Low-altitude Flight Research Articles

Seed and Seedling Detection Using Unmanned Aerial Vehicles and Automated Image Classification in the Monitoring of Ecological Recovery

Monitoring is a crucial component of ecological recovery projects, yet it can be challenging to achieve at scale and during the formative stages of plant establishment. The monitoring of seeds and seedlings, which represent extremely vulnerable stages in the plant life cycle, is particularly challenging due to their diminutive size and lack of distinctive morphological characteristics. Counting and classifying seedlings to species level can be time-consuming and extremely difficult, and there is a need for technological approaches offering restoration practitioners with fine-resolution, rapid and scalable plant-based monitoring solutions. Unmanned aerial vehicles (UAVs) offer a novel approach to seed and seedling monitoring, as the combination of high-resolution sensors and low flight altitudes allow for the detection and monitoring of small objects, even in challenging terrain and in remote areas. This study utilized low-altitude UAV imagery and an automated object-based image analysis software to detect and count target seeds and seedlings from a matrix of non-target grasses across a variety of substrates reflective of local restoration substrates. Automated classification of target seeds and target seedlings was achieved at accuracies exceeding 90% and 80%, respectively, although the classification accuracy decreased with increasing flight altitude (i.e., decreasing image resolution) and increasing background surface complexity (increasing percentage cover of non-target grasses and substrate surface texture). Results represent the first empirical evidence that small objects such as seeds and seedlings can be classified from complex ecological backgrounds using automated processes from UAV-imagery with high levels of accuracy. We suggest that this novel application of UAV use in ecological monitoring offers restoration practitioners an excellent tool for rapid, reliable and non-destructive early restoration trajectory assessment.

Validation of the sea ice surface albedo scheme of the regional climate model HIRHAM–NAOSIM using aircraft measurements during the ACLOUD/PASCAL campaigns

Abstract. For large-scale and long-term Arctic climate simulations appropriate parameterization of the surface albedo is required. Therefore, the sea ice surface (SIS) albedo parameterization of the coupled regional climate model HIRHAM–NAOSIM was examined against broadband surface albedo measurements performed during the joint ACLOUD (Arctic CLoud Observations Using airborne measurements during polar Day) and PASCAL (Physical feedbacks of Arctic boundary layer, Sea ice, Cloud and AerosoL) campaigns, which were performed in May–June 2017 north of Svalbard. The SIS albedo parameterization was tested using measured quantities of the prognostic variables surface temperature and snow depth to calculate the surface albedo and the individual fractions of the ice surface subtypes (snow-covered ice, bare ice, and melt ponds) derived from digital camera images taken on board the Polar 5 and 6 aircraft. The selected low-altitude (less than 100 m) flight sections of overall 12 flights were performed over surfaces dominated by snow-covered ice. It was found that the range of parameterized SIS albedo for individual days is smaller than that of the measurements. This was attributed to the biased functional dependence of the SIS albedo parameterization on temperature. Furthermore, a time-variable bias was observed with higher values compared to the modeled SIS albedo (0.88 compared to 0.84 for 29 May 2017) in the beginning of the campaign, and an opposite trend towards the end of the campaign (0.67 versus 0.83 for 25 June 2017). Furthermore, the surface type fraction parameterization was tested against the camera image product, which revealed an agreement within 1 %. An adjustment of the variables, defining the parameterized SIS albedo, and additionally accounting for the cloud cover could reduce the root-mean-squared error from 0.14 to 0.04 for cloud free/broken cloud situations and from 0.06 to 0.05 for overcast conditions.

A Low-Altitude Flight Conflict Detection Algorithm Based on a Multilevel Grid Spatiotemporal Index

Flight conflict detection is fundamental to flight dispatch, trajectory planning, and flight safety control. An ever-increasing aircraft population and higher speeds, particularly the emergence of hypersonic/supersonic aircrafts, are challenging the timeliness and accuracy of flight conflict detection. Traditional trajectory conflict detection algorithms rely on traversing multivariate equations of every two trajectories, in order to yield the conflict result and involve extensive computation and high algorithmic complexity; these algorithms are often unable to provide the flight conflict solutions required quickly enough. In this paper, we present a novel, low-altitude flight conflict detection algorithm, based on the multi-level grid spatiotemporal index, that transforms the traditional trajectory-traversing multivariate conflict computation into a grid conflict state query of distributed grid databases. Essentially, this is a method of exchanging "storage space" for "computational time". First, we build the spatiotemporal subdivision and encoding model based on the airspace. The model describes the geometries of the trajectories, low-altitude obstacles, or dangerous fields and identifies the grid with grid codes. Next, we design a database table structure of the grid and create a grid database. Finally, we establish a multilevel grid spatiotemporal index, design a query optimization scheme, and examine the flight conflict detection results from the grid database. Experimental verification confirms that the computation efficiency of our algorithm is one order of magnitude higher than those of traditional methods. Our algorithm can perform real-time (dynamic/static) conflict detection on both individual aircraft and aircraft flying in formation with more efficient trajectory planning and airspace utilization.

