Applications Of Unmanned Aerial Systems Research Articles

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part III: Best Practices for Efficient Aerial Surveying

With the increasing use of unmanned aerial systems (UASs) in the agricultural domain, ensuring the consistency and completeness of aerial surveys is critical in order to establish repeatability and consistency in data collection activities. This publication covers five main steps to ensure that aerial data collections are repeatable and consistent among missions. It is one of a three-part series focusing on the applications, configuration, and best practices for using UASs in agricultural operations management. Written by Aditya Singh and James Fletcher, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2021.

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part II: Platforms and Payloads

Unmanned aerial systems (UASs, UAVs, or drones) have emerged as an important tool for farmers, Extension agents, and landowners to map, monitor, and manage their properties. This 5-page publication provides an overview of the primary components of typical UASs to help growers, landowners, and/or Extension agents who want to configure and/or purchase a UAS or sensor system for agricultural operations. This document is one of a three-part series focusing on the applications, configuration, and best practices for using UASs in agricultural operations management. Written by Aditya Singh and James Fletcher, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2021.

Strategic framework of Unmanned Aerial Systems integration in the disaster management public organisations of the Dominican Republic

In the Caribbean region, there is a significant yearly number of natural events incidents. The impact of those events has affected the well-being, social and economic structures of the countries in this region. The investment in disaster management strategies is a fundamental decision to the region for improving capabilities, understanding the liabilities, dealing with the disaster stages, and integrating systems and tools to obtain greater results in resilience strategies. Therefore, information technology and robotics have played an effective role bringing innovation to the traditional approaches by improving the capabilities of personnel and serving as a catalyst to rapid data collection tools for effective decision-making during disastrous situation, as has been the case of the Dominican Republic. The organic adoption process of Unmanned Aerial System (UAS) has represented a large step forward regarding this matter. Examples of the adoption process occur based on their flexibility in their regulatory context, funding investments in testing the applications of UAS, such as surveying, delivering medical samples and spray disinfection during the COVID-19 pandemic. However, there are only some mature applications that address a future-proofing concept as well as the integration into a typical institutional workflow. Therefore, this paper aims to be an introductory guide for decision and policymakers, educators and technicians to reduce the scepticism, lack of knowledge and know-how in the adoption of reliable, practical and effective tools by providing a combination of theories from socio-technical systems (socio-technical change impact model (SCI)) and organisational level (technology-organisational-environmental) frameworks of the UAS adoption process for natural events, tasks and critical roles in disaster management taking into account the Dominican Republic context. Qualitative case of study was evaluated from 7 organisations involved to disaster management in the Dominican Republic, and a semi-structured interview were used to approach the adoption process of Unmanned Aerial System (UAS) in these organisations.The findings of these studies reveal the lessons learned from the internal strategies and policies in the adoption process between public and private institutions. The task of capturing images and videos has been shown to be a faster way to acquire data, which allow infrastructure projects to tend to be a significant benefit at the moment of applying UAS for 3D reconstructions and simulations of seismic behaviour, and finally, identification of inaccessible vulnerable areas after disasters. Other tasks, such as search and rescue, are also mentioned. Recommendations for policymakers are related to establish guidance for learning organisations approaches on focusing on digitalisation of cities through UAS applications. Other context can be benefit from the lessons and can also beas an additional complement of the Regional Comprehensive Disaster Management Framework of 2014–2024.

AREIOM: adaptive research multicopter platform

The applications of unmanned aerial systems have dramatically increased in the late days, in particular within dull, dirty and dangerous missions, where it has been clearly shown that unmanned systems are better suited than manned aircrafts. The reduction of human involvements leads to an increasing demand for autonomous and operationally adaptive airborne systems. In addition, unmanned aerial systems share similar limitations with most computer embedded systems, e.g., limited space and power resources, and increasing computation requirements and complexity of the applications running onboard. To fulfill these requirements and achieve the fundamental features of autonomy and adaptability, the AREIOM—Adaptive Research Multicopter Platform—along with its layered modular architecture and the specific details of its underlying hardware and software components are presented.

Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities

Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability.

Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of unmanned aerial systems (UASs). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1 m resolution snow depth product across seven areas of interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories, Canada. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n = 8434) and snow depth (n = 7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (<0.01 m bias), similar to the snow depth product (UASSD) RMSE of 0.15 m (+0.04 m bias). The distribution of snow depth between in situ measurements and UASSD was similar along the transects where in situ snow depth was collected, although similarity varies by AOI. Finally, we provide a discussion of factors that may influence the accuracy of the snow depth products including vegetation, environmental conditions, and study design.

