Unmanned Aerial Systems Flight Research Articles

Assessment of Unmanned Aerial System Flight Plans for Data Acquisition from Erosional Terrain

Accurate data mapping and visualization are of crucial importance for the detection and monitoring of slope morphodynamics, including erosion processes and studying small erosional landforms (rills and gullies). The purpose of the current research is to examine how the flight geometry of unmanned aerial systems (UASs) could affect the accuracy of photogrammetric processing products, concerning small erosion landforms that are a result of slope wash and temporary small streams formed by rain. In October 2021, three UAS flights with a different geometry were carried out in a hilly to a low-mountain area with an average altitude of about 650 m where erosion processes are observed. UAS imagery processing was carried out using structure-from-motion (SfM) photogrammetry. High-resolution products such as photogrammetric-based point clouds, digital surface models (DSMs) and orthophotos were generated. The obtained data were compared and evaluated by the root mean square error (RMSE), length measurement, cloud-to-cloud comparison, and 3D spatial GIS analysis of DSMs. The results show small differences between the considered photogrammetric products generated by nadir-viewing and oblique-viewing (45°—single strip and 60°—cross strips) geometry. The complex analysis of the obtained photogrammetric products gives an advantage to the 60°—cross strips imagery, in studying erosional terrains with slow slope morphodynamics.

Using Spatial Uncertainty to Dynamically Determine UAS Flight Paths

In the aftermath of radiation or chemical accidents, responders must rapidly map out regions of contamination as quickly and accurately as possible. One important and relevant statistical method for this kind of disaster response is spatial kriging, which makes predictions based on incomplete knowledge of spatially referenced observations. Given an Unmanned Aerial System (UAS) equipped with radiation sensors, we develop a spatial statistics-based approach to optimally map out a contamination field over a geographic region. In this article, we evaluate three approaches to UAS mapping: a Variance Driven Sampling (VDS) approach that minimizes kriging variance, a more computationally intensive Hybrid Entropy Search (HES), and a baseline Levy Flight search. Considering limited UAS range, we also implement a restricted version of these approaches that only considers nearby points. We find that HES is optimal for small numbers of sampled points with the restricted versions of HES and VDS becoming optimal for larger samples. Ultimately, the best method is dependent on the number of samples to be taken, with each method providing clear benefits over a random search in terms of both mean squared error and path length. We demonstrate the advantages of our methodology using actual radiation field test data from the Idaho National Lab.

Use of high-resolution unmanned aerial systems imagery and machine learning to evaluate grain sorghum tolerance to mesotrione

Manual evaluation of crop injury to herbicides is time-consuming. Unmanned aerial systems (UAS) and high-resolution multispectral sensors and machine learning classification techniques have the potential to save time and improve precision in the evaluation of herbicide injury in crops, including grain sorghum (Sorghum bicolor L. Moench). The objectives of our research are to (1) evaluate three supervised classification algorithms [support vector machine (SVM), maximum likelihood, and random forest] for categorizing high-resolution UAS imagery to aid in data extraction and (2) evaluate the use of vegetative indices (VIs) collected from UAS imagery as an alternative to traditional methods of visible herbicide injury assessment in mesotrione-tolerant grain sorghum breeding trials. An experiment was conducted in a randomized complete block design using a factorial treatment arrangement of three genotypes by four mesotrione doses. Herbicide injury was rated visually on a scale of 0 (no injury) to 100 (complete plant mortality). The UAS flights were flown at 9, 15, 21, 27, and 35 days after treatment. Results show the SVM algorithm to be the most consistently accurate, and high correlations (r = − 0.83 to −0.94; p < 0.0001) were observed between the normalized difference VI and ground-measured herbicide injury. Therefore, we conclude that VIs collected with UAS coupled with machine learning image classification has the potential to be an effective method of evaluating mesotrione injury in grain sorghum.

ANALYSIS OF GULLY EROSION IN A CATCHMENT AREA IN OLIVE GROVES USING UAS PHOTOGRAMMETRY TECHNIQUES

Abstract. An approach based on images captured by means of an Unmanned Aerial System (UAS) and Structure from Motion – Multi Video Stereo (SfM-MVS) photogrammetry is presented for the study of the gully erosion in a catchment area of about 16 ha. The study area is located in the province of Jaén (SW Spain) where the main land use is the olive groves. Three UAS flights have been made in April 2019, May 2019 and February 2020 with an average GSD of 2–3 cm. The image processing has been carried out using some field surveyed GNSS ground control points and the RTK positioning of the UAV with errors lower than 0.03 m and 0.04 m in XY and Z, respectively. Then DSMs and orthophotographs were obtained with a resolution of 0.10 m and 0.05 m, respectively, and DSM of differences (DoDs) were calculated, with an uncertainty of about ±0.15 m. Finally, the DoDs were analysed in the GIS, in order to calculate the height differences and volumes between the three flights. The analysis has showed the areas affected by the gully erosion processes that correspond mainly to ground descents due to significant channel erosion and mass movements at the steep sidewalls. Considering the balance between depletion and deposition processes, the average height differences is about −0.03 m and the volume is about −42 m3, that is the depletion predominates. The main changes have been detected in the second period (May 2019–February 2020), while in the first one (April 2019–May 2019) they are practically insignificant and limited to small areas.

