Aerial Unmanned Aerial Vehicle Research Articles

Landslide Deposit Erosion and Reworking Documented by Geomatic Surveys at Mount Meager, BC, Canada

Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 106 m3) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 106 m3 of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 105 m3 of that sediment was redeposited inside the study area. About 5.8 × 105 m3 of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems.

A stochastic geometry-based performance analysis of a UAV corridor-assisted IoT network

The exploitation of unmanned aerial vehicles (UAVs) in enhancing network performance in the context of beyond-fifth-generation (5G) communications has shown a variety of benefits compared to terrestrial counterparts. In addition, they have been largely conceived to play a central role in data dissemination to Internet of Things (IoT) devices. In the proposed work, a novel stochastic geometry unified framework is proposed to study the downlink performance in a UAV-assisted IoT network that integrates both UAV-base stations (UAV-BSs) and terrestrial IoT receiving devices. The framework builds upon the concept of the aerial UAV corridor, which is modeled as a finite line above the IoT network, and the one-dimensional (1D) binomial point process (BPP) is employed for modeling the spatial locations of the UAV-BSs in the aerial corridor. Subsequently, a comprehensive SNR-based performance analysis in terms of coverage probability, average rate, and energy efficiency is conducted under three association strategies, namely, the nth nearest-selection scheme, the random selection scheme, and the joint transmission coordinated multi-point (JT-CoMP) scheme. The numerical results reveal valuable system-level insights and trade-offs and provide a firm foundation for the design of UAV-assisted IoT networks.

Enhanced stability of aerial refueling process of UAV based on fuzzy PID

This essay aims to study UAV(Unmanned Aerial Vehicle) aerial refueling technology, and by understanding the control analysis of aerial UAV refueling by comparing multiple R&D(Research And Development) teams, a more practical and feasible method is obtained, that is, through the discussion and design of UAV motion modeling, UAV trajectory tracking control system, fuzzy PID(proportion integral derivative) controller design, etc., the purpose of UAV aerial refueling is finally achieved, and the time of UAV is increased to enhance the stability and durability of UAV in air operation.

In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV

Most conventional speech recognition systems have mainly concentrated on voice-driven control of personal user devices such as smartphones. Therefore, a speech recognition system used in a special environment needs to be developed in consideration of the environment. In this study, a speech recognition framework for voice-driven control of unmanned aerial vehicles (UAVs) is proposed in a collaborative environment between manned aerial vehicles (MAVs) and UAVs, where multiple MAVs and UAVs fly together, and pilots on board MAVs control multiple UAVs with their voices. Standard speech recognition systems consist of several modules, including front-end, recognition, and post-processing. Among them, this study focuses on recognition and post-processing modules in terms of in-vehicle speech recognition. In order to stably control UAVs via voice, it is necessary to handle the environmental conditions of the UAVs carefully. First, we define control commands that the MAV pilot delivers to UAVs and construct training data. Next, for the recognition module, we investigate an acoustic model suitable for the characteristics of the UAV control commands and the UAV system with hardware resource constraints. Finally, two approaches are proposed for post-processing: grammar network-based syntax analysis and transaction-based semantic analysis. For evaluation, we developed a speech recognition system in a collaborative simulation environment between a MAV and an UAV and successfully verified the validity of each module. As a result of recognition experiments of connected words consisting of two to five words, the recognition rates of hidden Markov model (HMM) and deep neural network (DNN)-based acoustic models were 98.2% and 98.4%, respectively. However, in terms of computational amount, the HMM model was about 100 times more efficient than DNN. In addition, the relative improvement in error rate with the proposed post-processing was about 65%.

