UAVs Research Articles

Sensor-Based Quantification of Peanut Disease Defoliation Using an Unmanned Aircraft System and Multispectral Imagery.

Early leaf spot (Passalora arachidicola) and late leaf spot (Nothopassalora personata) are two of the most economically important foliar fungal diseases of peanut, often requiring seven to eight fungicide applications to protect against defoliation and yield loss. Rust (Puccinia arachidis) may also cause significant defoliation depending on season and location. Sensor technologies are increasingly being utilized to objectively monitor plant disease epidemics for research and supporting integrated management decisions. This study aimed to develop an algorithm to quantify peanut disease defoliation using multispectral imagery captured by an unmanned aircraft system. The algorithm combined the Green Normalized Difference Vegetation Index and the Modified Soil-Adjusted Vegetation Index and included calibration to site-specific peak canopy growth. Beta regression was used to train a model for percent net defoliation with observed visual estimations of the variety 'GA-06G' (0 to 95%) as the target and imagery as the predictor (train: pseudo-R2 = 0.71, test k-fold cross-validation: R2 = 0.84 and RMSE = 4.0%). The model performed well on new data from two field trials not included in model training that compared 25 (R2 = 0.79, RMSE = 3.7%) and seven (R2 = 0.87, RMSE = 9.4%) fungicide programs. This objective method of assessing mid-to-late season disease severity can be used to assist growers with harvest decisions and researchers with reproducible assessment of field experiments. This model will be integrated into future work with proximal ground sensors for pathogen identification and early season disease detection.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Perception of noise from unmanned aircraft systems: Efficacy of metrics for indoor and outdoor listener positions.

This paper presents the results of a listening experiment designed to assess annoyance and perceived loudness (PL) for several unmanned aircraft system (UAS) operations, with the listener simulated in indoor and outdoor positions. This research investigated (i) how participant responses change depending on UAS operation, (ii) which broadband metrics are most suitable for representing annoyance and PL, (iii) differences in noise level required to result in equal participant responses to different operations, and (iv) which sound quality metrics (SQMs) are significant for UAS noise perception. Results indicate annoyance and PL responses were greatest for landing operations with flyovers being the least annoying or loud. LAeq, LASmax, and loudness (N5) were the strongest predictors in representing annoyance. Offset analysis predicted small differences in annoyance responses between flyovers and other operations, but also indicated that flyovers would require an increase to LASmax of 3.3 to 6.3 dB compared to other operations to achieve equal PL. Loudness was the most significant SQM, with minor contributions from impulsivity for annoyance and PL when outside, and tonality for PL when indoors. These findings contribute to the understanding of UAS noise perception for the development of metrics and assessment methods accounting for the characteristics of UAS operations.

Analysis of the accuracy of cadastral surveys using UaVs

This article is dedicated to analyzing the accuracy of cadastral surveys using uncrewed aerial vehicles (UAVs). It explores the efficiency and precision of surveys conducted with UAVs in the context of cadastral work. The article examines the methods and technologies used to ensure high accuracy of geospatial data in the cadastral field through UAVs. Utilizing cutting-edge technologies and analyzing research results, the article highlights the possibilities and limitations of UAV applications in cadastral surveys and land mapping tasks. The research findings can benefit geodesy, cartography, and cadastre professionals, as well as developers and operators of uncrewed aerial vehicles. This work investigates a network of phototriangulation conducted using uncrewed aerial vehicles (UAVs). Network parameters evaluated include the number of images, flight altitude, camera focal length, image size, pixel size, and coverage area. The images were processed using Agisoft Metashape Professional software. The main results of automatic image measurement are provided, including the number of tie points in the network, average ray intersection residuals, and random root mean square errors of ground points. The work shows that the provided accuracy characteristics need to provide more information for a reliable assessment of results. Despite optimistic results, the accuracy distribution may need to be improved for confident cadastral data quality. A modernized software complex for phototriangulation calculation, BlockMSG, was used for a more detailed assessment. It is noted that the calculation results correspond to all initial data of the Agisoft Metashape program but without additional information and analysis of possible errors. The paper also discusses the importance of measurement accuracy and its impact on creating a 3D cadastre. Data on the distribution of linear errors of points are presented, indicating the importance of choosing the number of images to obtain accurate coordinates. The primary focus is the need for a thorough analysis of results and possible error sources to ensure high-quality cadastral data. Keywords: accuracy, cadastral surveys, unmanned aerial vehicles (UAVs), geodetic methods, geospatial data, analysis.

Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods

AbstractMeasuring grain sizes in gravel‐bed rivers is crucial when studying river dynamics and sediment transport. Automated methodologies have been developed in recent years for detecting individual grains and measuring their size on digital imagery. These object‐based methodologies have mainly been applied to handheld imagery. Low‐cost and high‐resolution orthoimages covering long river reaches are nowadays accessible with the improvements in uncrewed aerial vehicles (UAV) and structure‐from‐motion (SfM) photogrammetry. Applying object‐based grain‐sizing methodologies to such orthoimages may provide wide‐scale information about the grain size spatial distribution along streambeds. We examined how accurate three object‐based models (BASEGRAIN, PebbleCountsAuto and GALET) were, by comparing their outcomes to in‐field manual measurements of grain sizes and manual grain labelling. We found that BASEGRAIN and PebbleCountsAuto underestimated grain sizes on average, whereas GALET generally overestimated grain size percentiles. Grain size measurements obtained by manually labelling grain features were consistent with in‐field measurements.

AI-Embedded UAV System for Detecting and Pursuing Unwanted UAVs

In recent years, the use of unmanned aerial vehicle (UAV) platforms in civil and military applications has surged, highlighting the critical role of artificial intelligence (AI) embedded UAV systems in the future. This study introduces the Autonomous Drone (Vechür-SIHA), a novel AI-embedded UAV system designed for real-time detection and tracking of other UAVs during flight sequences. Leveraging advanced object detection algorithms and an LSTM-based tracking mechanism, our system achieves an impressive 80% accuracy in drone detection, even in challenging conditions like varying backgrounds and adverse weather.
 Our system boasts the capability to simultaneously track multiple drones within its field of view, maintaining flight for up to 35 minutes, making it ideal for extended missions that require continuous UAV tracking. Moreover, it can lock onto and track other UAVs in mid-air for durations of 4-10 seconds without losing contact, a feature with significant potential for security applications.
 This research marks a substantial contribution to the development of AI-embedded UAV systems, with broad implications across diverse domains such as search and rescue operations, border security, and forest fire prevention. These results provide a solid foundation for future research, fostering the creation of similar systems tailored to different applications, ultimately enhancing the efficiency and safety of UAV operations. The novel approach to real-time UAV detection and tracking presented here holds promise for driving innovations in UAV technology and its diverse applications.

Autonomous Drone Landing: Marked Landing Pads and Solidified Lava Flows

Landing is the most challenging and risky aspect of basic multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than global positioning system (GPS) can provide (e.g. package delivery in an urban canyon). We expand on previous methods by actuating the drone’s camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the Rover-Aerial Vehicle Exploration Network (RAVEN) project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavily influence the drone’s behavior, mission structure, or operational environments.

Modular Multiplatform Compatible Air Measurement System (MoMuCAMS): a new modular platform for boundary layer aerosol and trace gas vertical measurements in extreme environments