Influence of Drone Altitude, Image Overlap, and Optical Sensor Resolution on Multi-View Reconstruction of Forest Images

Recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.

SIMULATION OF REFLECTIONS FROM SURFACE-DISTRIBUTED OBJECTS ON EXAMPLE OF FRAGMENT OF UNDERLYING SURFACE

The paper considers the problem of the synthesis of a low-point model of the relief of the underlying surface. The model can be used to conduct semi-null simulation when the radar is operating in low-altitude flight mode. A method for defining a relief in the form of a piecewise broken approximation is proposed. An algorithm is presented that allows real-time, for a given relief, to calculate for each of the elements of the resolution in range its angular position and angular dimensions. These parameters determine the expectation of the angular noise and the width of their probability density, respectively. The ability to work in real time is provided using a straight line in a spherical coordinate system when defining the relief. The recommendations on the choice of a geometric model from the previously justified family are given. The synthesized models provide a given form of the correlation functions of angular noise and adequately replace reflections from distributed objects. Geometric models can be used as the basis for matrix simulators of reflected electromagnetic waves.

Comparison of GNSS-, TLS- and Different Altitude UAV-Generated Datasets on the Basis of Spatial Differences

In this study, different in-situ and close-range sensing surveying techniques were compared based on the spatial differences of the resultant datasets. In this context, the DJI Phantom 3 Advanced and Trimble UX5 Unmanned Aerial Vehicle (UAV) platforms, Zoller + Fröhlich 5010C phase comparison for continuous wave-based Terrestrial Laser Scanning (TLS) system and Network Real Time Kinematic (NRTK) Global Navigation Satellite System (GNSS) receiver were used to obtain the horizontal and vertical information about the study area. All data were collected in a gently (mean slope angle 4%) inclined, flat vegetation-free, bare-earth valley bottom near Istanbul, Turkey (the size is approximately 0.7 ha). UAV data acquisitions were performed at 25-, 50-, 120-m (with DJI Phantom 3 Advanced) and 350-m (with Trimble UX5) flight altitudes (above ground level, AGL). The imagery was processed with the state-of-the-art SfM (Structure-from-Motion) photogrammetry software. The ortho-mosaics and digital elevation models were generated from UAV-based photogrammetric and TLS-based data. GNSS- and TLS-based data were used as reference to calculate the accuracy of the UAV-based geodata. The UAV-results were assessed in 1D (points), 2D (areas) and 3D (volumes) based on the horizontal (X- and Y-directions) and vertical (Z-direction) differences. Various error measures, including the RMSE (Root Mean Square Error), ME (Mean Error) or MAE (Mean Average Error), and simple descriptive statistics were used to calculate the residuals. The comparison of the results is simplified by applying a normalization procedure commonly used in multi-criteria-decision-making analysis or visualizing offset. According to the results, low-altitude (25 and 50 m AGL) flights feature higher accuracy in the horizontal dimension (e.g., mean errors of 0.085 and 0.064 m, respectively) but lower accuracy in the Z-dimension (e.g., false positive volumes of 2402 and 1160 m3, respectively) compared to the higher-altitude flights (i.e., 120 and 350 m AGL). The accuracy difference with regard to the observed terrain heights are particularly striking, depending on the compared error measure, up to a factor of 40 (i.e., false positive values for 120 vs. 50 m AGL). This error is attributed to the “doming-effect”—a broad-scale systematic deformation of the reconstructed terrain surface, which is commonly known in SfM photogrammetry and results from inaccuracies in modeling the radial distortion of the camera lens. Within the scope of the study, the “doming-effect” was modeled as a functional surface by using the spatial differences and the results were indicated that the “doming-effect” increases inversely proportional to the flight altitude.

