable discrepancies but they have not been fully evaluated, primarily due to the lack of ground reference data for evaluation. This study evaluated the SOS dates of a deciduous broadleaf forest estimated by VI-based phenological models from three satellite sensors (PlanetScope, Sentinel-2A/B, and Landsat-7/8/9) by using ground reference data collected by a small unmanned aerial vehicle (sUAV). Daily sUAV imagery (0.035-meter resolution) was used to identify and generate green leaf maps. These green leaf maps were further aggregated to generate Green Leaf Fraction (GLF) maps at the spatial resolutions of PlanetScope (3-meter), Sentinel-2A/B (10-meter), and Landsat-7/8/9 (30-meter). The temporal changes of GLF differ from those of vegetation indices in spring, with the peak dates of GLF being much earlier than those of VIs. At the SOS dates estimated by VI-based phenological models in 2022 (Julian days from 105 to 111), GLF already ranges from 62% to 96%. The moderate accuracy and large uncertainties of SOS dates from VI-based phenological models arise from the limitations of vegetation indices in accurately tracking the number of green leaves and the inherent uncertainties of the mathematical models used. The results of this study clearly highlight the need for new research on spring phenology of deciduous forests. [ABSTRACT FROM AUTHOR]

In this article, an attitude-position obstacle avoidance trajectory tracking control scheme based on barrier functions is proposed for a quadrotor unmanned aerial vehicle (UAV). First, the barrier functions are designed based on the relationship between the position and attitude of the quadrotor UAV and obstacles. Then, by decoupling the quadrotor UAV system into a position control subsystem and an attitude control subsystem and incorporating integral barrier Lyapunov functions (IBLFs) into the backstepping process, a novel obstacle avoidance tracking control strategy for the position subsystem and the attitude subsystem is constructed. Compared with the existing obstacle avoidance results, the designed obstacle avoidance strategy can simultaneously achieve obstacle avoidance on the position and attitude of the quadrotor UAV. Finally, the efficiency of the proposed method is verified by simulation results. [ABSTRACT FROM AUTHOR]

Despite comprising only one per cent of global forests, mangroves provide vital ecological and economic benefits to their ecosystems. Due to its decreasing extent over the past decade, there is a rise in research innovations supporting mangrove conservation. Specifically, consumer-grade Unmanned Aerial Vehicles (UAV) were proven effective as potential remote sensing alternatives to support mangrove research and monitoring in recent studies. As most studies use custom UAV-mounted sensors for mangrove species classification, similar studies using a UAV's default red–green–blue (RGB) cameras were scarce. This study explores the potential of high-resolution RGB aerial images through state-of-the-art object detection algorithm, YOLOv5 to detect the dominant Rhizophora mangroves in Sarawak, Malaysia. A total of 400 RGB images were equally selected from two study areas and allocated into three datasets, two corresponding to each study area and one combining all images. The annotation process was performed using a previously proposed novel method, assisted by YOLOv5 for a semi-automated annotation process with expert verification. Systematic training experiments were conducted to select an optimal epoch size across models trained with each dataset. The final models produced an average true positive rate of 73.8% and 71.7% for each study site, while the combined dataset model produced an average true positive rate of 73.7%. Overall, this study demonstrated the potential of UAV-based RGB images and deep learning object detection architectures to identify specific mangrove objects, while also highlighting key considerations for similar future research. [ABSTRACT FROM AUTHOR]

The diverse range of uses of unmanned aerial vehicles has garnered significant attention in research. The scientific literature that supports the data obtained from UAVs recording information from various sensors is presented in this manuscript. It summarizes current developments in remote sensing, including radar, photogrammetry, thermal imaging, light detection and ranging sensors (LiDAR), data gathering, and analysis. It is predicated on the instruments' ability to gather and analyze accurate data. To identify some of the most urgent research problems, it also shows surveys based on research methodologies. The present research focuses on the proliferation and social effects of unmanned aerial vehicles (UAVs). It also encourages novice researchers to pursue this area of study and suggest novel approaches to the design or setup of these flying machines. UAVs have entirely transformed due to advancements in internet technology and current technologies which include camera defects, environmental monitoring, charging, impediments, crop monitoring, energy consumption, military applications, and technology gaps. [ABSTRACT FROM AUTHOR]

This survey provides a comprehensive comparison of prominent open-source unmanned aerial vehicle (UAV) autopilots, focusing on their hardware compatibility, software features, and communication protocols. Additionally, it assesses the impact of these autopilots on research and education by examining their potential for integration with companion computers, compatibility with robot operating system (ROS) middleware and the MATLAB/Simulink environment, and the availability of simulation-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulation tools. The paper concludes with a discussion of the advantages and disadvantages of these leading open-source autopilots. [ABSTRACT FROM AUTHOR]