Investigating the Potential Use of RADARSAT-2 and UAS imagery for Monitoring the Restoration of Peatlands

The restoration of peatlands is critical to help reduce the effects of climate change and further prevent the loss of habitat for many species of flora and fauna. The objective of this research was to evaluate RADARSAT-2 satellite imagery and high-resolution Unmanned Aerial Systems (UASs) to determine if they could be used as surrogates for monitoring the success of peatland restoration. Areas of peatland that were being actively harvested, had been restored from past years (1994–2003), and natural shrub bog in Lac St. Jean, Quebec were used as a test case. We compared the Freeman–Durden and Touzi decompositions by applying the Bhattacharyya Distance (BD) statistic to see if the spectral signatures of restored peatland could be separated from harvested peat and natural shrub bog. We flew Unmanned Aerial Surveys (UASs) over the study site to identify Sphagnum and Polytrichum strictum, two indicator species of early peatland restoration success. Results showed that the Touzi decomposition was better able to separate the spectral signatures of harvested, restored, and natural shrub bog (BD values closer to 9). Symmetric scattering type αs1, Helicity |τ1,2,3|, a steep incidence angle, and peak growing season appear to be important for separating the spectral signatures. We had moderate success in detecting Sphagnum and Polytrichum strictum visually by using texture and pattern but were unable to use colour due to differences in sun angle and clouds during the UAS flights. Results suggest that RADARSAT-2 data using the Touzi decomposition and UAS imagery show potential for monitoring peatland restoration success over time.

UAS-based High-resolution Record of the Response of a Seminatural Sandy Spit to a Severe Storm

Talavera, L.; del Rio, L.; and Benavente, J., 2020. UAS-based high-resolution record of the response of a seminatural sandy spit to a severe storm. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 679-683. Coconut Creek (Florida), ISSN 0749-0208. Human infrastructures in barrier islands restrict the natural storm-induced overwash needed by these systems to evolve, further increasing their vulnerability in the medium and long term. For this reason, accurate overwash measurements in coastal environments subject to varying degrees of occupation are needed. In this work, two Unmanned Aerial System (UAS) flights were performed over two distinct sectors of a sandy spit located in Cadiz (SW Spain) prior and after the landfall of storm Emma. This storm was a high-energy event that lasted 153 hours,with an average significant wave height of 4 m and a storm peak of 6.81 m., and water levels (tide and surge) of up to 4 m. The images derived from the UAS flights were processed with Structure From Motion (SfM) algorithms using Pix4D software, resulting in high-resolution mosaics and Digital Elevation Models (DEMs) of the study area. These allowed (1) the digitization of the washover deposits in both sectors before and after the storm, and (2) the computation of DEMs of Difference (DoDs), which in turn allowed the analysis of volumetric estimates of erosion/accretion triggered by the significant overwash. The results revealed a nonuniform coastal response between both sectors. The area subject to higher degree of development showed a clear restriction in the onshore delivery of sediment during overwash due to the presence of a channel and a road, the coalescence of the nine pre-existing washovers into a unique deposit of 77546 m2, and much higher damage extents and associated recovery costs. In the natural sector, the overwash processes reactivated the three pre-existing washovers (which enlarged their areas up to 200%) with no significant damages reported. These findings serve as examples of coastal responses to future similar hydrodynamic conditions, and suggest that this spit will likely evolve following different trajectories in the long-term.

Developing an autonomous unmanned aerial system to estimate field terrain corrections for gravity measurements

We have developed a method employing an autonomous unmanned aerial system (UAS) to provide a more robust measure of the field terrain correction (FTC) for gravity measurements than is offered by traditional methods. The resolution of digital terrain that is typically available for the United States (10 or 30 m) is too low to adequately estimate the FTC in steep terrain. The FTC corresponds to the innermost zone around the gravity station (e.g., extending to a 68 m radius for the Hayford-Bowie zones A and B) and is most often estimated in the field with the aid of templates and charts that approximate sectors of the terrain as uniform slopes. These techniques can incur significant error if they are not performed by experienced practitioners. In our approach, we dispatch a UAS to collect camera images around the gravity station, which we use to construct a digital elevation model (DEM) of the area with the structure from motion (SfM) method. The resulting DEMs allow us to precisely calculate the FTC. We have developed software to automate most of the procedure, including the autonomous flight and image capturing by the UAS. We have experimented with a variety of flight paths at several sites spanning a range of terrain conditions to determine the most efficient flight characteristics for this application. Selecting field sites with existing light detection and ranging data has enabled us to characterize errors in the DEMs derived from SfM and to assess the trade-offs between flight time, processing time, and accuracy of the resulting FTCs. Our methodology is fast (flight time: 3–4 minutes) and robust, primarily because the UAS flight is automated. It can be used to calculate the FTC of a variety of terrain conditions and delivers results that are much more precise than existing methods that do not make use of high-resolution digital terrain data.

