Integrating Unmanned Aircraft Systems to Measure Linear and Areal Features into Undergraduate Forestry Education

The use of Unmanned Aircraft Systems (UAS) in undergraduate forestry education continues to expand and develop. Accuracy of data collection is an important aspect of preparation for “society-ready” foresters to meet the complex sustainable environment managing for ecological, social and economic interests. Hands-on use of a DJI Phantom 4 Pro UAS by undergraduates to measure the length and area of 30 linear features and areal features on Earth’s surface were estimated. These measurements were compared (measured within the ArcMap 10.5.2 interface) to hyperspectral Pictometry imagery measured on the web-based interface and the Google Earth Pro interface. Each remotely estimated measurement was verified with the actual ground measurements and the methods compared. An analysis of variance, conducted on the absolute length errors resulting in a p-value of 0.000057, concluded that the three length estimating techniques were statistically different at a 95% confidence interval. A Tukey pair-wise test found that the remotely sensed DJI Phantom 4 Pro data was statistically less accurate than the Pictometry and Google Earth Pro data, while both of which were found to be not different statistically in terms of accuracy. The areal feature area measurements were not normally distributed and therefore tested for equal medians using a Kruskal-Wallis test. The test found that there was no significant difference between sample medians, indicating that all three methods of estimating area are statistically equal in accuracy. The results indicate that Pictometry and Google Earth Pro could both be used to accurately estimate linear feature lengths remotely in lieu of in situ linear measurements while all three remote sensing techniques can be used to accurately estimate areal feature areas remotely in lieu of in situ areal measurements.

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Environmental and Sensor Integration Influences on Temperature Measurements by Rotary-Wing Unmanned Aircraft Systems

Sensors ◽

10.3390/s19061470 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1470 ◽

Cited By ~ 10

Author(s):

Brian Greene ◽

Antonio Segales ◽

Tyler Bell ◽

Elizabeth Pillar-Little ◽

Phillip Chilson

Keyword(s):

Unmanned Aircraft ◽

The Body ◽

Unmanned Aircraft Systems ◽

Sensor Integration ◽

Heat Sources ◽

Aircraft Systems ◽

Ducted Fan ◽

Rotary Wing

Obtaining thermodynamic measurements using rotary-wing unmanned aircraft systems (rwUAS) requires several considerations for mitigating biases from the aircraft and its environment. In this study, we focus on how the method of temperature sensor integration can impact the quality of its measurements. To minimize non-environmental heat sources and prevent any contamination coming from the rwUAS body, two configurations with different sensor placements are proposed for comparison. The first configuration consists of a custom quadcopter with temperature and humidity sensors placed below the propellers for aspiration. The second configuration incorporates the same quadcopter design with sensors instead shielded inside of an L-duct and aspirated by a ducted fan. Additionally, an autopilot algorithm was developed for these platforms to face them into the wind during flight for kinematic wind estimations. This study will utilize in situ rwUAS observations validated against tower-mounted reference instruments to examine how measurements are influenced both by the different configurations as well as the ambient environment. Results indicate that both methods of integration are valid but the below-propeller configuration is more susceptible to errors from solar radiation and heat from the body of the rwUAS.

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An efficient method for estimating dormant season grass biomass in tallgrass prairie from ultra-high spatial resolution aerial imaging produced with small unmanned aircraft systems

International Journal of Wildland Fire ◽

10.1071/wf19026 ◽

2020 ◽

Vol 29 (8) ◽

pp. 696

Author(s):

Deon van der Merwe ◽

Carol E. Baldwin ◽

Will Boyer

Keyword(s):