Given rapid glacier thinning and retreat observed in the Pyrenees in recent decades, an updated glacier inventory and continuous mass balance assessments are important to understand the ongoing variability and changes of these very small glaciers (< 0.5 km2). The mass balance years 2021/22 and 2022/23 were characterised by prolonged extreme heat waves and reduced snow duration that severely affected the Pyrenees, which also impacted their glaciers. This paper reviews the criteria for classifying ice bodies as glaciers or ice patches, presents the latest high-resolution glacier inventory for the Pyrenees, and quantifies the mass losses caused by the extreme climate conditions in 2022 and 2023. The glacierised area was determined by manual mapping of high-resolution (0.2 m spatial resolution) aerial orthomosaics acquired by unmanned aerial vehicles (UAVs) and aerial orthophotos (0.25 m spatial resolution) for the few glaciers not surveyed by UAVs. 3D point clouds, also obtained from UAV flights, were used to update the results for the change in surface elevation (glacier thickness) and mass balance between 2020 and 2023. For the Pyrenees, the total glacierised area in 2023 is 143.2 ± 1.8 ha in 15 different glaciers and 8 ice masses were degraded to ice patches according to our criteria. The resulting area change between 2020 and 2023 is -94.8 ha, representing a -39.8% decrease of the glaciarised area from 2020 to 2023, increasing the annual ratio of area change from 2020 to 2023 by -8.7% yr−1 compared to the period 2011–2020 (-2.4% yr−1). The change in glacier thickness measured on 12 glaciers shows a decrease of -2.52 m yr−1 for the period 2020–2023, which represents a significant acceleration in glacier thickness loss compared to -0.80 m yr−1 for the period 2011–2020. The three glaciers (Infiernos, Monte Perdido and Aneto) on which annual geodetic measurements were carried out showed slightly higher glacier thickness losses (-0.91 m yr−1) in the first mass balance year (2020/21) than in the previous decade (2011–2020), while the losses in the last two mass balance years (2021/22 and 2022/23) were three to four times higher (-3.42 m yr−1 and -3.07 m yr−1 respectively) and exceeded the record values. [ABSTRACT FROM AUTHOR]

Mining-induced ground fissures are common problems associated with mining damage in shallowly buried coal seams in the western mining area of China. To evaluate the surface mining damage of the 12203 working face of the Huojitu Colliery in Shendong mining area, low-altitude infrared aerial surveys were conducted on the ground at the static fissure area (O-A1) and the dynamic fissure area (O-A2) of the working face. The temperature evolution patterns of fissures, sand and plants in the infrared images were analysed. The relationship between overburden fractures and surface fissure temperature was revealed, and the influence range and temperature self-healing period of the surface affected by underground mining were determined. The results indicated that underground mining could lead to a decrease in the ground temperature above the working face. The surface temperature evolution can be divided into three zones: a temperature stabilization zone before mining, a temperature cooling zone during mining, and a temperature recovery zone after mining. The temperature of sand and plants above the working face exhibited quadratic curve changes in O-A1 and O-A2, respectively. The length of the temperature reduction zone affected by mining is 40 m in O-A2, and 46.8 m in O-A1. The temperature recovery periods of ground fissures in O-A1 and O-A2 were 4.0 and 4.6 d, respectively. These findings could provide a basis for evaluating mining ground damage. [ABSTRACT FROM AUTHOR]

Introduction: The emergence rate of crop seedlings is an important indicator for variety selection, evaluation, field management, and yield prediction. To address the low recognition accuracy caused by the uneven size and varying growth conditions of crop seedlings under salt-alkali stress, this research proposes a peanut seedling recognition model, MS-YOLOv8. Methods: This research employs close-range remote sensing from unmanned aerial vehicles (UAVs) to rapidly recognize and count peanut seedlings. First, a lightweight adaptive feature fusion module (called MSModule) is constructed, which groups the channels of input feature maps and feeds them into different convolutional layers for multi-scale feature extraction. Additionally, the module automatically adjusts the channel weights of each group based on their contribution, improving the feature fusion effect. Second, the neck network structure is reconstructed to enhance recognition capabilities for small objects, and the MPDIoU loss function is introduced to effectively optimize the detection boxes for seedlings with scattered branch growth. Results: Experimental results demonstrate that the proposed MS-YOLOv8 model achieves an AP50 of 97.5% for peanut seedling detection, which is 12.9%, 9.8%, 4.7%, 5.0%, 11.2%, 5.0%, and 3.6% higher than Faster R-CNN, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and RT-DETR, respectively. Discussion: This research provides valuable insights for crop recognition under extreme environmental stress and lays a theoretical foundation for the development of intelligent production equipment. [ABSTRACT FROM AUTHOR]

Despite its flexibility, unmanned aerial vehicle (UAV) communications are susceptible to eavesdropping due to the open nature of wireless channels and the broadcasting nature of wireless signals. This paper studies secure UAV communications and proposes a method to optimize the minimum secrecy rate of the system by using interference technology to enhance it. To this end, the system not only deploys multiple UAV base stations (BSs) to provide services to legitimate users but also assigns dedicated UAV jammers to send interference signals to active or potential eavesdroppers to disrupt their eavesdropping effectiveness. Based on this configuration, we formulate the optimization process of parameters such as the user association variables, UAV trajectory, and output power as a sequential decision-making problem and use the single-agent soft actor-critic (SAC) algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm to achieve joint optimization of the core parameters. In addition, for specific scenarios, we also use the multi-agent soft actor-critic (MASAC) algorithm to solve the joint optimization problem mentioned above. The numerical results show that the normalized average secrecy rate of the MASAC algorithm increased by more than 6.6% and 14.2% compared with that of the SAC and TD3 algorithms, respectively. [ABSTRACT FROM AUTHOR]

