Subjects -> TRANSPORTATION (Total: 216 journals)
    - AIR TRANSPORT (9 journals)
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AIR TRANSPORT (9 journals)

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Drones     Open Access   (Followers: 5)
International Journal of Aerospace Psychology     Hybrid Journal   (Followers: 23)
International Journal of Aviation Management     Hybrid Journal   (Followers: 9)
International Journal of Micro Air Vehicles     Full-text available via subscription   (Followers: 11)
Journal of Air Transport Management     Hybrid Journal   (Followers: 13)
Journal of Air Transportation     Hybrid Journal   (Followers: 11)
Journal of Airline and Airport Management     Open Access   (Followers: 12)
Journal of Airport Management     Full-text available via subscription   (Followers: 7)
Transport and Aerospace Engineering     Open Access   (Followers: 1)
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Drones
Number of Followers: 5  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2504-446X
Published by MDPI Homepage  [233 journals]
  • Drones, Vol. 5, Pages 24: Biomimetic Drones Inspired by Dragonflies Will
           Require a Systems Based Approach and Insights from Biology

    • Authors: Javaan Chahl, Nasim Chitsaz, Blake McIvor, Titilayo Ogunwa, Jia-Ming Kok, Timothy McIntyre, Ermira Abdullah
      First page: 24
      Abstract: Many drone platforms have matured to become nearly optimal flying machines with only modest improvements in efficiency possible. “Chimera” craft combine fixed wing and rotary wing characteristics while being substantially less efficient than both. The increasing presence of chimeras suggests that their mix of vertical takeoff, hover, and more efficient cruise is invaluable to many end users. We discuss the opportunity for flapping wing drones inspired by large insects to perform these mixed missions. Dragonflies particularly are capable of efficiency in all modes of flight. We will explore the fundamental principles of dragonfly flight to allow for a comparison between proposed flapping wing technological solutions and a flapping wing organism. We chart one approach to achieving the next step in drone technology through systems theory and an appreciation of how biomimetics can be applied. New findings in dynamics of flapping, practical actuation technology, wing design, and flight control are presented and connected. We show that a theoretical understanding of flight systems and an appreciation of the detail of biological implementations may be key to achieving an outcome that matches the performance of natural systems. We assert that an optimal flapping wing drone, capable of efficiency in all modes of flight with high performance upon demand, might look somewhat like an abstract dragonfly.
      Citation: Drones
      PubDate: 2021-03-27
      DOI: 10.3390/drones5020024
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 25: A New Method for High Resolution Surface Change
           Detection: Data Collection and Validation of Measurements from UAS at the
           Nevada National Security Site, Nevada, USA

    • Authors: Brandon Crawford, Erika Swanson, Emily Schultz-Fellenz, Adam Collins, Julian Dann, Emma Lathrop, Damien Milazzo
      First page: 25
      Abstract: The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.
      Citation: Drones
      PubDate: 2021-04-14
      DOI: 10.3390/drones5020025
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 26: Hybrid LoRa-IEEE 802.11s Opportunistic Mesh
           Networking for Flexible UAV Swarming

    • Authors: Luca Davoli, Emanuele Pagliari, Gianluigi Ferrari
      First page: 26
      Abstract: Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases.
      Citation: Drones
      PubDate: 2021-04-15
      DOI: 10.3390/drones5020026
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 27: UAVs Trajectory Optimization for Data Pick Up
           and Delivery with Time Window

    • Authors: Ines Khoufi, Anis Laouiti, Cedric Adjih, Mohamed Hadded
      First page: 27
      Abstract: Unmanned Aerial Vehicles (UAVs), also known as drones, are a class of aircraft without the presence of pilots on board. UAVs have the ability to reduce the time and cost of deliveries and to respond to emergency situations. Currently, UAVs are extensively used for data delivery and/or collection to/from dangerous or inaccessible sites. However, trajectory planning is one of the major UAV issues that needs to be solved. To address this question, we focus in this paper on determining the optimized routes to be followed by the drones for data pickup and delivery with a time window with an intermittent connectivity network, while also having the possibility to recharge the drones’ batteries on the way to their destinations. To do so, we formulated the problem as a multi-objective optimization problem, and we showed how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve this problem. Several experiments were conducted to validate the proposed algorithm by considering different scenarios.
      Citation: Drones
      PubDate: 2021-04-16
      DOI: 10.3390/drones5020027
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 28: SeeCucumbers: Using Deep Learning and Drone
           Imagery to Detect Sea Cucumbers on Coral Reef Flats

    • Authors: Joan Y. Q. Li, Stephanie Duce, Karen E. Joyce, Wei Xiang
      First page: 28
      Abstract: Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species.
      Citation: Drones
      PubDate: 2021-04-16
      DOI: 10.3390/drones5020028
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 29: Assessing the Potential of Remotely-Sensed Drone
           Spectroscopy to Determine Live Coral Cover on Heron Reef

    • Authors: Valerie J. Cornet, Karen E. Joyce
      First page: 29
      Abstract: Coral reefs, as biologically diverse ecosystems, hold significant ecological and economic value. With increased threats imposed on them, it is increasingly important to monitor reef health by developing accessible methods to quantify coral cover. Discriminating between substrate types has previously been achieved with in situ spectroscopy but has not been tested using drones. In this study, we test the ability of using point-based drone spectroscopy to determine substrate cover through spectral unmixing on a portion of Heron Reef, Australia. A spectral mixture analysis was conducted to separate the components contributing to spectral signatures obtained across the reef. The pure spectra used to unmix measured data include live coral, algae, sand, and rock, obtained from a public spectral library. These were able to account for over 82% of the spectral mixing captured in each spectroscopy measurement, highlighting the benefits of using a public database. The unmixing results were then compared to a categorical classification on an overlapping mosaicked drone image but yielded inconclusive results due to challenges in co-registration. This study uniquely showcases the potential of using commercial-grade drones and point spectroscopy in mapping complex environments. This can pave the way for future research, by increasing access to repeatable, effective, and affordable technology.
      Citation: Drones
      PubDate: 2021-04-17
      DOI: 10.3390/drones5020029
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 30: Drones, Gulls and Urbanity: Interaction between
           New Technologies and Human Subsidized Species in Coastal Areas

    • Authors: Martín G. Frixione, Christian Salvadeo
      First page: 30
      Abstract: The use of drones has expanded the boundaries of several activities, which is expected to be utilized intensively in the near future. Interactions between urbanity and naturalness have been increasing while urban expansion amplifies the proximity between urban and natural areas. In this scenario, the interactions between drones and fauna could be augmented. Therefore, the aim of this study was to depict and evaluate the responses of the opportunistic and territorial seagull Larus livens to a small-sized drone during the non-breeding stage in urban areas and natural surroundings. The results evidenced that gulls do not react to drone sounds, coloration, or distance between them and the drone take-off spot. Clearly, the take-off vertical movement triggers an agonistic behavior that is more frequent in groups conformed by two adults, evidencing some kind of territorial response against the device, expressed as characteristic mobbing behavior. Thus, adult settled gulls in touristic and non-urbanized areas displayed agonistic behavior more frequently against the drone. Despite the coastal urban area being a free interaction environment, it evidences a low risk between drone management and territorial seabirds.
      Citation: Drones
      PubDate: 2021-04-22
      DOI: 10.3390/drones5020030
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 31: Comparison of Outdoor Compost Pile Detection
           Using Unmanned Aerial Vehicle Images and Various Machine Learning
           Techniques

    • Authors: Bonggeun Song, Kyunghun Park
      First page: 31
      Abstract: Since outdoor compost piles (OCPs) contain large amounts of nitrogen and phosphorus, they act as a major pollutant that deteriorates water quality, such as eutrophication and green algae, when the OCPs enter the river during rainfall. In South Korea, OCPs are frequently used, but there is a limitation that a lot of manpower and budget are consumed to investigate the current situation, so it is necessary to efficiently investigate the OCPs. This study compared the accuracy of various machine learning techniques for the efficient detection and management of outdoor compost piles (OCPs), a non-point pollution source in agricultural areas in South Korea, using unmanned aerial vehicle (UAV) images. RGB, multispectral, and thermal infrared UAV images were taken in August and October 2019. Additionally, vegetation indices (NDVI, NDRE, ENDVI, and GNDVI) and surface temperature were also considered. Four machine learning techniques, including support vector machine (SVM), decision tree (DT), random forest (RF), and k-NN, were implemented, and the machine learning technique with the highest accuracy was identified by adjusting several variables. The accuracy of all machine learning techniques was very high, reaching values of up to 0.96. Particularly, the accuracy of the RF method with the number of estimators set to 10 was highest, reaching 0.989 in August and 0.987 in October. The proposed method allows for the prediction of OCP location and area over large regions, thereby foregoing the need for OCP field measurements. Therefore, our findings provide highly useful data for the improvement of OCP management strategies and water quality.
      Citation: Drones
      PubDate: 2021-04-26
      DOI: 10.3390/drones5020031
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 32: MIMO Relaying UAVs Operating in Public Safety
           Scenarios

    • Authors: Joseanne Viana, Francisco Cercas, Américo Correia, Rui Dinis, Pedro Sebastião
      First page: 32
      Abstract: Methods to implement communication in natural and humanmade disasters have been widely discussed in the scientific community. Scientists believe that unmanned aerial vehicles (UAVs) relays will play a critical role in 5G public safety communications (PSC) due to their technical superiority. They have several significant advantages: a high degree of mobility, flexibility, exceptional line of sight, and real-time adaptative planning. For instance, cell edge coverage could be extended using relay UAVs. This paper summarizes the sidelink evolution in the 3GPP standardization associated with the usage of the device to device (D2D) techniques that use long term evolution (LTE) communication systems, potential extensions for 5G, and a study on the impact of circular mobility on relay UAVs using the software network simulator 3 (NS3). In this simulation, the transmitted packet percentage was evaluated where the speed of the UAV for users was changed. This paper also examines the multi-input multi-output (MIMO) communication applied to drones and proposes a new trajectory to assist users experiencing unfortunate circumstances. The overall communication is highly dependent on the drone speed and the use of MIMO and suitable antennas may influence overall transmission between users and the UAVs relay. When the UAVs relaying speed was configured at 108 km/h the total transmission rate was reduced to 55% in the group with 6 users allocated to each drone.
      Citation: Drones
      PubDate: 2021-04-26
      DOI: 10.3390/drones5020032
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 33: Communication Aware UAV Swarm Surveillance Based
           on Hierarchical Architecture

    • Authors: Chengtao Xu, Kai Zhang, Yushan Jiang, Shuteng Niu, Thomas Yang, Houbing Song
      First page: 33
      Abstract: Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects.
      Citation: Drones
      PubDate: 2021-04-30
      DOI: 10.3390/drones5020033
      Issue No: Vol. 5, No. 2 (2021)
       
  • Drones, Vol. 5, Pages 4: Automated Agave Detection and Counting Using a
           Convolutional Neural Network and Unmanned Aerial Systems

    • Authors: Donovan Flores, Iván González-Hernández, Rogelio Lozano, Jesus Manuel Vazquez-Nicolas, Jorge Luis Hernandez Toral
      First page: 4
      Abstract: We present an automatic agave detection method for counting plants based on aerial data from a UAV (Unmanned Aerial Vehicle). Our objective is to autonomously count the number of agave plants in an area to aid management of the yield. An orthomosaic is obtained from agave plantations, which is then used to create a database. This database is in turn used to train a Convolutional Neural Network (CNN). The proposed method is based on computer image processing, and the CNN increases the detection performance of the approach. The main contribution of the present paper is to propose a method for agave plant detection with a high level of precision. In order to test the proposed method in a real agave plantation, we develop a UAV platform, which is equipped with several sensors to reach accurate counting. Therefore, our prototype can safely track a desired path to detect and count agave plants. For comparison purposes, we perform the same application using a simpler algorithm. The result shows that our proposed algorithm has better performance reaching an F1 score of 0.96 as opposed to 0.57 for the Haar algorithm. The obtained experimental results suggest that the proposed algorithm is robust and has considerable potential to help farmers manage agave agroecosystems.
      Citation: Drones
      PubDate: 2021-01-01
      DOI: 10.3390/drones5010004
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 5: Quantifying Waterfowl Numbers: Comparison of
           Drone and Ground-Based Survey Methods for Surveying Waterfowl on
           Artificial Waterbodies

    • Authors: Shannon J. Dundas, Molly Vardanega, Patrick O’Brien, Steven R. McLeod
      First page: 5
      Abstract: Drones are becoming a common method for surveying wildlife as they offer an aerial perspective of the landscape. For waterbirds in particular, drones can overcome challenges associated with surveying locations not accessible on foot. With the rapid uptake of drone technology for bird surveys, there is a need to compare and calibrate new technologies with existing survey methods. We compared waterfowl counts derived from ground- and drone-based survey methods. We sought to determine if group size and waterbody size influenced the difference between counts of non-nesting waterfowl and if detection of species varied between survey methods. Surveys of waterfowl were carried out at constructed irrigation dams and wastewater treatment ponds throughout the Riverina region of New South Wales (NSW), Australia. Data were analyzed using Bayesian multilevel models (BMLM) with weakly informative priors. Overall, drone-derived counts of waterfowl were greater (+36%) than ground counts using a spotting scope (β_ground= 0.64 [0.62–0.66], (R2 = 0.973)). Ground counts also tended to underestimate the size of groups. Waterbody size had an effect on comparative counts, with ground counts being proportionally less than drone counts (mean = 0.74). The number of species identified in each waterbody type was similar regardless of survey method. Drone-derived counts are more accurate compared to traditional ground counts, but drones do have some drawbacks including initial equipment costs and time-consuming image or photo processing. Future surveys should consider using drones for more accurately surveying waterbirds, especially when large groups of birds are present on larger waterbodies.
      Citation: Drones
      PubDate: 2021-01-13
      DOI: 10.3390/drones5010005
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 6: A Citizen Science Unmanned Aerial System Data
           Acquisition Protocol and Deep Learning Techniques for the Automatic
           

    • Authors: Apostolos Papakonstantinou, Marios Batsaris, Spyros Spondylidis, Konstantinos Topouzelis
      First page: 6
      Abstract: Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.
      Citation: Drones
      PubDate: 2021-01-18
      DOI: 10.3390/drones5010006
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 7: The Methodological Aspects of Constructing a
           High-Resolution DEM of Large Territories Using Low-Cost UAVs on the
           Example of the Sarycum Aeolian Complex, Dagestan, Russia

    • Authors: Artur Gafurov
      First page: 7
      Abstract: Unmanned aerial vehicles (UAV) have long been well established as a reliable way to construct highly accurate, up-to-date digital elevation models (DEM). However, the territories which were modeled by the results of UAV surveys can be characterized as very local. This paper presents the results of surveying the Sarycum area of the Dagestan Nature Reserve of Russia with an area of 15 sq. km using a DJI Phantom 4 UAV, as well as the methodological recommendations for conducting work on such a large territory. As a result of this work, a DEM with 0.5 m resolution as well as an ultrahigh resolution orthophotoplane were obtained for the first time for this territory, which make it possible to assess the dynamics of aeolian processes at a qualitatively different level.
      Citation: Drones
      PubDate: 2021-01-19
      DOI: 10.3390/drones5010007
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 8: The Drone Revolution of Shark Science: A Review