Radio Resource Management for Cellular-Connected UAV: A Learning Approach

Integrating unmanned aerial vehicles (UAVs) into existing cellular networks encounters lots of challenges, among which one of the most striking concerns is how to achieve harmonious coexistence of aerial transceivers, inter alia, UAVs, and terrestrial user equipments (UEs). In this paper, a cellular-connected UAV network is focused, where multiple UAVs receive messages from base stations (BSs) in the down-link, while BSs are serving ground UEs in their cells. For effectively managing inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first p-tier based RB coordination criterion is proposed and adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design minimizing UAV’s ergodic outage duration (EOD) is investigated. To cope with conventional optimization techniques’ inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is initiated, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in discrete action domain and beamforming design in continuous action regime, respectively. The hybrid D3QN-TD3 solution is trained via interacting with the considered outer and inner environments in an online centralized manner so that it can then help achieve the suboptimal EOD minimization performance during its offline decentralized exploitation. Simulation results have illustrated the effectiveness of the proposed hybrid D3QN-TD3 algorithm, compared to several representative baselines.

Design of Airborne Large Aperture Infrared Optical System Based on Monocentric Lens.

Conventional reconnaissance camera systems have been flown on manned aircraft, where the weight, size, and power requirements are not stringent. However, today, these parameters are important for unmanned aerial vehicles (UAVs). This article provides a solution to the design of airborne large aperture infrared optical systems, based on a monocentric lens that can meet the strict criteria of aerial reconnaissance UAVs for a wide field of view (FOV) and lightness of airborne electro-optical pod cameras. A monocentric lens has a curved image plane, consisting of an array of microsensors, which can provide an image with 368 megapixels over a 100° FOV. We obtained the initial structure of a five-glass (5GS) asymmetric monocentric lens with an air gap, using ray-tracing and global optimization algorithms. According to the design results, the ground sampling distance (GSD) of the system is 0.33 m at 3000 m altitude. The full-field modulation transfer function (MTF) value of the system is more than 0.4 at a Nyquist frequency of 70 lp/mm. We present a primary thermal control method, and the image quality was steady throughout the operating temperature range. This compactness and simple structure fulfill the needs of uncrewed airborne lenses. This work may facilitate the practical application of monocentric lens in UAVs.

Study on UAV Based on Fused Filament Fabrication

Air Unmanned Aerial Vehicles (UAVs) have the characteristics of simple structure and convenient carrying, while quadrotor UAVs have better manoeuvrability, can achieve fixed-point hover, and have vertical takeoff and landing capabilities. Additive Manufacturing (AM) is also often referred to as 3D printing. In the past ten years, AM technology has received extensive attention. Among the seven categories of AM technologies released by the American Society for Testing and Materials, Fused Filament Fabrication (FFF) is one of the most widely used and essential processing technologies in additive manufacturing. It mainly manufactures models by extruding molten wire (such as plastics, resins, composites, etc.). First, consider the additive manufacturing of the quadrotor aerial UAV entirely through fused filament fabrication, and control the UAV's flexibility by adjusting the filling material's parameter during the additive manufacturing process. However, the increased flexibility makes it difficult to control the UAV, and the structural, aerodynamic and aeroelastic effects must be further explored.

ASSESSING RED PINE SEEDLINGS USING UAV POINT CLOUDS AND FIELD-VERIFIED DATA

Abstract. Accurate, reliable, and cost-efficient approaches to forest monitoring are critical for sustainable forest management. The use of digital photogrammetry for tree height estimation is well-known among forest managers and remote sensing researchers. Satellite remote sensing has not been very successful in providing detailed and reliable estimates of tree height. Unmanned Aerial Vehicles (UAVs) are one of the latest remote sensing platforms to get forest attributes information at very high temporal and spatial resolution. This study assessed the potential of using digital aerial photogrammetry point clouds and UAV acquired high-resolution imagery to estimate red pine seedlings' height in Adirondacks, New York. Seedling's location, height, crown width, and diameter were measured from 16 fixed area sample plots, and multispectral imagery was acquired with DJI Matrice 100- UAV fitted with Micasense RedEdge-M camera. UAV was flown under clear sky conditions at 93-meter height in a single grid pattern with 80% front and side overlap. PIX4D software was used to process UAV multispectral imagery and generate Digital Surface Model (DSM) and Orthomosiac at 6.08 cm/pixel resolution along with 3D Digital Terrain Model (DTM). 3D densified point cloud layers of regeneration canopy were generated at an average density of 1.54 per m3. Seedlings were differentiated from bare ground cover through supervised image classification methods. Preliminary results of this study highlight that multispectral imagery acquired from UAVs has the potential to characterize and provide detailed structural information to estimate red pine seedlings' height.