Species-level phenology models are essential for predicting shifts in tree species under climate change. This study quantified phenological differences among dominant miombo tree species and modeled seasonal variability using climate variables. We used TIMESAT version 3.3 software and the Savitzky–Golay filter to derive phenology metrics from bi-monthly PlanetScope Normalized Difference Vegetation Index (NDVI) data from 2017 to 2024. A repeated measures Analysis of Variance (ANOVA) assessed differences in phenology metrics between species, while a regression analysis modeled the Start of Season (SOS) and End of Season (EOS). The results show significant seasonal and species-level variations in phenology. Brachystegia spiciformis differed from other species in EOS, Length of Season (LOS), base value, and peak value. Surface solar radiation and skin temperature one month before SOS were key predictors of SOS, with an adjusted R-squared of 0.90 and a Root Mean Square Error (RMSE) of 13.47 for Brachystegia spiciformis. SOS also strongly predicted EOS, with an adjusted R-squared of 1 and an RMSE of 3.01 for Brachystegia spiciformis, indicating a shift in the growth cycle of tree species due to seasonal variability. These models provide valuable insights into potential phenological shifts in miombo species due to climate change. [ABSTRACT FROM AUTHOR]

Remote visual inspections are valuable tools for maintaining bridges in safe operation. In the case of old structures with incomplete documentation, the verification of dimensions is also an essential aspect. This paper presents an attempt to use a Scanning Total Station (STS) and Unmanned Aerial Vehicle (UAV) for the inspection and inventory of bridge dimensions. The STS's measurements are conducted by applying two methods: the direct method using a total station (TS) and advanced geometric analyses of the collected point cloud. The UAV's measurements use a Structure from Motion (SfM) method. Verification tests were conducted on a steel truss railway bridge over the largest river in Poland. The measurements concerned both the basic dimensions of the bridge and the details of a selected truss connection. The STS identified a significant deviation in the actual geometry of the measured connection and the design documentation. The UAV's inspection confirmed these findings. The integration of STS and UAV technologies has demonstrated significant advantages, including STS's high accuracy in direct measurements, with deviations within acceptable engineering tolerances (below a few mm), and the UAV's efficiency in covering large areas, achieving over 90% compliance with reference dimensions. This combined approach not only reduces operating costs and enhances safety by minimizing the need for heavy machinery or scaffolding but also provides a more comprehensive understanding of the structural condition. [ABSTRACT FROM AUTHOR]

In recent years, micro-sized electric unmanned aerial vehicle (UAVs) have gained widespread applications in both defense and civilian sectors owing to their advantages of lightweight construction, portability, and cost-effectiveness. However, a significant drawback is the difficulty in discerning and controlling the flight attitude and trajectory of these UAVs when operating beyond the line of sight. Consequently, they heavily rely on remote control for flight operations, leading to a high degree of operational complexity. Hence, it is challenging to maintain the desired characteristics of micro-sized aerial vehicle systems, such as high sensitivity and stability. In this study, we aimed to enhance the longitudinal flight stability of micro-sized electric UAVs. To achieve this goal, control of a UAV's elevator was utilized to ensure stability in pitch and roll during flight. We employed the Digital Airborne Tactical Communications System (DATCOM) software developed by the United States Air Force to calculate the fundamental aerodynamic coefficients of the UAV. Subsequently, the longitudinal motion state space equation was employed to derive transfer functions for the pitch angle θ and the horizontal stabilizer deflection de. Furthermore, we utilized Simulink to compare the effects of two control methods, traditional Proportional-Integral-Derivative (PID) and fuzzy PID, on the longitudinal flight stability of the UAV. We aimed to identify the optimal PID values for the UAV. Finally, we validated through practical flight tests that Fuzzy PID can enhance the longitudinal flight stability of the UAV while also contributing to new technological solutions for stability in micro-sized UAV flight. [ABSTRACT FROM AUTHOR]

Rural wetlands are complex landscapes where rivers, croplands, and villages coexist, making water quality monitoring crucial for the well-being of nearby residents. UAV-based imagery has proven effective in capturing detailed features of water bodies, making it a popular tool for water quality assessments. However, few studies have specifically focused on drone-based water quality monitoring in rural wetlands and their seasonal variations. In this study, Xiangfudang Rural Wetland Park, Jiaxin City, Zhejiang Province, China, was taken as the study area to evaluate water quality parameters, including total nitrogen (TN), total phosphors (TP), chemical oxygen demand (COD), and turbidity degree (TUB). We assessed these parameters across summer and winter seasons using UAV multispectral imagery and field sample data. Four machine learning algorithms were evaluated and compared for the inversion of the water quality parameters, based on the situ sample survey data and UAV multispectral images. The results show that ANN algorithm yielded the best results for estimating TN, COD, and TUB, with validation R2 of 0.78, 0.76, and 0.57, respectively; CatBoost performed best in TP estimation, with validation R2 and RMSE values of 0.72 and 0.05 mg/L. Based on spatial estimation results, the average COD concentration in the water body was 16.05 ± 9.87 mg/L in summer, higher than it was in winter (13.02 ± 8.22 mg/L). Additionally, mean TUB values were 18.39 Nephelometric Turbidity Units (NTU) in summer and 20.03 NTU in winter. This study demonstrates the novelty and effectiveness of using UAV multispectral imagery for water quality monitoring in rural wetlands, providing critical insights into seasonal water quality variations in these areas. [ABSTRACT FROM AUTHOR]

Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) as an aerial base station or relay device is a promising technology to rapidly provide wireless connectivity to ground device. Given UAV's agility and mobility, ground user's mobility, a key question is how to analyze and value the performance of UAV-based wireless channel in the terahertz (THz) band. In this paper, a three-dimensional (3D) time-varying channel model is proposed for UAV-based dual-mobility wireless channels based on geometric channel model theory in THz band. In this proposed channel model, the small-scale fading (e.g., scattering fading and reflection fading) on rough surfaces of communication environment and the atmospheric molecule absorption attenuations are considered in THz band. Moreover, the statistical properties of the proposed channel model, including path loss, time autocorrelation function (T-ACF) and Doppler power spectrum density (DPSD), have been derived and the impact of several important UAV-related and vehicle-related parameters have been investigated and compared to millimeter wave (mm-wave) band. Furthermore, the correctness of the proposed channel model has been verified via simulation, and some useful observations are provided for the system design of THz UAV-based dual-mobility wireless communication systems. [ABSTRACT FROM AUTHOR]

Identification of nutritious rice varieties through non-destructive detection technology is important for high-quality seed production. With the development of technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining attention in the scientific community. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain filling stage. Different models (stepwise multiple linear regression (SMLR) and the Back Propagation Neural Network (BPNN)) for estimating rice protein content based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R2 = 0.9516 and RMSE = 0.3492, indicating high accuracy and stability in the model. Overall, hyperspectral imaging technology combined with various models could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner. [ABSTRACT FROM AUTHOR]

In beyond fifth-generation (B5G) networks, free-space optical (FSO) communication is anticipated to play a crucial role thanks to its high data rates and minimal system complexity. Therefore, infrequently occurring poor weather can impair its performance. The combination of FSO and radio frequency (RF) has proven a successful solution to address the increasing demand for high data rates in wireless communication networks. Due to their adaptability in terms of deployment and movement, unmanned aerial vehicles (UAVs) are also projected to be crucial in B5G networks. This paper investigates a UAV-aided hybrid FSO/DRF backhauling system using a matching game theory and Multi-Agent deep reinforcement learning (MARL) framework. We deploy a UAV to provide a user offloading service to an existing ground base station (GBS) facing a reduced backhaul capacity due to weather attenuation (e.g., fog). It is considered that the GBS has a pre-installed FSO backhaul connection to a macro-base station. However, during adverse weather conditions, the FSO backhaul is severely affected, compromising the reliability of the FSO link. The novelty here is the hybrid FSO/RF backhauling system and how it is utilized to address weather-related challenges. The UAV is deployed at the ideal height to increase system throughput using MARL, and the frequency division between the GBS and the UAV is also tuned. The system's effectiveness is assessed using meteorological data from the British cities of Edinburgh and London. The numerical outcomes demonstrate the suggested scheme's superiority and efficacy over traditional approaches. The time estimation shows that for a maximum of 30 users, the time consumed is 300 s which is lesser and effective. [ABSTRACT FROM AUTHOR]

The Galileo High Accuracy Service (HAS) is a free of charge Global Navigation Satellite System (GNSS) augmentation service provided by the European Union. It is designed to enable real-time Precise Point Positioning (PPP) with a target accuracy (at the 95% confidence level) of 20 cm and 40 cm in the horizontal and vertical components, respectively, to be achieved within 300 s. The performance of the service has been confirmed with geodetic-grade receivers. However, mass market applications require low-cost GNSS receivers connected to low-cost antennae. This paper focuses on the performance of the real-time static and kinematic positioning achieved with Galileo HAS and low-cost GNSS receivers. The study is limited to GPS + Galileo dual-frequency positioning, thus exploiting the full potential of Galileo HAS SL1. We demonstrate that the target accuracy of Galileo HAS SL1 is reached with both geodetic-grade and low-cost receivers in dual-frequency static and kinematic applications in open-sky conditions. Precision of a few centimeters is reached for static positioning, while kinematic positioning results in subdecimeter precision. Vertical accuracy is limited by missing phase center offset models for low-cost antennas. In general, the performance of low-cost hardware using Galileo HAS for real-time PPP is comparable to that of geodetic-grade hardware. Therefore, combining low-cost GNSS receivers with Galileo HAS is feasible and justified. [ABSTRACT FROM AUTHOR]

