Small Unmanned Aerial Systems Research Articles

Detecting Geo-Positional Bias in Imagery Collected Using Small UASs

Statistical methods for detecting bias in global positioning system (<small>GPS</small>) error are presented and applied to imagery collected using three common unmanned aerial systems (<small>UASs</small>). Imagery processed without ground control points (<small>GCPs</small>) had horizontal errors of 1.0–2.5 m; however, the errors had unequal variances, significant directional bias, and did not conform to the expected statistical distribution and so should be considered unreliable. When <small>GCPs</small>were used, horizontal errors decreased to less than 5 cm, and the errors had equal variances, directional uniformity, and they conformed to the expected distribution. The analysis identified a longitudinal bias in some of the reference data, which were subsequently excluded from the analysis. Had these data been retained, the estimates of positional accuracy would have been unreliable and inaccurate. These results strongly suggest that examining <small>GPS</small> data for bias should be a much more common practice.

Collision Risk Modeling and Analysis for Lateral Separation to Support Unmanned Traffic Management.

This article investigates the utility and extension of well-established collision risk modeling approaches for lateral separation standard development for unmanned traffic management and network design. The applicability of standard assumptions and simplifications made in the manned environment to a scaled-down unmanned aerial systems (UAS) environment is first investigated. The results are used to derive an iso-risk surface that shows the tradeoff between separation distance and navigation performance for a given risk level or target level of safety. The model is then extended to consider collision risk from one to tracks, and finally to determine the total collision risk and the related iso-risk surfaces for systems of parallel tracks with additions to account for the effects of more than two parallel tracks. The extended model is applied to a case study where airspace design for an urban area is conducted via maximization of the number of same-direction parallel tracks while meeting the target level of safety. The results suggest that lateral separation distances less than 100 m are achievable for small UAS, and that the separation standards are mostly affected by the proportion of poorly navigatingaircraft.

RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System

Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., Spartina alterniflora) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; R2 = 0.39) and excess green index (ExG; R2 = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for S. alterniflora biomass estimation using sUAS as an on-demand, personal remote sensing tool.

Dynamic Analysis of a Series Hybrid–Electric Powertrain for an Unmanned Aerial Vehicle

Hybrid–electric powertrains offer the potential for performance improvements in unmanned aerial systems. However, for small unmanned aerial systems, potential gains in range and endurance can depend significantly on the aircraft flight profile and powertrain control logic. Subsequently, these impact the performance of individual powertrain components. This study uses dynamic simulations of an unmanned aerial system (UAS) with different powertrain control logic approaches to evaluate the performance of a series hybrid–electric powertrain. Component models generated using lookup table approaches and model parameterization are combined to generate a dynamic system model of the unmanned aerial system. The performance of the powertrain is evaluated for three representative mission profiles. Fuel consumption and battery state of charge form two metrics that are used to evaluate the performance of a baseline controller against an ideal operating line strategy. The ideal operating line strategy, which uses a performance map obtained by engine characterization on a specialized dynamometer, produces an average fuel economy improvement ranging from 0.5–2.0 g/km for a 30-min-long mission profile. The study demonstrates the need to consider a dynamic analysis aided by detailed component performance maps and a robust control strategy in evaluating hybridization approaches for UAS powertrains.

Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project.