Toward flexible and persistent UAV service: Multi-period and multi-objective system design with task assignment for disaster management

Usability of lightweight unmanned aerial vehicles (UAVs) limited in a variety of activities in disaster management due to two fundamental drawbacks: limited flight duration, and limited loading capacity. With the proper system design and task assignment methodologies, these limitations can be overcome to enable long-duration UAV missions over large areas. The present study set out to determine the optimal unmanned aerial system (UAS) system design and UAV task assignment for such applications. Based on a proposed multi-objective mathematical model, exact Pareto optimal solutions were derived using the ε–constraint method. To find approximate Pareto solutions, an approximate two-phase (ATP) approach was developed, which is computationally inexpensive compare to the optimal solution approach. Through numerical experiments based on a realistic case study, it was shown that the ATP approach results in high accuracy and a two-orders-of-magnitude reduction in computation time. Additionally, for prompt application of our study to continuously changing situations, a rolling-horizon UAS approach herein is suggested.

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning.

Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.

Cinematography Drone with Automated Ability for Self-flight and Maneuverability

Abstract: Unmanned Aerial Vehicles or UAVs are an amazing piece of machinery that have proven their usefulness in various industries like construction, military, logistics, filmmaking, etc. They’re either operated remotely by a human controller from the ground or have automated capabilities to control its own flight. But due to several technological and environmental restrictions there are many models that can guarantee complete autonomous behavior of the drone. For being truly autonomous, UAVs will need to get far better at sensing its surroundings and obstacles to decide its own path and react in time to avoid collisions. This project specifically aims at enhancing the drones used in the tourism sector for the betterment of traveling experience of people by automating the process of capturing their experience along with autonomous flight capabilities. ML enabled cinematography drones to tackle the gap in the market where good camera drones are unavailable at a lower price point with an added benefit of hands-free automated flight patterns to capture cinematic shots. We have tried to achieve this milestone by using sensors that can detect the surrounding obstacles in real-time and manipulate its flight path to capture cinematic shots of the target object. Through the project we’re also automating various shots like hover, 360 degree view, follow at focus, etc that the drone will include into its flight path on the go. Operating an unmanned flying machine is very challenging especially when you’re flying the drone out of your line of sight and detecting objects using just one FPV camera or monitoring several cameras at the same time is not humanly possible. Hence, through this project we’re not only allowing travellers to enjoy their journey but also take videos of places they can’t reach without having to worry about losing or crashing their drones. There can be further research on this flying model to inculcate it for various industries as the flying pattern and requirement changes in each sector. Keywords: UAV (Unmanned Aerial Vehicle), Drone, Cinematography, ML, FPV, Automated flight

On Tethered UAV-Assisted Heterogeneous Network

Unmanned aerial vehicle (UAV)-assisted heterogeneous network has drawn great research interests as it can significantly improve the capacity and coverage of the cellular networks. An UAV can act as a flying base station (BS) or a relay when the regular terrestrial infrastructure is malfunctioned or overloaded. However, the deployment of UAV-mounted communication infrastructure is confined by the limited on-board energy and short battery life. To prolong the lifetime of each UAV, connecting it with a ground charging station (GCS) through a tether could be a promising and feasible solution. In this paper, we aim to maximize the sum rate of all users by jointly optimizing the user association, resource allocation and placement of the GCSs and the aerial UAVs. In addition, the constraint of each user's quality of service (QoS) requirement and total available resource are considered. To tackle this problem, we propose a cyclic iterative algorithm to efficiently obtain suboptimal solutions. Specifically, the primal problem is decomposed into three subproblems, i.e., the TUAV placement problem, the resource allocation problem and the user association problem. Then, the three sub-problems are alternately and iteratively optimized by using the outputs of the first two as the input for the third. Numerical experiments demonstrate that our proposed algorithm outperforms baseline algorithms under different setups.