AbstractLandmines significantly hinder the rapid movement of military forces and pose major obstacles during critical operations. Unmanned aerial vehicles (UAVs), commonly known as drones, offer several significant advantages for landmine detection. Most studies have tested drone-based systems at controlled sites and demonstrated their effectiveness in limited scenarios, with no examples of using these systems to detect landmines and relay the results to mine clearance machines (MCMs) for actual removal in real military operations. This study evaluated the use of UAVs equipped with magnetometers to detect metallic landmines in military applications. By conducting a series of controlled experiments, the research identified optimal flight conditions—2 m/s flight speed, 1 m survey interval, and 0.5 m sensor altitude—that balance accuracy and operational efficiency. The findings demonstrate that UAV-based magnetometer systems can significantly enhance mine clearance operations by providing near real-time data to MCMs. This approach offers a safer, faster, and more cost-effective alternative to traditional landmine detection methods by addressing the limitations of ground-based operations, such as high risk to human operators and inefficiency. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Unmanned aerial vehicle (UAV) imagery highlights the extent of illegal deforestation in protected areas for the biodiverse humid forest of central Madagascar. The ultra‐high‐resolution (<10‐cm pixel) images enable the creation of detailed forest 3D base maps and provide the means to quantify forest stand losses. To help communities safeguard their forests, local non‐governmental organisations can use UAV maps in combination with weekly deforestation alerts to facilitate an immediate on‐ground response that significantly restricts illegal activity. Integrating ultra‐high‐resolution UAV mapping and coarse‐resolution freely available satellite imagery should have much wider applications in Madagascar and the humid tropics for community‐based conservation. Summary: This study of the Ambohimahamasina humid forest shows that small UAVs offer a detailed (<10‐cm pixel), rapid and cost‐effective solution to provide maps of detailed deforestation patterns not visible in satellite imagery.Calculating forest extent and volume are valuable ways to rapidly assess forest losses and prioritise areas for ground patrols. The use of 3‐dimensional measurements for above ground carbon estimates indicate how, in the future, these metrics could be used to calculate carbon payments for conservation programs.By combining UAV and free satellite imagery, an effective alert system has been developed that supports community initiatives in the protection of their natural forest resources.The wealth of ultra‐high‐resolution UAV data collected in this study provides insights into forest dynamics, supports local community forest management, and has the potential to measure the value of the forest. [ABSTRACT FROM AUTHOR]

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Recent advancements in Unmanned Aerial Vehicle (UAV) technology have made them effective platforms for data capture in applications like environmental monitoring. UAVs, acting as mobile data ferries, can significantly improve ground network performance by involving ground network representatives in data collection. These representatives communicate opportunistically with accessible UAVs. Emerging technologies such as Software Defined Wireless Sensor Networks (SDWSN), wherein the role/function of sensor nodes is defined via software, can offer a flexible operation for UAV data-gathering approaches. In this paper, we introduce the "UAV Fuzzy Travel Path", a novel approach that utilizes Deep Reinforcement Learning (DRL) algorithms, which is a subfield of machine learning, for optimal UAV trajectory planning. The approach also involves the integration between UAV and SDWSN wherein nodes acting as gateways (GWs) receive data from the flexibly formulated group members via software definition. A UAV is then dispatched to capture data from GWs along a planned trajectory within a fuzzy span. Our dual objectives are to minimize the total energy consumption of the UAV system during each data collection round and to enhance the communication bit rate on the UAV-Ground connectivity. We formulate this problem as a constrained combinatorial optimization problem, jointly planning the UAV path with improved communication performance. To tackle the NP-hard nature of this problem, we propose a novel DRL technique based on Deep Q-Learning. By learning from UAV path policy experiences, our approach efficiently reduces energy consumption while maximizing packet delivery. [ABSTRACT FROM AUTHOR]

This research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analysed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees (r/h), in order to classify the gaps as small‐size, medium‐size, large‐size, or open areas at both sites independently. Then Kendall's correlation coefficients between the snow depth, FSCG and topographic variables were computed and a Random Forest (RF) model for each survey was implemented, to determine the influence of these variables in explaining snow depth patterns. The results demonstrate the consistency of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At the northeast exposed Site 1, the larger the r/h observed, the greater was the snow depth obtained. This pattern was not evident at the southwest exposed Site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth and was consistently the highest‐ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at Site 1. The findings suggest differences in the main drivers throughout each site and surveys of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains. [ABSTRACT FROM AUTHOR]

Providing seamless connectivity during transit in vehicular ad hoc networks (VANETs) is the most promising and challenging task. In VANET, the vehicles are able to communicate with other vehicles and also with the infrastructure units such as road side unit (RSU). The coverage range of RSU is not sufficient to cover the whole road. The vehicle can drive to an area which is not covered by any of the RSUs, and it is termed as congested zone (CZ). This will extremely lead to traffic congestion and maximize blocking probability. In order to provide connectivity between the road segments and to avoid long delay in message transmission, unmanned aerial vehicles (UAVs) are placed in CZs, which act as relay nodes. To enhance the connectivity of the vehicles, a fuzzy-based UAV-assisted handoff scheme (F-UAV-HO) is proposed in this work. By adopting fuzzy, the unwanted participation of UAV is limited in non-congested traffic, thereby saving the battery resource. To validate the proposed work, the simulations are executed in Network Simulator 2.35. The proposed system outperforms other conventional UAV-assisted schemes in congested traffic in terms of average end-to-end delay, PDR and throughput. [ABSTRACT FROM AUTHOR]

