national oceanic and atmospheric administration (noaa) small unmanned...

National Oceanic and Atmospheric Administration (NOAA)

Small Unmanned Aircraft Systems (sUAS)

Avon Park Demonstration

Executive Summary

National Oceanic and Atmospheric Administration Small Unmanned Aircraft Systems Avon Park Demonstration Executive Summary

Contents 1.0 Background ....................................................................................................................................... 1

2.0 Objective ........................................................................................................................................... 1

3.0 Test Site ............................................................................................................................................. 2

4.0 Observations ..................................................................................................................................... 2

5.0 System Observations......................................................................................................................... 3

SenseFly eBee: .......................................................................................................................................... 3

AV Puma AE: ............................................................................................................................................. 4

Altavian Nova F6500: ................................................................................................................................ 4

DJI Phantom 2 ........................................................................................................................................... 5

6.0 Resolution and positional accuracy .................................................................................................. 5

7.0 Processing Summary ......................................................................................................................... 7

8.0 Data Recording - Sound Meter ......................................................................................................... 9

9.0 Summary ........................................................................................................................................... 9

10.0 Recommendations .......................................................................................................................... 10

Appendix 1-7 ............................................................................................................................................... 13


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1.0 BACKGROUND Unmanned Aircraft Systems (UAS) provide scientists with a way to look longer, closer, and more frequently at remote areas where until now it has been too intrusive, too dangerous, or expensive to monitor. UAS provide data in real-time and permit data acquisition under marginal weather conditions (acquisition under clouds), which results in better science, safer acquisition of data, and savings over conventional remote sensing techniques. The flexibility of operations and relatively low-cost of small unmanned aircraft systems (sUAS) make them an ideal platform for acquiring remote sensing data in many situations. Not since the invention of the airplane or global positioning system (GPS) has a technology had such a disruptive and transformational impact on the remote sensing community. UAS missions have been conducted to monitor environmental conditions, analyze the impacts of climate change, respond to natural hazards, understand landscape change rates and consequences, conduct wildlife inventories, and support related land management and law enforcement missions. Many scientists and resource managers within the National Oceanic and Atmospheric Administration (NOAA) are eager to create new uses of the technology. They are also turning to UAS and associated remote sensing tools to perform many traditional tasks more inexpensively. Using UAS platforms NOAA will be able to tailor solutions to meet project requirements and obtain very high-resolution video, acquire thermal imagery, detect chemical plumes, and collect point cloud data at a fraction of the cost of conventional surveying methods.

2.0 OBJECTIVE The primary objective of the demonstration was to provide NOAA with information to evaluate specific UAS systems and their potential to safely supplement or replace current observation methods (i.e. satellite, manned aircraft, and terrestrial based techniques). Calibrated targets and ground control for quantification of optical payloads were deployed to evaluate sensor resolution and positional accuracy at various flying heights. The multiple platforms and payloads were flown at a variety of altitudes over a standardized repeatable geographic area inclusive of a variety of terrain to provide direct mapping data for comparison. A secondary objective was to provide NOAA personnel with the ability to witness, evaluate and inspect the different UAS platforms and payloads. The demonstration will assist NOAA in the development of UAS mission Standard Operating Procedures (SOPs), establishing airworthiness criteria and developing a way forward for sUAS deployment across the NOAA enterprise. Due to operational constraints not all goals were achieved fully but numerous lessons as noted below were learned regarding further evaluations.

The systems evaluated were: 1. Puma- AeroVironment, https://www.avinc.com/ 2. F6500- Altavian, https://www.altavian.com/ 3. eBee- SenseFly, https://www.sensefly.com/home.html 4. Phantom 2- DJI, http://www.dji.com/product/phantom

The demonstration included direct observations and comparisons of: 1. Flight characteristics of each system 2. Launch and landing 3. Mission Planning – programmable flight patterns 4. Autonomous operation 5. Safety considerations 6. Capabilities of camera systems 7. Data management and processing capabilities

https://www.avinc.com/

https://www.avinc.com/

https://www.altavian.com/

https://www.altavian.com/

https://www.sensefly.com/home.html

https://www.sensefly.com/home.html

http://www.dji.com/product/phantom


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8. Operation and features of the Ground Control Systems

3.0 TEST SITE

The demonstrations were conducted from January 20-22, 2015 using Special Use Airspace (R-2901 A/B; Figure 1) at Avon Park, FL. Operations were allowed up to 1,000 ft. AGL. Weather conditions were clear and warm with mild winds.