THE ESTIMATION OF PRECISIONS IN THE PLANNING OF UAS PHOTOGRAMMETRIC SURVEYS

Abstract. The Unmanned Aerial System (UAS) is widely used in the photogrammetric surveys both of structures and of small areas. Geomatics focuses the attention on the metric quality of the final products of the survey, creating several 3D modelling applications from UAS images. As widely known, the quality of results derives from the quality of images acquisition phase, which needs an a priori estimation of the expected precisions. The planning phase is typically managed using dedicated tools, adapted from the traditional aerial-photogrammetric flight plan. But UAS flight has features completely different from the traditional one. Hence, the use of UAS for photogrammetric applications today requires a growth in knowledge in planning. The basic idea of this research is to provide a drone photogrammetric flight planning tools considering the required metric precisions, given a priori the classical parameters of a photogrammetric planning: flight altitude, overlaps and geometric parameters of the camera. The created “office suite” allows a realistic planning of a photogrammetric survey, starting from an approximate knowledge of the Digital Surface Model (DSM), and the effective attitude parameters, changing along the route. The planning products are the overlapping of the images, the Ground Sample Distance (GSD) and the precision on each pixel taking into account the real geometry. The different tested procedures, the obtained results and the solution proposed for the a priori estimates of the precisions in the particular case of UAS surveys are here reported.

General queuing model for optimal seamless delivery of payload processing in multi-core processors

Recent developments in unmanned aerial systems (UAS) provide new opportunities in remote sensing application. In contrast to satellite and conventional (manned) aerial tasks, UAS flights can be operated in a very short period of time. UAS can also be more specifically focused toward a given task such as crop reconnaissance or electric line tower inspection. For some applications, the delivery time of the remote sensing results is crucial. The current three-phase procedure of data acquisition, data downloading and data processing, performed sequentially in time, represents a drawback that reduces the benefits of using unmanned aerial systems. In this paper, we present a parallel processing strategy, based on queuing theory, in which the data processing phase is performed on board in parallel with data acquisition. The unmanned aerial system payload has been enlarged with low-cost, lightweight, multi-core boards to facilitate remote sensing data processing during flight. The storage of the raw sensing data is also done for possible further analysis; however, the ultimate decision support information can be seamless delivered to the customer upon landing. Furthermore, text alarms and limited imagery can also be provided during flight.

Applicability of unmanned aerial system (UAS) for safety inspection on construction sites

The use of technologies on jobsites is one of the recent challenges for construction management. To exemplify this, studies report the use of Unmanned Aerial Vehicles/Systems (UAV/UAS) for transportation and jobsite monitoring, and these potential features have caught the attention of the construction industry. However, studies are still required to orient the development of operational procedures for using this technology effectively, mainly for on-site safety monitoring. This study aims to assess the applicability of UAV for safety inspection on construction sites, focusing on the identification of the requirement that can be inspected and the non-compliances with the safety requirements established. This study was based on two case studies, conducted by a protocol developed for data collection, processing and analysis of the visual assets (photos and video recording) gathered from the UAS flights on jobsites. The results show that the visual assets collected by UAV can improve the safety inspection on jobsites by means of a better visualization of working conditions. This fact was pointed out by the analysis of the visual assets which provided detailed information about the compliance of safety items according to the safety regulations. The main contribution of this research is the development of a set of procedures and guidelines for collecting, processing and analyzing safety requirements from UAS visual assets on jobsite in order to identify the potential to incorporate this technology into the project’s safety management routine.

Exploratory Study of Potential Applications of Unmanned Aerial Systems for Construction Management Tasks

Despite studies exploring potential applications of unmanned aerial systems (UASs), the particular use and value of visual assets (photographs or video) collected with UASs for construction management tasks is not well understood. This paper presents an exploratory case study to identify potential applications of visual assets obtained from UASs for construction management tasks. The case study involved the development of a visual assets database from UAS-based images and videos collected during UAS flights at jobsites in the United States and Brazil as well as semi-structured interviews with construction project personnel. The results revealed potential applications of UASs mainly for project progress monitoring, job site logistics, evaluating safety conditions, and quality inspections among other secondary management tasks. In addition, an analysis of costs related to the use of UASs was performed. The main contribution of this case study is a better understanding of the use of UASs for construction management tasks and their regulatory and cost implications.