    • Authors: Paul A. Butcher, Andrew P. Colefax, Robert A. Gorkin, Stephen M. Kajiura, Naima A. López, Johann Mourier, Cormac R. Purcell, Gregory B. Skomal, James P. Tucker, Andrew J. Walsh, Jane E. Williamson, Vincent Raoult
      First page: 8
      Abstract: Over the past decade, drones have become a popular tool for wildlife management and research. Drones have shown significant value for animals that were often difficult or dangerous to study using traditional survey methods. In the past five years drone technology has become commonplace for shark research with their use above, and more recently, below the water helping to minimise knowledge gaps about these cryptic species. Drones have enhanced our understanding of shark behaviour and are critically important tools, not only due to the importance and conservation of the animals in the ecosystem, but to also help minimise dangerous encounters with humans. To provide some guidance for their future use in relation to sharks, this review provides an overview of how drones are currently used with critical context for shark monitoring. We show how drones have been used to fill knowledge gaps around fundamental shark behaviours or movements, social interactions, and predation across multiple species and scenarios. We further detail the advancement in technology across sensors, automation, and artificial intelligence that are improving our abilities in data collection and analysis and opening opportunities for shark-related beach safety. An investigation of the shark-based research potential for underwater drones (ROV/AUV) is also provided. Finally, this review provides baseline observations that have been pioneered for shark research and recommendations for how drones might be used to enhance our knowledge in the future.
      Citation: Drones
      PubDate: 2021-01-21
      DOI: 10.3390/drones5010008
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 9: Ears in the Sky: Potential of Drones for the
           Bioacoustic Monitoring of Birds and Bats

    • Authors: Adrien Michez, Stéphane Broset, Philippe Lejeune
      First page: 9
      Abstract: In the context of global biodiversity loss, wildlife population monitoring is a major challenge. Some innovative techniques such as the use of drones—also called unmanned aerial vehicle/system (UAV/UAS)—offer promising opportunities. The potential of UAS-based wildlife census using high-resolution imagery is now well established for terrestrial mammals or birds that can be seen on images. Nevertheless, the ability of UASs to detect non-conspicuous species, such as small birds below the forest canopy, remains an open question. This issue can be solved with bioacoustics for acoustically active species such as bats and birds. In this context, UASs represent an interesting solution that could be deployed on a larger scale, at lower risk for the operator, and over hard-to-reach locations, such as forest canopies or complex topographies, when compared with traditional protocols (fixed location recorders placed or handled by human operators). In this context, this study proposes a methodological framework to assess the potential of UASs in bioacoustic surveys for birds and bats, using low-cost audible and ultrasound recorders mounted on a low-cost quadcopter UAS (DJI Phantom 3 Pro). The proposed methodological workflow can be straightforwardly replicated in other contexts to test the impact of other UAS bioacoustic recording platforms in relation to the targeted species and the specific UAS design. This protocol allows one to evaluate the sensitivity of UAS approaches through the estimate of the effective detection radius for the different species investigated at several flight heights. The results of this study suggest a strong potential for the bioacoustic monitoring of birds but are more contrasted for bat recordings, mainly due to quadcopter noise (i.e., electronic speed controller (ESC) noise) but also, in a certain manner, to the experimental design (use of a directional speaker with limited call intensity). Technical developments, such as the use of a winch to safely extent the distance between the UAS and the recorder during UAS sound recordings or the development of an innovative platform, such as a plane–blimp hybrid UAS, should make it possible to solve these issues.
      Citation: Drones
      PubDate: 2021-01-26
      DOI: 10.3390/drones5010009
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 10: StratoTrans: Unmanned Aerial System (UAS) 4G
           Communication Framework Applied on the Monitoring of Road Traffic and
           Linear Infrastructure

    • Authors: Robert Guirado, Joan-Cristian Padró, Albert Zoroa, José Olivert, Anica Bukva, Pedro Cavestany
      First page: 10
      Abstract: This study provides an operational solution to directly connect drones to internet by means of 4G telecommunications and exploit drone acquired data, including telemetry and imagery but focusing on video transmission. The novelty of this work is the application of 4G connection to link the drone directly to a data server where video (in this case to monitor road traffic) and imagery (in the case of linear infrastructures) are processed. However, this framework is appliable to any other monitoring purpose where the goal is to send real-time video or imagery to the headquarters where the drone data is processed, analyzed, and exploited. We describe a general framework and analyze some key points, such as the hardware to use, the data stream, and the network coverage, but also the complete resulting implementation of the applied unmanned aerial system (UAS) communication system through a Virtual Private Network (VPN) featuring a long-range telemetry high-capacity video link (up to 15 Mbps, 720 p video at 30 fps with 250 ms of latency). The application results in the real-time exploitation of the video, obtaining key information for traffic managers such as vehicle tracking, vehicle classification, speed estimation, and roundabout in-out matrices. The imagery downloads and storage is also performed thorough internet, although the Structure from Motion postprocessing is not real-time due to photogrammetric workflows. In conclusion, we describe a real-case application of drone connection to internet thorough 4G network, but it can be adapted to other applications. Although 5G will -in time- surpass 4G capacities, the described framework can enhance drone performance and facilitate paths for upgrading the connection of on-board devices to the 5G network.
      Citation: Drones
      PubDate: 2021-01-28
      DOI: 10.3390/drones5010010
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 11: Acknowledgment to Reviewers of Drones in 2020

    • Authors: Drones Editorial Office Drones Editorial Office
      First page: 11
      Abstract: Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Drones maintains its standards for the high quality of its published papers [...]
      Citation: Drones
      PubDate: 2021-01-29
      DOI: 10.3390/drones5010011
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 12: Going Batty: The Challenges and Opportunities of
           Using Drones to Monitor the Behaviour and Habitat Use of Rays

    • Authors: Semonn Oleksyn, Louise Tosetto, Vincent Raoult, Karen E. Joyce, Jane E. Williamson
      First page: 12
      Abstract: The way an animal behaves in its habitat provides insight into its ecological role. As such, collecting robust, accurate datasets in a time-efficient manner is an ever-present pressure for the field of behavioural ecology. Faced with the shortcomings and physical limitations of traditional ground-based data collection techniques, particularly in marine studies, drones offer a low-cost and efficient approach for collecting data in a range of coastal environments. Despite drones being widely used to monitor a range of marine animals, they currently remain underutilised in ray research. The innovative application of drones in environmental and ecological studies has presented novel opportunities in animal observation and habitat assessment, although this emerging field faces substantial challenges. As we consider the possibility to monitor rays using drones, we face challenges related to local aviation regulations, the weather and environment, as well as sensor and platform limitations. Promising solutions continue to be developed, however, growing the potential for drone-based monitoring of behaviour and habitat use of rays. While the barriers to enter this field may appear daunting for researchers with little experience with drones, the technology is becoming increasingly accessible, helping ray researchers obtain a wide range of highly useful data.
      Citation: Drones
      PubDate: 2021-02-02
      DOI: 10.3390/drones5010012
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 13: A Quickly Deployed and UAS-Based Logistics
           Network for Delivery of Critical Medical Goods During Healthcare System
           Stress Periods: A Real Use Case in Valencia (Spain)

    • Authors: Israel Quintanilla Quintanilla García, Norberto Vera Vera Vélez, Pablo Alcaraz Alcaraz Martínez, Jordi Vidal Vidal Ull, Beatriz Fernández Fernández Gallo
      First page: 13
      Abstract: On the one hand, Unmanned Aircraft Systems (UASs) have experienced great applicability surge in the recent years, arising as a promising technology with a wide field of use. On the other hand, healthcare, a critical system in modern society, is subject to a heavy and unexpected pressure in the case of situations such as the COVID-19 pandemic. This article aims to leverage the flexibility of UASs as complementary support for healthcare logistic systems when under high-stress conditions, via quick deployment of an air delivery network. We have defined a logistics network model and created three scenarios based on the model and current needs in Valencia (Spain). Flight tests have been performed in these scenarios, which include urban areas and controlled airspace. Operations complied with requirements derived from the application of Specific Operations Risk Assessment (SORA) methodology, recently adopted by the European Aviation Safety Agency (EASA). Flights were successful, being able to swiftly deliver medical goods without requiring any dedicated infrastructure. However, a moderate number of contingencies took place during the tests, mainly related to control link quality and Air Traffic Management (ATM) integration, forcing the use of dedicated procedures to cope with them. Although additional development is required to ensure the safety of large-scale automated operations, the use of UASs as part of logistic networks is a feasible means to support existing structures, especially in situations in dire need.
      Citation: Drones
      PubDate: 2021-02-17
      DOI: 10.3390/drones5010013
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 14: Drone-Monitoring: Improving the Detectability of
           Threatened Marine Megafauna

    • Authors: Jonathas Barreto, Luciano Cajaíba, João Batista Teixeira, Lorena Nascimento, Amanda Giacomo, Nelson Barcelos, Ticiana Fettermann, Agnaldo Martins
      First page: 14
      Abstract: Unmanned aerial vehicles (UAVs; or drones) are an emerging tool to provide a safer, cheaper, and quieter alternative to traditional methods of studying marine megafauna in a natural environment. The UFES Nectology Laboratory team developed a drone-monitoring to assess the impacts on megafauna related to the Fundão dam mining tailings disaster in the Southeast Brazilian coast. We have developed a systematic pattern to optimize the available resources by covering the largest possible area. The fauna observer can monitor the environment from a privileged angle with virtual reality and subsequently analyzes each video captured in 4k, allowing to deepening behavioral ecology knowledge. Applying the drone-monitoring method, we have observed an increasing detectability by adjusting the camera angle, height, orientation, and speed of the UAV; which saved time and resources for monitoring turtles, sea birds, large fish, and especially small cetaceans efficiently and comparably.
      Citation: Drones
      PubDate: 2021-02-20
      DOI: 10.3390/drones5010014
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 15: Unmanned Aerial Vehicles for Wildland Fires:
           Sensing, Perception, Cooperation and Assistance

    • Authors: Moulay A. Akhloufi, Andy Couturier, Nicolás A. Castro
      First page: 15
      Abstract: Wildfires represent a significant natural risk causing economic losses, human death and environmental damage. In recent years, the world has seen an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small-scale environments. However, wildland fires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, unmanned aerial vehicles (UAV) and unmanned aerial systems (UAS) were proposed. UAVs have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper, previous works related to the use of UAV in wildland fires are reviewed. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, some of the recent frameworks proposing the use of both aerial vehicles and unmanned ground vehicles (UGV) for a more efficient wildland firefighting strategy at a larger scale are presented.
      Citation: Drones
      PubDate: 2021-02-22
      DOI: 10.3390/drones5010015
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 16: Safety Enhancement of UAVs from the Signal
           Processing’s Perspectives: A Bird’s Eye View

    • Authors: Chiman Kwan
      First page: 16
      Abstract: Unmanned air vehicles (UAVs) or drones have gained popularity in recent years. However, the US Federal Aviation Administration (FAA) is still hesitant to open up the national air space (NAS) to UAVs due to safety concerns because UAVs have several orders of magnitude of more accidents than manned aircraft. To limit the scope in this paper, we focus on large, heavy, and expensive UAVs that can be used for cargo transfer and search and rescue operations, not small radio-controlled toy drones. We first present a general architecture for enhancing the safety of UAVs. We then illustrate how signal processing technologies can help enhance the safety of UAVs. In particular, we provide a bird’s eye view of the application of signal processing algorithms on condition-based maintenance, structural health monitoring, fault diagnostics, and fault mitigation, which all play critical roles in UAV safety. Some practical applications are used to illustrate the importance of the various algorithms.
      Citation: Drones
      PubDate: 2021-02-26
      DOI: 10.3390/drones5010016
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 17: Drone Swarms in Fire Suppression Activities: A
           Conceptual Framework

    • Authors: Ausonio, Bagnerini, Ghio
      First page: 17
      Abstract: The recent huge technological development of unmanned aerial Vehicles (UAVs) can provide breakthrough means of fighting wildland fires. We propose an innovative forest firefighting system based on the use of a swarm of hundreds of UAVs able to generate a continuous flow of extinguishing liquid on the fire front, simulating the effect of rain. Automatic battery replacement and extinguishing liquid refill ensure the continuity of the action. We illustrate the validity of the approach in Mediterranean scrub first computing the critical water flow rate according to the main factors involved in the evolution of a fire, then estimating the number of linear meters of active fire front that can be extinguished depending on the number of drones available and the amount of extinguishing fluid carried. A fire propagation cellular automata model is also employed to study the evolution of the fire. Simulation results suggest that the proposed system can provide the flow of water required to fight low-intensity and limited extent fires or to support current forest firefighting techniques.
      Citation: Drones
      PubDate: 2021-03-07
      DOI: 10.3390/drones5010017
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 18: Operational Study of Drone Spraying Application
           for the Disinfection of Surfaces against the COVID-19 Pandemic

    • Authors: Higinio González-Jorge, Luis Miguel González-deSantos, Noelia Fariñas-Álvarez, Joaquin Martínez-Sánchez, Fermin Navarro-Medina
      First page: 18
      Abstract: The COVID-19 pandemic has shown the need to maximize the cleanliness of outside public services and the need to disinfect these areas to reduce the virus transmission. This work evaluates the possibilities of using unmanned aircraft systems for disinfection tasks in these aeras. The operational study focuses on evaluating the static and dynamic behavior, as well as the influence of the flying height, mission speed and flow of spraying. The most recommended height for correct spraying with the drone system under study is 3.0 m. The dynamic test shows that the lower height, 3.0 m, also provides the most adequate spraying footprint, achieving 2.2 m for a speed of 0.5 m/s. The operational behavior is evaluated on three different scenarios, a skatepark with an area around 882.7 m2, an outdoor gym with an area around 545.0 m2 and a multisport court with an area around 2025.7 m2. The cleaning time evaluates the flying duration, battery change and tank refill and results in 41 min for the skatepark (5 tank refills and 2 battery changes), 28.6 min for the outdoor gym (3 tank refills and 2 battery changes) and 96.4 min for the multisport court (11 tank refills and 5 battery changes). Each battery change and each tank refill are estimated to take 4 min each, with a drone autonomy of 7 min. The technology appears competitive compared to other forms of cleaning based, for example, on human operators.
      Citation: Drones
      PubDate: 2021-03-07
      DOI: 10.3390/drones5010018
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 19: An Integrated Spectral–Structural Workflow for
           Invasive Vegetation Mapping in an Arid Region Using Drones

    • Authors: Arnold Chi Kedia, Brandi Kapos, Songmei Liao, Jacob Draper, Justin Eddinger, Christopher Updike, Amy E. Frazier
      First page: 19
      Abstract: Mapping invasive vegetation species in arid regions is a critical task for managing water resources and understanding threats to ecosystem services. Traditional remote sensing platforms, such as Landsat and MODIS, are ill-suited for distinguishing native and non-native vegetation species in arid regions due to their large pixels compared to plant sizes. Unmanned aircraft systems, or UAS, offer the potential to capture the high spatial resolution imagery needed to differentiate species. However, in order to extract the most benefits from these platforms, there is a need to develop more efficient and effective workflows. This paper presents an integrated spectral–structural workflow for classifying invasive vegetation species in the Lower Salt River region of Arizona, which has been the site of fires and flooding, leading to a proliferation of invasive vegetation species. Visible (RGB) and multispectral images were captured and processed following a typical structure from motion workflow, and the derived datasets were used as inputs in two machine learning classifications—one incorporating only spectral information and one utilizing both spectral data and structural layers (e.g., digital terrain model (DTM) and canopy height model (CHM)). Results show that including structural layers in the classification improved overall accuracy from 80% to 93% compared to the spectral-only model. The most important features for classification were the CHM and DTM, with the blue band and two spectral indices (normalized difference water index (NDWI) and normalized difference salinity index (NDSI)) contributing important spectral information to both models.
      Citation: Drones
      PubDate: 2021-03-08
      DOI: 10.3390/drones5010019
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 20: Modeling Streamflow and Sediment Loads with a
           Photogrammetrically Derived UAS Digital Terrain Model: Empirical
           Evaluation from a Fluvial Aggregate Excavation Operation

    • Authors: Joseph P. Hupy, Cyril O. Wilson
      First page: 20
      Abstract: Soil erosion monitoring is a pivotal exercise at macro through micro landscape levels, which directly informs environmental management at diverse spatial and temporal scales. The monitoring of soil erosion can be an arduous task when completed through ground-based surveys and there are uncertainties associated with the use of large-scale medium resolution image-based digital elevation models for estimating erosion rates. LiDAR derived elevation models have proven effective in modeling erosion, but such data proves costly to obtain, process, and analyze. The proliferation of images and other geospatial datasets generated by unmanned aerial systems (UAS) is increasingly able to reveal additional nuances that traditional geospatial datasets were not able to obtain due to the former’s higher spatial resolution. This study evaluated the efficacy of a UAS derived digital terrain model (DTM) to estimate surface flow and sediment loading in a fluvial aggregate excavation operation in Waukesha County, Wisconsin. A nested scale distributed hydrologic flow and sediment loading model was constructed for the UAS point cloud derived DTM. To evaluate the effectiveness of flow and sediment loading generated by the UAS point cloud derived DTM, a LiDAR derived DTM was used for comparison in consonance with several statistical measures of model efficiency. Results demonstrate that the UAS derived DTM can be used in modeling flow and sediment erosion estimation across space in the absence of a LiDAR-based derived DTM.
      Citation: Drones
      PubDate: 2021-03-12
      DOI: 10.3390/drones5010020
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 21: Of Course We Fly Unmanned—We’re
           Women!