Applications of Unmanned Aerial Systems in Agricultural Operation Management: Part I: Overview

Unmanned aerial systems (UASs, UAVs, or drones) have emerged as one of the most promising technologies for agricultural operation management in recent decades. This 6-page publication provides an overview of the broad areas where UASs can be utilized for monitoring and managing farm operations. Written by James Fletcher and Aditya Singh, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2020.

Role of Unmanned Aerial Systems for Natural Resource Management

Natural resources management (NRM) is an important application of satellite remote sensing. The need for cost-effective, accurate, demand-based high-resolution data for natural resources management is growing in developing countries. However, the higher cost of satellite data, limitations of temporal and spatial resolution are the biggest hurdle for continuous monitoring of the dynamics of natural resources, i.e. agriculture, forest, water and land resources. Due to these limitations, unmanned aerial system (UAS)-based remote sensing studies for natural resource management are rapidly growing, as it offers several advantages, i.e. flexibility, efficiency, easy operation, higher spatial resolution, multispectral resolution and cost-effectiveness over high-resolution satellite data. The role of UAS is optimal in remote areas for mapping and monitoring. The constantly improving technology of UAS has led to several advantages over satellite data, however; some challenges remain the same as the main focus of UAS developers is to reduce the cost and payload weight. The challenges include poor radiometric and geometric performance, small payload, large dataset, data processing, regulation and restrictions. The application of UAS is still in its initial phase; however, its ability to produce application-specific data at relatively lower cost has proved that UAS has a great potential for natural resources management. Based on the existing literature, this study is an attempt to evaluate the current status of the use of UAS for NRM along with its major strength, opportunity, weakness, threats and future perspective.

Forecasting Yield and Lignocellulosic Composition of Energy Cane Using Unmanned Aerial Systems

Crop monitoring and appropriate agricultural management practices of elite germplasm will enhance bioenergy’s efficiency. Unmanned aerial systems (UAS) may be a useful tool for this purpose. The objective of this study was to assess the use of UAS with true color and multispectral imagery to predict the yield and total cellulosic content (TCC) of newly created energy cane germplasm. A trial was established in the growing season of 2016 at the Texas A&M AgriLife Research Center in Weslaco, Texas, where 15 energy cane elite lines and three checks were grown on experimental plots, arranged in a complete block design and replicated four times. Four flights were executed at different growth stages in 2018, at the first ratoon crop, using two multi-rotor UAS: the DJI Phantom 4 Pro equipped with RGB camera and the DJI Matrice 100, equipped with multispectral sensor (SlantRange 3p). Canopy cover, canopy height, NDVI (Normalized Difference Vegetation Index), and ExG (Excess Green Index) were extracted from the images and used to perform a stepwise regression to obtain the yield and TCC models. The results showed a good agreement between the predicted and the measured yields (R2 = 0.88); however, a low coefficient of determination was found between the predicted and the observed TCC (R2 = 0.30). This study demonstrated the potential application of UAS to estimate energy cane yield with high accuracy, enabling plant breeders to phenotype larger populations and make selections with higher confidence.

A Systematic Approach to Development Assurance and Safety of Unmanned Aerial Systems

<div class="section abstract"><div class="htmlview paragraph">The unrestrained design space for unmanned aerial systems (UAS) presents challenges to accurate safety assessment and the assurance of development to appropriate levels of rigor within those systems. The established safety and development assurance standards and practices were developed for vehicles operating in highly controlled conditions with continuous oversight. The very nature of unmanned systems introduce new failure conditions, even in those systems operating within the strict rules of the National Airspace System (NAS), particularly failures of control and command, situational awareness, and control security. Beyond those, the new concepts of operation being conceived by UAS developers introduce their own new set of considerations with regards to operating in uncontrolled airspace, often in close proximity to bystanders. These new concepts require new technologies beyond those currently supported by the hardware and software development assurance processes. However, the established standards and practices of aircraft development assurance and safety assessment can be adapted for the broader world of UAS applications, provided that the considerations driven by the new concepts of operation can be identified and properly analyzed. The two major challenges to this are the identification and determination of criticality of failure conditions in the new operational environment, and the analysis of the new architectures, technologies, and the “new and novel” mitigations that will be possible from them. If these challenges can be addressed, it will be possible to apply established principles of development assurance and system safety to these new products, and thus give confidence both to the developers and the regulators tasked with certificating them.</div></div>