Daily evapotranspiration (ETd) plays a key role in irrigation water management and is particularly important in drought-stricken areas, such as California and high-value crops. Remote sensing allows for the cost-effective estimation of spatial evapotranspiration (ET), and the advent of small unmanned aerial systems (sUAS) technology has made it possible to estimate instantaneous high-resolution ET at the plant, row, and subfield scales. sUAS estimates ET using “instantaneous” remote sensing measurements with half-hourly/hourly forcing micrometeorological data, yielding hourly fluxes in W/m2 that are then translated to a daily scale (mm/day) under two assumptions: (a) relative rates, such as the ratios of ET-to-net radiation (Rn) or ET-to-solar radiation (Rs), are assumed to be constant rather than absolute, and (b) nighttime evaporation (E) and transpiration (T) contributions are negligible. While assumption (a) may be reasonable for unstressed, full cover crops (no exposed soil), the E and T rates may significantly vary over the course of the day for partially vegetated cover conditions due to diurnal variations of soil and crop temperatures and interactions between soil and vegetation elements in agricultural environments, such as vineyards and orchards. In this study, five existing extrapolation approaches that compute the daily ET from the “instantaneous” remotely sensed sUAS ET estimates and the eddy covariance (EC) flux tower measurements were evaluated under different weather, grapevine variety, and trellis designs. Per assumption (b), the nighttime ET contribution was ignored. Each extrapolation technique (evaporative fraction (EF), solar radiation (Rs), net radiation-to-solar radiation (Rn/Rs) ratio, Gaussian (GA), and Sine) makes use of clear skies and quasi-sinusoidal diurnal variations of hourly ET and other meteorological parameters. The sUAS ET estimates and EC ET measurements were collected over multiple years and times from different vineyard sites in California as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Optical and thermal sUAS imagery data at 10 cm and 60 cm, respectively, were collected by the Utah State University AggieAir sUAS Program and used in the Two-Source Energy Balance (TSEB) model to estimate the instantaneous or hourly sUAS ET at overpass time. The hourly ET from the EC measurements was also used to validate the extrapolation techniques. Overall, the analysis using EC measurements indicates that the Rs, EF, and GA approaches presented the best goodness-of-fit statistics for a window of time between 1030 and 1330 PST (Pacific Standard Time), with the Rs approach yielding better agreement with the EC measurements. Similar results were found using TSEB and sUAS data. The 1030–1330 time window also provided the greatest agreement between the actual daily EC ET and the extrapolated TSEB daily ET, with the Rs approach again yielding better agreement with the ground measurements. The expected accuracy of the upscaled TSEB daily ET estimates across all vineyard sites in California is below 0.5 mm/day, (EC extrapolation accuracy was found to be 0.34 mm/day), making the daily scale results from TSEB reliable and suitable for day-to-day water management applications.

Morphological evolution of a non-engineered managed realignment site following tidal inundation

There is growing evidence that managed realignment (MR) sites have lower biodiversity than natural saltmarshes, which has been associated with differences in the physical function and morphological evolution following site breaching. This evidence has been derived from MR sites previously used for intensive arable agriculture or modified during site construction. Therefore, the development of these sites may not be representative of the sedimentological evolution that would have otherwise occurred. This paper presents analysis of high spatial resolution digital surface models derived from the images collected using a small-unmanned aerial system from a non-engineered MR site at Cwm Ivy Marsh, Gower Peninsula, Wales. These models are examined alongside a novel combination of high frequency measurements of the rate and patterns of sedimentation, suspended sediment concentration (SSC), and the sub-surface structure and geochemical profiles. Results indicated that although the site became topographically less variable over a four year period, the intertidal morphology developed through an increase in the abundance of higher order creek systems and sediment being deposited at a rate of between 3 and 7 cm/year. The SSC followed an inverse pattern to water depth, with bed elevation increasing and then decreasing during both the flood and ebb tidal phases. Analysis of the sediment subsurface geochemical composition indicated redox profiles similar to natural intertidal environments; evidence of a fluctuating water table was found at a saltmarsh site, in comparison to waterlogging identified at an anoxic mudflat site. These findings provide a new insight to the sedimentological processes in a MR site without the influence of landscaping or site engineering prior to site breaching, which can be used to inform the design of future MR sites.

Magnetic Surveys With Unmanned Aerial Systems: Software for Assessing and Comparing the Accuracy of Different Sensor Systems, Suspension Designs and Compensation Methods

