scholarly journals An Improved Algorithm for Low-Level Turbulence Forecasting

2018 ◽  
Vol 57 (6) ◽  
pp. 1249-1263 ◽  
Author(s):  
Domingo Muñoz-Esparza ◽  
Robert Sharman
Keyword(s):  
Probability Distributions ◽  
Atmospheric Stability ◽  
Energy Dissipation Rate ◽  
Probability Of Detection ◽  
Percentage Error ◽  
Unmanned Aerial Systems ◽  
Low Level ◽  
Forecasting System ◽  
The One ◽  
Aerial Systems

AbstractA low-level turbulence (LLT) forecasting algorithm is proposed and implemented within the Graphical Turbulence Guidance (GTG) turbulence forecasting system. The LLT algorithm provides predictions of energy dissipation rate (EDR; turbulence dissipation to the one-third power), which is the standard turbulence metric used by the aviation community. The algorithm is based upon the use of distinct log-Weibull and lognormal probability distributions in a statistical remapping technique to represent accurately the behavior of turbulence in the atmospheric boundary layer for daytime and nighttime conditions, respectively, thus accounting for atmospheric stability. A 1-yr-long GTG LLT calibration was performed using the High-Resolution Rapid Refresh operational model, and optimum GTG ensembles of turbulence indices for clear-air and mountain-wave turbulence that minimize the mean absolute percentage error (MAPE) were determined. Evaluation of the proposed algorithm with in situ EDR data from the Boulder Atmospheric Observatory tower covering a range of altitudes up to 300 m above the surface demonstrates a reduction in the error by a factor of approximately 2.0 (MAPE = 55%) relative to the current operational GTG system (version 3). In addition, the probability of detection of typical small and large EDR values at low levels is increased by approximately 15%–20%. The improved LLT algorithm is expected to benefit several nonconventional turbulence-prediction sectors such as unmanned aerial systems and wind energy.

scholarly journals Validating UAS-Based Photogrammetry with Traditional Topographic Methods for Surveying Dune Ecosystems in the Spanish Mediterranean Coast

2019 ◽  
Vol 7 (9) ◽  
pp. 297 ◽  
Author(s):  
Luis Bañón ◽  
José Ignacio Pagán ◽  
Isabel López ◽  
Carlos Banon ◽  
Luis Aragonés
Keyword(s):  
Low Cost ◽  
Temporal Frequency ◽  
Satellite System ◽  
Mean Value ◽  
Mediterranean Coast ◽  
Unmanned Aerial Systems ◽  
Aerial Photogrammetry ◽  
The One ◽  
Global Navigation Satellite ◽  
Aerial Systems

In the past few years, unmanned aerial systems (UAS) have achieved great popularity for civil uses. One of the present main uses of these devices is low-cost aerial photogrammetry, being especially useful in coastal environments. In this work, a high-resolution 3D model of a beach section in Guardamar del Segura (Spain) has been produced by employing a low maximum takeoff mass (MTOM) UAS, in combination with the use of structure-from-motion (SfM) techniques. An unprecedented extensive global navigation satellite system (GNSS) survey was simultaneously carried out to statistically validate the model by employing 1238 control points for that purpose. The results show good accuracy, obtaining a vertical root mean square error (RMSE) mean value of 0.121 m and a high point density, close to 30 pt/m2, with similar or even higher quality than most coastal surveys performed with classical techniques. UAS technology permits the acquisition of topographic data with low time-consuming surveys at a high temporal frequency. Coastal managers can implement this methodology into their workflow to study the evolution of complex, highly anthropized dune-beach systems such as the one presented in this study, obtaining more accurate surveys at lower costs.

scholarly journals Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations

2016 ◽  
Vol 10 (3) ◽  
pp. 1075-1088 ◽  
Author(s):  
Yves Bühler ◽  
Marc S. Adams ◽  
Ruedi Bösch ◽  
Andreas Stoffel
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Depth Distribution ◽  
Cost Effective ◽  
Test Site ◽  
Measurement Technology ◽  
Unmanned Aerial Systems ◽  
Tall Grass ◽  
The One ◽  
Aerial Systems

