An uncrewed aerial vehicle (UAV), commonly known as a drone, is an aircraft without a human pilot aboard. Originating mostly from military applications, their use is rapidly expanding to commercial, recreational, agricultural, and scientific applications. Unlike crewed aircraft, UAVs were initially used for missions too "dull, dirty, or dangerous" for humans. Nowadays however, many modern scientific experiments have begun to use UAVs as a tool to collect different types of data. Their flexibility and relatively simple usability now allow scientist to accomplish tasks that previously required expensive equipment like piloted aircrafts, gas, or hot air balloons. Even the industry has begun to adapt and offer extensive options in UAV characteristics and capabilities. At this session, we would like people to share their experience in using UAVs for scientific research. We are interested to hear about specific scientific tasks accomplished or attempted, types of UAVs used, and instruments deployed.
vPICO presentations: Thu, 29 Apr
archaeological sites have been always a subject of curiosity and search, the archaeologists and scientists from different specialties have been wondering about the origins of the man civilization, about the way our forefathers lived, how they nourished, dressed, and housed themselves, what techniques were used for the transport, the fishing, and the business, about the culture and the spiritual practices. in fact, the modern technologies, practices, and innovations are only a continuation of what was once; this is why the human being believes it is imperative to revive and understand the heritage and to discover its secrets. in the present work which pours in the same direction, we decided to revive and explore a wealthy site located in rabat, the Moroccan capital, this site is named chellah, which represents the summing up of historical eras from the antiquity to the Islamic period and which is marked by the presence of antique and Islamic constructions which reflect this continuity. our research aims to build a model for the detection of areas that are not yet excavated but are already mentioned by archaeologists, geographers, and historians to validate their hypothesis and to find out where exactly these areas are located. our methodology is based on the processing of unmanned aerial vehicle (uav) images to generate high-resolution photogrammetric products with low cost, those datasets will be analyzed with a technique that has been in use since the '80s and which is using crop, soil, and shadow marks visualized on images taken by aerial photography. this analysis gave us the vision to select the zones on which a geophysical investigation by electrical tomography was carried out to approve the presence of the archeological components that require future excavation. our study focused on the importance of non-invasive methodologies for the study, preservation, and valorization of archaeological sites.
How to cite: el guertet, I., aarab, A., larabi, A., Jemmal, M., and benchekroun, S.: The archaeological site of chellah, new technologies for investigation, modeling, and mapping., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-755, https://doi.org/10.5194/egusphere-egu21-755, 2021.
3D landscape reconstruction derived from imagery acquired by unmanned aerial systems (UAS) is an increasingly applied method within the field of geosciences. Low-cost UAS and subsequent Structure from Motion (SfM) and multi-view stereo (MVS) processing provides the opportunity to study landforms and processes in high detail; for instance mapping of river terraces (Li et al. 2019) or landslide monitoring (Devoto et al. 2020).
Due to an almost complete drainage of the Urft Lake reservoir in the northern Eifel mountains (W-Germany) in the autumn of 2020, the lake’s entire ground could be surveyed using a low-cost UAS.
The lake stretches for 12 km and has a maximum impoundment volume of approximately 45 million m³. Its shape is characterized by multiple fluvial bends and steep slopes, which required an elaborated flight layout. A DJI Phantom 4 Pro V2.0 was used. Each flight was carried out in two parallel heights (90 and 120 m), 80° camera inclination, and in double-grid pattern. Five full days of surveying yielded over 6,000 aerial images. Despite the difficulty to access the drained reservoir, 154 evenly distributed ground control points were taken using a Leica RTK dGPS instrument (accuracy <5 cm). SfM-MVS photogrammetric processing was conducted with Agisoft Metashape Professional 1.6, using an optimized workflow based on USGS (2017) and James et al. (2020).
The resulting 3D model features high accuracy and precision making it suitable for further detailed stationary as well as multi-temporal geomorphologic analyses. The derived DEM features a spatial resolution of <6 cm and will be used to calculate geometric changes of the reservoir body since its construction in 1905; in particular, due to sedimentation and mass movements along the hillslopes. Moreover, the products can be used to study the anthropogenic influences of the water reservoir on the fluvial morphology of the Urft.