AbstractA typical problem for magnetic surveys with small Unmanned Aerial Systems (sUAS) is the heading error caused by undesired magnetic signals that originate from the aircraft. This can be addressed by suspending the magnetometers on sufficiently long tethers. However, tethered payloads require skilled pilots and are difficult to fly safely. Alternatively, the magnetometer can be fixed on the aircraft. In this case, aircraft magnetic signals are removed from the recordings with a process referred to as magnetic compensation, which requires parameters estimated from calibration flights flown in an area with magnetically low‐gradients prior to the survey. We present open‐source software fully written in Python to process data and compute compensations for two fundamentally different magnetometer systems (scalar and vector). We used the software to compare the precision of two commercially available systems by flying dense grid patterns over a 135 × 150 m area using different suspension configurations. The accuracy of the magnetic recordings is assessed using both standard deviations of the calibration pattern and tie‐line cross‐over differences from the survey. After compensation, the vector magnetometer provides the lowest heading error. However, the magnetic field intensity recovered with this system is relative and needs to be adjusted with absolute data if absolute intensity values are needed. Overall, the highest accuracy of all suspension configurations tested was obtained by fixing the magnetometer 0.5 m below the sUAS onto a self‐built carbon‐fiber frame, which also offered greater stability and allowed fully autonomous flights in a wide range of conditions.

The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems

Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes).

Atmospheric aerosol, gases, and meteorological parameters measured during the LAPSE-RATE campaign by the Finnish Meteorological Institute and Kansas State University

Abstract. Small unmanned aerial systems (sUASs) are becoming very popular as affordable and reliable observation platforms. The Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE), conducted in the San Luis Valley (SLV) of Colorado (USA) between 14 and 20 July 2018, gathered together numerous sUASs, remote-sensing equipment, and ground-based instrumentation. Flight teams from the Finnish Meteorological Institute (FMI) and the Kansas State University (KSU) co-operated during LAPSE-RATE to measure and investigate the properties of aerosol particles and gases at the surface and in the lower atmosphere. During LAPSE-RATE the deployed instrumentation operated reliably, resulting in an observational dataset described below in detail. Our observations included aerosol particle number concentrations and size distributions, concentrations of CO2 and water vapor, and meteorological parameters. All datasets have been uploaded to the Zenodo LAPSE-RATE community archive (https://zenodo.org/communities/lapse-rate/, last access: 21 August 2020). The dataset DOIs for FMI airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3993996, Brus et al. (2020a), and those for KSU airborne measurements and surface measurements are available here: https://doi.org/10.5281/zenodo.3736772, Brus et al. (2020b).

Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping

Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one hour dwell time and no GCPs.

Onboard and External Magnetic Bias Estimation for UAS through CDGNSS/Visual Cooperative Navigation.

This paper describes a calibration technique aimed at combined estimation of onboard and external magnetic disturbances for small Unmanned Aerial Systems (UAS). In particular, the objective is to estimate the onboard horizontal bias components and the external magnetic declination, thus improving heading estimation accuracy. This result is important to support flight autonomy, even in environments characterized by significant magnetic disturbances. Moreover, in general, more accurate attitude estimates provide benefits for georeferencing and mapping applications. The approach exploits cooperation with one or more “deputy” UAVs and combines drone-to-drone carrier phase differential GNSS and visual measurements to attain magnetic-independent attitude information. Specifically, visual and GNSS information is acquired at different heading angles, and bias estimation is modelled as a non-linear least squares problem solved by means of the Levenberg–Marquardt method. An analytical error budget is derived to predict the achievable accuracy. The method is then demonstrated in flight using two customized quadrotors. A pointing analysis based on ground and airborne control points demonstrates that the calibrated heading estimate allows obtaining an angular error below 1°, thus resulting in a substantial improvement against the use of either the non-calibrated magnetic heading or the multi-sensor-based solution of the DJI onboard navigation filter, which determine angular errors of the order of several degrees.

Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique

Powdery mildew (PM) in apple orchards is a critical fungal disease that considerably reduces yield, harvested fruit quality, and orchard health. Rapid detection and mapping of resulting infestation at orchard scale is a challenge with existing laborious manual scouting approaches. Therefore, this study explored the feasibility of detecting and mapping PM infestation in an apple orchard block using high-resolution visible (red-green-blue [RGB]) and multispectral imaging technique. Imaging campaigns were conducted over an experimental orchard using small unmanned aerial systems (UAS) integrated with the above optical sensors. K-means classifier trained on individual snapshots of RGB imagery had mean disease detection accuracy of 77%. Eight multispectral vegetation indices also showed significant differences (p < 0.001) between healthy (Mean: 0.25–0.84) and infected (Mean: 0.01–0.25) leaves. Modified Simple Ratio-Red (MSRR), Modified Simple Ratio-Blue (MSRB), and Optimized Soil Adjusted Vegetation Index (OSAVI) showed the highest contrast (Mean: 0.46–0.79). Orchard block-scale mapping using RGB orthomosaic layer, classified with k-means and spectral angle mapper techniques had accuracies up to 73%. Overall, high resolution imagery in the visible and multispectral domain could sufficiently detect the infection and generate site-specific disease heat maps. These maps could be useful to apple growers in directing prescriptive resources ( pruning labor or fungicide application) for PM management.