Citrus orchards under formation evaluated by UAV-Based RGB Imagery

Few studies have investigated the biometric attributes of citrus orchards under formation that use RGB sensors on board unmanned aerial vehicles (UAV) and the challenges are great. This study aimed to develop and validate a method of using aerial UAV images by automated routines to evaluate the biometric attributes of a crop of ‘Tahiti’ acid lime under formation. We used a multirotor UAV, programmed to capture images at three different map scales, with a frontal and side overlap of 80 %. Geoprocessing was carried out both with and without ground control points on each scale. An automated routine was developed in an open-source environment, consisting of three processing phases: i) Estimation of the plant biometric attributes, ii) Statistical analysis, and iii) Statistical Report Map (SRM). The use of the developed routine allowed to delimit and estimate the crown projection area with an accuracy of more than 95 % as well as identify and quantify the plants with an accuracy of over 97 %. The use of ground control points during the processing stage does not increase accuracy in estimating the biometric attributes under evaluation. On the other hand, map scale is strongly correlated with the quality of the estimates, especially plant height. The results allowed to define a method for the acquisition and analysis of aerophotogrammetric data using a UAV, which can be used to measure the plant biometric attributes under analysis and the method can be easily adapted to perennial crops.

"무인항공기를 활용한 금강 하도내의 퇴적지형 변화 특성 연구: 공주보 개방 전·후를 중심으로"

In this study is aerial photos and UAV(Unmanned Aerial Vehicle) images were used to analyzed the characteristics of depositional landform changes in the Geum river channels before and after the opening gate of Gongju weir. Based on the depositional landform classification result, the main stream and the bare land occupied most of the area in all periods, and also found that the main stream, mid-channel island, and sand bar occupied a greater degree of area increase or decrease compared to other landforms in the classification items. As a result of analyzing the characteristics of depositional landform changes before and after the opening gate of Gongju weir, it is judged that the depositional landforms have changed due to the decreased water level of the Geum river after the opening of the weir, the summer rainy season and typhoons, river stabilization after the effluence of Daecheong dam, supply and deposition of river sediments and fixation of vegetation. The results derived from this study can be used as basic data for the study of river depositional landforms and the establishment of management and conservation plans for the landforms in river channels.

Development of automatic system for Unmanned Aerial Vehicle (UAV) motion control for mine conditions

Underground mining operations are connected with significant risks of technogenic accidents, which can be catastrophic. Mitigating the consequences of such phenomena directly depends on the reliability and efficiency of information about the state of parameters of many technological processes, mine workings and facilities located in them. At failure of standard systems of industrial telemetry in conditions of underground mining the creation of new information channels and places of information measurementbecomes practically impossible in case of emergency situation development. This predetermines necessity of use of essentially new systems of gathering and transfer of the information, based on robotized autonomous complexes. The task of acquiring reliable information about the situation in an emergency mine working with the help of drones (unmanned aerial vehicles or UAV) in order to make rational decisions in the course of the rescue operation is quite relevant. The aim of the paper was to develop a system of automatic control of an unmanned aerial vehicle (UAV) movement in confined space of a mine working, with significant perturbations of the mine air flow. The mathematical model of UAV movement in mine conditions, based on Euler angles or quaternions, was substantiated. The method of positioning through triangulation with the use of radio beacons was accepted as the basic method that allowed to determine the current position of an UAV. It was proposed to solve the problem of creation of the automatic system for an unmanned aerial vehicle movement control with the use of a hierarchical multiloop control system. The route planning algorithm was formed on the basis of the Dijkstra algorithm. For this purpose, discretization of the future motion space was performed, a labeled connected graph was constructed, on which the arc weights were the distances between the route points. A simulation experiment was implemented. The average deviation from the planned trajectory when flying at a speed of 10 m/s with payload mass up to 0.6 kg did not exceed 1 m, and the maximum deviation was unacceptably large. When flying at 6 m/s with payload mass up to 0.6 kg the average deviation did not exceed 0.3 m, and the maximum deviation, 1.2 m. The results of simulation of movement along the route towards the disturbing mine airflow showed that the control system allowed the UAV with payload of 0.6 kg to withstand the oncoming flow up to 8 m/s. It was obtained that with payload mass of 0.6 kg, the braking distance does not exceed 6 m if the UAV had a speed of 6 m/s, and the braking distance does not exceed 12 m at the speed of 10 m/s. The performed simulation studies confirmed the operating capability of the developed system for automatic motion control.