ABSTRACT The target‐attacker‐defenders game (TADs) is an important topic in multi‐agent systems, concerning group cooperation and decision‐making. To effectively counter fast attackers, this study introduces a passive defensive approach, where a group of defenders position themselves around a formation point, rather than intercept the attacker actively. An evaluation model is developed to determine the optimal formation point, and a comprehensive analysis of the model is conducted, demonstrating that the formation point changes at a slower rate than the attacker, allowing the defenders to effectively track the attacker's movements. To enhance the global search capabilities, a backbone exchange mechanism is integrated into the original pigeon‐inspired optimization (PIO) algorithm. Finally, a series of comparative experiments are conducted, providing a comprehensive assessment of the defense strategy and the improved algorithm. The simulation results indicate the defenders can block the attacker with a slower speed following the proposed strategy, while the UAVs using the comparison method fail to follow the attacker, and the attacker escape the encirclement of defenders. The results prove the effectiveness of the proposed passive method in the article. [ABSTRACT FROM AUTHOR]

Combining near-earth remote sensing spectral imaging technology with unmanned aerial vehicle (UAV) remote sensing sensing technology, we measured the Ningqi No. 10 goji variety under conditions of health, infestation by psyllids, and infestation by gall mites in Shizuishan City, Ningxia Hui Autonomous Region. The results indicate that the red and near-infrared spectral bands are particularly sensitive for detecting pest and disease conditions in goji. Using UAV-measured data, a remote sensing monitoring model for goji pest and disease was developed and validated using near-earth remote sensing hyperspectral data. A fully connected neural network achieved an accuracy of over 96.82% in classifying gall mite infestations, thereby enhancing the precision of pest and disease monitoring in goji. This demonstrates the reliability of UAV remote sensing. The pest and disease remote sensing monitoring model was used to visually present predictive results on hyperspectral images of goji, achieving data visualization. [ABSTRACT FROM AUTHOR]

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure. [ABSTRACT FROM AUTHOR]

The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored. [ABSTRACT FROM AUTHOR]

[Objective] Severe gully erosion in the black soil region of Northeast China is continuously eroding arable land resources, posing a threat to the country's food security. The development of unmanned aerial vehicle (UA V) technology provides an effective way for monitoring gully erosion, but the higher the resolution of data obtained, the longer it takes. The challenge lies in configuring UA V parameters to simultaneously meet accuracy requirements and enhance aerial photography efficiency. _Methods］ The Hebei catchment in the rolling hills region of Northeast China was chosen as the study area. Using actual cross-section measurement data as a validation value, the accuracy of gully parameters extracted from UA V data at different resolutions, flight directions, and types was assessed to explore the suitable conditions for various UAV parameter configurations. Results] (1) Compared with field measurement data, data at a 1 cm resolution extracted the gully parameters with the highest accuracy, the average percentage error for all parameters was less than 5.0%, suitable for monitoring typical gully development processes. Data extracted at 3 cm and 5 cm resolutions had an average percentage error of less than 1().()% for gully width, and the average error increased as gully depth decreased. For gullies deeper than 1 m, the average percentage error was less than 1().()%，suitable for rapid regional sampling surveys. Data extracted at resolutions of 8 cm and 10 cm had average percentage errors greater than 4().()% for gully depth and cross-sectional area, suitable for extracting gully distribution locations and planar parameters. (2) Although fixed-wing UAV obtaining data slightly outperformed in extracting two-dimensional gully features, multirotor UAV using oblique photogrammetry were better in extracting three-dimensional gully features. (3) The average percentage errors for gully depth and cross-sectional area extracted from single-direction oblique photogrammetry data were 1.7 and 1.9 times those from cross-direction data. UAV data obtained from cross-direction flights provided higher accuracy in gully parameter extraction and richer details. _ Conclusion H Setting UA V parameters to a resolution of less than 5 cm and obtaining cross-direction oblique aerial photography data can meet the accuracy requirements for monitoring gully morphology in the black soil region of Northeast China. [ABSTRACT FROM AUTHOR]

This paper introduces the use of point cloud processing for extracting 3D rock structure and the 3DEC-related reconstruction of slope failure, based on a case study of the 2019 Pinglu rockfall. The basic processing procedure involves: (1) computing the point normal for HSV-rendering of point cloud; (2) automatically clustering the discontinuity sets; (3) extracting the set-based point clouds; (4) estimating of set-based mean orientation, spacing, and persistence; (5) identifying the block-forming arrays of discontinuity sets for the assessment of stability. The effectiveness of our rock structure processing has been proved by 3D distinct element back analysis. The results show that SfM modelling and rock structure computing provides enormous cost, time and safety incentives in standard engineering practice. [ABSTRACT FROM AUTHOR]

