Small Unmanned Aerial Systems Research Articles

Radiative heat loss estimation of building envelopes based on 3D thermographic models utilizing small unmanned aerial systems (sUAS)

The advent of sUAS (small unmanned aerial systems – or surveying drones) technology has made comprehensive thermographic analyses of the built environment feasible. Advantages over traditional thermal survey techniques include a significant increase in ease of three-dimensional (3D) navigation around large structures as well as the ability to perform data-rich measurements. 3D building envelope thermographic models represent the building surface temperatures and can be created using structure-from-motion photogrammetry techniques with the collected high-overlap sUAS thermographic images. This paper introduces an end-to-end workflow for measuring radiative heat loss from building envelopes. This includes the setup and process required for quickly creating accurate 3D thermographic models and their analysis with software developed for this purpose. Three analytical models for building envelope radiative heat loss are applied and tested for viability through controlled experiments. The workflow is then demonstrated by evaluating the thermal radiation losses of two buildings on the campuses of the University of North Georgia. Our findings suggest the workflow can quickly provide good radiative heat loss measurements and may be a valuable addition to more comprehensive thermal analysis techniques.

Multirotor-assisted measurements of wind-induced drift of irregularly shaped objects in aquatic environments

Ocean hazardous spills and search and rescue incidents are becoming more prevalent as maritime activities increase across all sectors of society. However, emergency response time remains a factor due to the lack of information available to accurately forecast the location of small objects. Existing drifting characterization techniques are limited to objects whose drifting properties are not affected by on-board wind and surface current sensors. To address this challenge, we study the application of multirotor small unmanned aerial systems (sUAS), and embedded navigation technology, for on-demand wind velocity and surface flow measurements to characterize drifting properties of small objects. An off-the-shelf quadrotor sUAS was used to measure the speed and direction of the wind field at 10 m above surface level near a drifting object. We also leveraged sUAS-grade attitude and heading reference systems and GPS antennas to build water-proof tracking modules that recorded the position and orientation, as well of translational and rotational velocities, of objects drifting in water. The quadrotor and water-proof tracking modules were deployed during field experiments conducted in Claytor Lake, Virginia and the Atlantic Ocean south of Martha’s Vineyard, Massachusetts to characterize the leeway parameters of manikins that simulated a person in water. Leeway parameters were found to be an order of magnitude within previous estimates that were derived using conventional wind and surface current observations. We also determined that multirotor sUAS and water-proof tracking modules can provide accurate and high-resolution ambient information that is critical to understand how changes in orientation affect the downwind displacement and jibing characteristics of small objects floating in water. These findings support further development and application of multirotor sUAS technology for leeway characterization and understanding the effect of an object’s downwind-relative orientation on its drifting characteristics.

An indoor landscape for instruction of 3-D aerial drone imagery

ABSTRACT An introduction in the use of small unmanned aerial systems for mapping is becoming a desirable course for undergraduate students. The controllability of drone aircraft and quality cameras, even in less expensive aerial models, offers opportunities for students to learn and collect their own imagery for a variety of applications. This opportunity can be taught with an active learning approach. The challenge for instructors is to cover the three fundamentals in the remote sensing workflow – planning, collection, and image processing – while minimizing logistical issues associated with actual flight operation. The logistical issues (e.g. policy, legal, safety, weather) for an outdoor aerial drone learning experience are quite daunting, particularly for urban campuses with local/state restrictions on the use of drone aircraft. In this article, we separate the fundamental learning concepts in the remote sensing workflow from the flight operation and provide a learning environment for unlimited experiments. Using a synthetic indoor scaled landscape, students are given the opportunity for repeat experiments in a tightly controlled environment, saving the focus on actual flight operation for later times in the semester. The advantages of the indoor landscape are many, and also minimize the logistical issues for actual flight operation.

Evaluating the Ability of Multi-Sensor Techniques to Capture Topographic Complexity.

This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography.