Application of Dempster–Shafer Networks to a Real-Time Unmanned Systems Risk Analysis Framework

Unmanned aerial systems (UASs) are continuing to proliferate. Quantitative risk assessment for UAS operations, both a priori and during the operation, are necessary for governing authorities and insurance companies to understand the risks and properly approve operations and assign insurance premiums, respectively. In this paper, the problem of UAS risk analysis and decision making is treated through a novel application of Dempster–Shafer (DS) networks using auto-updating transition matrices. This method was motivated by the results of the 2018 UAS Safety Symposium held at the Georgia Institute of Technology, which was conducted as part of the research detailed in this paper. The paper describes training a DS network based on simulated operation data, testing the capabilities of the trained network to make real-time decisions on a small UAS against a baseline system in a representative mission, and exploring how this system would extend to a more inclusive UAS ecosystem. Conclusions are drawn with respect to the research performed, and additional research directions are proposed.

State-level statutes governing unmanned aerial vehicle use in academic research in the United States

ABSTRACT In the United States of America (USA), the Federal Aviation Administration (FAA) maintains operational control of the national airspace and the aircraft in it – including small unmanned aerial systems (sUAS). However, states and local jurisdictions can regulate collection of imagery/data and where the operator of the aircraft is located. Surprisingly, numerous states have enacted laws that impact unmanned aircraft operations in the national airspace. The proliferation of state statutes regulating sUAS operations and imagery/data collections has resulted in a patchwork quilt of laws affecting the academic or researcher using sUAS in the United States for image collections. These legal challenges are often known in a scientist’s home state but unknown and challenging to operate in another state. This research examined the state-level statutes (currently as of 2020) that govern sUAS operation and associated image collection in the United States as they would apply to academic research/instruction, in particular. All state statutes were categorized into the preparatory, operations, image/data collection regulations and the geographic space affected. The chronology, of when and by state, the laws enacted were also examined. These regulations are presented with interpretation of how the sUAS operator is affected and the possible exemptions available for university personnel lawfully operating a sUAS in their home state and in a non-home state. Problematic issues, such as the restriction of overflights over critical facilities, when the locations of these type facilities are unknown (because of security), are also presented and discussed. Recommendations on how an academic/researcher can qualify for an exemption to statute restrictions are provided.

Case Study of a SCAT Survey and Successful Remediation Strategy by Mechanical Mixing of a Fuel Oil Spill into a Mountain Stream

ABSTRACT # 1141233 The cargo of a double-tank truck carrying diesel and gasoline was released directly into a fast-flowing upland stream following an accident on a mountainous section of road in British Columbia (BC), Canada. High concentrations of the product were trapped in the interstitial spaces of coarse (cobble-boulder) sediments during a period of rising water levels. Almost the entire river backshore in the affected area was characterized by steep wooded slopes so that access everywhere was very difficult. These constraints for the SCAT program largely were overcome where direct backshore access was not possible using river rafts, boats (on the reservoir above the dam) and small Unmanned Aerial System (sUASs). Based on the survey results, a 4x4 Spider Walking Excavator equipped with a Universal grab on the hydraulic arm was deployed over a 2.5 km section of river immediately downstream of the accident site over a 9-day period. The grab rotated to mix the sediment or lifted and moved cobbles and boulders along the channel margin and in river bed sediments to release the oil. Swift Water Rescue personnel and river rescue response equipment were positioned with the Spider operations and the SCAT river bank surveys throughout the project, and used to scout river conditions ahead of SCAT rafting operations. Air monitoring was maintained throughout the response during all operations both along river banks as well as in the cab of the Spider while working in the river. A small UAS quadcopter was deployed to monitor the mixing activity in real time where the excavator could operate but ground access was unsafe or physically not possible. Standard SCAT practices were followed to provide the Unified Command (UC) with Shoreline Treatment Recommendation (STR) forms to guide the operations activities and once the treatment criteria were achieved STR Inspection Reports (SIRs) were submitted for approval by the UC. A downstream daily water sampling program monitored for PHs, VOCs and PAHs in the river waters during the mixing operations downstream of the operations area. At no time during the mechanical mixing activities (April 3 – 12) did the results of the analyses exceed Canadian and BC Water Quality Guidelines standards downstream past the confluence with the Salmo River and standards only were exceeded for the first few days of mechanical mixing (April 3 – April 5) during the period that the Spider was working on the upper reaches of the South Salmo.