Unmanned Aerial Systems for the Oil and Gas Industry: Overview, Applications, and Challenges

Unmanned aerial systems (UAS) represent an evolving technology with enormous potential to revolutionize the oil and gas industry by providing a more efficient, fast, safe, and cost-effective way to perform a variety of field activities. Recently, UAS has received considerable attention from the oil and gas industry. The objective of this review article is to highlight the usability of UAS for the oil and gas industry, focussing mainly on UAS applications and the opportunities and challenges of UAS deployment in such oil and gas applications. Additionally, this paper also covers the available sensory systems for UAS and provides some recommendations to select a UAS for a given oil and gas application.

Driver performance due to small unmanned aerial system applications in the vicinity of roadways

Small unmanned aerial systems (sUAS), or drones, have become increasingly utilized for a myriad of applications in the vicinity of roadways and can offer a low-cost alternative to many labor-intensive data collection techniques, including infrastructure inspection, roadway marking data collection, and more. To collect much of this data with a desired degree of accuracy, sUAS must be flown near moving vehicles, pedestrians, and/or bicyclists. However, sUAS, and their operators, have the potential to be a distraction to drivers. Through a full-immersion driver simulator study and post-experiment questionnaire, this study furthered the current state-of-the-literature and investigated the potential for sUAS to be flown in the vicinity of roadways in the future as well as potential safety implications of those circumstances. Specifically, driver performance due to drone height and the presence of drone operators was evaluated. The results of the driving simulation study revealed that participants were more visually distracted in situations where the operators and drone were both present compared to the drone only scenarios. Furthermore, in 11% of all analyzed situations, drivers were critically visually-distracted as shown by a continuous glance of two seconds or more at the drone or operators. The post-experiment questionnaire revealed that approximately 30% of participants had seen a drone in flight near a roadway prior to this study. Ultimately, this research provides recommendations to policymakers for creating regulations on the use of sUAS in the vicinity of roadways.

Comparison of the supervisory cost of using an unmanned aerial system and conventional methods in construction projects

Unmanned Aerial Systems (UAS) are a growing aircraft technology being used in all sorts of industries. Despite studies exploring the potential applications of UAS, such as photographs or video collected by UAS, its benefits for the supervisory job on construction sites is not well understood. This paper presents a comparison of the supervisory cost generated using UAS and that from the conventional method. In this study, both five projects applying UASs and another five using the conventional supervisory method participated voluntarily. The survey used questionnaires targeted to people working in a supervisory job within the five projects. The results showed that most potential applications were particularly suited for tracking and monitoring job progress, evaluating safety monitoring and support, and inspecting difficult areas. In comparison, it was found that applying UAS was more cost-effective than the conventional supervisory method. The average supervisory cost to total wage generated from the project supervisory job by applying UAS and conventional methods were 0.212% and 0.450% respectively. Despite insignificant differences, the study provided a picture that, by applying UAS properly, it would be able to have a potential cost saving in the supervisory job in construction.

Long‐duration fully autonomous operation of rotorcraft unmanned aerial systems for remote‐sensing data acquisition

AbstractRecent applications of unmanned aerial systems (UAS) to precision agriculture have shown increased ease and efficiency in data collection at precise remote locations. However, further enhancement of the field requires operation over long periods of time, for example, days or weeks. This has so far been impractical due to the limited flight times of such platforms and the requirement of humans in the loop for operation. To overcome these limitations, we propose a fully autonomous rotorcraft UAS that is capable of performing repeated flights for long‐term observation missions without any human intervention. We address two key technologies that are critical for such a system: full platform autonomy to enable mission execution independently from human operators and the ability of vision‐based precision landing on a recharging station for automated energy replenishment. High‐level autonomous decision making is implemented as a hierarchy of master and slave state machines. Vision‐based precision landing is enabled by estimating the landing pad's pose using a bundle of AprilTag fiducials configured for detection from a wide range of altitudes. We provide an extensive evaluation of the landing pad pose estimation accuracy as a function of the bundle's geometry. The functionality of the complete system is demonstrated through two indoor experiments with duration of 11 and 10.6 hr, and one outdoor experiment with a duration of 4 hr. The UAS executed 16, 48, and 22 flights, respectively, during these experiments. In the outdoor experiment, the ratio between flying to collect data and charging was 1–10, which is similar to past work in this domain. All flights were fully autonomous with no human in the loop. To our best knowledge, this is the first research publication about the long‐term outdoor operation of a quadrotor system with no human interaction.