4.0 OBSERVATIONS Upon arrival to the test site, each of the system operators received a safety briefing and an area of interest (AOI) was defined. Each system was required to be flown at multiple altitudes (100, 200, 300, 400, and 500 Foot). Set up time, battery endurance, sound meter recordings, safety procedures and other observations were recorded by members of the evaluation team. A sample observation worksheet for much of this information can be found in Table 1.

Figure 1: Map of Avon Park Air Ground Training Complex, located approximately 75 miles ESE of Tampa, FL.


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5.0 SYSTEM OBSERVATIONS SenseFly eBee: The eBee is a lightweight (less than 2 lbs.), electric, fully autonomous mapping system (Figure 2). It required only one person to operate the system. The Ground Control Station and mission planning software were extremely user friendly. The system allowed operators to select either autonomous or manual mode. Safety features include a “return home” lost link capability and geo-fence technology. The time required to set up the system, plan the mission and start flying were measured in minutes. The demonstrated battery life was nearly 90 minutes in duration. Installing a new battery and launching required only a few minutes. It is a system designed to “map” areas of approximately 300 acres. It was the least expensive of the systems evaluated. The eBee’s small size limits the available payload options, flight endurance, and weather resiliency. It is a system worthy of further consideration for small area topographic mapping projects, monitoring vegetation plots, subtle erosion change detection, or wildlife inventory missions.

Figure 2: eBee Images: Mission planning (left), UAS aircraft and carrying case (center), and Ground Control System (right)

Table 1: Sample worksheet for the eBee UAS platform, providing a record for several of the evaluated variables that were observed during the testing at Avon Park in January 2015


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AV Puma AE: NOAA acquired and has the most experience utilizing the AV Puma AE (Figure 3). As documented in GAO Report, GAO-14-566 (July 2014), NOAA identified and has been working with the industry and the manufacturer for the upgrade of the camera system. The Avon Park demonstration is another assessment of the upgraded camera system in a test environment to accomplish this task. Recent operational assessments of the 24 MP Camera, LiDAR and Super-Gimbal camera has fulfilled the operational requirements for real-world oil spills, Polar monitoring, maritime/coastal surveys, marine debris, and wildlife assessments.

AeroVironment provided a Puma UAS, operators and multiple payloads. The Puma AE vehicle and ground control station (GCS) are the same as those currently operated by NOAA. It was the largest and most expensive of the systems evaluated. It required up to three persons to operate and launch the system. The system demonstrated used a catapult launch system. The user interfaces and ground control system were the least user friendly of the systems evaluated. The set-up time far exceeded what the Altavian and eBee required.

The Puma was also the most robust system evaluated and supports multiple payloads: LiDAR/14 MP mapping/high resolution visible EO; 24 MP fixed nadir mapping camera, and Multispectral sensor. Of the systems evaluated, the Puma was the most water resistant and had the longest endurance (nearly 3 hours of battery life). Lost link procedures are adequate and the system allows manual or autonomous operations. The Department of Defense has worked with AeroVironment to establish a sound operator training program and there are many experienced operators available to supplement the current NOAA staff. The Puma was designed to support an Intelligence Surveillance and Reconnaissance (ISR) mission. It remains a capable tool for supplying NOAA with real time data collection. Based on this evaluation, the PUMA will remain a workhorse supporting NOAA offshore observations but will require enhancements to effectively support mapping missions.