    • Authors: Joyce, Anderson, Bartolo
      First page: 21
      Abstract: Striving to achieve a diverse and inclusive workplace has become a major goal for many organisations around the world [...]
      Citation: Drones
      PubDate: 2021-03-12
      DOI: 10.3390/drones5010021
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 22: Quantifying the Effects of Vibration on
           Medicines in Transit Caused by Fixed-Wing and Multi-Copter Drones

    • Authors: Andrew Oakey, Tim Waters, Wanqing Zhu, Paul G. Royall, Tom Cherrett, Patrick Courtney, Dennis Majoe, Nickolay Jelev
      First page: 22
      Abstract: The concept of transporting medical products by drone is gaining a lot of interest amongst the medical and logistics communities. Such innovation has generated several questions, a key one being the potential effects of flight on the stability of medical products. The aims of this study were to quantify the vibration present within drone flight, study its effect on the quality of the medical insulin through live flight trials, and compare the effects of vibration from drone flight with traditional road transport. Three trials took place in which insulin ampoules and mock blood stocks were transported to site and flown using industry standard packaging by a fixed-wing or a multi-copter drone. Triaxial vibration measurements were acquired, both in-flight and during road transit, from which overall levels and frequency spectra were derived. British Pharmacopeia quality tests were undertaken in which the UV spectra of the flown insulin samples were compared to controls of known turbidity. In-flight vibration levels in both the drone types exceeded road induced levels by up to a factor of three, and predominant vibration occurred at significantly higher frequencies. Flown samples gave clear insulin solutions that met the British Pharmacopoeia specification, and no aggregation of insulin was detected.
      Citation: Drones
      PubDate: 2021-03-13
      DOI: 10.3390/drones5010022
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 23: The Use of UAVs for the Characterization and
           Analysis of Rocky Coasts

    • Authors: Alejandro Gómez-Pazo, Augusto Pérez-Alberti
      First page: 23
      Abstract: Rocky coasts represent three quarters of all coastlines worldwide. These areas are part of ecosystems of great ecological value, but their steep configuration and their elevation make field surveys difficult. This fact, together with their lower variation rates, explains the lower numbers of publications about cliffs and rocky coasts in general compared with those about beach-dune systems. The introduction of UAVs in research, has enormously expanded the possibilities for the study of rocky coasts. Their relative low costs allow for the generation of information with a high level of detail. This information, combined with GIS tools, enables coastal analysis based on Digital Models and high spatial resolution images. This investigation summarizes the main results obtained with the help of UAVs between 2012 and the present day in rocky coastline sections in the northwest of the Iberian Peninsula. These investigations have particularly focused on monitoring the dynamics of boulder beaches, cliffs, and shore platforms, as well as the structure and function of ecosystems. This work demonstrates the importance of unmanned aerial vehicles (UAVs) for coastal studies and their usefulness for improving coastal management. The Galician case was used to explain their importance and the advances in the UAVs’ techniques.
      Citation: Drones
      PubDate: 2021-03-16
      DOI: 10.3390/drones5010023
      Issue No: Vol. 5, No. 1 (2021)
       
  • Drones, Vol. 5, Pages 1: Spatio-Temporal Change Monitoring of Outside
           Manure Piles Using Unmanned Aerial Vehicle Images

    • Authors: Geonung Park, Kyunghun Park, Bonggeun Song
      First page: 1
      Abstract: Water quality deterioration due to outdoor loading of livestock manure requires efficient management of outside manure piles (OMPs). This study was designed to investigate OMPs using unmanned aerial vehicles (UAVs) for efficient management of non-point source pollution in agricultural areas. A UAV was used to acquire image data, and the distribution and cover installation status of OMPs were identified through ortho-images; the volumes of OMP were calculated using digital surface model (DSM). UAV- and terrestrial laser scanning (TLS)-derived DSMs were compared for identifying the accuracy of calculated volumes. The average volume accuracy was 92.45%. From April to October, excluding July, the monthly average volumes of OMPs in the study site ranged from 64.89 m3 to 149.69 m3. Among the 28 OMPs investigated, 18 were located near streams or agricultural waterways. Establishing priority management areas among the OMP sites distributed in a basin is possible using spatial analysis, and it is expected that the application of UAV technology will contribute to the efficient management of OMPs and other non-point source pollutants.
      Citation: Drones
      PubDate: 2020-12-23
      DOI: 10.3390/drones5010001
      Issue No: Vol. 5, No. 1 (2020)
       
  • Drones, Vol. 5, Pages 2: Inter-UAV Routing Scheme Testbeds

    • Authors: Georgios Amponis, Thomas Lagkas, Panagiotis Sarigiannidis, Vasileios Vitsas, Panagiotis Fouliras
      First page: 2
      Abstract: With the development of more advanced and efficient control algorithms and communication architectures, UAVs and networks thereof (swarms) now find applications in nearly all possible environments and scenarios. There exist numerous schemes which accommodate routing for such networks, many of which are specifically designed for distinct use-cases. Validation and evaluation of routing schemes is implemented for the most part using simulation software. This approach is however incapable of considering real-life noise, radio propagation models, channel bit error rate and signal-to-noise ratio. Most importantly, existing frameworks or simulation software cannot sense physical-layer related information regarding power consumption which an increasing number of routing protocols utilize as a metric. The work presented in this paper contributes to the analysis of already existing routing scheme evaluation frameworks and testbeds and proposes an efficient, universal and standardized hardware testbed. Additionally, three interface modes aimed at evaluation under different scenarios are provided.
      Citation: Drones
      PubDate: 2020-12-28
      DOI: 10.3390/drones5010002
      Issue No: Vol. 5, No. 1 (2020)
       
  • Drones, Vol. 5, Pages 3: TomoSim: A Tomographic Simulator for Differential
           Optical Absorption Spectroscopy

    • Authors: Rui Valente de Almeida, Nuno Matela, Pedro Vieira
      First page: 3
      Abstract: TomoSim comes as part of project ATMOS, a miniaturised Differential Optical Absorption Spectroscopy (DOAS) tomographic atmospheric evaluation device, designed to fit a small drone. During the development of the project, it became necessary to write a simulation tool for system validation. TomoSim is the answer to this problem. The software has two main goals: to mathematically validate the tomographic acquisition method; and to allow some adjustments to the system before reaching final product stages. This measurement strategy was based on a drone performing a sequential trajectory and gathering projections arranged in fan beams, before using some classical tomographic methods to reconstruct a spectral image. The team tested three different reconstruction algorithms, all of which were able to produce an image, validating the team’s initial assumptions regarding the trajectory and acquisition strategy. All algorithms were assessed on their computational performance and their ability for reconstructing spectral “images”, using two phantoms, one of which custom made for this purpose. In the end, the team was also able to uncover certain limitations of the TomoSim approach that should be addressed before the final stages of the system.
      Citation: Drones
      PubDate: 2020-12-29
      DOI: 10.3390/drones5010003
      Issue No: Vol. 5, No. 1 (2020)
       
  • Drones, Vol. 4, Pages 61: Thermal and Multispectral Remote Sensing for the
           Detection and Analysis of Archaeologically Induced Crop Stress at a UK
           Site

    • Authors: Katherine James, Caroline J. Nichol, Tom Wade, Dave Cowley, Simon Gibson Poole, Andrew Gray, Jack Gillespie
      First page: 61
      Abstract: In intensively cultivated landscapes, many archaeological remains are buried under the ploughed soil, and detection depends on crop proxies that express subsurface features. Traditionally these proxies have been documented in visible light as contrasting areas of crop development commonly known as cropmarks. However, it is recognised that reliance on the visible electromagnetic spectrum has inherent limitations on what can be documented, and multispectral and thermal sensors offer the potential to greatly improve our ability to detect buried archaeological features in agricultural fields. The need for this is pressing, as ongoing agricultural practices place many subsurface archaeological features increasingly under threat of destruction. The effective deployment of multispectral and thermal sensors, however, requires a better understanding of when they may be most effective in documenting archaeologically induced responses. This paper presents the first known use of the FLIR Vue Pro-R thermal imager and Red Edge-M for exploring crop response to archaeological features from two UAV surveys flown in May and June 2019 over a known archaeological site. These surveys provided multispectral imagery, which was used to create vegetation index (VI) maps, and thermal maps to assess their effectiveness in detecting crop responses in the temperate Scottish climate. These were visually and statistically analysed using a Mann Whitney test to compare temperature and reflectance values. While the study was compromised by unusually damp conditions which reduced the potential for cropmarking, the VIs (e.g., Normalised Difference Vegetation Index, NDVI) did show potential to detect general crop stress across the study site when they were statistically analysed. This demonstrates the need for further research using multitemporal data collection across case study sites to better understand the interactions of crop responses and sensors, and so define appropriate conditions for large-area data collection. Such a case study-led multitemporal survey approach is an ideal application for UAV-based documentation, especially when “perfect” conditions cannot be guaranteed.
      Citation: Drones
      PubDate: 2020-09-24
      DOI: 10.3390/drones4040061
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 62: Drone-Based Participatory Mapping: Examining
           Local Agricultural Knowledge in the Galapagos

    • Authors: Mia Colloredo-Mansfeld, Francisco J. Laso, Javier Arce-Nazario
      First page: 62
      Abstract: Agriculture is cultural heritage, and studies of agricultural spaces and practices help this heritage to be valued and protected. In the Galapagos Islands, little focus has been placed on local agricultural practices and agroforestry, despite their increasing importance for food security and invasive species management. This article discusses the possibilities for unoccupied aerial vehicle (UAV) high-resolution imagery in examining agricultural and agroforestry spaces, techniques, and practices. It describes and assesses an UAV-assisted participatory methodology for on-farm qualitative research that aims to investigate the visible and invisible features of farming practices. An analysis of the types of responses elicited by different methods of interviews with Galapagos farmers demonstrates how incorporating UAV data affects what we took away from the interview, and how the perceived relationship between farmer and land is reflected. Specifically, we find that when interacting with orthomosaics created from UAV images of their farms, farmers’ responses reveal a greater focus on management strategies at larger spatial and temporal scales. UAV imagery thus supports studies of agricultural heritage not only by recording agricultural spaces but also by revealing agrarian knowledge and practices.
      Citation: Drones
      PubDate: 2020-09-24
      DOI: 10.3390/drones4040062
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 63: Generic Component-Based Mission-Centric Energy
           Model for Micro-Scale Unmanned Aerial Vehicles

    • Authors: Christoph Steup, Simon Parlow, Sebastian Mai, Sanaz Mostaghim
      First page: 63
      Abstract: The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%.
      Citation: Drones
      PubDate: 2020-09-25
      DOI: 10.3390/drones4040063
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 64: Operational Protocols for the Use of Drones in
           Marine Animal Research

    • Authors: Vincent Raoult, Andrew P Colefax, Blake M. Allan, Daniele Cagnazzi, Nataly Castelblanco-Martínez, Daniel Ierodiaconou, David W. Johnston, Sarah Landeo-Yauri, Mitchell Lyons, Vanessa Pirotta, Gail Schofield, Paul A Butcher
      First page: 64
      Abstract: The use of drones to study marine animals shows promise for the examination of numerous aspects of their ecology, behaviour, health and movement patterns. However, the responses of some marine phyla to the presence of drones varies broadly, as do the general operational protocols used to study them. Inconsistent methodological approaches could lead to difficulties comparing studies and can call into question the repeatability of research. This review draws on current literature and researchers with a wealth of practical experience to outline the idiosyncrasies of studying various marine taxa with drones. We also outline current best practice for drone operation in marine environments based on the literature and our practical experience in the field. The protocols outlined herein will be of use to researchers interested in incorporating drones as a tool into their research on marine animals and will help form consistent approaches for drone-based studies in the future.
      Citation: Drones
      PubDate: 2020-09-25
      DOI: 10.3390/drones4040064
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 65: A Review on Communications Perspective of Flying
           Ad-Hoc Networks: Key Enabling Wireless Technologies, Applications,
           Challenges and Open Research Topics

    • Authors: Fazal Noor, Muhammad Asghar Khan, Ali Al-Zahrani, Insaf Ullah, Kawther A. Al-Dhlan
      First page: 65
      Abstract: Unmanned aerial vehicles (UAVs), also known as drones, once centric to military applications, are presently finding their way in many civilian and commercial applications. If national legislations permit UAVs to operate autonomously, one will see the skies become populated with many small UAVs, each one performing various tasks such as mail and package delivery, traffic monitoring, event filming, surveillance, search and rescue, and other applications. Thus, advancing to multiple small UAVs from a single large UAV has resulted in a new clan of networks known as flying ad-hoc networks (FANETs). Such networks provide reliability, ease of deployment, and relatively low operating costs by offering a robust communication network among the UAVs and base stations (BS). Although FANETs offer many benefits, there also exist a number of challenges that need to be addressed; the most significant of these being the communication one. Therefore, the article aims to provide insights into the key enabling communication technologies through the investigation of data rate, spectrum type, coverage, and latency. Moreover, application scenarios along with the feasibility of key enabling technologies are also examined. Finally, challenges and open research topics are discussed to further hone the research work.
      Citation: Drones
      PubDate: 2020-09-30
      DOI: 10.3390/drones4040065
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 66: An Evaluation of the Drone Delivery of
           Adrenaline Auto-Injectors for Anaphylaxis: Pharmacists’ Perceptions,
           Acceptance, and Concerns

    • Authors: September Beck, Tam T. Bui, Andrew Davies, Patrick Courtney, Alex Brown, Jef Geudens, Paul G. Royall
      First page: 66
      Abstract: Anaphylaxis is a life-threatening condition where delays in medical treatment can be fatal. Such situations would benefit from the drone delivery of an adrenaline auto-injector such as EpiPen®. This study evaluates the potential risk, reward, and impact of drone transportation on the stability of adrenaline during episodes of anaphylaxis. Further, this study examines pharmacists’ perceptions on drone delivery—pharmacists approved the use of drones to deliver EpiPen® during emergencies but had concerns with drone safety and supply chain security. Laboratory simulated onboard drone conditions reflected typical missions. In these experiments, in vitro model and pharmaceutical equivalent formulations were subjected independently to 30 min vibrations at 5, 8.43, and 13.33 Hz, and temperature storage at 4, 25, 40, and 65 °C for 0, 0.5, 3, and 24 h. The chiral composition (an indicator of chemical purity that relates to molecular structure) and concentration of these adrenaline formulations were determined using ultraviolet (UV) and circular dichroism spectroscopy (CD). Adrenaline intrinsic stability was also explored by edge-of-failure experimentation to signpost the uppermost limits for safe transportation. During drone flight with EpiPen®, the temperature and vibration g-force were 10.7 °C and 1.8 g, respectively. No adverse impact on adrenaline was observed during drone flight and laboratory-simulated conditions shown by conformation to the British Pharmacopeia standards (p > 0.05 for CD and UV). This study showed that drone delivery of EpiPen® is feasible. There are more than 15,000 community pharmacies and ≈9000 GP surgeries spanning the UK, which are likely to provide achievable ranges and distances for the direct drone delivery of EpiPen®. The authors recommend that when designing future missions, in addition to medicine stability testing that models the stresses imposed by drone flight, one must conduct a perceptions survey on the relevant group of medical professionals, because their insights, acceptance, and concerns are extremely valuable for the design and evaluation of the mission.
      Citation: Drones
      PubDate: 2020-10-09
      DOI: 10.3390/drones4040066
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 67: Bridge Inspection with an Off-the-Shelf
           360° Camera Drone

