AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

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A Cohesive Simulation and Testing Platform for Civil Autonomous Aerial Sensing and Operations

Drones (also known as sUAS or small Uncrewed Aerial Systems) are often flown with cameras to take images of an area of land. These images can then be used to create a map by stitching these images together. This map can then be analyzed using scientific principles to learn things about the land and make decisions or take action based on the information. The scientific application of drones is very advantageous, but flying a drone is inherently dangerous, impacting the safety of the airspace (particularly in the event of a crash), and drones are more dangerous the bigger they are. Smaller off-the-shelf drones are readily available to the public and are quite safe and easy to use. Larger near 55-lb fixed-wing mapping drones that can fly for 2.5 hours are quite costly and bring new risks into the equation. There are many barriers and risks to being able to successfully test equipment and to improving drone mapping technology. This research focuses on creating a simulator that can simulate the entire process of creating these scientific maps. Simulating a drone, a camera payload, and a world for the drone to fly over. By having a simulator, researchers will be able to test out new technologies without having to risk flying a drone or without having to overcome the challenges mentioned above. This research also focuses on creating a smaller simple camera payload that can be attached to a drone for performing test flights. This allows researchers to do scientific tests without risking flying larger systems. This work enables the testing of sUAS payload systems many times in the simulation and then, when the system works as it should, the test flights with an actual drone can commence. This reduces the amount of time it takes to develop scientific drone systems and reduces the risk of flight

Innovative Payloads for Small Unmanned Aerial System-Based Personal Remote Sensing and Applications

Remote sensing enables the acquisition of large amounts of data, over a small period of time, in support of many ecological applications (i.e. precision agriculture, vegetation mapping, etc.) commonly from satellite or manned aircraft platforms. This dissertation focuses on using small unmanned aerial systems (UAS) as a remote sensing platform to collect aerial imagery from commercial-grade cameras and as a radio localization platform to track radio-tagged sh. The small, low-cost nature of small UAS enables remotely sensed data to be captured at a lower cost, higher spatial and temporal resolution, and in a more timely manner than conventional platforms. However, these same attributes limit the types of cameras and sensors that can be used on small UAS and introduce challenges in calibrating the imagery and converting it into actionable information for end users. A major contribution of this dissertation addresses this issue and includes a complete description on how to calibrate imagery from commercial-grade visual, near-infrared, and thermal cameras. This includes the presentation of novel surface temperature sampling methods, which can be used during the ight, to help calibrate thermal imagery. Landsat imagery is used to help evaluate these methods for accuracy; one of the methods performs very well and is logistically feasible for regular use. Another major contribution of this dissertation includes novel, simple methods to estimate the location of radio-tagged fish using multiple unmanned aircraft (UA). A simulation is created to test these methods, and Monte Carlo analysis is used to predict their performance in real-world scenarios. This analysis shows that the methods are able to locate the radio-tagged fish with good accuracy. When multiple UAs are used, the accuracy does not improve; however the fish is located much quicker than when one UA is used

Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature.

In recent years, the availability of lightweight microbolometer thermal cameras compatible with small unmanned aerial systems (sUAS) has allowed their use in diverse scientific and management activities that require sub-meter pixel resolution. Nevertheless, as with sensors already used in temperature remote sensing (e.g., Landsat satellites), a radiance atmospheric correction is necessary to estimate land surface temperature. This is because atmospheric conditions at any sUAS flight elevation will have an adverse impact on the image accuracy, derived calculations, and study replicability using the microbolometer technology. This study presents a vicarious calibration methodology (sUAS-specific, time-specific, flight-specific, and sensor-specific) for sUAS temperature imagery traceable back to NIST-standards and current atmospheric correction methods. For this methodology, a three-year data collection campaign with a sUAS called AggieAir , developed at Utah State University, was performed for vineyards near Lodi, California, for flights conducted at different times (early morning, Landsat overpass, and mid-afternoon ) and seasonal conditions. From the results of this study, it was found that, despite the spectral response of microbolometer cameras (7.0 to 14.0 μm), it was possible to account for the effects of atmospheric and sUAS operational conditions, regardless of time and weather, to acquire accurate surface temperature data. In addition, it was found that the main atmospheric correction parameters (transmissivity and atmospheric radiance) significantly varied over the course of a day. These parameters fluctuated the most in early morning and partially stabilized in Landsat overpass and in mid-afternoon times. In terms of accuracy, estimated atmospheric correction parameters presented adequate statistics (confidence bounds under ±0.1 for transmissivity and ±1.2 W/m²/sr/um for atmospheric radiance, with a range of RMSE below 1.0 W/m²/sr/um) for all sUAS flights. Differences in estimated temperatures between original thermal image and the vicarious calibration procedure reported here were estimated from -5 °C to 10 °C for early morning, and from 0 to 20 °C for Landsat overpass and mid-afternoon times

gRAID: A Geospatial Real-Time Aerial Image Display for a Low-Cost Autonomous Multispectral Remote Sensing Platform (AggieAir)