Figure 3: Puma images: Ground Control Station & antennas (left), UAS aircraft (center), and catapult launch (right)

Altavian Nova F6500: The Nova F6500 aircraft (Figure 4) is an all-electric system that provides precision 3D mapping and real-time thermal infrared and high definition video capabilities. Altavian provided the platform and flight crew. This system has previously been flown supporting the NOAA River Forecasting Center (RFC) mission. The system requires a two person team to operate, was hand launched and had a user friendly mission planning and ground control system. The system was designed to support photogrammetric mapping missions. The efficiency of mission planning, data collection, post processing, and analysis of the data reflected those roots. The system is resistant to water and able to operate in a wide range of


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environmental conditions. The system allows manual or autonomous flight modes. Battery life was one hour in duration and required nearly 20 minutes to swap out the battery and launch. In terms of cost, the system is less expensive than the Puma. However, the system failed to return home on a landing approach and “crashed”. The incident report is available on request. The system is being used successfully by several government agencies. It is a capable mapping system and could support several near shore applications.

Figure 4: Nova F6500 images: hand launch (far left), Ground Control Station (center left), UAS aircraft (center right), and

recovered aircraft (far right).

DJI Phantom 2 While not a robust mapping platform, the low cost and easy availability of the DJI Phantom 2 system will drive a demand by NOAA scientists, resource managers and programs to request authorization to acquire these systems (Figure 5). The Phantom is the most widely sold sUAS on the market. Primarily due to its massive user community, applications development and user support are both expanding rapidly. The battery life duration was limited to approximately 20 minutes but swapping out the battery required only a few minutes. Lost link procedures and geofence technology are on par with much larger, and more expensive systems. The system was operated only in manual mode. While not a formal participant in the Avon Park demonstration, the system was easy to assemble and operate. Development of a data life cycle for Phantom 2 observations is suggested.

Figure 5: Phantom Image: UAS aircraft

6.0 RESOLUTION AND POSITIONAL ACCURACY The project included flying all available systems and cameras over a resolution target (Figures 6 and 7). In addition, ground control points were established using GPS observations. Positional accuracy evaluations were not conducted due to an insufficient number of ground control points being withheld from the aero-triangulation solution. Jason Woolard of RSD and Wayne Perryman of NMFS analyzed the data. Their input on the quality of the various datasets and the corresponding cameras is summarized in Table 2 and is also included as Appendix 1-7.


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eBee 16MP: Canon Powershot ELPH 100HS @ 4.3mm

eBee 12MP: Canon Powershot S110 @

5.2mm

Puma: Sony Nex-7 w/

20mm lens

F6500: Canon EOS Rebel SL1 w/ 20mm

lens

3.02 cm 125 ft.

3.81 cm 185 ft

4.27 cm 201 ft.

4.82 cm 198 ft.

5.39 cm 285 ft.

6.04 cm 305 ft.

6.80 cm 175-200 ft. 175 ft. 348 ft.

7.62 cm 173-202 ft.

>7.62 cm > 351 ft. > 230 ft. > 435 ft. > 398 ft.

*Approximate Altitude (ft.) at which various Ground Resolved Distances (cm) were observed.

Table 2: Assessed quality of various payload imagery from the Avon Park tests

Altitudes and resolutions have ranges since the captured resolution is dependent on the angle between the flight path and the orientation of the lines on test chart

In general, the data resolution and accuracy were equivalent across all systems. The determining factor was the number of pixels in the image. Sensors are evolving at a rapid pace and are becoming more affordable. The vendors have all recently upgraded their EO cameras, post the NOAA evaluation. The systems tested would allow species identification for waterfowl such as ducks and geese. It is likely that affordable LiDAR, gas detection technology, hyper and multispectral cameras will be available on a sUAS within the next few years. All data is available at: http://www.ngi.msstate.edu/projects/noaa-uas/UASDataArchive/.

Figure 6: Images: establishing GPS control (left) and resolution target (right)

http://www.ngi.msstate.edu/projects/noaa-uas/UASDataArchive/

http://www.ngi.msstate.edu/projects/noaa-uas/UASDataArchive/


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7.0 PROCESSING SUMMARY Sense Fly was the only vendor that provided detailed information on the processing of the data they collected. They processed the data using Pix4D. The summary report for the 500 foot flight is included as an example:

Project 2015_01_20_resolution_s110_500ft Processed 2015-Jan-28 13:07:50 Camera Model Name CanonPowerShotS110_5.2_4048x3048 (RGB) Average Ground Sampling Distance (GSD) 5.43 cm / 2.14 in Area Covered 0.0569 km2 / 5.6945 ha / 0.022 sq. mi. / 14.0786 acres Image Coordinate System WGS84 Output Coordinate System WGS84 / UTM zone 17N Camera Model Parameter Optimization optimize externals and all internals Time for Initial Processing (without report) 06m: 04s Images median of 46083 key points per image Dataset 16 out of 16 images calibrated (100%), 71 images disabled Camera Optimization 0.9% relative difference between initial and final focal length Matching median of 18793.7 matches per calibrated image

Much of the information supplied by AeroVironment to NOAA is proprietary. The following information was extracted from the AeroVironment supplied report and briefing:

Data collected included 24MPixel DSLR EO Imagery with over 8000 high resolution images collected (Figure 7). Total processing time to convert the data to an ortho-rectified GeoTIFF and create a digital terrain model (DTM) was reported to be 1.1 days.

Figure 7: Aerial view of the resolution target used during the Avon Park tests; this image was

obtained from one the 24 Megapixel DSLR EO payload cameras.


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The Puma was the only system that collected LiDAR (Figure 8). Flight row spacing was set to 25 meters with an AGL elevation of 50m.

Figure 8: Classification imagery produced from data obtained by the LiDAR payload, carried

aboard the Puma AE UAS.

AV conducted a Puma mission with over 2500 false color and Normalized Difference Vegetation Index (NDVI) images collected. The camera collects six simultaneous images with various color filters. After the flight the raw color images are combined into standard JPG images using the classic LandSAT false color mapping of:

● JPG red channel = Near IR (800 nanometer band) ● JPG green channel = Red (680 nanometer band) ● JPG blue channel = Green (550 nanometer band)

The false color images are processed into a secondary set of images for NDVI analysis. Each false color JPG was processed with the following NDVI, low veg, med veg, high veg, cars, planes, utility poles, livestock, pipelines, etc. (Figure 9). The capability to operate several plug and play sensors is a valuable capability as image processing scientists are in the early stages of developing data fusion techniques. The ability to obtain several “views” of an area of concern often results in better science and selection of management alternatives.

Figure 9: Multi-spectral false color image developed from NDVI data, which is used to further classify certain features within the

imagery.


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8.0 DATA RECORDING - SOUND METER Acoustic data sheets were compiled for each system and flight to record sound meter readings (Table 3). All of the systems evaluated were powered by electric motors and the noise levels were considered insignificant compared to the background noise. “To provide further context, a whisper registers approximately 30 dBA; normal conversation about 50 to 60 dBA; a ringing phone 80 dBA; and a power mower 90 dBA. If noise levels exceed 80 dBA, people must speak very loudly to be heard, while at noise levels of 85 dBA, people have to shout to communicate with coworkers who are an arms length away”- Occupational Safety and Health Administration, OSHA FS-3463 8/2011 DSG. The Occupational Safety and Health Administration’s (OSHA’s) Noise standard (29 CFR 1910.95) requires employers to have a hearing conservation program in place if workers are exposed to a time-weighted average (TWA) noise level of 85 decibels (dBA) or higher over an 8-hour work shift. All of the systems tested were well below this standard.

System eBee Puma F6500 Phantom

Takeoff Decibels 80 81 82 80 100 Foot Decibels 44 44 54 55 200 Foot Decibels 39 N/A N/A 46

Table 3: Acoustic data recorded for each of the platforms at fixed altitudes, starting from the surface

While NOAA and others continue to monitor some wildlife with UAS, dolphins, whales, seals, and sea lions are protected species and harming or disturbing them can be a violation of Federal law. The Marine Mammal Protection Act of 1972 (MMPA) makes it illegal to harass marine mammals by changing their behavior, which may occur if they are approached too closely. Federal guidelines recommend keeping a safe aerial distance of at least 1000 feet (300 yards) from marine mammals in the wild. The Endangered Species Act of 1973 (ESA) also provides additional protections for those species of marine mammals listed as threatened or endangered. For example, Federal regulations restrict close approaches by air for humpback whales in Hawaii (1000 feet = 300 yards) and for North Atlantic right whales (1500 feet = 500 yards). Permits have been used to fly closer to wildlife, and continued UAS evaluation may lead to new guidelines.