    • Authors: Andreas Humpe
      First page: 67
      Abstract: The author proposes a new approach for bridge crack detection by a 360° camera on top of a drone. Traditionally, bridge inspection is performed manually and although the use of drones has been implemented before, researchers used standard high definition cameras underneath the drone. To make the approach comparable to the conventional approach, two bridges were selected in Germany and inspected for cracks and defects by applying both methods. The author follows an engineering design process and after developing a prototype of the drone with a 360° camera above the body of the drone, the system is built, tested, and the bridges are inspected. First, the critical parts of the bridges are inspected with an off-the-shelf drone with a high definition camera underneath the drone. The results provide a benchmark for comparison. Next, the new approach to bridge inspection by using a 360° camera on top of the drone is tested. The images of the critical parts of the bridge that were taken with the 360° camera on top of the drone are analyzed and compared to the images of the conventional approach with the camera underneath the drone. The results show that a 360° camera can be used for crack and defect detection with similar results to a standard high definition camera. Furthermore, the 360° camera is more suitable for inspecting corners or the ceiling of, e.g., an arch bridge.
      Citation: Drones
      PubDate: 2020-10-11
      DOI: 10.3390/drones4040067
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 68: Short-Range Transportation Using Unmanned Aerial
           Vehicles (UAVs) during Disasters in Japan

    • Authors: Koki Yakushiji, Hiroshi Fujita, Mikio Murata, Naoki Hiroi, Yuuichi Hamabe, Fumiatsu Yakushiji
      First page: 68
      Abstract: Larger types of small unmanned aerial vehicles (UAVs) are beginning to be used in the United States and Europe for commercial transportation. Additionally, some blood product transport systems have been commercialized in Rwanda and other countries and used in pandemic operations for the coronavirus disease 2019 (COVID-19) in infected areas. Conversely, implementing goods transportation for commercial purposes in Japan has been difficult, especially in urban areas, due to national legislation. This study examined UAV-assisted transportation in Japan, a natural disaster hotspot, with a focus on the potential uses of UAVs in situations where traffic blockages make ground transportation impossible. UAVs were used to transport 17 kg of medical supplies belonging to a disaster medical assistance team (DMAT), along with 100 emergency meals. We also transported insulin under controlled-temperature conditions, as well as many other emergency supplies. Using UAVs to transport emergency supplies could be an effective approach when dealing with disasters. This paper summarizes the effectiveness of this approach for medical care and disaster response activities. We present a method for using drones to bridge the gap between medical and firefighting personnel, such as DMAT personnel, who are engaged in life-saving activities at the time of a disaster, and those who are unable to transport necessary goods by land using terrestrial vehicles due to traffic interruptions.
      Citation: Drones
      PubDate: 2020-10-16
      DOI: 10.3390/drones4040068
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 69: Species Classification in a Tropical Alpine
           Ecosystem Using UAV-Borne RGB and Hyperspectral Imagery

    • Authors: Carol X. Garzon-Lopez, Eloisa Lasso
      First page: 69
      Abstract: Páramos host more than 3500 vascular plant species and are crucial water providers for millions of people in the northern Andes. Monitoring species distribution at large scales is an urgent conservation priority in the face of ongoing climatic changes and increasing anthropogenic pressure on this ecosystem. For the first time in this ecosystem, we explored the potential of unoccupied aerial vehicles (UAV)-borne red, green, and blue wavelengths (RGB) and hyperspectral imagery for páramo species classification by collecting both types of images in a 10-ha area, and ground vegetation cover data from 10 plots within this area. Five plots were used for calibration and the other five for validation. With the hyperspectral data, we tested our capacity to detect five representative páramo species with different growth forms using support vector machine (SVM) and random forest (RF) classifiers in combination with three feature selection methods and two class groups. Using RGB images, we could classify 21 species with an accuracy greater than 97%. From hyperspectral imaging, the highest accuracy (89%) was found using models built with RF or SVM classifiers combined with a binary grouping method and the sequential floating forward selection feature. Our results demonstrate that páramo species can be accurately mapped using both RGB and hyperspectral imagery.
      Citation: Drones
      PubDate: 2020-10-31
      DOI: 10.3390/drones4040069
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 70: Developing an Introductory UAV/Drone Mapping
           Training Program for Seagrass Monitoring and Research

    • Authors: Bo Yang, Timothy L. Hawthorne, Margot Hessing-Lewis, Emmett J. Duffy, Luba Y. Reshitnyk, Michael Feinman, Hunter Searson
      First page: 70
      Abstract: Unoccupied Aerial Vehicles (UAVs), or drone technologies, with their high spatial resolution, temporal flexibility, and ability to repeat photogrammetry, afford a significant advancement in other remote sensing approaches for coastal mapping, habitat monitoring, and environmental management. However, geographical drone mapping and in situ fieldwork often come with a steep learning curve requiring a background in drone operations, Geographic Information Systems (GIS), remote sensing and related analytical techniques. Such a learning curve can be an obstacle for field implementation for researchers, community organizations and citizen scientists wishing to include introductory drone operations into their work. In this study, we develop a comprehensive drone training program for research partners and community members to use cost-effective, consumer-quality drones to engage in introductory drone mapping of coastal seagrass monitoring sites along the west coast of North America. As a first step toward a longer-term Public Participation GIS process in the study area, the training program includes lessons for beginner drone users related to flying drones, autonomous route planning and mapping, field safety, GIS analysis, image correction and processing, and Federal Aviation Administration (FAA) certification and regulations. Training our research partners and students, who are in most cases novice users, is the first step in a larger process to increase participation in a broader project for seagrass monitoring in our case study. While our training program originated in the United States, we discuss our experiences for research partners and communities around the globe to become more confident in introductory drone operations for basic science. In particular, our work targets novice users without a strong background in geographic research or remote sensing. Such training provides technical guidance on the implementation of a drone mapping program for coastal research, and synthesizes our approaches to provide broad guidance for using drones in support of a developing Public Participation GIS process.
      Citation: Drones
      PubDate: 2020-11-03
      DOI: 10.3390/drones4040070
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 71: Development, Modeling and Control of a Dual
           Tilt-Wing UAV in Vertical Flight

    • Authors: Luz M. Sanchez-Rivera, Rogelio Lozano, Alfredo Arias-Montano
      First page: 71
      Abstract: Hybrid Unmanned Aerial Vehicles (H-UAVs) are currently a very interesting field of research in the modern scientific community due to their ability to perform Vertical Take-Off and Landing (VTOL) and Conventional Take-Off and Landing (CTOL). This paper focuses on the Dual Tilt-wing UAV, a vehicle capable of performing both flight modes (VTOL and CTOL). The UAV complete dynamic model is obtained using the Newton–Euler formulation, which includes aerodynamic effects, as the drag and lift forces of the wings, which are a function of airstream generated by the rotors, the cruise speed, tilt-wing angle and angle of attack. The airstream velocity generated by the rotors is studied in a test bench. The projected area on the UAV wing that is affected by the airstream generated by the rotors is specified and 3D aerodynamic analysis is performed for this region. In addition, aerodynamic coefficients of the UAV in VTOL mode are calculated by using Computational Fluid Dynamics method (CFD) and are embedded into the nonlinear dynamic model. To validate the complete dynamic model, PD controllers are adopted for altitude and attitude control of the vehicle in VTOL mode, the controllers are simulated and implemented in the vehicle for indoor and outdoor flight experiments.
      Citation: Drones
      PubDate: 2020-11-25
      DOI: 10.3390/drones4040071
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 72: Correlation among Earthwork and Cropmark
           Anomalies within Archaeological Landscape Investigation by Using LiDAR and
           Multispectral Technologies from UAV

    • Authors: Diego Ronchi, Marco Limongiello, Salvatore Barba
      First page: 72
      Abstract: This project aimed to systematically investigate the archaeological remains of the imperial Domitian villa in Sabaudia (Italy), using different three-dimensional survey techniques. Particular attention in the research was paid to the identification and documentation of traces that buried structures left on the surface occupied by the villa, which extended for 46 hectares, an area that was fully covered with structures. Since a dense pine forest was planted during the 1940s and is currently covering the site, this contribution investigates particularly the correlation among the presence of cropmarks, identifiable with the processing of multispectral maps and vegetation indices from RGB images, and earthwork anomalies identified in a Digital Terrain Model (DTM) built, by utilizing a light detection and ranging (LiDAR) flight from an Unmanned Aerial Vehicle (UAV). The study demonstrates how the use of vegetation maps—calculated starting from RGB and multispectral aerial photos—can provide a more expeditious preliminary analysis on the position and extension of areas characterized by the presence of buried structures, but also that, in order to investigate in-depth a context in similar conditions, the most effective approach remains the one based on LiDAR technology. The integration between the two techniques may prove fruitful in limiting the extension of the areas to be investigated with terrestrial survey techniques.
      Citation: Drones
      PubDate: 2020-11-30
      DOI: 10.3390/drones4040072
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 73: RPAS Automatic ADS-B Based Separation Assurance
           and Collision Avoidance System Real-Time Simulation Results

    • Authors: Vittorio Di Vito, Giulia Torrano
      First page: 73
      Abstract: Remotely piloted aircraft systems (RPAS) are increasingly becoming relevant actors that are flying through the airspace and will gain much more importance in the future. In order to allow for their safe integration with manned conventional traffic in non-segregated airspaces, in accordance with the overall air traffic management (ATM) paradigm, specific enabling technologies are needed. As is well known, the detect and avoid (DAA) technology is fundamental among the enabling technologies identified as crucial for RPAS integration into the overall ATM system. In the meantime, to support extended surveillance, the universal introduction of cooperative automatic dependent surveillance-broadcast (ADS-B) on-board aircraft is being increasingly implemented because it has the potential to allow for the coverage of the entire airspaces in remote areas not usually covered by conventional radar surveillance. In this paper, experimental results that were obtained through the real-time validation, with hardware and human in the loop (RTS-HIL) simulations, of an automatic ADS-B based separation assurance and collision avoidance system aimed to support RPAS automatic operations (as well as remote pilot decision making) are presented and discussed. In the paper, after an introductory outline of the concept of operations (ConOps) of the system and its architectural organization, in addition to basic information about the main system functionalities, a description of the tests that were carried out is reported, and the obtained results are described and discussed in order to emphasize the performance and limitations of the proposed system. In particular, the obtained quantitative performances are reported and commented on, and the feedback presented by pilots in order to improve the system, e.g., in terms of preferred typology of conflict resolution maneuver elaborated by the system, is described.
      Citation: Drones
      PubDate: 2020-12-07
      DOI: 10.3390/drones4040073
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 74: Elasmobranch Use of Nearshore Estuarine Habitats
           Responds to Fine-Scale, Intra-Seasonal Environmental Variation: Observing
           Coastal Shark Density in a Temperate Estuary Utilizing Unoccupied Aircraft
           Systems (UAS)

    • Authors: Alexandra E. DiGiacomo, Walker E. Harrison, David W. Johnston, Justin T. Ridge
      First page: 74
      Abstract: Many coastal shark species are known to use estuaries of the coastal southeastern United States for essential purposes like foraging, reproducing, and protection from predation. Temperate estuarine landscapes, such as the Rachel Carson Reserve (RCR) in Beaufort, NC, are dynamic habitat mosaics that experience fluctuations in physical and chemical oceanographic properties on various temporal and spatial scales. These patterns in abiotic conditions play an important role in determining species movement. The goal of this study was to understand the impact of environmental conditions around the RCR on shark density within the high-abundance summer season. Unoccupied Aircraft System (UAS) surveys of coastal habitats within the reserve were used to quantify shark density across varying environmental conditions. A combination of correlation analyses and Generalized Linear Modelling (GLM) revealed that density differs substantially across study sites and increases with rising water temperatures, conclusions that are supported by previous work in similar habitats. Additionally, density appears to increase moving towards dawn and dusk, potentially supporting crepuscular activity in coastal estuarine areas. By describing shark density dynamics in the RCR, this study provides new information on this population and presents a novel framework for studying elasmobranchs in temperate estuaries.
      Citation: Drones
      PubDate: 2020-12-08
      DOI: 10.3390/drones4040074
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 75: Cattle Detection Using Oblique UAV Images

    • Authors: Jayme Garcia Arnal Barbedo, Luciano Vieira Koenigkan, Patrícia Menezes Santos
      First page: 75
      Abstract: The evolution in imaging technologies and artificial intelligence algorithms, coupled with improvements in UAV technology, has enabled the use of unmanned aircraft in a wide range of applications. The feasibility of this kind of approach for cattle monitoring has been demonstrated by several studies, but practical use is still challenging due to the particular characteristics of this application, such as the need to track mobile targets and the extensive areas that need to be covered in most cases. The objective of this study was to investigate the feasibility of using a tilted angle to increase the area covered by each image. Deep Convolutional Neural Networks (Xception architecture) were used to generate the models for animal detection. Three experiments were carried out: (1) five different sizes for the input images were tested to determine which yields the highest accuracies; (2) detection accuracies were calculated for different distances between animals and sensor, in order to determine how distance influences detectability; and (3) animals that were completely missed by the detection process were individually identified and the cause for those errors were determined, revealing some potential topics for further research. Experimental results indicate that oblique images can be successfully used under certain conditions, but some practical limitations need to be addressed in order to make this approach appealing.
      Citation: Drones
      PubDate: 2020-12-08
      DOI: 10.3390/drones4040075
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 76: Estimating Rooftop Areas of Poultry Houses Using
           UAV and Satellite Images

    • Authors: A. Bulent Koc, Patrick T. Anderson, John P. Chastain, Christopher Post
      First page: 76
      Abstract: Poultry production requires electricity for optimal climate control throughout the year. Demand for electricity in poultry production peaks during summer months when solar irradiation is also high. Installing solar photovoltaic (PV) panels on the rooftops of poultry houses has potential for reducing the energy costs by reducing the electricity demand charges of utility companies. The objective of this research was to estimate the rooftop areas of poultry houses for possible PV installation using aerial images acquired with a commercially available low-cost unmanned aerial vehicle (UAV). Overhead images of 31 broiler houses were captured with a UAV to assess their potential for solar energy applications. Building plan dimensions were acquired and building heights were independently measured manually. Images were captured by flying the UAV in a double grid flight path at a 69-m altitude using an onboard 4K camera at an angle of −80° from the horizon with 70% and 80% overlaps. The captured images were processed using Agisoft Photoscan Professional photogrammetry software. Orthophotos of the study areas were generated from the acquired 3D image sequences using structure from motion (SfM) techniques. Building rooftop overhang obscured building footprint in aerial imagery. To accurately measure building dimensions, 0.91 m was subtracted from building roof width and 0.61 m was subtracted from roof length based on blueprint dimensions of the poultry houses. The actual building widths and lengths ranged from 10.8 to 184.0 m and the mean measurement error using the UAV-derived orthophotos was 0.69% for all planar dimensions. The average error for building length was 1.66 ± 0.48 m and the average error for widths was 0.047 ± 0.13 m. Building sidewall, side entrance and peak heights ranged from 1.9 to 5.6 m and the mean error was 0.06 ± 0.04 m or 1.2%. When compared to the horizontal accuracy of the same building measurements taken from readily available satellite imagery, the mean error in satellite images was −0.36%. The average length error was −0.46 ± 0.49 m and −0.44 ± 0.14 m for building widths. The satellite orthomosaics were more accurate for length estimations and the UAV orthomosaics were more accurate for width estimations. This disparity was likely due to the flight altitude, camera field of view, and building shape. The results proved that a low-cost UAV and photogrammetric SfM can be used to create digital surface models and orthomosaics of poultry houses without the need for survey-grade equipment or ground control points.
      Citation: Drones
      PubDate: 2020-12-09
      DOI: 10.3390/drones4040076
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 77: Using UAV to Capture and Record Torrent Bed and
           Banks, Flood Debris, and Riparian Areas