River and lake health assessment (RLHA) is an important approach to alleviating the conflict between protecting river and lake ecosystems and fostering socioeconomic development, aiming for comprehensive protection, governance, and management. Vegetation, a key component of the riparian zone, supports and maintains river and lake health (RLH) by providing a range of ecological functions. While research on riparian zone vegetation is ongoing, these studies have not yet been synthesized from the perspective of integrating RLHA with the ecological functions of riparian zone vegetation. In this paper, based on the bibliometric method, the relevant literature studies on the topics of RLHA and unmanned aerial vehicle (UAV) remote sensing of vegetation were screened and counted, and the keywords were highlighted, respectively. Based on the connotation of RLH, this paper categorizes the indicators of RLHA into five aspects: water space: the critical area from the river and lake water body to the land in the riparian zone; water resources: the amount of water in the river and lake; water environment: the quality of water in the river and lake; water ecology:aquatic organisms in the river and lake; and water services:the function of ecosystem services in the river and lake. Based on these five aspects, this paper analyzes the key role of riparian zone vegetation in RLHA. In this paper, the key roles of riparian zone vegetation in RLHA are summarized as follows: stabilizing riverbanks, purifying water quality, regulating water temperature, providing food, replenishing groundwater, providing biological habitats, and beautifying human habitats. This paper analyzes the application of riparian zone vegetation ecological functions in RLH, summarizing the correlation between RLHA indicators and these ecological functions. Moreover, this paper analyzes the advantages of UAV remote sensing technology in the quantitative monitoring of riparian zone vegetation. This analysis is based on the high spatial and temporal resolution characteristics of UAV remote sensing technology and focuses on monitoring the ecological functions of riparian zone vegetation. On this basis, this paper summarizes the content and indicators of UAV quantitative remote sensing monitoring of riparian zone vegetation for RLHA. It covers several aspects: delineation of riparian zone extent, identification of vegetation types and distribution, the influence of vegetation on changes in the river floodplain, vegetation cover, plant diversity, and the impact of vegetation distribution on biological habitat. This paper summarizes the monitoring objects involved in monitoring riparian zones, riparian zone vegetation, river floodplains, and biological habitats, and summarizes the monitoring indicators for each category. Finally, this paper analyzes the challenges of UAV quantitative remote sensing for riparian zone vegetation at the current stage, including the limitations of UAV platforms and sensors, and the complexity of UAV remote sensing data information. This paper envisages the future application prospects of UAV quantitative remote sensing for riparian zone vegetation, including the development of hardware and software such as UAV platforms, sensors, and data technologies, as well as the development of integrated air-to-ground monitoring systems and the construction of UAV quantitative remote sensing platforms tailored to actual management applications. [ABSTRACT FROM AUTHOR]

In this paper, the advantages of the joint use of MHz- and GHz-frequency band impulses when employing contactless ground penetration radar (GPR) for the remote sensing of biomass, the height of the wheat canopy, and underlying soil moisture were experimentally investigated. A MHz-frequency band nanosecond impulse with a duration of 1.2 ns (average frequency of 750 MHz and spectrum bandwidth of 580 MHz, at a level of –6 dB) was emitted and received by a GPR OKO-3 equipped with an AB-900 M3 antenna unit. A GHz-frequency band sub-nanosecond impulse with a duration of 0.5 ns (average frequency of 3.2 GHz and spectral bandwidth of 1.36 GHz, at a level of −6 dB) was generated using a horn antenna and a Keysight FieldFox N9917B 18 GHz vector network analyzer. It has been shown that changes in the relative amplitudes and time delays of nanosecond impulses, reflected from a soil surface covered with wheat at a height from 0 to 87 cm and fresh above-ground biomass (AGB) from 0 to 1.5 kg/m2, do not exceed 6% and 0.09 ns, respectively. GPR nanosecond impulses reflected/scattered by the wheat canopy have not been detected. In this research, sub-nanosecond impulses reflected/scattered by the wheat canopy have been confidently identified and make it possible to measure the wheat height (fresh AGB up to 2.3 kg/m2 and height up to 104 cm) with a determination coefficient (R2) of ~0.99 and a bias of ~−7 cm, as well as fresh AGB where R2 = 0.97, with a bias = −0.09 kg/m2, and a root-mean-square error of 0.1 kg/m2. The joint use of impulses in two different MHz- and GHz-frequency bands will, in the future, make it possible to create UAV-based reflectometers for simultaneously mapping the soil moisture, height, and biomass of vegetation for precision farming systems. [ABSTRACT FROM AUTHOR]

This paper proposed an improved ant colony optimization named TSACO and an enhanced velocity obstacle (VO) to solve the UAV path planning problem. The TSACO incorporated non-uniform initial pheromone distribution, a turning heuristic function, and an elite ant system to improve the convergence speed and reduce the number of path corners during global planning. The enhanced VO integrated the UAV's dynamic equation, adaptive collision radius and emergency collision cone, along with an optimal velocity selection approach to increase real-time and safe local obstacle avoidance during local planning. Simulation experiments demonstrate that the proposed algorithms have better effectiveness in terms of path length, number of turns, and dynamic obstacle avoidance compared to other algorithms. [ABSTRACT FROM AUTHOR]