Remote sensing helps many applications like precision irrigation, habitat mapping, and traffic monitoring. However, due to shortcomings of current remote sensing platforms - like high cost, low spatial, and temporal resolution - many applications do not have access to useful remote sensing data. A team at the Center for Self-Organizing and Intelligent Systems (CSOIS) together with the Utah Water Research Laboratory (UWRL) at Utah State University has been developing a new remote sensing platform to deal with these shortcomings in order to give more applications access to remote sensing data. This platform (AggieAir) is low cost, fully autonomous, easy to use, independent of a runway, has a fast turnover time, and a high spatial resolution. A program called the Geospatial Real-Time Aerial Image Display (gRAID) has also been developed to process the images taken from AggieAir. gRAID is able to correct the camera lens distortion, georeference, and display the images on a 3D globe, and export them in a conventional Geographic Information System (GIS) format for further processing. AggieAir and gRAID prove to be innovative and useful tools for remote sensing

Cyber-Physical Systems Enabled By Unmanned Aerial System-Based Personal Remote Sensing: Data Mission Quality-Centric Design Architectures

In the coming 20 years, unmanned aerial data collection will be of great importance to many sectors of civilian life. Of these systems, Personal Remote Sensing (PRS) Small Unmanned Aerial Systems (sUASs), which are designed for scientic data collection, will need special attention due to their low cost and high value for farming, scientic, and search-andrescue uses, among countless others. Cyber-Physical Systems (CPSs: large-scale, pervasive automated systems that tightly couple sensing and actuation through technology and the environment) can use sUASs as sensors and actuators, leading to even greater possibilities for benet from sUASs. However, this nascent robotic technology presents as many problems as possibilities due to the challenges surrounding the abilities of these systems to perform safely and eectively for personal, academic, and business use. For these systems, whose missions are dened by the data they are sent to collect, safe and reliable mission quality is of highest importance. Much like the dawning of civil manned aviation, civilian sUAS ights demand privacy, accountability, and other ethical factors for societal integration, while safety of the civilian National Airspace (NAS) is always of utmost importance. While the growing popularity of this technology will drive a great effort to integrate sUASs into the NAS, the only long-term solution to this integration problem is one of proper architecture. In this research, a set of architectural requirements for this integration is presented: the Architecture for Ethical Aerial Information Sensing or AERIS. AERIS provides a cohesive set of requirements for any architecture or set of architectures designed for safe, ethical, accurate aerial data collection. In addition to an overview and showcase of possibilities for sUAS-enabled CPSs, specific examples of AERIS-compatible sUAS architectures using various aerospace design methods are shown. Technical contributions include specic improvements to sUAS payload architecture and control software, inertial navigation and complementary lters, and online energy and health state estimation for lithium-polymer batteries in sUAS missions. Several existing sUASs are proled for their ability to comply with AERIS, and the possibilities of AERIS data-driven missions overall is addressed

Assessing the potential of drone-based thermal infrared imagery for quantifying river temperature heterogeneity

© 2019 Crown copyright. Hydrological Processes © 2019 John Wiley & Sons, Ltd. Climate change is altering river temperature regimes, modifying the dynamics of temperature-sensitive fishes. The ability to map river temperature is therefore important for understanding the impacts of future warming. Thermal infrared (TIR) remote sensing has proven effective for river temperature mapping, but TIR surveys of rivers remain expensive. Recent drone-based TIR systems present a potential solution to this problem. However, information regarding the utility of these miniaturised systems for surveying rivers is limited. Here, we present the results of several drone-based TIR surveys conducted with a view to understanding their suitability for characterising river temperature heterogeneity. We find that drone-based TIR data are able to clearly reveal the location and extent of discrete thermal inputs to rivers, but thermal imagery suffers from temperature drift-induced bias, which prevents the extraction of accurate temperature data. Statistical analysis of the causes of this drift reveals that drone flight characteristics and environmental conditions at the time of acquisition explain ~66% of the variance in TIR sensor drift. These results shed important light on the factors influencing drone-based TIR data quality and suggest that further technological development is required to enable the extraction of robust river temperature data. Nonetheless, this technology represents a promising approach for augmenting in situ sensor capabilities and improved quantification of advective inputs to rivers at intermediate spatial scales between point measurements and “conventional” airborne or satellite remote sensing

Improving Accuracy of Unmanned Aerial System Thermal Infrared Remote Sensing for Use in Energy Balance Models in Agriculture Applications

Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements

Utilization of a thermal camera in aerial photography taken with a drone

Dronejen käyttö on lisääntynyt voimakkaasti viimeisten vuosien aikana. Ensimmäiset dronet on kehitetty jo 1900-luvun alussa. Myös lämpökameroita on käytetty jo useamman vuosikymmenen ajan. Näiden yhteiskäyttö on yleistynyt 2010-luvun aikana. Dronen ja lämpökameran avulla on tutkittu erityisesti kasvien lämpöstressiä. Tavoitteena oli selvittää, millaisia käyttömahdollisuuksia droneen kiinnitetyllä lämpökameralla on ja ovatko kameran antamat tulokset riittävän tarkkoja esimerkiksi täsmäviljelyssä hyödyntämiseen. Lisäksi tutkittiin kuvausprosessin käyttökelpoisuutta ja lämpökameran kalibrointia. Tutkimuksessa käytettiin lämpökameran rinnalla kolmea muuta eri lämpötilan mittausmenetelmää. Tutkimus suoritettiin Helsingin yliopiston Viikin tutkimustilalla. Kuvattavana kohteina olivat nurmi, jonka kasvuaste oli Zadoksin (BBCH) asteikolla 12–13, edellisenä syksynä kultivoitu maa sekä kynnetty maa. Tutkimuksessa oli käytössä itserakennettu drone (Tarot Ironman:n runko) sekä Flir Duo Pro R -lämpökamera. Tutkimus suoritettiin touko-kesäkuussa 2022. Lämpökuvien käsittely tehtiin Pix4D ja Matlab-ohjelmilla. Lämpökameralla saatiin kuvattua kaikki peltolohkot. Jokaisesta koelohkosta mitattiin vertailulämpötilat, jotta voitiin tutkia ilmasta otetun kuvan paikkaansa pitävyyttä. Kontrollipisteet mitattiin GCP-pisteiden (Ground Control Point) läheisyydestä kolmen metrin etäisyydeltä merkkitolpasta. Dronella otettujen lämpökuvien ja Ahlbornin mittarin tulosten välinen korrelaatiokerroin oli 0,67; joka on kohtalaisen korkea. Flir-käsilämpökameran ja dronella otettujen lämpökuvien välinen korrelaatio ei osoittautunut tilastollisesti merkitseväksi. Tähän vaikutti luultavasti Flir-käsilämpökameralla otettujen mittauspisteiden epätarkkuus kunnollisen kuvaustelineen puuttuessa. Maaperäskannerin tuottaman lämpökartan ja dronella otettujen lämpökuvien välinen korrelaatio oli -0,11. Tutkimuksessa havaittiin myös kameran kulma-anturissa olevan jotain häiriötä, koska kaikki sen ottamat kuvat olivat virtuaalisella karttatasolla 90 astetta väärässä kulmassa. Tämä saatiin korjattua kuvankäsittelyohjelmalla. Dronen lämpökameran kalibrointi todettiin riittäväksi tutkimuksen olosuhteissa. Droneen kiinnitetty lämpökamera on riittävän tarkka mitattaessa lämpötiloja ilmasta, jos olosuhteet ovat kameralle oikeat. Tulevia kuvauksia varten kasvustoon tulisi saada lisää kiintopisteitä, jotta analysointiohjelma saisi muodostettua kohdealueelta luotettavan lämpökuvan. Myös säähän olisi kiinnitettävä huomiota, sillä vähäinenkin pilvisyys vaikuttaa lopputulokseen kameran ominaisuuksista johtuen. Myös maasta mitattujen kontrollipisteiden tarkkuuteen tulisi kiinnittää enemmän huomiota, sillä niillä on suuri vaikutus tuloksiin, koska maan lämpötila voi vaihdella hyvin pienenkin alueen sisällä. Tässäkin tutkimuksessa vierekkäisten mittauspisteiden välillä oli jopa useiden asteiden lämpötilaeroja

Multispectral Remote Sensing and Spatiotemporal Mapping of the Environment and Natural Disasters Using Small UAS

This dissertation focuses on development of new methods for multispectral remote sensing, measurement, and mapping of the environment and natural disasters using small Unmanned Aircraft Systems (UAS). Small UAS equipped with multispectral cameras such as true color (RGB), near infrared (NIR), and thermal can gather important information about the environment before, during, and after a disaster without risking pilots or operators. Additionally, small UAS are generally inexpensive, easy to handle, and can detect features at small spatiotemporal scales that are not visible in manned aircraft or satellite imagery. Four important problems in UAS remote sensing and disaster data representation are focused in this dissertation. First, key considerations for the development of UAS disaster sensing systems are provided, followed by detailed descriptions of the KHawk system and representative environment and disaster data sets. Second, a new method is proposed and demonstrated for accurate mapping and measurement of grass fire evolution using multitemporal thermal orthomosaics collected by a fixed-wing UAS flying at low altitudes. Third, a low-cost and effective solution is further developed for spatiotemporal representation and measurement of grass fire evolution using time-labeled UAS NIR orthomosaics and a novel Intensity Variance Thresholding (IVT) method is proposed for grass fire front extraction to support fire spread metrics measurement of fire front location and rate of spread (ROS). A UAS grass fire observation data set is also presented including thermal and NIR orthomosaics and supporting weather and fuel data. Fourth, a new Satellite-based Cross Calibration (SCC) method is proposed for surface reflectance estimation of UAS images in digital numbers (DN) using free and open calibrated satellite reflectance data. This also serves as a solid foundation for data-enabled multiscale remote sensing and large scale environmental observations. Finally, the main conclusions and future research considerations are summarized