    • Authors: Paschalis Koutalakis, Ourania Tzoraki, Giorgos Gkiatas, George N. Zaimes
      First page: 77
      Abstract: Capturing and recording fluvio-geomorphological events is essential since these events can be very sudden and hazardous. Climate change is expected to increase flash floods intensity and frequency in the Mediterranean region, thus enhancing such events will also impact the adjacent riparian vegetation. The aim of this study was to capture and record the fluvial-geomorphological changes of the torrent bed and banks and flood debris events with the use of UAV images along a reach of Kallifytos torrent in northern Greece. In addition, a novel approach to detecting changes and assessing the conditions of the riparian vegetation was conducted by using UAV images that were validated with field data based on a visual protocol. Three flights were conducted using the DJI Spark UAV. Based on the images collected from these flights, orthomosaics were developed. The orthomosaics clearly identified changes in the torrent bed and detected debris flow events after major flood events. In addition, the results on the assessment of riparian vegetation conditions were satisfactory. Utilizing UAV images shows great potential to capture, record, and monitor fluvio-geomorphological events and riparian vegetation. Their utilization would help water managers to develop more sustainable management solutions based on actual field data.
      Citation: Drones
      PubDate: 2020-12-14
      DOI: 10.3390/drones4040077
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 78: Volitional Swimming Kinematics of Blacktip
           Sharks, Carcharhinus limbatus, in the Wild

    • Authors: Marianne E. Porter, Braden T. Ruddy, Stephen M. Kajiura
      First page: 78
      Abstract: Recent work showed that two species of hammerhead sharks operated as a double oscillating system, where frequency and amplitude differed in the anterior and posterior parts of the body. We hypothesized that a double oscillating system would be present in a large, volitionally swimming, conventionally shaped carcharhinid shark. Swimming kinematics analyses provide quantification to mechanistically examine swimming within and among species. Here, we quantify blacktip shark (Carcharhinus limbatus) volitional swimming kinematics under natural conditions to assess variation between anterior and posterior body regions and demonstrate the presence of a double oscillating system. We captured footage of 80 individual blacktips swimming in the wild using a DJI Phantom 4 Pro aerial drone. The widespread accessibility of aerial drone technology has allowed for greater observation of wild marine megafauna. We used Loggerpro motion tracking software to track five anatomical landmarks frame by frame to calculate tailbeat frequency, tailbeat amplitude, speed, and anterior/posterior variables: amplitude and frequency of the head and tail, and the body curvature measured as anterior and posterior flexion. We found significant increases in tailbeat frequency and amplitude with increasing swimming speed. Tailbeat frequency decreased and tailbeat amplitude increased as posterior flexion amplitude increased. We found significant differences between anterior and posterior amplitudes and frequencies, suggesting a double oscillating modality of wave propagation. These data support previous work that hypothesized the importance of a double oscillating system for increased sensory perception. These methods demonstrate the utility of quantifying swimming kinematics of wild animals through direct observation, with the potential to apply a biomechanical perspective to movement ecology paradigms.
      Citation: Drones
      PubDate: 2020-12-18
      DOI: 10.3390/drones4040078
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 79: Aerial and Ground Robot Collaboration for
           Autonomous Mapping in Search and Rescue Missions

    • Authors: Dimitrios Chatziparaschis, Michail G. Lagoudakis, Panagiotis Partsinevelos
      First page: 79
      Abstract: Humanitarian Crisis scenarios typically require immediate rescue intervention. In many cases, the conditions at a scene may be prohibitive for human rescuers to provide instant aid, because of hazardous, unexpected, and human threatening situations. These scenarios are ideal for autonomous mobile robot systems to assist in searching and even rescuing individuals. In this study, we present a synchronous ground-aerial robot collaboration approach, under which an Unmanned Aerial Vehicle (UAV) and a humanoid robot solve a Search and Rescue scenario locally, without the aid of a commonly used Global Navigation Satellite System (GNSS). Specifically, the UAV uses a combination of Simultaneous Localization and Mapping and OctoMap approaches to extract a 2.5D occupancy grid map of the unknown area in relation to the humanoid robot. The humanoid robot receives a goal position in the created map and executes a path planning algorithm in order to estimate the FootStep navigation trajectory for reaching the goal. As the humanoid robot navigates, it localizes itself in the map while using an adaptive Monte-Carlo Localization algorithm by combining local odometry data with sensor observations from the UAV. Finally, the humanoid robot performs visual human body detection while using camera data through a Darknet pre-trained neural network. The proposed robot collaboration scheme has been tested under a proof of concept setting in an exterior GNSS-denied environment.
      Citation: Drones
      PubDate: 2020-12-19
      DOI: 10.3390/drones4040079
      Issue No: Vol. 4, No. 4 (2020)
       
  • Drones, Vol. 4, Pages 28: Correlating the Plant Height of Wheat with
           Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface
           Model, A Case Study from Nepal

    • Authors: Uma Shankar Panday, Nawaraj Shrestha, Shashish Maharjan, Arun Kumar Pratihast, Shahnawaz, Kundan Lal Shrestha, Jagannath Aryal
      First page: 28
      Abstract: Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information.
      Citation: Drones
      PubDate: 2020-07-01
      DOI: 10.3390/drones4030028
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 29: A Method for Selecting Strategic Deployment
           Opportunities for Unmanned Aircraft Systems (UAS) for Transportation
           Agencies

    • Authors: Sarah Hubbard, Bryan Hubbard
      First page: 29
      Abstract: Unmanned aircraft systems (UAS) are increasingly used for a variety of applications by state Departments of Transportation (DOT) and local transportation agencies due to technology advancements, lower costs, and regulatory changes that have simplified operations. There are numerous applications (e.g., bridge inspection, traffic management, incident response, construction and roadway mapping) and agencies find it challenging to prioritize which applications are most appropriate. Important factors to consider when prioritizing UAS applications include: (1) benefits, (2) ease of adoption, (3) stakeholder acceptance, and (4) technical feasibility. These factors can be evaluated utilizing various techniques such as the technology acceptance model, benefit analysis, and technology readiness level (TRL). This paper presents the methodology and results for the prioritization of UAS applications’ quality function deployment (QFD), which reflects both qualitative and quantitative components. The proposed framework can be used in the future as technologies mature, and the prioritization can be revised on a regular basis to identify future strategic implementation opportunities. Numerous transportation agencies have begun to use UAS, some have developed UAS operating policies and manuals, but there has been no documentation to support identification of the UAS applications that are most appropriate for deployment. This paper fills that gap and documents a method for identification of UAS applications for strategic deployment and illustrates the method with a case study.
      Citation: Drones
      PubDate: 2020-07-02
      DOI: 10.3390/drones4030029
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 30: The Application of Drones in Healthcare and
           Health-Related Services in North America: A Scoping Review

    • Authors: Bradley Hiebert, Elysée Nouvet, Vyshnave Jeyabalan, Lorie Donelle
      First page: 30
      Abstract: Using drone aircraft to deliver healthcare and other health-related services is a relatively new application of this technology in North America. For health service providers, drones represent a feasible means to increase their efficiency and ability to provide services to individuals, especially those in difficult to reach locations. This paper presents the results of a scoping review of the research literature to determine how drones are used for healthcare and health-related services in North America, and how such applications account for human operating and machine design factors. Data were collected from PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore using a block search protocol that combined 13 synonyms for “drone” and eight broad terms capturing healthcare and health-related services. Four-thousand-six-hundred-and-sixty-five documents were retrieved, and following a title, abstract, and full-text screening procedure completed by all authors, 29 documents were retained for analysis through an inductive coding process. Overall, findings indicate that drones may represent a financially feasible means to promote healthcare and health-related service accessibility for those in difficult-to-reach areas; however, further work is required to fully understand the costs to healthcare organizations and the communities they serve.
      Citation: Drones
      PubDate: 2020-07-04
      DOI: 10.3390/drones4030030
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 31: Inferring Visual Biases in UAV Videos from Eye
           Movements

    • Authors: Anne-Flore Perrin, Lu Zhang, Olivier Le Meur
      First page: 31
      Abstract: Unmanned Aerial Vehicle (UAV) imagery is gaining a lot of momentum lately. Indeed, gathered information from a bird-point-of-view is particularly relevant for numerous applications, from agriculture to surveillance services. We herewith study visual saliency to verify whether there are tangible differences between this imagery and more conventional contents. We first describe typical and UAV contents based on their human saliency maps in a high-dimensional space, encompassing saliency map statistics, distribution characteristics, and other specifically designed features. Thanks to a large amount of eye tracking data collected on UAV, we stress the differences between typical and UAV videos, but more importantly within UAV sequences. We then designed a process to extract new visual attention biases in the UAV imagery, leading to the definition of a new dictionary of visual biases. We then conduct a benchmark on two different datasets, whose results confirm that the 20 defined biases are relevant as a low-complexity saliency prediction system.
      Citation: Drones
      PubDate: 2020-07-04
      DOI: 10.3390/drones4030031
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 32: On the Self-Calibration of Aerodynamic
           Coefficients in Vehicle Dynamic Model-Based Navigation

    • Authors: Gabriel Laupré, Jan Skaloud
      First page: 32
      Abstract: The performance of vehicle dynamic model (VDM)-based navigation largely depends on the accurate determination of aerodynamic coefficients that are unknown a priori. Among different techniques, such as model simulations or experimental analysis in a wind tunnel, the method of self-calibration via state-space augmentation benefiting Global Navigation Satellite System (GNSS) positioning represents an interesting and economical alternative. We study this technique under simulation with the goal of determining the impact of aircraft maneuvers on the precision and (de)-correlation of the aerodynamic coefficients among themselves and with respect to other error-states. A combination of different maneuvers indicates to be essential for obtaining satisfactory aerodynamic coefficients estimation and reduce their uncertainty.
      Citation: Drones
      PubDate: 2020-07-12
      DOI: 10.3390/drones4030032
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 33: A Hybrid Voronoi Tessellation/Genetic Algorithm
           Approach for the Deployment of Drone-Based Nodes of a Self-Organizing
           Wireless Sensor Network (WSN) in Unknown and GPS Denied Environments

    • Authors: Khouloud Eledlebi, Hanno Hildmann, Dymitr Ruta, A. F. Isakovic
      First page: 33
      Abstract: Using autonomously operating mobile sensor nodes to form adaptive wireless sensor networks has great potential for monitoring applications in the real world. Especially in, e.g., disaster response scenarios—that is, when the environment is potentially unsafe and unknown—drones can offer fast access and provide crucial intelligence to rescue forces due the fact that they—unlike humans—are expendable and can operate in 3D space, often allowing them to ignore rubble and blocked passages. Among the practical issues faced are the optimizing of device–device communication, the deployment process and the limited power supply for the devices and the hardware they carry. To address these challenges a host of literature is available, proposing, e.g., the use of nature-inspired approaches. In this field, our own work (bio-inspired self-organizing network, BISON, which uses Voronoi tessellations) achieved promising results. In our previous approach the wireless sensors network (WSN) nodes were using knowledge about their coverage areas center of gravity, something which a drone would not automatically know. To address this, we augment BISON with a genetic algorithm (GA), which has the benefit of further improving network deployment time and overall coverage. Our evaluations show, unsurprisingly, an increase in energy cost. Two variations of our proposed GA-BISON deployment strategies are presented and compared, along with the impact of the GA. Counter-intuitively, performance and robustness increase in the presence of noise.
      Citation: Drones
      PubDate: 2020-07-14
      DOI: 10.3390/drones4030033
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 34: A Comprehensive Review of Applications of Drone
           Technology in the Mining Industry

    • Authors: Javad Shahmoradi, Elaheh Talebi, Pedram Roghanchi, Mostafa Hassanalian
      First page: 34
      Abstract: This paper aims to provide a comprehensive review of the current state of drone technology and its applications in the mining industry. The mining industry has shown increased interest in the use of drones for routine operations. These applications include 3D mapping of the mine environment, ore control, rock discontinuities mapping, postblast rock fragmentation measurements, and tailing stability monitoring, to name a few. The article offers a review of drone types, specifications, and applications of commercially available drones for mining applications. Finally, the research needs for the design and implementation of drones for underground mining applications are discussed.
      Citation: Drones
      PubDate: 2020-07-15
      DOI: 10.3390/drones4030034
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 35: Temperature Profiling of Waterbodies with a
           UAV-Integrated Sensor Subsystem

    • Authors: Cengiz Koparan, Ali Bulent Koc, Calvin Sawyer, Charles Privette
      First page: 35
      Abstract: Evaluation of thermal stratification and systematic monitoring of water temperature are required for lake management. Water temperature profiling requires temperature measurements through a water column to assess the level of thermal stratification which impacts oxygen content, microbial growth, and distribution of fish. The objective of this research was to develop and assess the functions of a water temperature profiling system mounted on a multirotor unmanned aerial vehicle (UAV). The buoyancy apparatus mounted on the UAV allowed vertical takeoff and landing on the water surface for in situ measurements. The sensor node that was integrated with the UAV consisted of a microcontroller unit, a temperature sensor, and a pressure sensor. The system measured water temperature and depth from seven pre-selected locations in a lake using autonomous navigation with autopilot control. Measurements at 100 ms intervals were made while the UAV was descending at 2 m/s until it landed on water surface. Water temperature maps of three consecutive depths at each location were created from the measurements. The average surface water temperature at 0.3 m was 22.5 °C, while the average water temperature at 4 m depth was 21.5 °C. The UAV-based profiling system developed successfully performed autonomous water temperature measurements within a lake.
      Citation: Drones
      PubDate: 2020-07-21
      DOI: 10.3390/drones4030035
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 36: Measures of Canopy Structure from Low-Cost UAS
           for Monitoring Crop Nutrient Status

    • Authors: Kellyn Montgomery, Josh Henry, Matthew Vann, Brian E. Whipker, Anders Huseth, Helena Mitasova
      First page: 36
      Abstract: Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.
      Citation: Drones
      PubDate: 2020-07-22
      DOI: 10.3390/drones4030036
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 37: An Open Simulation Strategy for Rapid Control
           Design in Aerial and Maritime Drone Teams: A Comprehensive Tutorial

    • Authors: Omar Velasco, João Valente, Pablo J. Alhama Blanco, Mohammed Abderrahim
      First page: 37
      Abstract: The deployment of robot controllers into the real robotic platform is cumbersome and time consuming, especially when testing scenarios involve several robots or are sites not easily accessible. Besides this, most of the time, testing on the real platforms or real conditions provides little value in the early stages of controller design and prototype, phases where debugging and suitability of the controller are the main objectives. This paper proposes a simulation strategy for developing and testing controllers for Unmanned Aerial and Surface Vehicle coordination and interaction with the environment. The simulation strategy is based on V-REP and Matlab/Simulink which provide a large set of features, modularity and compatibility across platforms. Results show that this approach significantly reduces development and delivery times by providing an off-the-shelf simulation environment and a step-by-step implementation guidelines. The source code to deploy the simulations is available in an open-source repository.
      Citation: Drones
      PubDate: 2020-07-23
      DOI: 10.3390/drones4030037
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 38: Investigating Methods for Integrating Unmanned
           Aerial Systems in Search and Rescue Operations