Devoto, S., Macovaz, V., Mantovani, M., Soldati, S., Furlani, S., 2020. Advantages of Using UAV Digital Photogrammetry in the Study of Slow-Moving Coastal Landslides. Remote Sensing 2020, 12, 3566. https://doi.org/10.3390/rs12213566
James, M.R., Antoniazza, G., Robson, S., Lane, S.N., 2020. Mitigating systematic error in topographic models for geomorphic change detection: accuracy, precision and considerations beyond off-nadir imagery. Earth Surface Processes and Landforms 45, 2251–2271. https://doi.org/10.1002/esp.4878
Li, H., Lin, C., Wang, Z., Yu, Z., 2019. Mapping of River Terraces with Low-Cost UAS Based Structure-from-Motion Photogrammetry in a Complex Terrain Setting. Remote Sensing 2019, 11, 464. https://doi.org/10.3390/rs11040464
United States Geological Survey (USGS), 2017. Unmanned Aircraft Systems Data Post Processing: Structure-from-Motion Photogrammtery. Section 2 – MicaSense 5-band MultiSpectral Imagery. USGS National UAS Project Office. https://uas.usgs.gov/nupo/pdf/PhotoScanProcessingMicaSenseMar2017.pdf (Retrieved: 24 July 2020).
How to cite: Dörwald, L., Esch, A., Stauch, G., and Walk, J.: Meso- to macro-scale landscape modelling with SfM-MVS photogrammetry: a case study from the Urft Lake water reservoir (Eifel Mountains, western Germany), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3248, https://doi.org/10.5194/egusphere-egu21-3248, 2021.
Remotely Piloted Aircraft (RPA), commonly known as drones, provide sensing capabilities that address the critical scale-gap between ground- and satellite-based observations. Their versatility allows researchers to deliver near-real-time information for society.
Key to delivering RPA information is the capacity to enable researchers to systematically collect, process, manage and share RPA-borne sensor data. Importantly, this should allow vertical integration across scales and horizontal integration across different RPA deployments. However, as an emerging technology, the best practice and standards are still developing and the large data volumes collected during RPA missions can be challenging.
Australia’s Scalable Drone Cloud (ASDC) aims to coordinate and standardise how scientists from across earth, environmental and agricultural research manage, process and analyse data collected by RPA-borne sensors, by establishing best practices in managing 3D-geospatial data and aligned with the FAIR data principles.
The ASDC is building a cloud-native platform for research drone data management and analytics, driven by exemplar data management practices, data-processing pipelines, and search and discovery of drone data. The aim of the platform is to integrate sensing capabilities with easy-to-use storage, processing, visualisation and data analysis tools (including computer vision / deep learning techniques) to establish a national ecosystem for drone data management.
The ASDC is a partnership of the Monash Drone Discovery Platform, CSIRO and key National Collaborative Research Infrastructure (NCRIS) capabilities including the Australian Research Data Commons (ARDC), Australian Plant Phenomics Facility (APPF), Terrestrial Ecosystem Research Network (TERN), and AuScope.
This presentation outlines the roadmap and first proof-of-concept implementation of the ASDC.
How to cite: Klump, J., Brown, T., Clarke, R., Glasgow, R., Micklethwaite, S., Guru, S., Morris, B., Quenette, S., Rawling, T., Reid, N., and Robinson, S. and the ASDC Project Team: Building Australia’s Scalable Drone Cloud, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3820, https://doi.org/10.5194/egusphere-egu21-3820, 2021.
Rotary wing UAV’s are used in aeromagnetic measurements for UXO detection. That way, contaminated areas can be mapped fast and with high resolution. Until today, only the total magnetic intensity (TMI) is evaluated, even when a three axis fluxgate magnetometer is flown. In this project, we use two three component fluxgate sensors, an inertial measurement unit (IMU) and a GPS antenna. The IMU allows for a projection of the magnetic data into the geographic coordinate system as well as the calculation of the sensor positions relative to the GPS antenna. With this system, it is possible for the first time to evaluate the component gradients between the magnetometers.
The sensors are attached to the UAV via a versatile, T-shaped boom hanging below the UAV with the sensors positioned in a horizontal distance of 50 cm. The total mass of the flight system is about 5 kg with an air time of 15 minutes.
For the inversion, we use a dipole model which calculates the magnetic data for all sensor positions. Because the sources of the magnetic anomalies are unknown as a general rule, there is no distinction between induced and remanent magnetisation. Instead, the three components of the magnetic moment are fitted alongside the positions of the anomaly sources. The number of dipoles to be fitted and their initial parameters are arbitrary. For the inversion, the TMI and component gradients between the sensors are considered.
In order to analyse the accuracy of the complete system, we conducted surveys over a test field of 100 x 20 m, separated into four sections with varying anomaly configurations. As anomaly sources, we used neodymium magnets which we characterised in laboratory measurements. For optimal coverage and to compare flight directions, the test field was surveyed both lengthways and crossways with a sensor height of 1.5 m above ground. Inversion results show that when component gradients are used, overlapping anomalies can be separated and parameterised. The mean errors of the derived anomaly positions are 5 cm, the total magnetic moment can be determined with an accuracy of 0.35 Am2, whereby the errors in direction (declination and inclination) are 4 ° and 2 °, respectively.