Antarctic atmospheric boundary layer observations with the Small Unmanned Meteorological Observer (SUMO)

Abstract. Between January 2012 and June 2017 a small unmanned aerial system (sUAS), known as the Small Unmanned Meteorological Observer (SUMO), was used to observe the state of the atmospheric boundary layer in the Antarctic. During six Antarctic field campaigns, 116 SUMO flights were completed. These flights took place during all seasons over both permanent ice and ice-free locations on the Antarctic continent and over sea ice in the western Ross Sea. Sampling was completed during spiral ascent and descent flight paths that observed the temperature, humidity, pressure and wind up to 1000 m above ground level and sampled the entire depth of the atmospheric boundary layer, as well as portions of the free atmosphere above the boundary layer. A wide variety of boundary layer states were observed, including very shallow, strongly stable conditions during the Antarctic winter and deep, convective conditions over ice-free locations in the summer. The Antarctic atmospheric boundary layer data collected by the SUMO sUAS, described in this paper, can be retrieved from the United States Antarctic Program Data Center (https://www.usap-dc.org, last access: 8 March 2021). The data for all flights conducted on the continent are available at https://doi.org/10.15784/601054 (Cassano, 2017), and data from the Ross Sea flights are available at https://doi.org/10.15784/601191 (Cassano, 2019).

A Post-Classification Change Detection Model with Confidences in High Resolution Multi-Date sUAS Imagery in Coastal South Carolina

ABSTRACT Small unmanned aerial systems, or sUAS, remote sensing has much potential for monitoring vegetation cover and other phenomena due to its innate ability to capture very high spatial resolution imagery at low altitudes, for small areas, and with rapid planning. Remote sensing methodologies used in monitoring change from orbital and manned aerial remote sensing have been extensively researched and implemented. However, these same historic change detection methodologies have not been thoroughly tested with the new, very high spatial resolution imagery captured by sUAS, particularly the modest sUAS proliferating in the resource management agencies around the world. This study seeks to examine and understand the variability involved in using sUAS for change detection of vegetation cover by designing a confidence model, calibrating the model, and demonstrating the use of the model. Our research design involves a novel approach using multiple collections in a 1.6-hour period over a controlled environment where the land cover did not change. A confidence model was developed and calibrated in the controlled environment. A demonstration of the developed confidence model from the control environment was used for a hurricane impacted area. Coastal dune vegetation cover, essential for dune strength and growth, was monitored before and after Hurricane Irma impacted Harbor Island in coastal South Carolina. The results indicate that even though no actual change occurred during the controlled experiment, an average of 5.6% of the pixels indicated a false change of land cover. These false land cover discrepancies are caused from slight shadow movements, georegistration accuracies, multiple look angles and variable spectral response from a mosaic (i.e. 120 images) of imagery. It was also determined that much change in vegetation cover occurred as a result of inundation from Hurricane Irma, and confidences were high in assessing the change.

Navigator Notes

Navigator Notes

High resolution aerial photogrammetry based 3D mapping of fruit crop canopies for precision inputs management