    • Authors: William T. Weldon, Joseph Hupy
      First page: 38
      Abstract: Unmanned aerial systems (UAS) are increasingly being used in search and rescue (SAR) operations to assist in the discovery of missing persons. UAS are useful to first responders in SAR operations due to rapid deployment, high data volume, and high spatial resolution data collection capabilities. Relying on traditional manual interpretation methods to find a missing person in imagery data sets containing several hundred images is both challenging and time consuming. To better find small signs of missing persons in large UAS datasets, computer assisted interpretation methods have been developed. This article presents the results of an initial evaluation of a computer assisted interpretation method tested against manual methods in a simulated SAR operation. The evaluation performed focused on using resources available to first responders performing SAR operations, specifically: RGB data, volunteers, and a commercially available software program. Results from this field test were mixed, as the traditional group discovered more objects but required more time, in man hours, to discover the objects. Further field experiments, based on the capabilities of current first responder groups, should be conducted to determine to what extent computer assisted methods are useful in SAR operations.
      Citation: Drones
      PubDate: 2020-07-24
      DOI: 10.3390/drones4030038
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 39: Towards Bio-Inspiration, Development, and
           Manufacturing of a Flapping-Wing Micro Air Vehicle

    • Authors: P. Lane, G. Throneberry, I. Fernandez, M. Hassanalian, R. Vasconcellos, A. Abdelkefi
      First page: 39
      Abstract: Throughout the last decade, there has been an increased demand for intricate flapping-wing drones with different capabilities than larger drones. The design of flapping-wing drones is focused on endurance and stability, as these are two of the main challenges of these systems. Researchers have recently been turning towards bioinspiration as a way to enhance aerodynamic performance. In this work, the propulsion system of a flapping-wing micro air vehicle is investigated to identify the limitations and drawbacks of specific designs. Each system has a tandem wing configuration inspired by a dragonfly, with wing shapes inspired by a bumblebee. For the design of this flapping-wing, a sizing process is carried out. A number of actuation mechanisms are considered, and two different mechanisms are designed and integrated into a flapping-wing system and compared to one another. The second system is tested using a thrust stand to investigate the impact of wing configurations on aerodynamic force production and the trend of force production from varying flapping frequency. Results present the optimal wing configuration of those tested and that an angle of attack of two degrees yields the greatest force production. A tethered flight test is conducted to examine the stability and aerodynamic capabilities of the drone, and challenges of flapping-wing systems and solutions that can lead to successful flight are presented. Key challenges to the successful design of these systems are weight management, force production, and stability and control.
      Citation: Drones
      PubDate: 2020-07-25
      DOI: 10.3390/drones4030039
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 40: Unmanned Aircraft Systems (UAS) for Bridge
           Inspection Safety

    • Authors: Bryan Hubbard, Sarah Hubbard
      First page: 40
      Abstract: Unmanned aircraft systems (UAS) are an excellent tool to remove bridge inspection workers from potential harm. Previous research has documented that UAS for bridge inspection is a strategic priority of a state’s Department of Transportation (DOT), and this paper presents how they can increase safety and presents one methodology to quantify the economic benefit. Although previous studies have documented the potential benefits of using UAS for bridge inspection, these studies have primarily focused on efficiency and capabilities. This paper investigates in greater detail the potential to use UAS to increase the safety of bridge inspection, and includes the results of a survey of bridge inspectors, as well as a benefit cost methodology that utilizes worker compensation rates to quantify the safety benefits of UAS; the methodology is demonstrated using a case study for a DOT. The results of this research present evidence that UAS can increase the safety of bridge inspection, and the benefit–cost methodology and analysis suggest that using UAS to increase safety will provide benefits that are greater than agency costs.
      Citation: Drones
      PubDate: 2020-08-04
      DOI: 10.3390/drones4030040
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 41: A Review on Drone-Based Data Solutions for
           Cereal Crops

    • Authors: Uma Shankar Panday, Arun Kumar Pratihast, Jagannath Aryal, Rijan Bhakta Kayastha
      First page: 41
      Abstract: Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.
      Citation: Drones
      PubDate: 2020-08-12
      DOI: 10.3390/drones4030041
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 42: Virtual Modelling and Testing of the Single and
           Contra-Rotating Co-Axial Propeller

    • Authors: Balram Panjwani, Cecile Quinsard, Dominik Gacia Przemysław, Jostein Furseth
      First page: 42
      Abstract: Propellers are a vital component to achieve successful and reliable operation of drones. However, the drone developer faces many challenges while selecting a propeller and a common approach is to perform static thrust measurement. However, the selection of a propeller using a static thrust measurement system is time-consuming. To overcome a need for the static thrust system a virtual model has been developed for measuring both the static and dynamic thrust of a single and coaxial propeller. The virtual model is reliable enough to minimize the need for full-scale tests. The virtual model has been built using two open-source software Qblade and OpenFoam. Qblade is employed to obtain the lift and drag coefficients of the propeller’s airfoil section. OpenFoam is utilized to perform the flow simulations of propellers and for obtaining the thrust and torque data of the propeller. The developed virtual model is validated with experimental data and the experimental data are obtained by developing a multi-force balance system for measuring thrusts and torques of a single and a pair of coaxial contra-rotating propellers. The data obtained from the propeller virtual model are compared with the measurement data. For a single propeller, the virtual model shows that the estimated forces are close to the experiment at lower rotational speeds. For coaxial propellers, there are some deviations at the rear propeller due to the turbulence and flow disturbance caused by the front propeller. However, the computed thrust data are still accurate enough to be used in selecting the propeller. The studies indicate that in the future, these virtual models will minimize a need for experimental testing.
      Citation: Drones
      PubDate: 2020-08-12
      DOI: 10.3390/drones4030042
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 43: Modeling and Investigations on Surface Colors of
           Wings on the Performance of Albatross-Inspired Mars Drones and
           Thermoelectric Generation Capabilities

    • Authors: Devyn Rice, Samah Ben Ayed, Stephen Johnstone, Abdessattar Abdelkefi
      First page: 43
      Abstract: Thermal effects of wing color for Albatross-inspired drones performing in the Martian atmosphere are investigated during the summer and winter seasons. This study focuses on two useful consequences of the thermal effects of wing color: the drag reduction and the thermoelectric generation of power. According to its color, each wing side has a certain temperature affecting the drag. Investigations of various configurations have shown that the thermal effect on the wing boundary layer skin drag is insignificant because of the low atmospheric pressure. However, the total drag varies as much as 12.8% between the highest performing wing color configuration and the lowest performing configuration. Additionally, the large temperature differences between the top and the bottom wing surfaces show great potential for thermoelectric power generation. The maximum temperature differences between the top and bottom surfaces for the summer and winter seasons are, respectively, 65 K and 30 K. The drag reduction and the power generation via thermoelectric generators both contribute to enhancing the endurance of drones. Future drone designs will benefit from increased endurance through optimizing the wing color configuration.
      Citation: Drones
      PubDate: 2020-08-16
      DOI: 10.3390/drones4030043
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 44: Context-Specific Challenges, Opportunities, and
           Ethics of Drones for Healthcare Delivery in the Eyes of Program Managers
           and Field Staff: A Multi-Site Qualitative Study

    • Authors: Vyshnave Jeyabalan, Elysée Nouvet, Patrick Meier, Lorie Donelle
      First page: 44
      Abstract: Unmanned aerial vehicles (UAVs), also known as drones, have significant potential in the healthcare field. Ethical and practical concerns, challenges, and complexities of using drones for specific and diverse healthcare purposes have been minimally explored to date. This paper aims to document and advance awareness of diverse context-specific concerns, challenges, and complexities encountered by individuals working on the front lines of drones for health. It draws on original qualitative research and data from semi-structured interviews (N = 16) with drones for health program managers and field staff in nine countries. Directed thematic analysis was used to analyze interviews and identify key ethical and practical concerns, challenges, and complexities experienced by participants in their work with drones for health projects. While some concerns, challenges, and complexities described by study participants were more technical in nature, for example, those related to drone technology and approval processes, the majority were not. The bulk of context-specific concerns and challenges identified by participants, we propose, could be mitigated through community engagement initiatives.
      Citation: Drones
      PubDate: 2020-08-17
      DOI: 10.3390/drones4030044
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 45: Ice Detection on Aircraft Surface Using Machine
           Learning Approaches Based on Hyperspectral and Multispectral Images

    • Authors: Maria Angela Musci, Luigi Mazzara, Andrea Maria Lingua
      First page: 45
      Abstract: Aircraft ground de-icing operations play a critical role in flight safety. However, to handle the aircraft de-icing, a considerable quantity of de-icing fluids is commonly employed. Moreover, some pre-flight inspections are carried out with engines running; thus, a large amount of fuel is wasted, and CO2 is emitted. This implies substantial economic and environmental impacts. In this context, the European project (reference call: MANUNET III 2018, project code: MNET18/ICT-3438) called SEI (Spectral Evidence of Ice) aims to provide innovative tools to identify the ice on aircraft and improve the efficiency of the de-icing process. The project includes the design of a low-cost UAV (uncrewed aerial vehicle) platform and the development of a quasi-real-time ice detection methodology to ensure a faster and semi-automatic activity with a reduction of applied operating time and de-icing fluids. The purpose of this work, developed within the activities of the project, is defining and testing the most suitable sensor using a radiometric approach and machine learning algorithms. The adopted methodology consists of classifying ice through spectral imagery collected by two different sensors: multispectral and hyperspectral camera. Since the UAV prototype is under construction, the experimental analysis was performed with a simulation dataset acquired on the ground. The comparison among the two approaches, and their related algorithms (random forest and support vector machine) for image processing, was presented: practical results show that it is possible to identify the ice in both cases. Nonetheless, the hyperspectral camera guarantees a more reliable solution reaching a higher level of accuracy of classified iced surfaces.
      Citation: Drones
      PubDate: 2020-08-18
      DOI: 10.3390/drones4030045
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 46: UAS-Based Archaeological Remote Sensing: Review,
           Meta-Analysis and State-of-the-Art

    • Authors: Efstathios Adamopoulos, Fulvio Rinaudo
      First page: 46
      Abstract: Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms. These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes. In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques. We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows. Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research.
      Citation: Drones
      PubDate: 2020-08-19
      DOI: 10.3390/drones4030046
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 47: Photogrammetric Acquisitions in Diverse
           Archaeological Contexts Using Drones: Background of the Ager Mellariensis
           Project (North of Córdoba-Spain)

    • Authors: Massimo Gasparini, Juan Carlos Moreno-Escribano, Antonio Monterroso-Checa
      First page: 47
      Abstract: Unmanned aerial vehicles (UAVs) and aerial photogrammetry have greatly contributed to expanding research in scientific fields that employ geomatics techniques. Archaeology is one of the sciences that has advanced most as a result of this technological innovation. The geographic products obtained by UAV photogrammetric surveys can detect anomalies corresponding to ancient settlements and aid in designing future archaeological interventions. These acquisitions also offer attractive scientific dissemination products. We present five archaeological sites from different ages located in the Guadiato Valley of Córdoba, Spain, where a series of photogrammetric images were acquired for purposes of both research and dissemination. Acquisitions were designed based on the accessibility of the sites and on the end-user experience. The results present several photogrammetric products for use in research, and the mandatory dissemination of the results of a publicly-funded research project.
      Citation: Drones
      PubDate: 2020-08-25
      DOI: 10.3390/drones4030047
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 48: Improving Motion Safety and Efficiency of
           Intelligent Autonomous Swarm of Drones

    • Authors: Amin Majd, Mohammad Loni, Golnaz Sahebi, Masoud Daneshtalab
      First page: 48
      Abstract: Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to guarantee dependable operations. To maximize motion safety, we should design the swarm system in such a way that drones do not collide with each other and/or other objects in the operating environment. On other hand, to ensure that the drones have sufficient resources to complete the required task reliably, we should also achieve efficiency while implementing the mission, by minimizing the travelling distance of the drones. In this paper, we propose a novel integrated approach that maximizes motion safety and efficiency while planning and controlling the operation of the swarm of drones. To achieve this goal, we propose a novel parallel evolutionary-based swarm mission planning algorithm. The evolutionary computing allows us to plan and optimize the routes of the drones at the run-time to maximize safety while minimizing travelling distance as the efficiency objective. In order to fulfill the defined constraints efficiently, our solution promotes a holistic approach that considers the whole design process from the definition of formal requirements through the software development. The results of benchmarking demonstrate that our approach improves the route efficiency by up to 10% route efficiency without any crashes in controlling swarms compared to state-of-the-art solutions.
      Citation: Drones
      PubDate: 2020-08-26
      DOI: 10.3390/drones4030048
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 49: Determining the Optimal Number of Ground Control
           Points for Varying Study Sites through Accuracy Evaluation of Unmanned
           Aerial System-Based 3D Point Clouds and Digital Surface Models

    • Authors: Jae Jin Yu, Dong Woo Kim, Eun Jung Lee, Seung Woo Son
      First page: 49
      Abstract: The rapid development of drone technologies, such as unmanned aerial systems (UASs) and unmanned aerial vehicles (UAVs), has led to the widespread application of three-dimensional (3D) point clouds and digital surface models (DSMs). Due to the number of UAS technology applications across many fields, studies on the verification of the accuracy of image processing results have increased. In previous studies, the optimal number of ground control points (GCPs) was determined for a specific area of a study site by increasing or decreasing the amount of GCPs. However, these studies were mainly conducted in a single study site, and the results were not compared with those from various study sites. In this study, to determine the optimal number of GCPs for modeling multiple areas, the accuracy of 3D point clouds and DSMs were analyzed in three study sites with different areas according to the number of GCPs. The results showed that the optimal number of GCPs was 12 for small and medium sites (7 and 39 ha) and 18 for the large sites (342 ha) based on the overall accuracy. If these results are used for UAV image processing in the future, accurate modeling will be possible with minimal effort in GCPs.
      Citation: Drones
      PubDate: 2020-08-27
      DOI: 10.3390/drones4030049
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 50: Automating Drone Image Processing to Map Coral
           Reef Substrates Using Google Earth Engine

    • Authors: Mary K. Bennett, Nicolas Younes, Karen Joyce
      First page: 50
      Abstract: While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone imagery for analysis is time-consuming and complex. This study presents the first semi-automatic workflow for drone image processing with Google Earth Engine (GEE) and free and open source software (FOSS). With this workflow, we processed 230 drone images of Heron Reef, Australia and classified coral, sand, and rock/dead coral substrates with the Random Forest classifier. Our classification achieved an overall accuracy of 86% and mapped live coral cover with 92% accuracy. The presented methods enable efficient processing of drone imagery of any environment and can be useful when processing drone imagery for calibrating and validating satellite imagery.
      Citation: Drones
      PubDate: 2020-08-28
      DOI: 10.3390/drones4030050
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 51: Insights Before Flights: How Community
           Perceptions Can Make or Break Medical Drone Deliveries

    • Authors: Susan Truog, Luciana Maxim, Charles Matemba, Carla Blauvelt, Hope Ngwira, Archimede Makaya, Susana Moreira, Emily Lawrence, Gabriella Ailstock, Andrea Weitz, Melissa West, Olivier Defawe
      First page: 51
      Abstract: Drones are increasingly used to transport health products, but life-saving interventions can be stalled if local community concerns and preferences are not assessed and addressed. In order to inform the introduction of drones in new contexts, this paper analyzed similarities and differences in community perceptions of medical delivery drones in Malawi, Mozambique, the Democratic Republic of the Congo (DRC) and the Dominican Republic (DR). Community perceptions were assessed using focus group discussions (FGDs) and key informant interviews (KIIs) conducted with stakeholders at the national level, at health facilities and in communities. Data were collected on respondents’ familiarity with drones, perceptions of benefits and risks of drones, advice on drone operations and recommendations on sharing information with the community. The comparative analysis found similar perceptions around the potential benefits of using drones, as well as important differences in the perceived risks of flying drones and culturally appropriate communication mechanisms based on the local context. Because community perceptions are heavily influenced by culture and local experiences, a similar assessment should be conducted before introducing drone activities in new areas and two-way feedback channels should be established once drone operations are established in an area. The extent to which a community understands and supports the use of drones to transport health products will ultimately play a critical role in the success or failure of the drone’s ability to bring life-saving products to those who need them.
      Citation: Drones
      PubDate: 2020-08-30
      DOI: 10.3390/drones4030051
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 52: High Resolution Geospatial Evapotranspiration
           Mapping of Irrigated Field Crops Using Multispectral and Thermal Infrared
           Imagery with METRIC Energy Balance Model