The Puma was flown over several feeding sand hill cranes at approximately 100 ft. The birds were not affected and did not flush. Each species behaves differently based on several environmental and sensitivity factors (migration patterns, breeding, time of day, time of year, etc.) While there are an increasing number of videos of wildlife available on social media, it is strongly recommended that wildlife biologists be consulted before any wildlife missions are conducted.

9.0 SUMMARY NOAA can leverage readily available, low cost UAS technology to realize an increased flexibility of operations and sUAS are an ideal platform for NOAA employees and partners to acquire remote sensing data. NOAA can look at remote areas for longer periods, at closer range, and more frequently than in the past when it may have been too dangerous, too expensive, or too intrusive to monitor. NOAA can have real-time data acquired under even marginal weather conditions. NOAA can employ efficient, cost effective, safe UAS solutions offering the precise solutions tailored to meet program requirements at a fraction of the cost of conventional surveying methods.

● Compared to traditional data acquisition methods, UAS data acquisition can be more:

http://www.nmfs.noaa.gov/pr/dontfeedorharass.htm

http://www.nmfs.noaa.gov/pr/pdfs/fr/50cfr224-103.pdf


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Economical Safer Efficient

● Allows us to do things we couldn’t do before Enhanced Observations New Science More Informed Decisions

● Assessment of Technology Developing at an extraordinary rate Plug and Play Sensors Analysis Tools Lagging

● UAS technology will not replace other observation techniques, but will emerge as the primary platform for DOI remote sensing applications very high-resolution video thermal imagery acquisition

atmospheric measurements point & non-point data collection

10.0 RECOMMENDATIONS NOAA continues to execute an initial UAS strategy and is developing a comprehensive UXS strategy as unmanned systems mature and transition towards operations. This strategy, based on internal and inter-agency lessons learned, will be tailored to meet the complex NOAA mission requirements, decentralized delivery system, funding, personnel, and infrastructure. As part of the overarching strategies, the NOAA UAS team has published a Data Management Plan which follows the total lifecycle management model from the requirements identification inception through data dissemination to navigate every phase and aspect of remote sensing data acquisition and management.

Continued focus will include:

• Optimized solution architecting, procurement, development, testing, deployment, and Operations and Maintenance (O&M) by availing the experience of local Subject Matter Experts who participate daily in missions to monitor environmental conditions, analyze the impacts of climate change, respond to natural hazards, understand landscape change rates and consequences, conduct wildlife inventories, and support related land management and law enforcement missions.

• Ensure all NOAA UAS missions are in full compliance with Federal laws, FAA regulations, Department of Commerce, and NOAA policies and procedures, and conduct UAS missions to professional standards, codes of conduct, and case law with the public’s trust in mind.


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• Leverage partnerships with Federal and state agencies, universities, and partner organizations that possess UAS capabilities to avoid duplication and enhance capabilities.

• Efficiently acquire and retain data collected with UAS sensors within industry standards and consistent with data collected using other NOAA remote sensing systems.

• Data buying and end-user observation requirements may be fulfilled without an aircraft services component. This type of services acquisition should be considered for schedule, performance and cost consideration.

• Continued understanding of spectrum analysis and understanding, so that sensor payloads can be studied for spectral, resolution and C-SWAP.

• Provide UAS technology education and outreach across the NOAA organization and external partners.

• And continue to support previous small UAS recommendations documented in the Interagency Working Group on Facilities and Infrastructure (IWG-FI) established Subcommittee on Unmanned Systems (SUS):

1. Establish an overarching Inter-Agency Agreement (IAA) between Federal agencies. An IAA between agencies for unmanned systems would improve inter-agency relationships, workload sharing and allow for the transfer of unmanned systems and technology. The IAA will enhance inter-agency program transparency, coordinate the definition and efficiency of utilization rates across communities, decrease duplicative Federal resource expenditures, and coordinate acquisition, operations, training, and life-cycle maintenance.