Rapid and accurate canopy attributes estimation is highly critical in fruit crops production management as this information can be used for canopy and crop load management as well as to develop nutrient/chemical prescription application maps. However, the existing ground based canopy sensing and attribute estimation methods are laborious and often involve complexity with field data collection and analysis. Manual methods can be subjective as well. Therefore, this study explores aerial photogrammetry based method of tree–row–volume (TRV), leaf–wall–area (LWA), canopy volume (CV) and canopy cover (CC) estimation for grapevine and apple canopies. Remote sensing data was collected using a consumer–grade small unmanned aerial system (UAS) with an RGB imaging sensor flying at different flight altitudes i.e., 15 m (Ground sampling distance, GSD = 0.45 cm pixel−1 at 65° sensor inclination), 30 m (0.90 and 0.85 cm pixel−1 at 65°and 75°, respectively), 45 m (1.35 and 1.27 cm pixel−1 at 65°and 75°, respectively) and 60 m (1.81 and 1.69 cm pixel−1 at 65°and 75°, respectively). Crop surface model (CSM) was derived from such data to estimate canopy height, width and foliage vigor, which are further used to estimate TRV, LWA, CV and CC. The ground measured and aerial imagery estimated TRV had a strong relationship with the data collected at the lowest GSD within grapevine canopies (R2 = 0.77 at 0.45 cm pixel−1) as well as for apple canopies (R2 = 0.82 at 0.90 cm pixel−1). Similar trends were observed for the LWA (R2 = 0.77 and 0.86), CV (R2 = 0.43 and 0.64) and CC (R2 = 0.61 and 0.68) estimates for grapevine and apple canopies, respectively. Increasing GSD (≥0.45 cm pixel−1 in grapevine and ≥ 0.90 cm pixel−1 in apple) resulted in a weak relationship between ground measurements and aerial imagery data-based estimates for grapevines (R2 ≤ 0.36) and apple canopies (R2 = 0.39–0.78). Overall, the aerial flights with lower GSD and double grid missions with RGB imaging sensor in 65° orientation aided in the development of site–specific high–quality canopy vigor maps that can be used in precision crop inputs management related decision making.

A Review of High Precision Finite Element Modelling Methods for Light and Small UAS

In the dynamic mechanics analysis of light and small unmanned aerial systems (UAS), The importance of the high-precision modelling of UAS is unquestionable. Modelling can be classified into 3D geometric model modelling and finite element model modelling, of which the finite element model modelling is mainly divided into four parts: discretization, connection modelling, contact modelling and material attribute definition. This review summarizes the existing modelling methods of light and small UAS, and provides the basic methods and modelling methods for the simulation calculation of light and small UAS. Implications for practice and future research are provided.

Field evaluation of a compact, polarizing topo‐bathymetric lidar across a range of river conditions

AbstractThis paper summarizes field trials to evaluate the performance of a prototype compact topo‐bathymetric lidar sensor for surveying rivers. The sensor uses a novel polarization technique to distinguish between laser returns from the water surface and streambed and its size and weight permit deployment from a small unmanned aerial system (sUAS) or a boat. Field testing was designed to identify the range of operational conditions under which the sensor can provide accurate information on river depths. For accuracy assessment, conventional, field‐based depth measurements were collected by wading and sonar. Additionally, optical properties of the rivers were measured in situ. Wading and lidar bathymetry comparisons in relatively shallow channels yielded observed versus predicted (OP) regression R2 values ranging from 0.60 to 0.97. A comparison between sonar and lidar bathymetry in a deeper river resulted in an OP R2 of 0.72. Absorption and attenuation coefficients at the 532 nm wavelength of the lidar were recorded in the field and the highest values of these inherent optical properties were at sites with the highest turbidity and highest concentrations of colored dissolved organic matter, chlorophyll, and suspended sediment. At these sites, which included both sand and gravel/cobble beds, the point density of riverbed returns was not uniform, with areas of sparse coverage occurring primarily in deeper water. However, submerged objects and slopes could be resolved in the lidar point clouds.

Design and Development of a High-Speed UAS for beyond Visual Line-of-Sight Operations

This paper provides details on best practices for vehicle design for BVLOS missions, with a focus on robust control links and real-time health monitoring.The development work presented herein focuses on a specific mission to set the speed and distance world record for a fully autonomous vehicle, requiring development of analytic tools for estimating vehicle range and endurance, as well as models for predicting command and control link integrity while operating BVLOS. Range and endurance estimates are developed using a novel real-time system identification algorithm onboard the vehicle for aerodynamic parameter estimation, and propulsion system characterization. Flight-testing methods and results are presented demonstrating the capabilities of these algorithms, with results from a unique test-case involving an unintended fault during the record flight. In addition to the vehicle performance assessment, Friis-based models are introduced to provide a pseudo real-time communications link range assessment. The predictive communications range models were refined using specific antenna and system characterization data measured in a compact range facility resulting in a detailed link budget useful for addressing BVLOS command and control requirements. The combination of aircraft and radio-frequency performance prediction models with supporting flight-testing techniques applied to small unmanned aerial systems provides critical insight into the design of future vehicles, and validation of existing systems tasked with performing BVLOS missions.