Abstract. Detailed information on the spatiotemporal snow depth distribution is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Today, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network like the one in Switzerland, with more than one measurement station per 10 km2 on average, is not able to capture the large spatial variability of snow depth present in alpine terrain.Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, in most countries such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UASs), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Compared to manual measurements, such systems are relatively cost-effective and can be applied very flexibly to cover terrain not accessible from the ground. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) of less than 0.07 to 0.15 m on meadows and rocks and a RMSE of less than 0.30 m on sections covered by bushes or tall grass, compared to manual probe measurements. This new measurement technology opens the door for efficient, flexible, repeatable and cost-effective snow depth monitoring over areas of several hectares for various applications, if the national and regional regulations permit the application of UASs.

scholarly journals Pervasive Intelligence

2018 ◽  
Vol 4 (1) ◽  
pp. 107-132
Author(s):  
Sebastian Vehlken
Keyword(s):  
Swarm Robotics ◽  
Self Organization ◽  
Unmanned Aerial Systems ◽  
Spatial Intelligence ◽  
Changing Environments ◽  
Robot Swarms ◽  
Peculiar Form ◽  
The One ◽  
And Control ◽  
Aerial Systems

Abstract This article seeks to situate collective or swarm robotics (SR) on a conceptual pane which on the one hand sheds light on the peculiar form of AI which is at play in such systems, whilst on the other hand it considers possible consequences of a widespread use of SR with a focus on swarms of Unmanned Aerial Systems (Swarm UAS). The leading hypothesis of this article is that Swarm Robotics create a multifold “spatial intelligence”, ranging from the dynamic morphologies of such collectives via their robust self-organization in changing environments to representations of these environments as distributed 4D-sensor systems. As is shown on the basis of some generative examples from the field of UAS, robot swarms are imagined to literally penetrate space and control it. In contrast to classical forms of surveillance or even “sousveillance”, this procedure could be called perveillance.

scholarly journals UAV – a useful tool for monitoring woodlands

2014 ◽  
Vol 18 (2) ◽  
pp. 46-52 ◽  
Author(s):  
Anna Zmarz
Keyword(s):  
Field Research ◽  
Image Data ◽  
Large Field ◽  
Unmanned Aerial Systems ◽  
Digital Cameras ◽  
Forest Division ◽  
Human Costs ◽  
The One ◽  
Aerial Systems ◽  
Spectral Channels

Abstract Unmanned aerial systems are in many countries one of the most dynamically developing branches of technology. They have also been recognized and are being utilized by scientists who find remote sensing indispensable in their work. Today, it is increasingly common to find research teams utilizing so-called drones in field research. Unmanned systems are becoming ever more important for environment monitoring by, on the one hand, providing data from inaccessible or remote areas, and, on the other hand, reducing the human costs required by traditional large field teams while also increasing the efficiency of the work. This paper presents the possibility of utilizing UAVs for image data collection in woodland areas. Flights over Głuchów and an Arboretum were performed using two different UAVs (Mentor, AVI). The flights were made in 2010 in the middle of the growing season. Images were taken with Sigma DP2 digital cameras in four spectral channels: R (red), G (green), B (blue) and IR (infrared). Images were saved in 8-bit. The Głuchów forest complex is located in the Głuchów forest district, which forms a part of the Rogów forest division. From the administrative viewpoint, the forest division is located in the Łódzkie province, Skierniewicki Poviat. The Arboretum is a park with a collection of trees and shrubs from different regions of the world. The area is characterized by a high variability of species and trees of varying heights. It is located in the Łódzkie province, Skierniewicki Poviat.

scholarly journals Monitoring abundance of aggregated animals (Florida manatees) using an unmanned aerial system (UAS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Holly H. Edwards ◽  
Jeffrey A. Hostetler ◽  
Bradley M. Stith ◽  
Julien Martin
Keyword(s):  
New Technologies ◽  
Probability Of Detection ◽  
Unmanned Aerial Systems ◽  
Imperfect Detection ◽  
Trichechus Manatus Latirostris ◽  
Abundance Estimates ◽  
Survey Costs ◽  
Abundance And Distribution ◽  
Aerial Systems ◽  
Over Time