Abstract. The Modular Multiplatform Compatible Air Measurement System (MoMuCAMS) is a newly developed in situ aerosol and trace gas measurement platform for lower-atmospheric vertical profiling. MoMuCAMS has been primarily designed to be attached to a Helikite, a rugged tethered balloon type that is suitable for operations in cold and windy conditions. The system addresses the need for detailed vertical observations of atmospheric composition in the boundary layer and lower free troposphere, especially in polar and alpine regions. The MoMuCAMS encompasses a box that houses instrumentation, a heated inlet, a single-board computer to transmit data to the ground for in-flight decisions and a power distribution system. The enclosure can accommodate various combinations of instruments within its weight limit (e.g., 20 kg for a 45 m3 balloon). This flexibility represents a unique feature, allowing for the study of multiple aerosol properties (number concentration, size distribution, optical properties, chemical composition and morphology), as well as trace gases (e.g., CO, CO2, O3, N2O) and meteorological variables (e.g., wind speed and direction, temperature, relative humidity, pressure). Different instrumental combinations are therefore possible to address the specific scientific focus of the observations. It is the first tethered-balloon-based system equipped with instrumentation providing a size distribution for aerosol particles within a large range, i.e., from 8 to 3370 nm, which is vital to understanding atmospheric processes of aerosols and their climate impacts through interaction with radiation and clouds. Here we present a characterization of the specifically developed inlet system and previously unreported instruments, most notably the miniaturized scanning electrical mobility spectrometer and a near-infrared carbon monoxide monitor. As of December 2022, MoMuCAMS has been tested during two field campaigns in the Swiss Alps in winter and fall 2021. It was further deployed in Fairbanks, Alaska, USA, in January–February 2022, as part of the ALPACA (Alaskan Layered Pollution and Chemical Analysis) campaign and in Pallas, Finland, in September–October 2022, as part of the PaCE2022 (Pallas Cloud Experiment) study. Three cases from one of the Swiss Alpine studies are presented to illustrate the various observational capabilities of MoMuCAMS. Results from the first two case studies illustrate the breakup of a surface-based inversion layer after sunrise and the dilution of a 50–70 m thick surface layer. The third case study illustrates the capability of the system to collect samples at a given altitude for offline chemical and microscopic analysis. Overall, MoMuCAMS is an easily deployable tethered-balloon payload with high flexibility, able to cope with the rough conditions of extreme environments. Compared to uncrewed aerial vehicles (drones) it allows for observation of aerosol processes in detail over multiple hours, providing insights into their vertical distribution and processes, e.g., in low-level clouds, that were difficult to obtain beforehand.

Development of a continuous UAV-mounted air sampler and application to the quantification of CO2 and CH4 emissions from a major coking plant

Abstract. The development in uncrewed aerial vehicle (UAV) technologies over the past decade has led to a plethora of platforms that can potentially enable greenhouse gas emission quantification. Here, we report the development of a new air sampler, consisting of a pumped stainless coiled tube of 150 m in length with controlled time stamping, and its deployment from an industrial UAV to quantify CO2 and CH4 emissions from the main coking plant stacks of a major steel maker in eastern China. Laboratory tests show that the time series of CO2 and CH4 measured using the sampling system is smoothed when compared to online measurement by the cavity ring-down spectrometer (CRDS) analyzer. Further analyses show that the smoothing is akin to a convolution of the true time series signals with a heavy-tailed digital filter. For field testing, the air sampler was mounted on the UAV and flown in virtual boxes around two stacks in the coking plant of the Shagang Group (steel producer). Mixing ratios of CO2 and CH4 in air and meteorological parameters were measured from the UAV during the test flight. A mass-balance computational algorithm was used on the data to estimate the CO2 and CH4 emission rates from the stacks. Using this algorithm, the emission rates for the two stacks from the coking plant were calculated to be 0.12±0.014 t h−1 for CH4 and 110±18 t h−1 for CO2, the latter being in excellent agreement with material-balance-based estimates. A Gaussian plume inversion approach was also used to derive the emission rates, and the results were compared with those derived using the mass-balance algorithm, showing a good agreement between the two methods.

Towards Automated Target Picking in Scalar Magnetic Unexploded Ordnance Surveys: An Unsupervised Machine Learning Approach for Defining Inversion Priors

With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data. Many existing conventional methods require manual inspection of the survey data to pick out candidate areas for further analysis. We automate this initial process by implementing unsupervised machine learning techniques to identify small, well-defined regions. When further analysis is conducted with magnetic inversion algorithms, then our approach also reduces the nonlinear computation and time costs by breaking one huge inversion problem into several smaller ones. We also demonstrate robustness to noise and sidestep the requirement for large quantities of labeled training data: two pitfalls of current automation approaches. We propose first to use hierarchical clustering on filtered magnetic gradient data and then to fit ellipses to the resulting clusters to identify subregions for further analysis. In synthetic data experiments and on real-world datasets, our model successfully captures all true targets while simultaneously proposing fewer computationally costly false positives. With this approach, we take an important step towards fully automating the detection of high-risk subregions, but we wish to emphasize the importance of prudent skepticism until it has been tested and proven on more diverse data.