Road Extraction from UAV Images Using a Deep ResDCLnet Architecture

Obtaining near real-time road features is very important in emergent situations like flood and geological disaster cases. Remote sensing images with very high spatial resolution usually have many details in land use and land cover, which complicate the detection and extraction of road features. In this paper, we propose a deep residual deconvolutional network (Deep ResDCLnet), to extract road features from unmanned aerial vehicle (UAV) images. This proposed network is based on the deep neural network from SegNet architecture, the rich skip connection in a residual bottleneck, and the direct relationship among intermediate feature maps from the pixel deconvolution algorithm. It can improve the performance of a supervised learning model by differentiating and extracting complex road features on aerial photographs and UAV imagery. The proposed network is evaluated with the standard public Massachusetts road dataset and the UAV dataset collected alongside Yangtze River, and is compared with four state-of-art network architectures. The results show that the Deep ResDCLnet outperforms all four networks in terms of extraction accuracy, which demonstrates the effectiveness of the network in road extraction from very high spatial resolution imagery.

Online Identification-Verification-Prediction Method for Parallel System Control of UAVs

In order to solve the problem of how to efficiently control a large-scale swarm Unmanned Aerial Vehicle (UAV) system, which performs complex tasks with limited manpower in a non-ideal environment, this paper proposes a parallel UAV swarm control method. The key technology of parallel control is to establish a one-to-one artificial UAV system corresponding to the aerial swarm UAV on the ground. This paper focuses on the computational experiments algorithm for artificial UAV system establishment, including data processing, model identification, model verification and state prediction. Furthermore, this paper performs a comprehensive flight mission with four common modes (climbing, level flighting, turning and descending) for verification. The results of the identification experiment present a good consistency between the outputs of the refined dynamics model and the real flight data. The prediction experiment results show that the prediction method in this paper can basically guarantee that the prediction states error is kept within 10% about 16 s.

Comparação de imagens multiespectrais utilizando Satélites e VANT para a análise de mudanças estruturais em área de Floresta Seca

O sensoriamento remoto é uma ferramenta muito importante na aquisição de informações que permitem o monitoramento das características estruturais e de mudanças da vegetação nos biomas, e com o uso de índices espectrais de vegetação, é possível a análise de sua dinâmica ao longo do tempo. O presente estudo tem o objetivo de analisar a estrutura da cobertura vegetal em uma área do Bioma Caatinga comparando imagens multiespectrais adquiridas por satélite com diferentes resoluções e plataformas de veículos aéreos não tripulados (VANT) de baixa altitude e com câmeras em alta resolução. Foram realizados voos automatizados nos meses de novembro e dezembro de 2019 sobre a área de estudo e as imagens foram processadas para geração de orthomosaicos. Foram adquiridas gratuitamente imagens dos satélites Landsat-8 e Sentinel-2 para fins de comparação com o VANT. O vigor da vegetação verde foi analisado por meio do cálculo do Índice de Vegetação por Diferença Normalizada (NDVI) e verificado por meio da correlação entre produtos de alta resolução e baixa altitude com satélites. Ambos os produtos a partir de satélites mostraram-se eficazes e bons indicadores de vigor da vegetação, com destaque para as imagens do Sentinel-2 que obteve uma melhor correlação com as imagens aéreas de VANT chegando a (R = 0,7) em relação Landsat-8 (R = 0,6). Os produtos de Satélites mostraram bons indicadores para monitoramento de características estruturais da Caatinga, no entanto, não são indicados para avaliações de áreas com maior predominância de solo, agua ou outros alvos, pois eles podem afetar os valores de NDVI e impossibilitar uma avaliação mais detalhada das áreas. Comparison of multispectral images using satellites and UAV for the analysis of structural changes in a dry forest area A B S T R A C TRemote sensing is a very important tool in the acquisition of information that allows the monitoring of structural characteristics and changes in vegetation in biomes, and with the use of spectral indices of vegetation, it is possible to analyze its dynamics over time. This study aims to analyze the structure of vegetation cover in an area of the Caatinga Biome, comparing multispectral images acquired by satellite with different resolutions and low altitude unmanned aerial vehicle (UAV) platforms with high resolution cameras. Automated flights were carried out in November and December 2019 over the study area and the images were processed to generate orthomosaics. Landsat-8 and Sentinel-2 satellite images were acquired free of charge for comparison purposes with the UAV. The vigor of green vegetation was analyzed through the calculation of the Normalized Difference Vegetation Index (NDVI) and verified through the correlation between high resolution and low altitude products with satellites. Both products from satellites proved to be effective and good indicators of vegetation vigor, with emphasis on Sentinel-2 images, which obtained a better correlation with aerial UAV images reaching (R = 0.7) compared to Landsat-8 (R = 0.6). Satellite products showed good indicators for monitoring the structural characteristics of the Caatinga, however, they are not indicated for assessments of areas with a greater predominance of soil, water or other targets, as they can affect the NDVI values and make a more detailed assessment impossible. of the areas. Keywords: Remote Sensing, Satellite Images, Drones, NDVI, Dry Forest.