    • Authors: Abhilash K. Chandel, Behnaz Molaei, Lav R. Khot, R. Troy Peters, Claudio O. Stöckle
      First page: 52
      Abstract: Geospatial crop water use mapping is critical for field-scale site-specific irrigation management. Landsat 7/8 satellite imagery with a widely adopted METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) energy balance model (LM approach) estimates accurate evapotranspiration (ET) but limits field-scale spatiotemporal (30 m pixel−1, ~16 days) mapping. A study was therefore conducted to map actual ET of commercially grown irrigated-field crops (spearmint, potato, and alfalfa) at very high-resolution (7 cm pixel−1). Six small unmanned aerial system (UAS)-based multispectral and thermal infrared imagery campaigns were conducted (two for each crop) at the same time as the Landsat 7/8 overpass. Three variants of METRIC model were used to process the UAS imagery; UAS-METRIC-1, -2, and -3 (UASM-1, -2, and -3) and outputs were compared with the standard LM approach. ET root mean square differences (RMSD) between LM-UASM-1, LM-UASM-2, and LM-UASM-3 were in the ranges of 0.2–2.9, 0.5–0.9, and 0.5–2.7 mm day−1, respectively. Internal calibrations and sensible heat fluxes majorly resulted in such differences. UASM-2 had the highest similarity with the LM approach (RMSD: 0.5–0.9, ETdep,abs (daily ET departures): 2–14%, r (Pearson correlation coefficient) = 0.91). Strong ET correlations between UASM and LM approaches (0.7–0.8, 0.7–0.8, and 0.8–0.9 for spearmint, potato, and alfalfa crops) suggest equal suitability of UASM approaches as LM to map ET for a range of similar crops. UASM approaches (Coefficient of variation, CV: 6.7–24.3%) however outperformed the LM approach (CV: 2.1–11.2%) in mapping spatial ET variations due to large number of pixels. On-demand UAS imagery may thus help in deriving high resolution site-specific ET maps, for growers to aid in timely crop water management.
      Citation: Drones
      PubDate: 2020-09-01
      DOI: 10.3390/drones4030052
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 53: Fusion of UAV and Terrestrial Photogrammetry
           

    • Authors: Thomas Luhmann, Maria Chizhova, Denys Gorkovchuk
      First page: 53
      Abstract: In September 2018, photogrammetric images and terrestrial laser scans were carried out as part of a measurement campaign for the three-dimensional recording of several historic churches in Tbilisi (Georgia). The aim was the complete spatial reconstruction with a spatial resolution and accuracy of approx. 1 cm under partly difficult external conditions, which required the use of different measurement techniques. The local measurement data were collected by two laser scanning campaigns (Leica BLK360 and Faro Focus 3D X330), several UAV flights and two terrestrial image sets. The photogrammetric point clouds were calculated with the image-based modelling programs AgiSoft and RealityCapture taking into account the control points from the laser scans. The mean residual errors from the registrations or photogrammetric evaluations are 4–16 mm, depending on the selected software, size and complexity of the monument and environmental conditions. The best completeness and quality of the resulting 3D model was achieved by using laser scan data and images simultaneously. The article presents recent results obtained with RealityCapture and gives a critical analysis of accuracy and modelling quality.
      Citation: Drones
      PubDate: 2020-09-07
      DOI: 10.3390/drones4030053
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 54: Measuring High Levels of Total Suspended Solids
           and Turbidity Using Small Unoccupied Aerial Systems (sUAS) Multispectral
           Imagery

    • Authors: Elizabeth M. Prior, Frances C. O’Donnell, Christian Brodbeck, Wesley N. Donald, George Brett Runion, Stephanie L. Shepherd
      First page: 54
      Abstract: Due to land development, high concentrations of suspended sediment are produced from erosion after rain events. Sediment basins are commonly used for the settlement of suspended sediments before discharge. Stormwater regulations may require frequent sampling and monitoring of these basins, both of which are time and labor intensive. Potential remedies are small, unoccupied aerial systems (sUAS). The goal of this study was to demonstrate whether sUAS multispectral imagery could measure high levels of total suspended solids (TSS) and turbidity in a sediment basin. The sediment basin at the Auburn University Erosion and Sediment Control Testing Facility was used to simulate a local 2-year, 24-h storm event with a 30-min flow rate. Water samples were collected at three depths in two locations every 15 min for six hours with corresponding sUAS multispectral imagery. Multispectral pixel values were related to TSS and turbidity in separate models using multiple linear regressions. TSS and turbidity regression models had coefficients of determination (r2) values of 0.926 and 0.851, respectively. When water column measurements were averaged, the r2 values increased to 0.965 and 0.929, respectively. The results indicated that sUAS multispectral imagery is a viable option for monitoring and assessing sediment basins during high-concentration events.
      Citation: Drones
      PubDate: 2020-09-08
      DOI: 10.3390/drones4030054
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 55: Ground Control Point Distribution for Accurate
           Kilometre-Scale Topographic Mapping Using an RTK-GNSS Unmanned Aerial
           Vehicle and SfM Photogrammetry

    • Authors: Eilidh Stott, Richard D. Williams, Trevor B. Hoey
      First page: 55
      Abstract: Unmanned Aerial Vehicles (UAVs) have revolutionised the availability of high resolution topographic data in many disciplines due to their relatively low-cost and ease of deployment. Consumer-grade Real Time Kinematic Global Navigation Satellite System (RTK-GNSS) equipped UAVs offer potential to reduce or eliminate ground control points (GCPs) from SfM photogrammetry surveys, removing time-consuming target deployment. Despite this, the removal of ground control can substantially reduce the georeferencing accuracy of SfM photogrammetry outputs. Here, a DJI Phantom 4 RTK UAV is deployed to survey a 2 × 0.5 km reach of the braided River Feshie, Scotland that has local channel-bar relief of c.1 m and median grain size c.60 mm. Five rectangular adjacent blocks were flown, with images collected at 20° from the nadir across a double grid, with strips flown in opposing directions to achieve locally convergent imagery geometry. Check point errors for seven scenarios with varying configurations of GCPs were tested. Results show that, contrary to some published Direct Georeferencing UAV investigations, GCPs are not essential for accurate kilometre-scale topographic modelling. Using no GCPs, 3300 independent spatially-distributed RTK-GNSS surveyed check points have mean z-axis error −0.010 m (RMSE = 0.066 m). Using 5 GCPs gave 0.016 m (RMSE = 0.072 m). Our check point results do not show vertical systematic errors, such as doming, using either 0 or 5 GCPs. However, acquiring spatially distributed independent check points to check for systematic errors is recommended. Our results imply that an RTK-GNSS UAV can produce acceptable errors with no ground control, alongside spatially distributed independent check points, demonstrating that the technique is versatile for rapid kilometre-scale topographic survey in a range of geomorphic environments.
      Citation: Drones
      PubDate: 2020-09-08
      DOI: 10.3390/drones4030055
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 56: Mapping Temperate Forest Phenology Using Tower,
           UAV, and Ground-Based Sensors

    • Authors: Jeff W. Atkins, Atticus E. L. Stovall, Xi Yang
      First page: 56
      Abstract: Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.
      Citation: Drones
      PubDate: 2020-09-10
      DOI: 10.3390/drones4030056
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 57: Using Minidrones to Teach Geospatial Technology
           Fundamentals

    • Authors: Karen E. Joyce, Natalie Meiklejohn, Paul C.H. Mead
      First page: 57
      Abstract: With an increased level of interest in promoting science, technology, engineering, and maths (STEM) careers, there are many ways in which drone and geospatial technology can be brought into the education system to train the future workforce. Indeed, state-level government policies are even stipulating that they should be integrated into curriculum. However, in some cases, drones may be seen as the latest toy advertised to achieve an education outcome. Some educators find it difficult to incorporate the technology in a meaningful way into their classrooms. Further, educators can often struggle to maintain currency on rapidly developing technology, particularly when it is outside of their primary area of expertise as is frequently the case in schools. Here, we present a structured approach to using drones to teach fundamental geospatial technology concepts within a STEM framework across primary/elementary, middle, secondary, and tertiary education. After successfully working with more than 6000 participants around the world, we encourage other scientists and those in industry using drones as part of their research or operations to similarly reach out to their local community to help build a diverse and strong STEM workforce of the future.
      Citation: Drones
      PubDate: 2020-09-15
      DOI: 10.3390/drones4030057
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 58: An Approach for Route Optimization in
           Applications of Precision Agriculture Using UAVs

    • Authors: Kshitij Srivastava, Prem Chandra Pandey, Jyoti K. Sharma
      First page: 58
      Abstract: This research paper focuses on providing an algorithm by which (Unmanned Aerial Vehicles) UAVs can be used to provide optimal routes for agricultural applications such as, fertilizers and pesticide spray, in crop fields. To utilize a minimum amount of inputs and complete the task without a revisit, one needs to employ optimized routes and optimal points of delivering the inputs required in precision agriculture (PA). First, stressed regions are identified using VegNet (Vegetative Network) software. Then, methods are applied for obtaining optimal routes and points for the spraying of inputs with an autonomous UAV for PA. This paper reports a unique and innovative technique to calculate the optimum location of spray points required for a particular stressed region. In this technique, the stressed regions are divided into many circular divisions with its center being a spray point of the stressed region. These circular divisions would ensure a more effective dispersion of the spray. Then an optimal path is found out which connects all the stressed regions and their spray points. The paper also describes the use of methods and algorithms including travelling salesman problem (TSP)-based route planning and a Voronoi diagram which allows applying precision agriculture techniques.
      Citation: Drones
      PubDate: 2020-09-18
      DOI: 10.3390/drones4030058
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 59: Spray Deposition on Weeds (Palmer Amaranth and
           Morningglory) from a Remotely Piloted Aerial Application System and
           Backpack Sprayer

    • Authors: Daniel Martin, Vijay Singh, Mohamed A. Latheef, Muthukumar Bagavathiannan
      First page: 59
      Abstract: This study was designed to determine whether a remotely piloted aerial application system (RPAAS) could be used in lieu of a backpack sprayer for post-emergence herbicide application. Consequent to this objective, a spray mixture of tap water and fluorescent dye was applied on Palmer amaranth and ivyleaf morningglory using an RPAAS at 18.7 and 37.4 L·ha−1 and a CO2-pressurized backpack sprayer at a 140 L·ha−1 spray application rate. Spray efficiency (the proportion of applied spray collected on an artificial sampler) for the RPAAS treatments was comparable to that for the backpack sprayer. Fluorescent spray droplet density was significantly higher on the adaxial surface for the backpack sprayer treatment than that for the RPAAS platforms. The percent of spray droplets on the abaxial surface for the RPAAS aircraft at 37.4 L·ha−1 was 4-fold greater than that for the backpack sprayer at 140 L·ha−1. The increased spray deposition on the abaxial leaf surfaces was likely caused by rotor downwash and wind turbulence generated by the RPAAS which caused leaf fluttering. This improved spray deposition may help increase the efficacy of contact herbicides. Test results indicated that RPAASs may be used for herbicide application in lieu of conventional backpack sprayers.
      Citation: Drones
      PubDate: 2020-09-19
      DOI: 10.3390/drones4030059
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 60: Suitability of the Reforming-Controlled
           Compression Ignition Concept for UAV Applications

    • Authors: Amnon Eyal, Leonid Tartakovsky
      First page: 60
      Abstract: Reforming-controlled compression ignition (RefCCI) is a novel approach combining two methods to improve the internal combustion engine’s efficiency and mitigate emissions: low-temperature combustion (LTC) and thermochemical recuperation (TCR). Frequently, the combustion controllability challenge is resolved by simultaneous injection into the cylinder of two fuel types, each on the other edge of the reactivity scale. By changing the low-to-high-reactivity fuel ratio, ignition timing and combustion phasing control can be achieved. The RefCCI principles, benefits, and possible challenges are described in previous publications. However, the suitability of the RefCCI approach for aerial, mainly unmanned aerial vehicle (UAV) platforms has not been studied yet. The main goal of this paper is to examine whether the RefCCI approach can be beneficial for UAV, especially HALE (high-altitude long-endurance) applications. The thermodynamic first-law and the second-law analysis is numerically performed to investigate the RefCCI approach suitability for UAV applications and to assess possible efficiency gains. A comparison with the conventional diesel engine and the previously developed technology of spark ignition (SI) engine with high-pressure TCR is performed in view of UAV peculiarities. The results indicate that the RefCCI system can be beneficial for UAV applications. The RefCCI higher efficiency compared to existing commercial engines compensates the lower heating value of the primary fuel, so the fuel consumption remains almost the same. By optimizing the compression pressure ratio, the RefCCI system efficiency can be improved.
      Citation: Drones
      PubDate: 2020-09-22
      DOI: 10.3390/drones4030060
      Issue No: Vol. 4, No. 3 (2020)
       
  • Drones, Vol. 4, Pages 13: Accuracy of 3D Landscape Reconstruction without
           Ground Control Points Using Different UAS Platforms

    • Authors: Margaret Kalacska, Oliver Lucanus, J. Pablo Arroyo-Mora, Étienne Laliberté, Kathryn Elmer, George Leblanc, Andrew Groves
      First page: 13
      Abstract: The rapid increase of low-cost consumer-grade to enterprise-level unmanned aerial systems (UASs) has resulted in the exponential use of these systems in many applications. Structure from motion with multiview stereo (SfM-MVS) photogrammetry is now the baseline for the development of orthoimages and 3D surfaces (e.g., digital elevation models). The horizontal and vertical positional accuracies (x, y and z) of these products in general, rely heavily on the use of ground control points (GCPs). However, for many applications, the use of GCPs is not possible. Here we tested 14 UASs to assess the positional and within-model accuracy of SfM-MVS reconstructions of low-relief landscapes without GCPs ranging from consumer to enterprise-grade vertical takeoff and landing (VTOL) platforms. We found that high positional accuracy is not necessarily related to the platform cost or grade, rather the most important aspect is the use of post-processing kinetic (PPK) or real-time kinetic (RTK) solutions for geotagging the photographs. SfM-MVS products generated from UAS with onboard geotagging, regardless of grade, results in greater positional accuracies and lower within-model errors. We conclude that where repeatability and adherence to a high level of accuracy are needed, only RTK and PPK systems should be used without GCPs.
      Citation: Drones
      PubDate: 2020-04-24
      DOI: 10.3390/drones4020013
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 14: An Annular Wing VTOL UAV: Flight Dynamics and
           Control

    • Authors: Rajan Gill, Raffaello D’Andrea
      First page: 14
      Abstract: A vertical takeoff and landing, unmanned aerial vehicle is presented that features a quadrotor design for propulsion and attitude stabilization, and an annular wing that provides lift in forward flight. The annular wing enhances human safety by enshrouding the propeller blades. Both the annular wing and the propulsion units are fully characterized in forward flight via wind tunnel experiments. An autonomous control system is synthesized that is based on model inversion, and accounts for the aerodynamics of the wing. It also accounts for the dominant aerodynamics of the propellers in forward flight, specifically the thrust and rotor torques when subject to oblique flow conditions. The attitude controller employed is tilt-prioritized, as the aerodynamics are invariant to the twist angle of the vehicle. Outdoor experiments are performed, resulting in accurate tracking of the reference position trajectories at high speeds.
      Citation: Drones
      PubDate: 2020-04-26
      DOI: 10.3390/drones4020014
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 15: Drones as an Integral Part of Remote Sensing
           Technologies to Help Missing People