An approach to minimize aircraft motion bias in multi-hole probe wind measurements made by small unmanned aerial systems

Abstract. A multi-hole probe mounted on an aircraft provides the air velocity vector relative to the aircraft, requiring knowledge of the aircraft spatial orientation (e.g., Euler angles), translational velocity and angular velocity to translate this information to an Earth-based reference frame and determine the wind vector. As the relative velocity of the aircraft is typically an order of magnitude higher than the wind velocity, the extracted wind velocity is very sensitive to multiple sources of error including misalignment of the probe and aircraft coordinate system axes, sensor error and misalignment in time of the probe and aircraft orientation measurements in addition to aerodynamic distortion of the velocity field by the aircraft. Here, we present an approach which can be applied after a flight to identify and correct biases which may be introduced into the final wind measurement. The approach was validated using a ground reference, different aircraft and the same aircraft at different times. The results indicate a significant reduction in wind velocity variance at frequencies which correspond to aircraft motion.

SUAS Remote Sensing to Evaluate Geothermal Seep Interactions with the Yellowstone River, Montana, USA

Small unmanned aerial systems (sUAS) are becoming increasingly popular due to their affordability and logistical ease for repeated surveys. While sUAS-based remote sensing has many applications in water resource management, their applicability and limitations in fluvial settings is not well defined. This study uses a combined thermal-optic sUAS to monitor the seasonal geothermal influence of a 1-km-long reach of the Yellowstone River, paired with in-situ streambed temperature profiles to evaluate geothermal seep interactions with Yellowstone River in Montana, USA. Accurate river water surface elevation along the shoreline was estimated using structure from motion (SfM) photogrammetry digital surface models (DSMs); however, water surface elevations were unreliable in the main river channel. Water temperature in thermal infrared (TIR) orthomosaics was accurate in temperature ranges of tens of degrees (>≈30 °C), but not as accurate in temperature ranges of several degrees (>≈15 °C) as compared to in-situ water temperature measurements. This allowed for identification of geothermal features but limited the ability to identify small-scale temperature changes due to river features, such as pools and riffles. The study concludes that rivers with an average width greater than or equal to 123% of the ground area covered by a TIR image will be difficult to study using structure from motion photogrammetry, given Federal Aviation Administration (FAA) altitude restrictions and sensor field of view. This study demonstrates the potential of combined thermal-optic sUAS systems to collect data over large river systems, and when combined with in-situ measurements, can further increase the sUAS utility in identifying river characteristics.

Pixel-Based Calibration and Atmospheric Correction of a UAS-Mounted Thermal Camera for Land Surface Temperature Measurements

HighlightsA novel pixel-based calibration algorithm and an atmospheric correction method are developed.Application of the calibration methods reduces the RMSE of measurements to less than 1.32°C.The calibrations facilitate stitching of images together to form whole-field mosaics.Abstract. Thermal imagery can be used to provide insight into the water stress status and evapotranspiration demand of crops, but satellite-based sensors are generally too coarse spatially and too infrequent temporally to provide information of use for the management of specific fields. Thermal cameras mounted on small unmanned aerial systems (UAS) have potential to provide canopy temperature information at high spatial and temporal resolutions useful for crop management; however, without appropriate camera corrections, the measurement biases of these uncooled thermal cameras can be larger than ±5°C. Such uncertainty can render such camera measurements useless. In this research, a pixel-based (non-uniformity) calibration algorithm and an atmospheric correction method based on in-field approximate blackbody sources (water targets) were developed for a thermal camera. The objective was to improve the temperature measurement accuracy of the thermal camera on various land surfaces including soil and vegetation. With sufficient accuracy, temperature measurements can be used for the estimation of latent heat flux of field crops in the future. The thermal camera was first calibrated in a laboratory setting where the camera and environmental conditions were controlled. The results indicated that in the range between 10°C and 45°C, the calibrated temperatures were accurate, with an average bias of 1.76°C, and had a high linear correlation with reference temperatures (water target temperatures) (R2 > 0.99). Variability of measurements was also better constrained. In-field atmospheric correction is also important for obtaining high-accuracy thermal imagery. By applying both pixel-based calibration and atmospheric corrections, the RMSE (root mean square error) of validation targets from two dates in 2017 was reduced from 4.56°C and 6.36°C before calibration to 1.32°C and 1.24°C after calibration. The calibration process also increased the range of temperatures in the imagery, which enhanced contrast and may help with identification of tie-points and stitching of images together to form whole-field mosaics. Keywords: Atmospheric correction, Pixel-based calibration, Thermal remote sensing, UAS, Water targets.