2. Establish consolidated operations centers for Federal unmanned systems. In order to harness the full potential of unmanned systems and strengthen mission effectiveness, Federal agencies should establish consolidated operations centers. Standards and interface specifications need to be established to achieve modularity, commonality and interchangeability across payloads, control systems, telecommunications interfaces, data, and communication links.

3. Define common capability descriptions, metadata standards, data models and architectures. The enterprise-wide adoption and execution of proper data management practices, with emphasis on accepted metadata standardization fosters improved operating efficiencies in Federal and partner programs and reporting that supports government transparency. This model improves the single agency stovepipe model by applying consistent policy, improved organization, better governance, and understanding of the electorate to deliver outstanding results.

4. Establish asset pools for Federal unmanned systems. Federal organizations should share unmanned systems, personnel, technologies and information, strategic and operating plans, observing and performance requirements, technology assessments, impact studies, system and business case analyses, and lessons learned. A successful “asset pool” will generate an inventory of unmanned systems, data requirements, sensors, and operating facilities.

5. Develop a federally coordinated acquisition strategy for Federal unmanned systems. The Federal government, working with industry, academia and international partners, must take a coordinated, disciplined and comprehensive approach to the development and acquisition of unmanned systems from a “Program of Record” perspective. Understanding of full life-cycle


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costs and opportunities for intergovernmental asset sharing must be better exploited while capitalizing upon commonality, standardization, and acquisition strategies.


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Acknowledgement

Thanks to the on-site participants Matt Pickett (M4), Jason Woolard, Mark Rogers, Mike Hutt (CNT) representing NOAA, Dr. Robert Moorhead (Mississippi State University), Adam Zylka (SenseFly), Kevin Choate & Allan Austria (Altavian), Tom Stone, Eric Thompson & Jonathan Scott (AeroVironment). Thanks to the co-authors, Mike Hutt (Team Lead), Dr. Robert Moorhead, John Walker and John “JC” Coffey for their attention to and support of this test and for their input to and review of this document.

APPENDIX 1-7 Resolution Test Results

Tests conducted at Avon Park, FL, January 20-22, 2015

Camera on 1/21/2105 (Puma) was Sony NEX-7 w 20mm lens (30mm in 35mm equivalent) Camera on 1/20/2105 (Phantom) was Go-Pro Hero3+ Silver w/2.8mm lens (15mm in 35mm equivalent) Camera on 1/20/2015 (Ebee 16mp) was Canon Powershot ELPH 110HS w/4.3-21.5mm zoom (images at 4.3) (24-120mm 35mm equivalent) Camera on 1/20/2015 (Ebee 12mp) was Canon Powershot S110 w/5.2-26.0mm zoom (images at 5.2) (24-120mm 35mm equivalent) Camera on 1/22/2015 (Altavian) was Canon EOS Rebel SL1 w 20mm lens Sony NEX-7 pixel size =3.89 microns = 0.000012762 ft Go-Pro Hero3+ Silver pixel size = 2.2 microns = 0.000007218ft Canon Powershot ELPH 110HS pixel size = 1.34 microns = 000004396 Canon Powershot S110 pixel size = 1.87 microns = 0.000006135 ft Canon EOS Rebel SL1 pixel size = 4.3 microns = 0.000014108 ft 20mm focal length = 0.065616798 ft 2.8mm focal length = 0.009186352 ft 4.3mm focal length = 0.014107612 ft 5.2mm focal length = 0.017060367 ft. Length of Resolution target = 6.93 ft. Width of Resolution target = 2.91 ft. Do not know flight line, so parallel & perpendicular elements have unknown relation to flight line Target elements 7+ only (smaller photo lab produced target)


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Puma with Sony Nex-7

21 January 2015

Ground Distance

Photo Distance

Calculated Altitude

Average Altitude

Best Element

Resolution

Ground Resolved Distance


Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) 229 6.930 179.3 199 198 11 0.158 4.82

2.910 75.5 198

359(1) 6.930 98.1 363 348 8 0.223 6.80 2.910 44.9 333 396 6.930 79.8 447 435 >7 >0.25 >7.62