Helicopter forward looking alert method for low-altitude flight based on terrain matching

Helicopter forward looking alert method for low-altitude flight based on terrain matching

Autonomous Flight Control for Multi-Rotor UAVs Flying at Low Altitude

Unmanned aerial vehicles (UAVs) at low altitude flight may significantly degrade their performance and the safety under wind disturbances and incorrect operations. This paper presents a robust control strategy for UAVs to achieve good performance of low altitude flight and disturbance rejection. First, a novel second-order hexacopter dynamics is established and the position tracking is translated to the altitude and the rotational angle tracking problem. An integrated control scheme is created to deal with the challenges faced by hexacopter at low altitude flight, in which the influence of near-ground threshold distance and the desired roll, pitch, and yaw are analyzed. Moreover, an improved flying altitude planner and an attitude planner for low altitude conditions are designed respectively to avoid the overturning risk due to the big reaction torque and external disturbances. Second, a sliding-mode-based altitude tracking controller and an attitude tracking controller are designed to reduce the tracking errors and improve the robustness of the system. Finally, the proposed control scheme is tested on simulation and experiment platforms of multi-rotor UAV to show the feasibility and accurate trajectory tracking at low altitude flight.

A simplified virtual obstacle avoidance approach for aircraft altitude bounded flight maneuvers

Avoiding collision with obstacles and terrain has always been a critical issue for aircraft during low altitude flight landing and approach maneuvers, especially in airports located in mountainous areas. In low altitude flight, it is desirable to find a safe trajectory that is compatible with the aircraft capabilities and at the same time optimizes flight efficiencies. Safety is a fundamental concern when in terrain following (TF) and/or terrain avoidance (TA) flight modes, and aircraft design for these types of missions emphasizes the inclusion of terrain and obstacle avoidance flight (TOAF) capabilities.

Weed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier

Weed detection with aerial images is a great challenge to generate field maps for site-specific plant protection application. The requirements might be met with low altitude flights of unmanned aerial vehicles (UAV), to provide adequate ground resolutions for differentiating even single weeds accurately. The following study proposed and tested an image classifier based on a Bag of Visual Words (BoVW) framework for mapping weed species, using a small unmanned aircraft system (UAS) with a commercial camera on board, at low flying altitudes. The image classifier was trained with support vector machines after building a visual dictionary of local features from many collected UAS images. A window-based processing of the models was used for mapping the weed occurrences in the UAS imagery. The UAS flight campaign was carried out over a weed infested wheat field, and images were acquired between a 1 and 6 m flight altitude. From the UAS images, 25,452 weed plants were annotated on species level, along with wheat and soil as background classes for training and validation of the models. The results showed that the BoVW model allowed the discrimination of single plants with high accuracy for Matricaria recutita L. (88.60%), Papaver rhoeas L. (89.08%), Viola arvensis M. (87.93%), and winter wheat (94.09%), within the generated maps. Regarding site specific weed control, the classified UAS images would enable the selection of the right herbicide based on the distribution of the predicted weed species.

Optimization of longitudinal and lateral motion of vehicle near disturbed surface

The paper covers the tasks of flight control, where it is necessary to minimize the mean true (geometrical) altitude of low-altitude vehicle flight. Therein a developed control algorithm for longitudinal and lateral motion of vehicle is described, assessment criteria for trajectory selection efficiency are determined and efficiency dependencies upon parameters of control algorithm and medium are analyzed.

Bioinspired wind field estimation—part 1: Angle of attack measurements through surface pressure distribution

One of the major challenges of Mini-Unmanned Aerial Vehicle flight is the unsteady interaction with turbulent environment while flying in lower levels of atmospheric boundary layer. Following inspiration from nature we expose a new system for angle of attack estimation based on pressure measurements on the wing. Such an equipment can be used for real-time estimation of the angle of attack during flight or even further building of wind velocity vector with additional equipment. Those information can find purpose in control and stabilization of the aircraft due to inequalities seen by the wing or even for various soaring strategies that rely on active control for energy extraction. In that purpose, flying wing aircraft has been used with totally four span-wise locations for local angle of attack estimation. In-flight angle of attack estimation from differential pressure measurements on the wing has been compared with magnetic sensor with wind vane. The results have shown that pressure ports give more reliable estimation of angle of attack when compared to values given by wind vane attached to a specially designed air-boom. Difference in local angle of attack at four span-wise locations has confirmed spatial variation of turbulence in low altitude flight. Moreover, theoretical law of energy dissipation for wind components described by Kaimal spectrum has shown acceptable match with estimated ones.

Exploring tandem wing UAS designs for operation in turbulent urban environments

The stability of small unmanned air systems can be challenged by turbulence during low-altitude flight in cluttered urban environments. This paper explores the benefits of a tandem wing aircraft configuration with the implementation of a pressure-based phase-advanced turbulence sensory system on a small unmanned air system for gust mitigation. The objective was to utilise passive and active methods to minimise gust-induced perturbations. Experimentation in repeatable turbulence within a wind tunnel’s test section was conducted. The experiments focus on the roll axis, which is isolated through a specially designed roll-axis rig. The results show improvement over conventional aircraft. This work is part of a larger research project aimed at enabling safe, stable and steady small unmanned air systems flight in urban environments.