The accurate estimation of nitrogen content in crop plants is the basis of precise nitrogen fertilizer management. Unmanned aerial vehicle (UAV) imaging technology has been widely used to rapidly estimate the nitrogen in crop plants, but the accuracy will still be affected by the variety, the growth stage, and other factors. We aimed to (1) analyze the correlation between the plant nitrogen content of winter wheat and spectral, texture, and structural information; (2) compare the accuracy of nitrogen estimation at single versus multiple growth stages; (3) assess the consistency of UAV multispectral images in estimating nitrogen content across different wheat varieties; (4) identify the best model for estimating plant nitrogen content (PNC) by comparing five machine learning algorithms. The results indicated that for the estimation of PNC across all varieties and growth stages, the random forest regression (RFR) model performed best among the five models, obtaining R2, RMSE, MAE, and MAPE values of 0.90, 0.10%, 0.08, and 0.06%, respectively. Additionally, the RFR estimation model achieved commendable accuracy in estimating PNC in three different varieties, with R2 values of 0.91, 0.93, and 0.72. For the dataset of the single growth stage, Gaussian process regression (GPR) performed best among the five regression models, with R2 values ranging from 0.66 to 0.81. Due to the varying nitrogen sensitivities, the accuracy of UAV multispectral nitrogen estimation was also different among the three varieties. Among the three varieties, the estimation accuracy of SL02-1 PNC was the worst. This study is helpful for the rapid diagnosis of crop nitrogen nutrition through UAV multispectral imaging technology. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing search and rescue (SAR) operations by accessing inaccessible areas, accomplishing challenging tasks, and providing real-time monitoring and modeling in situations where human presence is unsafe. Multi-UAVs can collaborate more efficiently and cost-effectively than a single large UAV for performing SAR operations. In multi-UAV systems, task allocation (TA) is a critical and complex process involving cooperative decision making and control to minimize the time and energy consumption of UAVs for task completion. This paper offers an exhaustive review of both static and dynamic TA algorithms, confidently assessing their strengths, weaknesses, and limitations. It provides valuable insights into addressing research questions related to specific UAV operations in SAR. The paper rigorously discusses outstanding issues and challenges and confidently presents potential directions for the future development of task assignment algorithms. Finally, it confidently highlights the challenges of multi-UAV dynamic TA methods for SAR. This work is crucial for gaining a comprehensive understanding of multi-UAV dynamic TA algorithms and confidently emphasizes critical open issues and research gaps for future SAR research and development, ensuring that readers feel informed and knowledgeable. [ABSTRACT FROM AUTHOR]

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments. [ABSTRACT FROM AUTHOR]

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Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the start point to the destination point for each aerial vehicle. With the rapid development of UAV technology, UAVs are required to tackle missions in increasingly complex environments. Consequently, UAV path planning encounters more challenges, causing traditional deterministic algorithms to struggle to find the optimal path within a certain time. Evolutionary computation (EC) is a series of nature-inspired methodologies and algorithms, which have shown effectiveness and efficiency in solving many complex optimization problems in real-world applications. Recently, EC algorithms have been effectively applied in UAV path planning and have shown encouraging performance in obtaining high-quality solutions. Therefore, it is crucial to review the related research experience and literature in the field of using EC for UAV path planning. This paper presents a comprehensive survey to showcase the existing studies on EC in UAV path planning, especially in complex environments. The paper first proposes a novel taxonomy to categorize the relevant studies into three different categories according to the complex environmental properties of the application scenarios. These environmental properties include complex search space, complex time control, and complex optimization objectives. Then, the EC algorithms for UAV path planning in these complex environments are further systematically surveyed as constrained search space and large-scale search space in complex search space, dynamic UAV path planning and multi-UAV concurrent path planning in complex time control, and expensive objective and multiple objectives in complex optimization objectives. Finally, some potential future research directions for applying EC algorithms to UAV path planning are presented and discussed. [ABSTRACT FROM AUTHOR]

Photovoltaic energy (PV) is considered one of the pillars of the energy transition. However, this energy source is limited by a power density per unit surface lower than 200 W/m2, depending on the latitude of the installation site. Compared to fossil fuels, such low power density opens a sustainability issue for this type of renewable energy in terms of its competition with other land uses, and forces us to consider areas suitable for the installation of photovoltaic arrays other than farmlands. In this frame, floating PV plants, installed in internal water basins or even offshore, are receiving increasing interest. On the other hand, this kind of installation might significantly affect the water ecosystem environment in various ways, such as by the effects of solar shading or of anchorage installation. As a result, monitoring of floating PV (FPV) plants, both during the ex ante site evaluation phase and during the operation of the PV plant itself, is therefore necessary to keep such effects under control. This review aims to examine the technical and academic literature on FPV plant monitoring, focusing on the measurement and discussion of key physico-chemical parameters. This paper also aims to identify the additional monitoring features required for energy assessment of a floating PV system compared to a ground-based PV system. Moreover, due to the intrinsic difficulty in the maintenance operations of PV structures not installed on land, novel approaches have introduced autonomous solutions for monitoring the environmental impacts of FPV systems. Technologies for autonomous mapping and monitoring of water bodies are reviewed and discussed. The extensive technical literature analyzed in this review highlights the current lack of a cohesive framework for monitoring these impacts. This paper concludes that there is a need to establish general guidelines and criteria for standardized water quality monitoring (WQM) and management in relation to FPV systems. [ABSTRACT FROM AUTHOR]