2.910 35.4 423 398 6.930 80.2 444 435 >7 >0.25 >7.62

2.910 35.1 426 (1) – badly smeared, edges jagged

Phantom II Quad Copter with Hero 3+ Silver Go-Pro camera 20 January 2015

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Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) G0092348 6.930 218.2 40 40 16 0.088 2.68 2.910 92.4 40 G0092349(1) 6.930 208.1 42 42 14 0.111 3.38 13 0.125 3.81

2.910 90.6 41 G0092350 6.930 221.5 40 39 15 0.099 3.02 14 0.111 3.38

2.910 95.6 39 G0092351 6.930 222.5 40 39 15 0.099 3.02 14 0.111 3.38

2.910 95 39 G0092352 6.930 334 26 27 18 0.070 2.13 17 0.079 2.41

2.910 138.8 27 G0092353 6.930 344.3 26 26 20 0.056 1.71 2.910 145.4 25 G0092354 6.930 331.3 27 26 19 0.063 1.92

2.910 143.2 26 G0092355 6.930 337 26 26 19 0.063 1.92 18 0.07 2.13

2.910 146.6 25 (1) – looks more oblique


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SenseFly eBee with Canon Powershot ELPH 110HS w/4.3-21.5mm zoom (images at 4.3)

20 January 2015

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Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) IMG_00952 6.930 109 204 202 7 0.250 7.62 >7 >0.25 >7.62

2.910 46.9 199 IMG_00953(1) 6.930 123.2 181 175 8 0.223 6.80 7 0.250 7.62

2.910 55 170 IMG_00957 6.930 128.1 174 173 7 0.250 7.62 >7 >0.25 >7.62

2.910 54.2 172 IMG_00966 6.930 110.2 202 200 8 0.223 6.80

2.910 47.3 197 IMG_00970 6.930 107.9 206 198 7 0.250 7.62 >7 >0.25 >7.62

2.910 49.2 190 IMG_00973(2) 6.930 54.3 410 402 >7 >0.25 >7.62

2.910 23.7 394 IMG_00974 6.930 54.9 405 382 >7 >0.25 >7.62

2.910 26 359 IMG_00975 6.930 56.1 396 394 >7 >0.25 >7.62

2.910 23.8 392 IMG_00976 6.930 61.7 360 351 >7 >0.25 >7.62

2.910 27.3 342 IMG_00977 6.930 60.6 367 352 >7 >0.25 >7.62

2.910 27.7 337 (1) – near edge (2) – very blurry, edges of target not good, near edge


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SenseFly eBee with Canon Powershot S110 w/5.2-26.0mm zoom (images at 5.2) _RGB are JPEG images; same frame number without _RGB suffix are RAW (cr2).

RAW better resolution than jpeg by approx. 1 element. 20 January 2015

Ground

Distance Photo

Distance Calculated

Altitude Average Altitude

Best Element

Resolution



Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) IMG_0008_RGB 6.930 107.4 179 175 7 0.250 7.62 IMG_0008 2.910 47.4 171 8 0.223 6.80 IMG_0013_RGB(1) 6.930 85.5 225 230 >7 >0.25 >7.62 IMG_0013 2.910 34.5 235 >7 >0.25 >7.62 IMG_0029_RGB 6.930 102.8 187 185 7 0.250 7.62 IMG_0029 2.910 44.4 182 8 0.223 6.80 IMG_0034_RGB(2) 6.930 87.8 219 227 >7 >0.25 >7.62 IMG_0034 2.910 34.6 234 >7 >0.25 >7.62 IMG_0037_RGB(2) 6.930 51.5 374 384 >7 >0.25 >7.62 IMG_0037 2.910 20.53 394 >7 >0.25 >7.62 IMG_0038_RGB 6.930 56.5 341 330 >7 >0.25 >7.62 IMG_0038 2.910 25.3 320 >7 >0.25 >7.62 IMG_0039_RGB 6.930 57.3 336 311 >7 >0.25 >7.62 IMG_0039 2.910 28.3 286 >7 >0.25 >7.62 IMG_0043_RGB(1) 6.930 55.9 345 363 >7 >0.25 >7.62 IMG_0043 2.910 21.2 382 >7 >0.25 >7.62 IMG_0044_RGB 6.930 64.1 301 292 >7 >0.25 >7.62 IMG_0044 2.910 28.5 284 >7 >0.25 >7.62 IMG_0048_RGB(1) 6.930 44.1 437 389 >7 >0.25 >7.62 IMG_0048 2.910 23.7 341 >7 >0.25 >7.62 IMG_0049_RGB(3) 6.930 46.7 413 365 >7 >0.25 >7.62 IMG_0049 2.910 25.5 317 >7 >0.25 >7.62 IMG_0050_RGB(2) 6.930 46.4 415 390 >7 >0.25 >7.62 IMG_0050 2.910 22.2 365 >7 >0.25 >7.62 IMG_0054_RGB 6.930 67.3 286 285 >7 >0.25 >7.62 IMG_0054 2.910 28.6 283 >7 >0.25 >7.62