How to cite: Kulüke, C., Virgil, C., Stoll, J., and Hördt, A.: UAV-based aeromagnetic gradient measurements and inversion, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4219, https://doi.org/10.5194/egusphere-egu21-4219, 2021.
Magnetic mapping is commonly used in the academic and industrial sectors for a wide variety of objectives. To comply with a broad range of survey designs, the use of unmanned aerial vehicles (UAVs) has become frequent over the recent years. The majority of existing systems involves a magnetic acquisition equipment and its carrier (an UAV in this context) with no -or very few- connections between the two systems. Terremys is conceiving and optimizing UAVs specifically adapted for geophysical magnetic acquisitions together with the appropriate processing tools, and performs magnetic surveying in challenging environments. Terremys’ “Q6” system weights 2.5 kg in air, including UAV & instrumentation, and allows 30 min swarm or individual flights.
Rotary-wing UAVs are found to be the most adaptive systems for a wide range of contexts and constraints (extensive range of flights heights even with steep slopes). They offer more flight flexibility than fixed-wing aircrafts. One of the major problems in the use of rotary-wings UAVs for magnetic mapping is the magnetic field generated by the aircraft itself on the measurements. Towing the magnetic sensor 2 to 5 m under the aircraft reduces data positioning accuracy and decreases the performances of the UAV, which can be critical for high-resolution surveys. To overcome these problems, a deployable 1 m long boom is rigidly attached to the UAV. The UAV magnetic signal can be divided between 1-the magnetic field of the whole equipment and 2-a low to high frequency magnetic field mostly originating from the motors. The magnetization of the system is the principal source of magnetic noise. It is modelled and corrected by calibration-compensation processes permitted by the use of three-component fluxgate magnetometers. The time-varying noise depends on the motors rotational speed and is minimized by optimizing the UAV components and characteristics along with the boom’s length.
The final set-up is able to acquire magnetic data with a precision of 1 to 5 nT at any height from 1 to 150 m above ground level. The high-precision magnetic measurements are coupled with a centimetric RTK navigation system to allow for high-resolution surveying. The quality of the obtained data is similar to that obtained with ground or aerial surveys with conventional carriers and matches industrial standards. Moreover, Terremys’ systems merge in real-time data from all the aircraft instruments in order to integrate magnetic measurements, positioning information and all the UAV’s flight data (full telemetry) into a unique synchronized data file. This opens up many possibilities in terms of QA/QC, data processing and facilitates on-field workflows.
Case studies with diverse designs, flight altitudes and targets are presented to investigate the acquisition performances for different applications, as distinct as network positioning, archaeological prospecting or geological mapping.
The full integration of the magnetic sensor to the drone opens the possibility for implementation additional sensors to the system. The adjoining of other magnetic sensors would allow multi-sensors surveying and increases daily productivity. Diverse geophysical sensors can also be added, such as thermal/infrared cameras, spectrometers, radar/SAR.
How to cite: Mercier de Lépinay, J., Fréville, T., Kiemes, B., Sanabria, L. M., Gavazzi, B., Reiller, H., and Munschy, M.: Ultra-lightweight integrated unmanned aerial systems for a wide range of geophysical magnetic exploration: a single system for high-precision archaeological surveys to rugged topography geological acquisitions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7290, https://doi.org/10.5194/egusphere-egu21-7290, 2021.
M.A.R.S., Multiple Airdrones Response System, is an innovative platform for environmental monitoring. Monitoring is a prerequisite to design a land management plan to maintain its biodiversity and health, in order to optimally avoid the risk of hydrogeological instability and disaster, e.g., floods, volcanic eruptions, earthquakes, wildfires. The innovative potential of the M.A.R.S. project lies mainly in the ability to manage the logistics of drone swarms and in the modularity of the platform infrastructure, which is easy to move and equipped with an integrated system for automatically replacing payloads carried by drones, such as batteries, instruments, sensors, and disposable materials.
The platform is composed of several subsystems: one or more landing pads, a controller for the platform operation management, a cartridge case and a hive for the storage of payloads and drones respectively. In summary, M.A.R.S. drones are served, supplied, and housed, similar to a multi-copter drone carrier.
This type of technology would launch new possible applications in contexts where the use of Unmanned Aerial Vehicles has not yet been hypothesized, overcoming the current limits thanks to the use of individual drones in swarm configuration and to the possibility of extending the flight time by changing the batteries.