Linear Regression ◽

Tallgrass Prairie ◽

Efficient Method ◽

Unmanned Aircraft ◽

Unmanned Aircraft Systems ◽

Biomass Estimation ◽

P Value ◽

Aircraft Systems ◽

Flint Hills ◽

Dormant Season

Fire is used extensively in prairie grassland management in the Flint Hills region of the midwestern United States, particularly at the end of the dormant season (March–April). A model is used to manage grassland fires in the region to avoid deterioration of air quality beyond acceptable standards. Dormant season dry biomass is an important parameter in the model. The commonly used method for producing high-quality biomass estimates relies on clipping, drying and weighing small biomass samples, which is tedious, expensive and does not scale efficiently to larger areas to provide regional estimates. Small unmanned aircraft systems (sUAS) were used to develop a reliable and more efficient method of biomass estimation based on the correlation between biomass and vegetation canopy height derived from digital surface models (DSMs). A linear regression model was developed from data collected at 11 representative sites in the Kansas Flint Hills region, and the model was validated at two sites. Biomass and canopy heights derived from DSMs were correlated, with a Pearson product moment correlation value of 0.881 (P-value <0.001). Biomass estimated from clipped vegetation at two validation sites positively correlated with model-derived biomass estimates, resulting in linear regression R2-values of 0.90 and 0.74 and Pearson moment correlation coefficients of 0.99 (P<0.001) and 0.86 (P=0.003). The described sUAS method has the potential to increase the efficiency and reliability of dormant season grassland biomass estimates.

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Development and Deployment of Air-Launched Drifters from Small UAS

Sensors ◽

10.3390/s19092149 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2149 ◽

Cited By ~ 1

Author(s):

Sara Swenson ◽

Brian Argrow ◽

Eric Frew ◽

Steve Borenstein ◽

Jason Keeler

Keyword(s):

Unmanned Aircraft ◽

Unmanned Aircraft Systems ◽

High Fidelity ◽

Atmospheric Research ◽

Aircraft Systems ◽

Lagrangian Drifters ◽

Industry Standards ◽

Remotely Piloted Aircraft ◽

Gust Front

Supercell thunderstorms can form extremely dangerous and destructive tornadoes. While high fidelity supercell simulations have increased the understanding of supercell mechanics to help determine how and when tornadoes form, there is a lack of targeted, in situ measurements taken aboveground in supercells to validate these simulations. Pseudo-Lagrangian drifters (PLDs) are atmospheric probes that can be used to attain thermodynamic measurements in areas that are difficult or dangerous to access, such as from within supercells. Of particular interest in understanding tornadogenesis is the rear-flank downdraft (RFD). However, strong outflow winds behind the rear-flank gust front (RFGF) make the RFD particularly difficult to access with balloon-borne sensors launched from the ground. A specific type of PLD, an air-launched drifter (ALD) that is released from unmanned aircraft systems (UAS), can be used to access RFD inflows, present at higher altitudes. Results from initial tests of ALDs are shown, along with results from a ground-released PLD test during a supercell intercept in the Oklahoma Panhandle on 12 June 2018. In characterization tests performed at the 2018 International Society for Atmospheric Research using Remotely piloted Aircraft (ISARRA) flight week, it was found that the ALD sensor system performs reasonably well against industry standards. However, improvements will be made to increase the aspiration of the sensor.

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Lidar Ice nuclei estimates and how they relate with airborne in-situ measurements

EPJ Web of Conferences ◽

10.1051/epjconf/201817605018 ◽

2018 ◽

Vol 176 ◽

pp. 05018

Author(s):

Eleni Marinou ◽

Vassilis Amiridis ◽

Albert Ansmann ◽

Athanasios Nenes ◽

Dimitris Balis ◽

...

Keyword(s):

Particle Number ◽

Number Concentration ◽

Unmanned Aircraft ◽

Unmanned Aircraft Systems ◽

Aircraft Systems ◽

Ice Nuclei ◽

Aerosol Backscatter ◽

Lidar Observations ◽

Surface Area Concentration

By means of available ice nucleating particle (INP) parameterization schemes we compute profiles of dust INP number concentration utilizing Polly-XT and CALIPSO lidar observations during the INUIT-BACCHUS-ACTRIS 2016 campaign. The polarization-lidar photometer networking (POLIPHON) method is used to separate dust and non-dust aerosol backscatter, extinction, mass concentration, particle number concentration (for particles with radius > 250 nm) and surface area concentration. The INP final products are compared with aerosol samples collected from unmanned aircraft systems (UAS) and analyzed using the ice nucleus counter FRIDGE.

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