Small UAS and Delivery Drones: Challenges and Opportunities The 38th Alexander A. Nikolsky Honorary Lecture

During the past two decades, there has been major growth of small-unmanned aerial vehicles (sUAV) for hobbyists and rapidly expanding commercial and military applications. The impetus for this dramatic expansion has been due to the explosion of mobile technology in terms of microelectronics, data processing and transmission capability, high-energy-density batteries, miniaturized integrated programmable chips, and innovations in computer vision and videography/photography. However, there are many challenges to overcome before these small unmanned aerial systems (sUAS) can be used for routine commercial and military applications, which include reliability, sizable payload and range, stringent navigation/guidance requirements, and precision takeoff/landing and robust autonomous flight in constrained and low-altitude gusty environment. The objective of this paper is to cover state-of-the-art of sUAS and delivery drones, identify technology gaps and key scientific barriers, and present future research needs for high payoff applications.

Remote Pilot Situational Awareness with Augmented Reality Glasses: An Observational Field Study

With the use of small unmanned aerial systems (sUAS) proliferating throughout industry and public safety, it is imperative to ensure safety of flight. The Federal Aviation Administration (FAA) published regulations for commercial use of sUAS in 2016 and included the requirement to maintain visual line-of-sight with the aircraft at all times. However, due to the nature of the sUAS ground control stations (GCS), remote pilots time-share between observing the aircraft and interacting with the display on the GCS. Time-sharing between the aircraft and GCS can be seen as similar to the cross-check a pilot uses when flying a manned aircraft. While manned aircraft designers have invested in the ergonomics and understanding of the cognitive process to optimize situational awareness, it has not been a design requirement for sUAS. The result is that the unmanned operator must change head orientation, eye focus, and remove the aircraft from peripheral vision during the cross-check between the GCS and aircraft. This, coupled with the limited field of view of the sUAS GCS displayed camera, leads to loss of situational awareness through task saturation, and misprioritization. Mixed reality, virtual reality, and augmented reality visual devices are being adopted in the gaming and technical world. The application of these devices to the sUAS GCS could mitigate some of the degradation of situational awareness. Specifically, the incorporation of augmented reality devices where a synthetic display is overlaid on the real-world, allows the remote pilot to observe the aircraft, manipulate the camera, and interact with the GCS without changing head position. This participant observational study evaluated the difference between the remote pilot cross-check while flying with a typical GCS display and when flying with an augmented reality headset in a field setting. The results indicate a significant difference between the pilot’s crosscheck when using augmented reality glasses allowing the pilot to maintain the aircraft in their field of view 56.7% of the time compared to 20.5% when not using the glasses.