Investigation on improving aerodynamics and flight performance for an unmanned aircraft with a span-extendable wing

The foldable unmanned aerial vehicle can reduce the storage space and improve the platform adaptability. However, the conventional foldable wing aircraft is configured with a low aspect ratio, which leads to a high aerodynamic drag and limited flight endurance. In this study, a foldable wing unmanned aircraft that can increase its wingspan to improve aerodynamic performance and flight endurance is proposed. The aerodynamic characteristics and flight performance of the aircraft is evaluated using the computational fluid dynamic method. The numerical results show that the increased wingspan can effectively decrease the wingtip vorticity and aerodynamic drag. The fully-extended wing configuration achieves a lift-to-drag ratio of 16.72, which results in an increase of 36.94% than that of the non-extended wing. The flight endurance and range of the fully-extended wing configuration are 3.81 hour and 249.89 km, respectively, which creates 86.22% more endurance and 36.88% more range than the non-extended wing. To validate the feasibility of the proposed concept, a flight test was carried out and the flight performance was obtained from flight control system. The flight data corrects well with the numerical results, which proves the effectiveness and feasibility of the variable span-morphing wing in improving flight performance for unmanned aircraft.

Two new multirotor uncrewed aerial vehicles (UAVs) for glaciogenic cloud seeding and aerosol measurements within the CLOUDLAB project

Abstract. Uncrewed aerial vehicles (UAVs) have become widely used in a range of atmospheric science research applications. Because of their small size, flexible range of motion, adaptability, and low cost, multirotor UAVs are especially well-suited for probing the lower atmosphere. However, their use so far has been limited to conditions outside of clouds, first because of the difficulty of flying beyond visual line of sight and second because of the challenge of flying in icing conditions in supercooled clouds. Here, we present two UAVs for cloud microphysical research: one UAV (the measurement UAV) equipped with a Portable Optical Particle Spectrometer (POPS) and meteorological sensors to probe the aerosol and meteorological properties in the boundary layer and one UAV (the seeding UAV) equipped with seeding flares to produce a plume of particles that can nucleate ice in supercooled clouds. A propeller heating mechanism on both UAVs allows for operating in supercooled clouds with icing conditions. These UAVs are an integral part of the CLOUDLAB project in which glaciogenic cloud seeding of supercooled low stratus clouds is utilized for studying aerosol–cloud interactions and ice crystal formation and growth. In this paper, we first show validations of the POPS on board the measurement UAV, demonstrating that the rotor turbulence has a small effect on measured particle number concentrations. We then exemplify the applicability for profiling the planetary boundary layer, as well as for sampling and characterizing aerosol plumes, in this case, the seeding plume. We also present a new method for filtering out high-concentration data to ensure good data quality of POPS. We explain the different flight patterns that are possible for both UAVs, namely horizontal or vertical leg patterns or hovering, with an extensive and flexible parameter space for designing the flight patterns according to our scientific goals. Finally, we show two examples of seeding experiments: first characterizing an out-of-cloud seeding plume with the measurement UAV flying horizontal transects through the plume and, second, characterizing an in-cloud seeding plume with downstream measurements from a POPS and a holographic imager mounted on a tethered balloon. Particle number concentrations and particle number size distributions of the seeding plume from the experiments reveal that we can successfully produce and measure the seeding plume, both in-cloud (with accompanying elevated ice crystal number concentrations) and out-of-cloud. The methods presented here will be useful for probing the lower atmosphere, for characterizing aerosol plumes, and for deepening our cloud microphysical understanding through cloud seeding experiments, all of which have the potential to benefit the atmospheric science community.

Experiences with using ears for stakeholder requirements deve-lopment of the safe integration of unmanned aircrafts in the airspace project

Requirements permeate the entire lifecycle of a system or product, from identifying the stakeholders’ needs to disposal, including verification and validation activities. Therefore, the process for the requirements development must be carefully observed and established in order to ensure that the requirements are complete, consistent, and correct. The use of an appropriate predefined structure can improve the requirements’ elicitation, enabling the reduction of ambiguities and the detection of duplications or omissions. Thus, this article proposes the use of syntax patterns during the development of the stakeholders’ requirements in the context of an airspace project. The result of applying the proposal of this article in a real case was a list of initial requirements in standardized language with their traceability guaranteed.

Bacterial Emission Factors: A Foundation for the Terrestrial-Atmospheric Modeling of Bacteria Aerosolized by Wildland Fires.