Emergency Oil Spill Response: Data Collection Automation, and Dissemination

ABSTRACT The California Department of Fish and Wildlife (CDFW), Office of Spill Prevention and Response (OSPR) has employed Geographic Information System (GIS) technology for oil spill planning and response since the early 1990's, adding remote sensing tools to the mix by the turn of the Century. Discussed herein are OSPR's current smartphone and tablet-based field data collection applications. We use an off-the-shelf tool for pre-response protection strategy planning and use customized tools developed in-house for California Sea Otter Surveys, Wildlife Search and Collection and the Shoreline Cleanup Assessment Technique (SCAT). The benefits of automated field data collection include the omission of transcription errors during manual data input and streamlined data processing. This results in accurate and timely information delivered to responders, thus enhancing their capability for informed decision making. OSPR uses the National Oceanic and Atmospheric Administration's (NOAA) Environmental Response Management Application® (ERMA), a web-based GIS data viewer as the Common Operational Picture (COP) for data dissemination throughout the incident response community during an oil spill event, and for the post emergency response cooperative Natural Resource Damage Assessment (NRDA). OSPR also uses ERMA a planning tool by uploading significant data layers from the U.S Coast Guard's (USCG) six Area Contingency Plans (ACP) for coastal California and OSPR's Geographic Response Plans for inland waters of the state with higher risk of an oil spill. These response planning data are freely downloadable from the ERMA platform. In the Summer/Fall of 2019 at the Cymric Oil Field in Kern County, California, over 1,000,000 gallons of oil, mud and steam seeped into a dry stream bed from several “surface expressions”. Direct access to the spill site was not permitted for Health and Safety concerns. OSPR used an off-the-shelf small Unmanned Aerial System (sUAS, aka drone) equipped with a standard high definition (HD) camera and a thermal infrared camera for SCAT reconnaissance, resources at risk evaluations, and regularly scheduled mapping missions for overall site situational awareness.

UTM-Chain: Blockchain-Based Secure Unmanned Traffic Management for Internet of Drones.

Unmanned aerial systems (UAVs) are dramatically evolving and promoting several civil applications. However, they are still prone to many security issues that threaten public safety. Security becomes even more challenging when they are connected to the Internet as their data stream is exposed to attacks. Unmanned traffic management (UTM) represents one of the most important topics for small unmanned aerial systems for beyond-line-of-sight operations in controlled low-altitude airspace. However, without securing the flight path exchanges between drones and ground stations or control centers, serious security threats may lead to disastrous situations. For example, a predefined flight path could be easily altered to make the drone perform illegal operations. Motivated by these facts, this paper discusses the security issues for UTM’s components and addresses the security requirements for such systems. Moreover, we propose UTM-Chain, a lightweight blockchain-based security solution using hyperledger fabric for UTM of low-altitude UAVs which fits the computational and storage resources limitations of UAVs. Moreover, UTM-Chain provides secure and unalterable traffic data between the UAVs and their ground control stations. The performance of the proposed system related to transaction latency and resource utilization is analyzed by using cAdvisor. Finally, the analysis of security aspects demonstrates that the proposed UTM-Chain scheme is feasible and extensible for the secure sharing of UAV data.