AbstractImperfect detection is an important problem when counting wildlife, but new technologies such as unmanned aerial systems (UAS) can help overcome this obstacle. We used data collected by a UAS and a Bayesian closed capture-mark-recapture model to estimate abundance and distribution while accounting for imperfect detection of aggregated Florida manatees (Trichechus manatus latirostris) at thermal refuges to assess use of current and new warmwater sources in winter. Our UAS hovered for 10 min and recorded 4 K video over sites in Collier County, FL. Open-source software was used to create recapture histories for 10- and 6-min time periods. Mean estimates of probability of detection for 1-min intervals at each canal varied by survey and ranged between 0.05 and 0.92. Overall, detection probability for sites varied between 0.62 and 1.00 across surveys and length of video (6 and 10 min). Abundance varied by survey and location, and estimates indicated that distribution changed over time, with use of the novel source of warmwater increasing over time. The highest cumulative estimate occurred in the coldest winter, 2018 (N = 158, CI 141–190). Methods here reduced survey costs, increased safety and obtained rigorous abundance estimates at aggregation sites previously too difficult to monitor.

LIGHTER-THAN-AIR (LTA) UNMANNED AERIAL SYSTEM (UAS) CARRIER CONCEPT FOR SURVAILLANCE AND DISASTER MANAGEMENT

2019 ◽  
Vol 3 ◽  
pp. 1255
Author(s):  
Ahmad Salahuddin Mohd Harithuddin ◽  
Mohd Fazri Sedan ◽  
Syaril Azrad Md Ali ◽  
Shattri Mansor ◽  
Hamid Reza Jifroudi ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Disaster Management ◽  
Unmanned Aerial Systems ◽  
Unmanned Aerial System ◽  
Border Control ◽  
Aerial Vehicles ◽  
Operational Safety ◽  
In Situ Methods ◽  
Aerial Systems

Unmanned aerial systems (UAS) has many advantages in the fields of SURVAILLANCE and disaster management compared to space-borne observation, manned missions and in situ methods. The reasons include cost effectiveness, operational safety, and mission efficiency. This has in turn underlined the importance of UAS technology and highlighted a growing need in a more robust and efficient unmanned aerial vehicles to serve specific needs in SURVAILLANCE and disaster management. This paper first gives an overview on the framework for SURVAILLANCE particularly in applications of border control and disaster management and lists several phases of SURVAILLANCE and service descriptions. Based on this overview and SURVAILLANCE phases descriptions, we show the areas and services in which UAS can have significant advantage over traditional methods.

Beach and dune impacts due to Hurricane Florence in Dare County, North Carolina

Shore & Beach ◽  
2019 ◽  
pp. 44-49 ◽  
Author(s):  
Elizabeth Sciaudone ◽  
Liliana Velasquez-Montoya
Keyword(s):  
North Carolina ◽  
Prior Knowledge ◽  
Unmanned Aerial Systems ◽  
State University ◽  
National Wildlife Refuge ◽  
Sediment Samples ◽  
Wildlife Refuge ◽  
Full Report ◽  
Aerial Systems

Less than two weeks after Hurricane Florence made landfall in North Carolina (NC), a team of researchers from NC State University traveled to Dare County to investigate the storm’s effects on beaches and dunes. Using available post-storm imagery and prior knowledge of vulnerabilities in the system, the team identified several locations to visit in the towns of Kitty Hawk, Nags Head, Rodanthe, Buxton, and Hatteras, as well as a number of locations within the Pea Island National Wildlife Refuge (Figure 1). Data collected included topographic profiles, still imagery and video from unmanned aerial systems, sediment samples, and geo-located photography. This Coastal Observations piece presents some of the data and photos collected; the full report is available online (Sciaudone et al. 2019), and data collected will be made available to interested researchers upon request.

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