Location-Based Robust Beamforming Design for Cellular-Enabled UAV Communications

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for unmanned aerial vehicles (UAVs), which have been widely deployed to conduct various missions, e.g., precision agriculture, forest monitoring, and border patrol. However, the unique features of aerial UAVs, including high-altitude manipulation, 3-D mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe intercell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of the direction-of-Arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14-dB SINR gain compared with five benchmark beamforming algorithms, including linearly constrained minimum variance (LCMV), position-based beamforming, diagonal loading (DL), robust capon beamforming (RCB), and robust LCMV algorithm.

Monitoring long-term gully erosion and topographic thresholds in the marginal zone of the Chinese Loess Plateau

Gully erosion has been widely regarded as an extreme process of land degradation around the world. Gully development rate and topographic thresholds obtained by measuring the morphological parameters of multiple gullies and their influencing factors are important indicators used to characterize gully activity and potential locations of gully initiation. However, existing gully measurement and observation methods are time-consuming and labor-intensive, and usually only a single or several gullies can be measured at a time. The objectives of this study are to develop a multi-scale gully erosion monitoring method by combining unmanned aerial vehicle (UAV) technology with historical aerial photogrammetry and a field survey, achieve the measurement of gully morphological parameters (length, width, depth, area, width-depth ratio, cross-sectional area and volume) and environmental factors (land use, slope gradient and drainage basin area) for numerous gullies in larger regions, and finally, solve the issue of the accurate calculation of regional topographic thresholds and gully development rates. We applied a multi-scale gully erosion monitoring method in the marginal zone of the Chinese Loess Plateau to investigate the gully changes from 1959 to 2018 and further identified long-term gully development patterns and their main control factors. We identified 46 permanent gullies via a field survey. Long-term developments in 30 permanent gullies were obtained using historical aerial photographs and UAV images. The topographic threshold relationship between the upslope drainage basin area (A) and local slope gradient (S) was determined using the remaining 16 incipient gullies derived from UAV images. The results indicated that the gully dimensions experienced significant expansion; the linear gully-head retreat was considerably stronger than the gully widening and deepening. The contribution of the eroded volume from the gully-head retreat to the total eroded volume was 6.1 times that of the gully sidewall retreat, indicating that gully-head retreat was the primary mechanism for gully development during the study period. The area of the gully-head drainage basin played a more significant role than the slope gradient and land use to explain the gully-head retreat. The S-A topographic threshold relationship was S = 0.0478A−0.4915, implying that overland flow erosion was the dominant gully erosion process in the study area. Our results provide the gully development rate and topographic threshold relationship in the marginal zone of the Loess Plateau and clarify the potential location of gully development, while proposing that effective reduction of the drainage basin area can aid in curbing gully erosion. This study suggests a multi-scale gully erosion monitoring method for rapidly and precisely obtaining long-term gully development characteristics in various regions.