Development and prospect of unmanned aerial vehicle technologies for agricultural production management

Unmanned aerial vehicles have been developed and applied to support agricultural production management. Compared with piloted aircraft, an Unmanned Aerial Vehicle (UAV) can focus on small crop fields at lower flight altitudes than regular aircraft to perform site-specific farm management with higher precision. They can also fill in the gap in locations where fixed winged or rotary winged aircraft are not readily available. In agriculture, UAVs have primarily been developed and used for remote sensing and application of crop production and protection materials. Application of fertilizers and chemicals is frequently needed at specific times and locations for site-specific management. Routine monitoring of crop plant health is often required at very high resolution for accurate site-specific management as well. This paper presents an overview of research involving the development of UAV technology for agricultural production management. Technologies, systems and methods are examined and studied. The limitations of current UAVs for agricultural production management are discussed, as well as future needs and suggestions for development and application of the UAV technologies in agricultural production management.

Changes in Pediatric-Sized Endotracheal Tube Cuff Pressure With Elevation Gain: Observations in Ex Vivo Simulations and In Vivo Air Medical Transport.

Prolonged excessive endotracheal cuff pressure greater than 30 cmH2O is thought to cause ischemic airway injury. Excessive cuff pressure with altitude gain during air medical transport has been previously described in adult patients. It is poorly understood how pediatric-sized endotracheal tube (ETT) cuffs behave with atmospheric pressure change during flight. In ex vivo models 4.0, 6.0, and 8.0, ETTs restricted within scaled syringe tubing were inflated to 20 cmH2O. Pressure was measured against 1500 ft elevation gain in ground and flight models. In an in vivo observation of pediatric patient transport, change in cuff pressure was measured between takeoff and helicopter peak flight altitudes. In the ex vivo ground model, endotracheal cuff pressure increased linearly with altitude and exceeded 40 cmH2O in all tube sizes. Comparable pressure change was demonstrated in the flight model. No difference was demonstrated in the degree of pressure change between ETT sizes. In the in vivo observations during patient transport, pressure increase was consistent with that seen in the ex vivo models. Children who are intubated with cuffed ETTs for air medical transport are subject to excessive endotracheal cuff pressure at even low flight altitudes. Endotracheal tube size did not affect the degree of cuff pressure change, contrary to previous study. These findings need to be validated and correlated to patient clinical outcomes. The implications of these data need to be considered clinically particularly for prolonged transport of intubated pediatric patients at elevation.

Airspace map design to implement customer-friendly service on unmanned aerial vehicles

Today, as the number of drones has suddenly increased, the problems of collision between drones in low altitude flight and Traffic control in drone flight have been highlighted. The collision between drones has a potential danger that can be extended to a very serious accident, which is directly linked to the second or third accident rather than the first accident. More detailed and thorough air traffic management is essential in order to minimize the collisions between drones and predict or address the collision before the collision occurs. However, the current unmanned traffic management technology is a basic research level and it is urgent to establish the concept. Therefore, the purpose of this study is to design and compare the airspace maps for drones, which is a core technical element in construction of drone Traffic control system. As a result of this study, based on the building height values in the conventional navigation map, a drone obstacle map for each airspace area is displayed. Also, the airspace classification is analyzed and the design definition and method for the uncontrolled drones are suggested.

LiDAR from drones employed for mapping archaeology – Potential, benefits and challenges

AbstractAlthough the use of both drones and LiDAR (light detection and ranging) has become common in archaeology in recent years, LiDAR scanning from drones is still in its infancy. The technological development related to drones as well as laser scanner instruments has gradually reached the point where these can be integrated. In this paper we present the results from a test where the applicability of LiDAR used from a drone was studied. The study had two objectives – both based on comparative studies: (i) whether LiDAR from drones represents an improvement in terms of detection success; and (ii) whether LiDAR from drones can increase the quality of the documentation of archaeological features and their physical properties based on remote sensing. A modest improvement of detection success was found, but was not as convincing as one would perhaps expect given the relatively large increase in terms of ground points. This has led us to the conclusion that very dense vegetation obstructs laser beams from reaching all the way to the bare earth. As regards accuracy in documenting archaeological features, the study showed more significant improvements. The last part of the paper is dedicated to a discussion of the pros and cons of using LiDAR from drones compared to conventional airborne laser scanning from aeroplanes or helicopters. The main advantages concern flexibility, low flight altitude and small laser footprint as well as the advantages of a far‐reaching field of view. The disadvantages are related to price, battery capacity, size of area and especially the requirement of line of sight between the drone operator and the drone, a fact that restricts the efficiency in terms of mapping large areas. Nevertheless, the final conclusion is that LiDAR from drones has the potential to make a substantial improvement to archaeological remote sensing.