Therefore, we propose and demonstrate the applicability of M.A.R.S. in forest firefighting, as fires constitute the most critical and widespread threat to Mediterranean forests. After computing the critical water flow rate according to the main time-varying factors involved in the evolution of a fire, we obtain the number of linear meters of active fire front that can be extinguished depending on the amount of fluid carried by the available drones. Finally, by means of a cellular automata model, the development and evolution of a Mediterranean scrub fire are simulated and the change of the fire area over time is estimated both without any extinguishing effort and in case of M.A.R.S. drones intervention.
Parallel to the work of scientific research, computation, and simulation, we started to build the platform and test the technologies to be implemented for the concrete development of the system. Since precision landing is of fundamental importance to the project, flight and landing tests were performed. The purpose of this in-depth study was to verify the landing error range using two hexacopter drones (DJI F550 and S900) on which two Pixhawk Flight Controllers and two different GNSS RTK modules were mounted, also comparing the results with those obtained using GPS only.
M.A.R.S. is based on an industrial patent (2016) owned by Inspire S.r.l., start-up and spin-off of the University of Genoa. The project is by its nature highly interdisciplinary, as is the professional knowledge that characterizes the members who make up the working group.
Forest fire research received support from Regione Liguria in the context of the European Social Fund 2014-2020 (POR-FSE). Further studies and experiments will be carried out.
How to cite: Ausonio, E., Bagnerini, P., and Ghio, M.: Multiple Airdrones Response System in forest firefighting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10250, https://doi.org/10.5194/egusphere-egu21-10250, 2021.
Traditionally, the inversion of magnetic data assumes the magnetization of the local geology to run parallel to the Earth’s internal magnetic field that is usually modelled using International Geomagnetic Reference Field (IGRF). Assuming the magnetization parallel to the main field, only the total (scalar) magnetic data are the sufficient input for the inversion of source susceptibility.
Local magnetization may alter from the main field direction in areas of remanent magnetization. Recently, magnetization vector inversion (MVI) using the total field has become an important tool trying to distinguish magnetic data affected by remanenence. Total field as a scalar field exclude all information of the direction of the internal magnetization and more information is required to reveal any remanent magnetization from the main field direction. Compared to total field using the 3-component XYZ vector magnetic measurements provide more information of the source. More measurements increase the unambiguous nature of data and may reveal the areas of possible remanence.
To measure XYZ vector magnetic field we use fluxgate 3-component magnetometer with rigid installation on a fixed-wing UAV. With the help of accurate inertial measurement units the measured magnetic field can be determined in the direction of fixed coordinate system. The components of the measured magnetic field rotated into the geographical coordinate represent the magnetic field at survey area.
UAV survey provided the data as the input for the inversions. We made the inversion separately for both susceptibility and magnetization vector. Susceptibility inversion means inversion of induced magnetization, i.e., a single component of magnetization parallel to the main field direction. Magnetization vector inversion, however, resolves all three components of magnetization, which may or may not include remanent magnetization in addition to induced one.
The benefits from utilizing XYZ components of the magnetic field with magnetization inversion seem promising in finding remanenence magnetization.
How to cite: Karinen, A.: Magnetic vector inversion using XYZ measured by fluxgate magnetometer in UAV , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13006, https://doi.org/10.5194/egusphere-egu21-13006, 2021.
Since 1900 AD, 64-71% of the world’s natural wetlands have been lost due to anthropogenic influences. Wetland restoration projects, such as managed realignment and tidal salt marsh restoration, act to combat these losses, but are also being used as a form of nature-based adaptation to the effects of climate change, including sea level rise. New advances in Unmanned Aerial Vehicle (UAV) technology offer a unique opportunity to quantify the restoring landscape at resolutions and accuracies previously unachievable. This presentation will focus on the use of hyperspatial datasets collected with a Real-Time Kinematic (RTK) GNSS enabled UAV at a managed realignment site in the Bay of Fundy, Canada, to monitor and quantify the geomorphic evolution of the site, including the development of a semi-automated method for mapping embryonic creek networks. Analyzed datasets were collected seasonally over the course of 1 year following the reintroduction of tidal flow, and range in resolution from 2.0 - 3.5 cm. Preliminary results show significant spatial variation in channel evolution patterns, related to the presence and absence of antecedent landscape features. A greater understanding of restoration site evolution, and the effects of the antecedent landscape on that evolution, will allow for a more informed design and implementation of future restoration projects to encourage site resilience and sustainability in terms of climate change adaptation.
How to cite: Lewis, S., Baker, G., Bowron, T., Graham, J., and van Proosdij, D.: Monitoring the Evolution of a Tidal Salt Marsh Restoration Site with an RTK-enabled UAV, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13638, https://doi.org/10.5194/egusphere-egu21-13638, 2021.
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We are sorry, but presentations are only available for users who registered for the conference. Thank you.