Quantifying Scales of Spatial Variability of Cyanobacteria in a Large, Eutrophic Lake Using Multiplatform Remote Sensing Tools

Harmful algal blooms of cyanobacteria are increasing in magnitude and frequency globally, degrading inland and coastal aquatic ecosystems and adversely affecting public health. Efforts to understand the structure and natural variability of these blooms range from point sampling methods to a wide array of remote sensing tools. This study aims to provide a comprehensive view of cyanobacterial blooms in Clear Lake, California — a shallow, polymictic, naturally eutrophic lake with a long record of episodic cyanobacteria blooms. To understand the spatial heterogeneity and temporal dynamics of cyanobacterial blooms, we evaluated a satellite remote sensing tool for estimating coarse cyanobacteria distribution with coincident, in situ measurements at varying scales and resolutions. The Cyanobacteria Index (CI) remote sensing algorithm was used to estimate cyanobacterial abundance in the top portion of the water column from data acquired from the Ocean and Land Color Instrument (OLCI) sensor on the Sentinel-3a satellite. We collected hyperspectral data from a handheld spectroradiometer; discrete 1 m integrated surface samples for chlorophyll-a and phycocyanin; multispectral imagery from small Unmanned Aerial System (sUAS) flights (∼12 cm resolution); Autonomous Underwater Vehicle (AUV) measurements of chlorophyll-a, turbidity, and colored dissolved organic matter (∼10 cm horizontal spacing, 1 m below the water surface); and meteorological forcing and lake temperature data to provide context to our cyanobacteria measurements. A semivariogram analysis of the high resolution AUV and sUAS data found the Critical Scale of Variability for cyanobacterial blooms to range from 70 to 175 m, which is finer than what is resolvable by the satellite data. We thus observed high spatial variability within each 300 m satellite pixel. Finally, we used the field spectroscopy data to evaluate the accuracy of both the original and revised CI algorithm. We found the revised CI algorithm was not effective in estimating cyanobacterial abundance for our study site. Satellite-based remote sensing tools are vital to researchers and water managers as they provide consistent, high-coverage data at a low cost and sampling effort. The findings of this research support continued development and refinement of remote sensing tools, which are essential for satellite monitoring of harmful algal blooms in lakes and reservoirs.

Detection and Tracking of Aircraft from Small Unmanned Aerial Systems

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small unmanned aerial system (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with Federal Aviation Administration well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of linear Kalman filtering and extended Kalman filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS versus fixed-wing sUAS and multirotor sUAS versus general aviation (GA) plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.

Registration of multi-platform point clouds using edge detection for rockfall monitoring

Remote sensing methods which produce point clouds, such as terrestrial laser scanning (TLS), terrestrial photogrammetry (TP), and small unmanned aerial systems (sUAS) have become an integral component of geotechnical monitoring programs. Applications such as rock-slope monitoring benefit from multi-platform datasets to be acquired in two or more different epochs. Accurate registration of these datasets in a common coordinate system is essential for detecting slope changes. Their registration often relies on initial feature-based alignment followed by fine alignment with the iterative closest point (ICP) algorithm. When practical, ground control points (GCPs) and other surveying targets with well-defined coordinates are used. However, establishing such GCPs on rock surfaces can be difficult, expensive and dangerous. In addition, GCPs and targets can be lost or destroyed with time and re-establishing them is difficult. This paper develops an automated registration algorithm based on edge detection that can register multi-platform and multi-epoch point clouds. Rock-surface edges are expected to remain largely the same and be captured in point clouds collected in two different epochs. For edge detection, we use α-molecules that offer a unified framework of most multi-scale transforms that can be adapted to any rock-surface. Then the algorithm identifies edge correspondences based on the discrete Fréchet distance. From the corresponding edges we derive matching points between datasets. Transformation parameters are then derived through Procrustes analysis. Using real and simulated scenarios, we demonstrate the utility and performance of the proposed algorithm. For example, sUAS scenarios with 0 and 1 GCPs show that initial root mean square error (RMSE) values of a few decimeters drop to a few centimeters. Scenarios with simulated translations, rotations, and scale showed that the developed algorithm registers multi-platform point clouds with mm differences from their original RMSE values. Results demonstrate that the algorithm can successfully register multi-platform point clouds and support rockfall monitoring.