National Oceanic and Atmospheric Administration (NOAA) Small Unmanned Aircraft Systems (suas) Avon Park Demonstration Executive Summary

Transcription

1 (NOAA) (suas) Avon Park Demonstration Executive Summary

2 Contents 1.0 Background Objective Test Site Observations System Observations... 3 SenseFly ebee:... 3 AV Puma AE:... 4 Altavian Nova F6500:... 4 DJI Phantom Resolution and positional accuracy Processing Summary Data Recording - Sound Meter Summary Recommendations Appendix

3 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, 2. F6500- Altavian, 3. ebee- SenseFly, 4. Phantom 2- DJI, 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 Page 1 of 18

4 8. Operation and features of the Control Systems 3.0 TEST SITE Figure 1: Map of Avon Park Air Training Complex, located approximately 75 miles ESE of Tampa, FL. 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. Page 2 of 18

5 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 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 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 Control System (right) Page 3 of 18

6 AV Puma AE: NOAA acquired and has the most experience utilizing the AV Puma AE (Figure 3). As documented in GAO Report, GAO (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: 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 realtime 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 Page 4 of 18

7 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), 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. Page 5 of 18

8 ebee 16MP: Canon Powershot ELPH 4.3mm ebee 12MP: Canon Powershot 5.2mm Puma: Sony Nex-7 w/ 20mm lens F6500: Canon EOS Rebel SL1 w/ 20mm lens 3.02 cm 125 ft cm 185 ft 4.27 cm 201 ft cm 198 ft cm 285 ft cm 305 ft cm ft. 175 ft. 348 ft cm ft. >7.62 cm > 351 ft. > 230 ft. > 435 ft. > 398 ft. *Approximate (ft.) at which various s (cm) were observed. Table 2: Assessed quality of various payload imagery from the Avon Park tests s 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: Figure 6: Images: establishing GPS control (left) and resolution target (right) Page 6 of 18

9 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 Sampling (GSD) 5.43 cm / 2.14 in Area Covered km2 / ha / sq. mi. / 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 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 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. Page 7 of 18

10 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. Page 8 of 18

11 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 /2011 DSG. The Occupational Safety and Health Administration s (OSHA s) Noise standard (29 CFR ) 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 Foot Decibels 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: Page 9 of 18

12 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. Page 10 of 18

13 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 Page 11 of 18

14 costs and opportunities for intergovernmental asset sharing must be better exploited while capitalizing upon commonality, standardization, and acquisition strategies. Page 12 of 18

15 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/ mm zoom (images at 4.3) (24-120mm 35mm equivalent) Camera on 1/20/2015 (Ebee 12mp) was Canon Powershot S110 w/ mm 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 = ft Go-Pro Hero3+ Silver pixel size = 2.2 microns = ft Canon Powershot ELPH 110HS pixel size = 1.34 microns = Canon Powershot S110 pixel size = 1.87 microns = ft Canon EOS Rebel SL1 pixel size = 4.3 microns = ft 20mm focal length = ft 2.8mm focal length = ft 4.3mm focal length = ft 5.2mm focal length = 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) Page 13 of 18

16 Photo Puma with Sony Nex-7 21 January 2015 Calculated Average Best Element Resolution Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (1) >7 >0.25 > >7 >0.25 > (1) badly smeared, edges jagged Phantom II Quad Copter with Hero 3+ Silver Go-Pro camera 20 January 2015 Photo Calculated Average Best Element Resolution Worst Element Resolution Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) G G (1) G G G G G G (1) looks more oblique Page 14 of 18

17 SenseFly ebee with Canon Powershot ELPH 110HS w/ mm zoom (images at 4.3) 20 January 2015 Photo Calculated Average Best Element Resolution Worst Element Resolution Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) IMG_ >7 >0.25 > IMG_00953 (1) IMG_ >7 >0.25 > IMG_ IMG_ >7 >0.25 > IMG_00973 (2) >7 >0.25 > IMG_ >7 >0.25 > IMG_ >7 >0.25 > IMG_ >7 >0.25 > IMG_ >7 >0.25 > (1) near edge (2) very blurry, edges of target not good, near edge Page 15 of 18

18 SenseFly ebee with Canon Powershot S110 w/ mm 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 Photo Calculated Average Best Element Resolution Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) IMG_0008_RGB IMG_ IMG_0013_RGB (1) >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0029_RGB IMG_ IMG_0034_RGB (2) >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0037_RGB (2) >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0038_RGB >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0039_RGB >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0043_RGB (1) >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0044_RGB >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0048_RGB (1) >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0049_RGB (3) >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0050_RGB (2) >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 IMG_0054_RGB >7 >0.25 >7.62 IMG_ >7 >0.25 >7.62 (1) very oblique (2) very oblique, near edge (3) near edge Page 16 of 18

19 Altavian Nova with Canon EOS Rebel SL1 w 20mm lens All frame names have prefix of IMG_ January 2015 Photo Calculated Average Best Element Resolution Worst Element Resolution Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) (1) (1) (2) >7 >0.25 > >7 >0.25 > >7 >0.25 > (1) >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > (1) near edge (2) on edge, target cut Page 17 of 18

20 Altavian Nova with Canon EOS Rebel SL1 w 20mm lens All frame names have prefix of IMG_ January 2015 Photo Calculated Average Best Element Resolution Worst Element Resolution Frame (ft.) (pxl) (ft.) (ft.) (ft.) (cm) (ft.) (cm) 875 (1) >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > (2) >7 >0.25 > >7 >0.25 > >7 >0.25 > >7 >0.25 > (1) very blurry (2) near edge Page 18 of 18