Wildland fire is a major global driver in the exchange of aerosols between terrestrial environments and the atmosphere. This exchange is commonly quantified using emission factors or the mass of a pollutant emitted per mass of fuel burned. However, emission factors for microbes aerosolized by fire have yet to be determined. Using bacterial cell concentrations collected on unmanned aircraft systems over forest fires in Utah, USA, we determine bacterial emission factors (BEFs) for the first time. We estimate that 1.39 × 1010 and 7.68 × 1011 microbes are emitted for each Mg of biomass consumed in fires burning thinning residues and intact forests, respectively. These emissions exceed estimates of background bacterial emissions in other studies by 3-4 orders of magnitude. For the ∼2631 ha of similar forests in the Fishlake National Forest that burn each year on average, an estimated 1.35 × 1017 cells or 8.1 kg of bacterial biomass were emitted. BEFs were then used to parametrize a computationally scalable particle transport model that predicted over 99% of the emitted cells were transported beyond the 17.25 x 17.25 km model domain. BEFs can be used to expand understanding of global wildfire microbial emissions and their potential consequences to ecosystems, the atmosphere, and humans.

Automated Hyperspectral Feature Selection and Classification of Wildlife Using Uncrewed Aerial Vehicles

Timely and accurate detection and estimation of animal abundance is an important part of wildlife management. This is particularly true for invasive species where cost-effective tools are needed to enable landscape-scale surveillance and management responses, especially when targeting low-density populations residing in dense vegetation and under canopies. This research focused on investigating the feasibility and practicality of using uncrewed aerial systems (UAS) and hyperspectral imagery (HSI) to classify animals in the wild on a spectral—rather than spatial—basis, in the hopes of developing methods to accurately classify animal targets even when their form may be significantly obscured. We collected HSI of four species of large mammals reported as invasive species on islands: cow (Bos taurus), horse (Equus caballus), deer (Odocoileus virginianus), and goat (Capra hircus) from a small UAS. Our objectives of this study were to (a) create a hyperspectral library of the four mammal species, (b) study the efficacy of HSI for animal classification by only using the spectral information via statistical separation, (c) study the efficacy of sequential and deep learning neural networks to classify the HSI pixels, (d) simulate five-band multispectral data from HSI and study its effectiveness for automated supervised classification, and (e) assess the ability of using HSI for invasive wildlife detection. Image classification models using sequential neural networks and one-dimensional convolutional neural networks were developed and tested. The results showed that the information from HSI derived using dimensionality reduction techniques were sufficient to classify the four species with class F1 scores all above 0.85. The performances of some classifiers were capable of reaching an overall accuracy over 98%and class F1 scores above 0.75, thus using only spectra to classify animals to species from existing sensors is feasible. This study discovered various challenges associated with the use of HSI for animal detection, particularly intra-class and seasonal variations in spectral reflectance and the practicalities of collecting and analyzing HSI data over large meaningful areas within an operational context. To make the use of spectral data a practical tool for wildlife and invasive animal management, further research into spectral profiles under a variety of real-world conditions, optimization of sensor spectra selection, and the development of on-board real-time analytics are needed.

Essential approaches to combat the use of drones. Specific elements of the armed conflict in Ukraine

Although used many years ago, by multinational allied forces acting in coalition in Iraq and Afghanistan, combat drones known as “unmanned aircraft systems” (UAS), “unmanned aerial vehicles” (UAV)”, “unmanned combat aerial vehicles” (UCAV) or “remotely piloted vehicles” (RPV) represent a formidable weapon both for NATO states and for the forces fighting in Ukraine. Considering their essential characteristics, dimensions, load capacity, speed, range, flexibility, ease of operation and low costs, these means of combat come with huge advantages in achieving the operational objectives of combatant structures - regardless of the category they belong to. Thus, the present study includes a theoretical approach that can actually be implemented in the military operational realm, because it includes details and essential references to combat drones designed, produced and used by belligerent and non-belligerent states in connection with the armed conflict taking place on Ukrainian territory. Starting from the objective of the study, a series of important directions resulted for strengthening the actual potential of the joint and tactical forces to be used in national and multinational context within the North Atlantic Alliance, against the aggressive force of an adversary state.

Research on Lateral Safety Spacing for Fusion Operation Based on Unmanned and Manned Aircraft-Event Modeling.