Modeling sensitivity of topographic change with sUAS imagery

Mapping the topographic surface and monitoring the change in such topographic surfaces has largely been a remote sensing-based solution for the last eighty years. The last few years has seen the dramatic rise in the use of small unmanned aerial systems (sUAS) for mapping both the topographic surface (in largely un-vegetated areas) and particularly, the overlying surface layer. How accurate are the sUAS derived elevation surfaces? How accurate are the change surfaces from a comparison of multi-date surfaces? How confident can the user be in the changes detected? Probing the expected accuracy for a topographic surface derived from low altitude sUAS imagery is a tad more problematic than many other types of remotely sensed imaging sensors. The precision and spatial resolution of the sUAS imagery, and subsequent digital elevation models (DEMs) is similar to the precision and spatial resolution of the very reference data sources used for accessing accuracy. In this research an approach was used to evaluate the performance of sUAS for creating digital elevation models on coastal sand dunes that did not change during ten repeat aerial collections at 40 m above ground level. A simple error budget model was used to empirically derive the intrinsic accuracy of the sUAS-derived topographic surface. The overall accuracy of the ten DEMs derived from independent aerial missions was 0.033 m root mean squared error (RMSE). The results indicate a confidence threshold of ~0.030 m can be typically used to separate 95% of the ‘false’ topographic changes mapped from two digital elevation models in this collection/processing context. By modeling and removing the reference data error (i.e. survey grade global navigation satellite systems (GNSS)-derived validation points) the average accuracy of the ten DEMs was 0.022 m (RMSE).

Application of a Small Unmanned Aerial System to Measure Ammonia Emissions from a Pilot Amine-CO2 Capture System.

The quantification of atmospheric gases with small unmanned aerial systems (sUAS) is expanding the ability to safely perform environmental monitoring tasks and quickly evaluate the impact of technologies. In this work, a calibrated sUAS is used to quantify the emissions of ammonia (NH3) gas from the exit stack a 0.1 MWth pilot-scale carbon capture system (CCS) employing a 5 M monoethanolamine (MEA) solvent to scrub CO2 from coal combustion flue gas. A comparison of the results using the sUAS against the ion chromatography technique with the EPA CTM-027 method for the standard emission sampling of NH3 shows good agreement. Therefore, the work demonstrates the usefulness of sUAS as an alternative method of emission measurement, supporting its application in lieu of traditional sampling techniques to collect real time emission data.

Evaluation of small unmanned aerial system highway volume and speed‐sensing applications

Small unmanned aerial systems (sUAS) have been utilised in the transportation industry in recent years to decrease the cost of projects and tasks while increasing safety. This is due to their ability to capture aerial images with reduced effort and time. Recently, these devices have begun to be used for traffic monitoring, given their ability to capture video above a roadway. Combined with object-tracking techniques, vehicle data such as speeds, volumes, and trajectories could be extracted, providing an opportunity to revolutionize traffic data collection techniques. There exists a need to improve upon origin–destination volume and speed data collection procedures through the development of a low-cost methodology to capture detailed data that would allow for more accurate analysis. This study evaluates a methodology and measures the accuracy of volume and speed data collected through sUAS aerial imagery using object tracking techniques. Using the developed methodology, vehicle volumes were tracked at 93% accuracy, and vehicle speeds were recorded with a 6.6% relative error. While future improvements could be made on this methodology as technology advances, this study reveals a low-cost solution to collect vehicle data which could improve the efficiency of transportation studies, and in turn, improve safety.

Performance Assessment of Portable Optical Particle Spectrometer (POPS).

Accurate representation of atmospheric aerosol properties is a long-standing problem in atmospheric research. Modern pilotless aerial systems provide a new platform for atmospheric in situ measurement. However, small airborne platforms require miniaturized instrumentation due to apparent size, power, and weight limitations. A Portable Optical Particle Spectrometer (POPS) is an emerged instrument to measure ambient aerosol size distribution with high time and size resolution, designed for deployment on a small unmanned aerial system (UAS) or tethered balloon system (TBS) platforms. This study evaluates the performance of a POPS with an upgraded laser heater and additional temperature sensors in the aerosol pathway. POPS maintains its performance under different environmental conditions as long as the laser temperature remains above 25 °C and the aerosol flow temperature inside the optical chamber is 15 °C higher than the ambient temperature. The comparison between POPS and an Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) suggests that the coincidence error is less than 25% when the number concentration is less than 4000 cm−3. The size distributions measured by both of them remained unaffected up to 15,000 cm−3. While both instruments’ sizing accuracy is affected by the aerosol chemical composition and morphology, the influence is more profound on the POPS.