A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data

Recent years have witnessed enormous interest in the application of Unmanned Aerial Systems (UAS) for precision agriculture. This study presents a novel approach to use multi-temporal UAS data for comparison of two management practices in cotton, conventional tillage (CT) and no-tillage (NT). The plant parameters considered for the comparison are: canopy height (CH), canopy cover (CC), canopy volume (CV) and Normalized Difference Vegetation Index (NDVI). Initially, the whole study area was divided into approximately one square meter size grids. Measurements were extracted grid wise using high resolution UAS data captured ten times over whole crop growing season of the cotton. One tailed Z-test hypothesis reveals that there is a significant difference between cotton growth under CT and NT for almost all the epochs. With 95% confidence interval, the crop grown under NT found to have taller canopy, higher canopy cover, bigger biomass and higher NDVI, as compared to those under CT cropping system.

Detection of floating plastics from satellite and unmanned aerial systems (Plastic Litter Project 2018)

A rapidly rising amount of plastic litter on land and at sea is becoming a global wicked environmental problem. Here, we present an innovative exploratory application of unmanned aerial systems (UAS) and open-access satellite imagery in remote detection of floating plastics in natural seawater, through a dedicated aquatic environment experiment. We aimed to extract meaningful spectral measurements in near-real scenarios and to compare the geospatial information ranging from moderate to very high resolution. A set of three artificial floating plastic targets were setup for remote detection in the waters close to Tsamakia beach of Mytilene on Lesvos Island, Greece. These floating targets consisted of 100 m2 PET-1 1.5 L water bottles, LDPE plastic bags and nylon fishing ghost nets. Spectral properties of the controlled targets as well as surrounding seawater were investigated for Sentinel-2A satellite data. We demonstrate how UAS very high geospatial resolution images can be useful in improving geo-referencing of satellite images and how UAS can be used to assess the plastic percentage coverage of satellite images. We observed very weak to strong relationships between percentage pixel coverage and the spectral reflectance at p-value < 0.1 significance level. Effects of atmospheric correction algorithms was evaluated using Sen2Cor and ACOLITE, derive unbiased percentage differences were less than 65%. Our feasibility study demonstrated the importance of very high geo-spatial resolution UAS datasets in validating and enhancing the geo-spatial accuracy of satellite data for monitoring plastics in the aquatic environment. Monitoring and identifying plastics needs an integrated suite of sensors, we therefore present how available tools can be utilized to improve current efforts and contribute to advancing relevant future remote sensing technologies.

Modelling glacier topography in Antarctica using unmanned aerial survey: assessment of opportunities

ABSTRACTWe discuss the application of unmanned aerial systems (UASs) and UAS-derived data to model glacier topography for detecting and analysing slow and rapid changes in glacier surfaces. The study was conducted in East Antarctica in the austral summer 2016/2017. The surveyed areas included an eastern part of the Larsemann Hills, an Airfield of the Progress Station, an initial section of a Sledge route from the Progress to Vostok Stations, and a north-western margin of the Dålk Glacier. We used a Geoscan 201 Geodesy, a professional-grade flying-wing UAS. For the photogrammetric processing, we utilized the Agisoft PhotoScan software. The direct georeferencing approach was applied. Several high-resolution, multitemporal digital elevation models (DEMs) for surveyed areas were produced. The DEM accuracy was estimated in the absence of ground control points and reference DEMs. We determined the ice flow velocity within the Airfield area and the Sledge route zone, where marked points were installed and moved together with the ice surface. We found that UAS imagery can be used for real-time monitoring of open and some snow-covered crevasses of various sizes. Wind-driven snow microforms are also well recognizable on UAS-derived aerial images. The 2017 Dålk Glacier catastrophic subsidence demonstrated that monitoring and studying such events in glaciers and ice sheets are almost impossible without UASs. Optimal meteorological conditions were empirically determined for conducting unmanned aerial survey to obtain images suitable for subsequent photogrammetric processing and DEM generation. Finally, we discuss adaptation of equipment and software to the Antarctic environment.

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an F 1 score of 99.09 % for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.