(1) – very oblique (2) – very oblique, near edge (3) – near edge


Page 17 of 18

Altavian Nova with Canon EOS Rebel SL1 w 20mm lens All frame names have prefix of IMG_2015-1-22-19-2-57-

22 January 2015

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Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) 42 6.930 161.1 200 201 12 0.140 4.27

2.910 67.2 201 43 6.930 169.1 191 185 13 0.125 3.81

2.910 75.5 179 92(1) 6.930 259.1 124 125 15 0.099 3.02

2.910 107.6 126 144(1) 6.930 157.5 205 201 13 0.125 3.81 11 0.158 4.82

2.910 68.7 197 145 6.930 167.6 192 192 13 0.125 3.81

2.910 70.3 193 146 6.930 169.1 191 192 13 0.125 3.81

2.910 70.1 193 198(2)

199 6.930 104.8 308 305 9 0.198 6.04 2.910 44.7 303

200 6.930 112.1 288 285 10 0.177 5.39 9 0.198 6.04 2.910 47.9 283

257 6.930 80.4 401 398 >7 >0.25 >7.62 2.910 34.2 396

258 6.930 80.3 401 399 >7 >0.25 >7.62 2.910 34.2 396

259 6.930 81.2 397 398 >7 >0.25 >7.62 2.910 33.9 399 260(1) 6.930 81 398 386 >7 >0.25 >7.62

2.910 36.1 375 330 6.930 62.7 514 515 >7 >0.25 >7.62

2.910 26.2 517 331 6.930 65.7 491 499 >7 >0.25 >7.62

2.910 26.7 507 332 6.930 64.8 497 492 >7 >0.25 >7.62

2.910 27.8 487 333 6.930 64.3 501 491 >7 >0.25 >7.62

2.910 28.1 482 334 6.930 63.1 511 495 >7 >0.25 >7.62

2.910 28.2 480 (1) – near edge (2) – on edge, target cut


Page 18 of 18

Altavian Nova with Canon EOS Rebel SL1 w 20mm lens All frame names have prefix of IMG_2015-1-22-20-18-30-

22 January 2015

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Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) 875(1) 6.930 70.8 455 448 >7 >0.25 >7.62

2.910 30.7 441 876 6.930 69.6 463 446 >7 >0.25 >7.62

2.910 31.6 428 877 6.930 71.5 451 430 >7 >0.25 >7.62

2.910 33.1 409 1077 6.930 71 454 445 >7 >0.25 >7.62

2.910 31 437 1078 6.930 73.1 441 432 >7 >0.25 >7.62

2.910 32 423 1079 6.930 72.3 446 431 >7 >0.25 >7.62

2.910 32.5 416 1080 6.930 71.8 449 430 >7 >0.25 >7.62

2.910 32.9 411 1081(2) 6.930 72.8 443 423 >7 >0.25 >7.62 2.910 33.6 403

1135 6.930 70.8 455 449 >7 >0.25 >7.62 2.910 30.6 442

1136 6.930 71 454 445 >7 >0.25 >7.62 2.910 31 437

1137 6.930 71.1 453 438 >7 >0.25 >7.62 2.910 32 423

(1) – very blurry (2) – near edge

national oceanic and atmospheric administration (noaa) small unmanned...

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