3D real-time path planning based on cognitive behavior optimization algorithm for UAV with TLP model

Real-time path planning of unmanned aerial vehicle (UAV) in the three-dimension environment is a complicated global optimum problem. This paper formulates the real-time path planning problem for the unmanned aerial vehicles (UAVs) as a complicated global optimum problem in the three-dimensional environment and proposes an improved a tree-dimensional (3D) real-time path planning algorithm based on tri-level programing model to generate an optimal feasible route. The flight routeis designed to have a short length and a low flight altitude. The multiple constraints based on the realistic scenarios are taken into account as the threat level function. Firstly, from the view of control performance of UAV, tri-level programing (TLP) model is designed to generate a smooth path. Then, the objective functions including the leader, middle-level follower and bottom-level follower function and decision variables of TLP are defined to guarantee the flight toward the target. In addition, the cognitive behavior optimization algorithm (COA) embedded with the proposed optimization strategies is used to solve this problem. Numerical experimental results and the comparison with PSO, RRT algorithm demonstrated the efficiency and effectiveness of the proposed approach.

Investigating the performances of commercial and non-commercial software for ground filtering of UAV-based point clouds

ABSTRACTIn recent years, the advent of unmanned aerial vehicles (UAV)-based photogrammetry has enabled the collection of accurate and comprehensive information from the surface of the Earth. Owing to low-altitude flights, it is possible to generate high-density point clouds, which are useful for accurate representation of topography of the land surface. Ground filtering is the removal of the points belonging to above-ground objects in order to retrieve ground points to be used in generating digital terrain models. It is essential in most applications for modelling the environment and is performed by using various types of commercial and non-commercial software. This study investigates the performances of seven widely used ground filtering algorithms on UAV-based point clouds. These algorithms are (1) the adaptive triangulated irregular network implemented into the commercial Agisoft Photoscan Professional software, (2) the multi-scale curvature classification implemented into the commercial global mapper software, (3) the cloth simulation filtering (CSF) applied with a MATLAB script, (4) the interpolation-based Boise Centre Aerospace Laboratory-lidar algorithm embedded in the commercial environment for visualizing images software, (5) the interpolation-based gLiDAR non-commercial software, (6) the 2D progressive morphological algorithms, and (7) elevation threshold with expand window algorithms embedded in the non-commercial airborne lidar data processing and analysis tools software. The results showed that the CSF algorithm presented the best filtering results for both test sites with Total Errors of 6% and 4.5% in the sites 1 and 2, respectively.

Mapping of radiation anomalies using UAV mini-airborne gamma-ray spectrometry

Localization of size-limited gamma-ray anomalies plays a fundamental role in uranium prospecting and environmental studies. Possibilities of a newly developed mini-airborne gamma-ray spectrometric equipment were tested on a uranium anomaly near the village of Třebsko, Czech Republic. The measurement equipment was based on a scintillation gamma-ray spectrometer specially developed for unmanned aerial vehicles (UAV) mounted on powerful hexacopter. The gamma-ray spectrometer has two 103 cm3 BGO scintillation detectors of relatively high sensitivity. The tested anomaly, which is 80 m by 40 m in size, was investigated by ground gamma-ray spectrometric measurement in a detail rectangular measurement grid. Average uranium concentration is 25 mg/kg eU attaining 700 mg/kg eU locally. The mini-airborne measurement across the anomaly was carried out on three 100 m long parallel profiles at eight flight altitudes from 5 to 40 m above the ground. The resulting 1 s 1024 channel gamma-ray spectra, recorded in counts per second (cps), were processed to concentration units of K, U and Th, while total count (TC) was reported in cps. Increased gamma ray intensity of the anomaly was indicated by mini-airborne measurement at all profiles and altitudes, including the highest altitude of 40 m, at which the recorded intensity is close to the natural radiation background. The reported instrument is able to record data with comparable quality as standard airborne survey, due to relative sensitive detector, lower flight altitude and relatively low flight speed of 1 m/s. The presented experiment brings new experience with using unmanned semi-autonomous aerial vehicles and the latest mini-airborne radiometric instrument. The experiment has demonstrated the instrument's ability to localize size-limited uranium anomalies.