    • Authors: Maria Gaia Pensieri, Mauro Garau, Pier Matteo Barone
      First page: 15
      Abstract: Due to the versatility of the drone, it can be applied in various areas and for different uses and as a practical support for human activities. In particular, this paper focuses on the situation in Italy and how the authorities use drones for the search and rescue of missing persons, especially now that a 10-year plague that has afflicted Italy with a large number of such incidents annually. Knowledge of the current legislation, the implementation of the drone with other instruments, specific pilot training, and experiential contributions are all essential elements that can provide exceptional assistance in search and rescue operations. However, to guarantee maximum effectiveness of the rescue device, they should seriously consider including teams with proven expertise in operating drones and count on their valuable contribution. Besides drones’ capacity to search large areas, thereby reducing the use of human resources and possibly limiting intervention times, to operate in difficult terrain and/or dangerous conditions for rescue teams, remote sensing tools (such as GPR or ground penetrating radar) as well as other disciplines (such as forensic archeology and, more generally, forensic geosciences) can be implemented to carry out search and rescue missions in case of missing persons.
      Citation: Drones
      PubDate: 2020-04-27
      DOI: 10.3390/drones4020015
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 16: Reliable Long-Range Multi-Link Communication for
           Unmanned Search and Rescue Aircraft Systems in Beyond Visual Line of Sight
           Operation

    • Authors: Johannes Güldenring, Philipp Gorczak, Fabian Eckermann, Manuel Patchou, Janis Tiemann, Fabian Kurtz, Christian Wietfeld
      First page: 16
      Abstract: With the increasing availability of unmanned aircraft systems, their usage for search and rescue is close at hand. Especially in the maritime context, aerial support can yield significant benefits. This article proposes and evaluates the concept of combining multiple cellular networks for highly reliable communication with those aircraft systems. The proposed approach is experimentally validated in several unprecedented large-scale experiments in the maritime context. It is found that in this scenario, conventional methods do not suffice for reliable connectivity to the aircraft with significantly varying overall availabilities between 68% and 97%. The underlying work, however, overcomes the limitations of single-link connectivity by providing availability of up to 99.8% in the analyzed scenarios. Therefore, the approach and the experimental data presented in this work yield a solid contribution to search and rescue drones. All results and flight recording data sets are published along with this article to enable future related work and studies, external reproduction, and validation of the underlying results and findings.
      Citation: Drones
      PubDate: 2020-05-01
      DOI: 10.3390/drones4020016
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 17: Development of a Simplified Radiometric
           Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial
           System (UAS) Operations

    • Authors: Christopher M. Zarzar, Padmanava Dash, Jamie L. Dyer, Robert Moorhead, Lee Hathcock
      First page: 17
      Abstract: The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for the reduction of atmospheric contributions in unmanned aerial systems (UAS) imagery and for the production of scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions were used to develop and test an ELM radiometric calibration framework designed for the reduction of atmospheric contributions from UAS imagery in studies with limited site accessibility or data acquisition time constraints. The ELM radiometric calibration framework was developed specifically for water-based operations and the efficacy of using generalized study area calibration equations averaged across variable illumination and atmospheric conditions was assessed. The framework was effective in reducing atmospheric and other external contributions in UAS imagery. Unique to the proposed radiometric calibration framework is the radiance-to-reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equation development. While image-by-image calibrations are still preferred for high accuracy applications, this paper provides an ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in UAS imagery in situations with limited site accessibility or data acquisition constraints.
      Citation: Drones
      PubDate: 2020-05-02
      DOI: 10.3390/drones4020017
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 18: Sharkeye: Real-Time Autonomous Personal Shark
           Alerting via Aerial Surveillance

    • Authors: Robert Gorkin, Kye Adams, Matthew J Berryman, Sam Aubin, Wanqing Li, Andrew R Davis, Johan Barthelemy
      First page: 18
      Abstract: While aerial shark spotting has been a standard practice for beach safety for decades, new technologies offer enhanced opportunities, ranging from drones/unmanned aerial vehicles (UAVs) that provide new viewing capabilities, to new apps that provide beachgoers with up-to-date risk analysis before entering the water. This report describes the Sharkeye platform, a first-of-its-kind project to demonstrate personal shark alerting for beachgoers in the water and on land, leveraging innovative UAV image collection, cloud-hosted machine learning detection algorithms, and reporting via smart wearables. To execute, our team developed a novel detection algorithm trained via machine learning based on aerial footage of real sharks and rays collected at local beaches, hosted and deployed the algorithm in the cloud, and integrated push alerts to beachgoers in the water via a shark app to run on smartwatches. The project was successfully trialed in the field in Kiama, Australia, with over 350 detection events recorded, followed by the alerting of multiple smartwatches simultaneously both on land and in the water, and with analysis capable of detecting shark analogues, rays, and surfers in average beach conditions, and all based on ~1 h of training data in total. Additional demonstrations showed potential of the system to enable lifeguard-swimmer communication, and the ability to create a network on demand to enable the platform. Our system was developed to provide swimmers and surfers with immediate information via smart apps, empowering lifeguards/lifesavers and beachgoers to prevent unwanted encounters with wildlife before it happens.
      Citation: Drones
      PubDate: 2020-05-04
      DOI: 10.3390/drones4020018
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 19: Estimating Tree Height and Volume Using Unmanned
           Aerial Vehicle Photography and SfM Technology, with Verification of Result
           Accuracy

    • Authors: Shohei Kameyama, Katsuaki Sugiura
      First page: 19
      Abstract: This study aimed to investigate the effects of differences in shooting and flight conditions for an unmanned aerial vehicle (UAV) on the processing method and estimated results of aerial images. Forest images were acquired under 80 different conditions, combining various aerial photography methods and flight conditions. We verified errors in values measured by the UAV and the measurement accuracy with respect to tree height and volume. Our results showed that aerial images could be processed under all the studied flight conditions. However, although tree height and crown were decipherable in the created 3D model in 64 conditions, they were undecipherable in 16. The standard deviation (SD) in crown area values for each target tree was 0.08 to 0.68 m2. UAV measurements of tree height tended to be lower than the actual values, and the RMSE (root mean square error) was high (5.2 to 7.1 m) through all the 64 modeled conditions. With the estimated volume being lower than the actual volume, the RMSE volume measurements for each flight condition were from 0.31 to 0.4 m3. Therefore, irrespective of flight conditions for UAV measurements, accuracy was low with respect to the actual values.
      Citation: Drones
      PubDate: 2020-05-11
      DOI: 10.3390/drones4020019
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 20: Evaluating the Efficacy and Optimal Deployment
           of Thermal Infrared and True-Colour Imaging When Using Drones for
           Monitoring Kangaroos

    • Authors: Elizabeth A. Brunton, Javier X. Leon, Scott E. Burnett
      First page: 20
      Abstract: Advances in drone technology have given rise to much interest in the use of drone-mounted thermal imagery in wildlife monitoring. This research tested the feasibility of monitoring large mammals in an urban environment and investigated the influence of drone flight parameters and environmental conditions on their successful detection using thermal infrared (TIR) and true-colour (RGB) imagery. We conducted 18 drone flights at different altitudes on the Sunshine Coast, Queensland, Australia. Eastern grey kangaroos (Macropus giganteus) were detected from TIR (n=39) and RGB orthomosaics (n=33) using manual image interpretation. Factors that predicted the detection of kangaroos from drone images were identified using unbiased recursive partitioning. Drone-mounted imagery achieved an overall 73.2% detection success rate using TIR imagery and 67.2% using RGB imagery when compared to on-ground counts of kangaroos. We showed that the successful detection of kangaroos using TIR images was influenced by vegetation type, whereas detection using RGB images was influenced by vegetation type, time of day that the drone was deployed, and weather conditions. Kangaroo detection was highest in grasslands, and kangaroos were not successfully detected in shrublands. Drone-mounted TIR and RGB imagery are effective at detecting large mammals in urban and peri-urban environments.
      Citation: Drones
      PubDate: 2020-05-27
      DOI: 10.3390/drones4020020
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 21: Comparison of Machine Learning Algorithms for
           Wildland-Urban Interface Fuelbreak Planning Integrating ALS and UAV-Borne
           LiDAR Data and Multispectral Images

    • Authors: Francisco Rodríguez-Puerta, Rafael Alonso Ponce, Fernando Pérez-Rodríguez, Beatriz Águeda, Saray Martín-García, Raquel Martínez-Rodrigo, Iñigo Lizarralde
      First page: 21
      Abstract: Controlling vegetation fuels around human settlements is a crucial strategy for reducing fire severity in forests, buildings and infrastructure, as well as protecting human lives. Each country has its own regulations in this respect, but they all have in common that by reducing fuel load, we in turn reduce the intensity and severity of the fire. The use of Unmanned Aerial Vehicles (UAV)-acquired data combined with other passive and active remote sensing data has the greatest performance to planning Wildland-Urban Interface (WUI) fuelbreak through machine learning algorithms. Nine remote sensing data sources (active and passive) and four supervised classification algorithms (Random Forest, Linear and Radial Support Vector Machine and Artificial Neural Networks) were tested to classify five fuel-area types. We used very high-density Light Detection and Ranging (LiDAR) data acquired by UAV (154 returns·m−2 and ortho-mosaic of 5-cm pixel), multispectral data from the satellites Pleiades-1B and Sentinel-2, and low-density LiDAR data acquired by Airborne Laser Scanning (ALS) (0.5 returns·m−2, ortho-mosaic of 25 cm pixels). Through the Variable Selection Using Random Forest (VSURF) procedure, a pre-selection of final variables was carried out to train the model. The four algorithms were compared, and it was concluded that the differences among them in overall accuracy (OA) on training datasets were negligible. Although the highest accuracy in the training step was obtained in SVML (OA=94.46%) and in testing in ANN (OA=91.91%), Random Forest was considered to be the most reliable algorithm, since it produced more consistent predictions due to the smaller differences between training and testing performance. Using a combination of Sentinel-2 and the two LiDAR data (UAV and ALS), Random Forest obtained an OA of 90.66% in training and of 91.80% in testing datasets. The differences in accuracy between the data sources used are much greater than between algorithms. LiDAR growth metrics calculated using point clouds in different dates and multispectral information from different seasons of the year are the most important variables in the classification. Our results support the essential role of UAVs in fuelbreak planning and management and thus, in the prevention of forest fires.
      Citation: Drones
      PubDate: 2020-06-11
      DOI: 10.3390/drones4020021
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 22: 5G-Enabled Security Scenarios for Unmanned
           Aircraft: Experimentation in Urban Environment

    • Authors: Erina Ferro, Claudio Gennaro, Alessandro Nordio, Fabio Paonessa, Claudio Vairo, Giuseppe Virone, Arturo Argentieri, Andrea Berton, Andrea Bragagnini
      First page: 22
      Abstract: The telecommunication industry has seen rapid growth in the last few decades. This trend has been fostered by the diffusion of wireless communication technologies. In the city of Matera, Italy (European capital of culture 2019), two applications of 5G for public security have been tested by using an aerial drone: the recognition of objects and people in a crowded city and the detection of radio-frequency jammers. This article describes the experiments and the results obtained.
      Citation: Drones
      PubDate: 2020-06-12
      DOI: 10.3390/drones4020022
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 23: Measuring Wind Speed Using the Internal
           Stabilization System of a Quadrotor Drone

    • Authors: Magdalena Simma, Håvard Mjøen, Tobias Boström
      First page: 23
      Abstract: This article proposes a method of measuring wind speed using the data logged by the autopilot of a quadrotor drone. Theoretical equations from works on quadrotor control are utilized and supplemented to form the theoretical framework. Static thrust tests provide the necessary parameters for calculating wind estimates. Flight tests were conducted at a test site with laminar wind conditions with the quadrotor hovering next to a static 2D ultrasonic anemometer with wind speeds between 0–5 m/s. Horizontal wind estimates achieve exceptionally good results with root mean square error (RMSE) values between 0.26–0.29 m/s for wind speed, as well as between 4.1–4.9 for wind direction. The flexibility of this new method simplifies the process, decreases the cost, and adds new application areas for wind measurements.
      Citation: Drones
      PubDate: 2020-06-16
      DOI: 10.3390/drones4020023
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 24: Deep Learning Classification of 2D Orthomosaic
           Images and 3D Point Clouds for Post-Event Structural Damage Assessment

    • Authors: Yijun Liao, Mohammad Ebrahim Mohammadi, Richard L. Wood
      First page: 24
      Abstract: Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures.
      Citation: Drones
      PubDate: 2020-06-22
      DOI: 10.3390/drones4020024
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 25: Using Multispectral Drone Imagery for Spatially
           Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

    • Authors: Antoine Mury, Antoine Collin, Thomas Houet, Emilien Alvarez-Vanhard, Dorothée James
      First page: 25
      Abstract: Offering remarkable biodiversity, coastal salt marshes also provide a wide variety of ecosystem services: cultural services (leisure, tourist amenities), supply services (crop production, pastoralism) and regulation services including carbon sequestration and natural protection against coastal erosion and inundation. The consideration of this coastal protection ecosystem service takes part in a renewed vision of coastal risk management and especially marine flooding, with an emerging focus on “nature-based solutions.” Through this work, using remote-sensing methods, we propose a novel drone-based spatial modeling methodology of the salt marsh hydrodynamic attenuation at very high spatial resolution (VHSR). This indirect modeling is based on in situ measurements of significant wave heights (Hm0) that constitute the ground truth, as well as spectral and topographical predictors from VHSR multispectral drone imagery. By using simple and multiple linear regressions, we identify the contribution of predictors, taken individually, and jointly. The best individual drone-based predictor is the green waveband. Dealing with the addition of individual predictors to the red-green-blue (RGB) model, the highest gain is observed with the red edge waveband, followed by the near-infrared, then the digital surface model. The best full combination is the RGB enhanced by the red edge and the normalized difference vegetation index (coefficient of determination (R2): 0.85, root mean square error (RMSE): 0.20%/m).
      Citation: Drones
      PubDate: 2020-06-24
      DOI: 10.3390/drones4020025
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 26: Low-Altitude Terrain-Following Flight Planning
           for Multirotors

    • Authors: Carmelo Donato Melita, Dario Calogero Guastella, Luciano Cantelli, Giuseppe Di Marco, Irene Minio, Giovanni Muscato
      First page: 26
      Abstract: Surveying with unmanned aerial vehicles flying close to the terrain is crucial for the collection of details that are not visible when flying at higher altitudes. This type of missions can be applied in several scenarios such as search and rescue, precision agriculture, and environmental monitoring, to name a few. We present a strategy for the generation of low-altitude trajectories for terrain following. The trajectory is generated taking into account the morphology of the area of interest, represented as a georeferenced Digital Surface Model (DSM), while ensuring a safe separation from any obstacle. The surface model of the scenario is created by using a UAV-based photogrammetry software, which processes the images acquired during a preliminary mission at high altitude. The solution was developed, tested, and verified both in simulation and in real scenarios with a multirotor equipped with low-cost sensing. The experimental results proved the validity of the generation of trajectories at altitudes lower than most of the works available in the literature. The images acquired during the low-altitude mission were processed to obtain a high-resolution reconstruction of the area as a representative application result.
      Citation: Drones
      PubDate: 2020-06-25
      DOI: 10.3390/drones4020026
      Issue No: Vol. 4, No. 2 (2020)
       
  • Drones, Vol. 4, Pages 27: Vegetation Extraction Using Visible-Bands from
           Openly Licensed Unmanned Aerial Vehicle Imagery

    • Authors: Athos Agapiou
      First page: 27
      Abstract: Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.
      Citation: Drones
      PubDate: 2020-06-26
      DOI: 10.3390/drones4020027
      Issue No: Vol. 4, No. 2 (2020)
       
 
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