With the rapid development of unmanned aerial vehicle technology and its increasing application across various fields, current airspace resources are insufficient for unmanned aerial vehicles' needs. This paper, taking Zigong General Aviation Airport in Sichuan as a case study, explores the lateral safety spacing in a mixed operation mode of unmanned aerial vehicles and manned aircraft. Currently, there are no standardized regulations for the safe spacing of the fusion operation of unmanned and manned aircraft. Theoretical research is essential to provide a reference for actual operations. It introduces the UM-Event (unmanned and manned aircraft-event) collision risk model, an adaptation of the Event collision risk model, considering factors like communication, navigation, surveillance performance, human factors, collision avoidance equipment performance, and meteorology. Safety spacing was determined via simulation experiments and actual data analysis, adhering to the target safety level (TSL). Findings indicate that surveillance performance has a minor impact on safety spacing, while communication and navigation significantly influence it. The safety spacing, influenced solely by CNS (communication performance, navigation performance, surveillance performance) and combined factors, increased from 4.42 to 4.47 nautical miles. These results offer theoretical guidance for unmanned aerial vehicle safety in non-segregated airspace.

Coupling numerical models of deltaic wetlands with AirSWOT, UAVSAR, and AVIRIS-NG remote sensing data

Abstract. Coastal marsh survival relies on the ability to increase elevation and offset sea level rise. It is therefore important to realistically model sediment fluxes between marshes, tidal channels, and bays as sediment availability controls accretion. Traditionally, numerical models have been calibrated and validated using in situ measurements at a few locations within the domain of interest. These datasets typically provide temporal information but lack spatial variability. This paper explores the potential of coupling numerical models with high-resolution remote sensing imagery. Products from three sensors from the NASA Delta-X airborne mission are used. Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) provides vertical water level change on the marshland and was used to adjust the bathymetry and calibrate water fluxes over the marsh. AirSWOT yields water surface elevation within bays, lakes, and channels, and was used to calibrate the Chezy bottom friction coefficient. Finally, imagery from AVIRIS-NG provides maps of total suspended solids (TSS) concentration that were used to calibrate sediment parameters of settling velocity and critical shear stress for erosion. Three numerical models were developed at different locations along coastal Louisiana using Delft3D. The coupling enabled a spatial evaluation of model performance that was not possible using simple point measurements. Overall, the study shows that calibration of numerical models and their general performance will greatly benefit from remote sensing.

Pemantauan Jumlah Batang Eucalyptus pellita Umur 6 Bulan Berdasarkan Hasil Foto Drone

The use of unmanned aircraft (UAV) or drones, apart from being used for mapping, can be a solution for quickly obtaining information and carrying out forest inventories with very detailed resolution results. Plantation Monitoring Assessment (PMA) aged 6 months is an initial inventory activity, later the data from PMA 6 will be the basic data for subsequent PMA. So, with drones, it is hoped that satisfactory results will be obtained. Apart from that, drones can be used to count the number of live and dead trees as well as empty spots. This research aims to compare the results of tree calculations directly in the field with the results of drone photos. This research was carried out on 6 month old Eucalyptus pellita plants in compartment A034, Baserah Sector, PT. RAPP. Data collection was carried out using systematic random sampling with a sampling intensity of 1%. The data taken is in the form of data on the number of live and dead/empty plants in the field and photographed with a drone. The test was carried out to test the differences in the number of dead/empty plants directly in the field and calculations using drone photos, namely the t test. The results showed that there was no difference between the number of live and dead/empty plants in the field and from drone photos.

Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems

Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.

Providing Air Traffic Services to Unmanned Aircraft Vehicles in Banda Aceh’s Airspace

Nowadays, aircraft are becoming more and more important and talked about due to the rapid development of aircraft in the past few decades. In addition, we must also remember that this aircraft is an important object under air law. Aircraft that were once the result of modifications from hot air balloons have now far developed into aircraft that can be flown remotely, or currently known as drones. Even with the rapid development of drones in the last few decades, it cannot be matched by the progress of air law arrangements both internationally and nationally. With such conditions, both international air law and national air law have not been able to fully guarantee flight safety (safety first), which is the spirit of aviation as contained in the 1944 Chicago Convention, which states that the convention mandates that the growth of safe and orderly international civil aviation be guaranteed. The 1944 Chicago Convention obliges the state to ensure flight safety, including Indonesia. The intended flight safety is where every flight must be guaranteed safety by the country concerned, including civil aviation, military aviation, and drone flights or unmanned aircraft, because their operation using remote pilots has the potential to cause accidents.