(IVAR) - Final Report - Strategic Environmental Research and ...

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Figure 6-77. A Google Earth display of a track history from SEA. This display was generated using data generated by the TVW application and exported in the kml format. Conclusion We have successfully demonstrated Performance criterion PB6.2 by illustrating three ways in which the avian radar systems evaluated by the IVAR project can display and/or export track data in a format suitable to input by a GIS. 6.2.2.7 Provides Bird Abundance over Periods of Time [PB7.2] Objective Our goal when we designed Performance Criterion PB7.2 was to demonstrate the ability of the digital avian radar systems the IVAR project is evaluating to accumulate number-of-targets (abundance) data over specified periods of time. This ability will be important for anyone who wants to analyze patterns of daily, seasonal, annual, and inter-annual bird activity. The most powerful method of generating abundance data is to extract it directly from the RDS, which includes a relational database that can be accessed using the Structured Query Language 186
(SQL). Extracting target data from the RDS involves structuring and performing queries to retrieve data based on specific criteria. A query can extract target counts over any time period, and filter them according to any combination of the following criteria: speed, heading, range, duration, latitude/longitude, intensity, height, azimuth, target ID, and start time. The data extracted from the RDS and used to demonstrate PB7.2 apply equally well to PB8.2, Provides seasonal comparison distributions. We therefore combined the demonstration of PB7.2 and PB8.2 into one analysis, reported in this section. Inasmuch as PB7.2 and PB8.2 are qualitative Performance Criteria, we proposed to demonstrate them by displaying bird abundance data collected from SEA over a full year and presenting the target counts over hourly and daily intervals, with the latter presentations spanning the four seasons of a year as well. Methods In order to demonstrate Performance Criteria PB7.2 and PB8.2, we chose a suitable radar and dataset from it. We selected the SEAAR2l radar because it experiences few outages during regular operation, and it has a high-speed (wired) connection to the Internet. We chose the interval December 2008 to November 2009 as the dataset to demonstrate bird abundance data because it had few discontinuities. We wrote and executed the SQL query described below: • The dataset was defined by identifying its “start” and “stop” timestamps as: 1 December 2008 00:00:00 GMT, and 30 November 2009 23:59:59 GMT, respectively. • The (counting) interval was defined (hourly/daily). • We structured the query to create a unique track ID for each track in a given interval. This unique ID was a combination of the track ID and the track start time. • The query saved the values of maximum velocity and duration for each track. This enabled post-query track filtering, ensuring that only tracks of interest were counted (i.e., with speeds above 0 m/s with at least 1 update). • We executed the query, which generated an output text file was containing the timestamps of each interval and the corresponding track counts. An example can be seen in Table 6-22. Table 6-22. Output from hourly track count query from the SEAAR2l radar. Interval Timestamp Start Time Interval Hourly Date (UNIX Time) (GMT Number Count (mm/dd/yyyy) Start Stop hh:mm:ss) 1 1228089600 1228093199 332 12/1/2008 00:00:00 2 1228093200 1228096799 329 12/1/2008 01:00:00 3 1228096800 1228100399 316 12/1/2008 02:00:00 We used the files generated from this query as the basis for our analyses and graphic representation of bird abundance counts using Microsoft Excel®. Results The SEAAR2l radar was originally intended to operate in “short pulse” mode, looking for small targets out to approximately 4.0 km (2.5 miles) in range. This was changed on October 27, 2009, 187
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FINAL REPORT Integration and Valida
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Table of Contents List of Tables ..
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APPENDIX A. POINTS OF CONTACT .....
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Table 6-17. Altitudinal distributio
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List of Figures Figure 2-1. Chronol
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Figure 6-17. Plot of the northing c
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Figure 6-42. eBirdRad display based
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Figure 6-82. Hourly track count ove
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Figure 6-115: COP display showing f
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List of Acronyms Acronym AGL AIP AR
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Acronym RST RT SEA SQL SSC Pacific
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Carmen Lombardo Naval Air Station,
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EXECUTIVE SUMMARY Military bases an
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database and redistributed to other
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1 INTRODUCTION 1.1 BACKGROUND Encro
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A seventh objective, the Functional
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Table 1-1 (cont.). IVAR TASK/ OBJEC
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environmental stewardship with miss
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2 TECHNOLOGY/METHODOLOGY DESCRIPTIO
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The original BirdRad system was des
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2.4 TECHNOLOGY/METHODOLOGY DEVELOPM
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Figure 2-2. Configuration of BirdRa
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2.4.3 eBirdRad - Digital Radars wit
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Figure 2-5. Interior view of the eB
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accuracy and target continuity over
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permanently installed at SEA. Prior
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2.5 ADVANTAGES AND LIMITATIONS OF T
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3 PERFORMANCE OBJECTIVES Table 3-1
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Table 3-1 (cont.). Performance Obje
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For the third test, Validation Usin
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temporal domain by recording all bi
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3.1.2.2.7 Provides Bird Abundance o
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difference in the time (i.e., the l
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3.1.6.2.4 Maintenance [SE4.2] We es
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Table 4-1. Summary of the IVAR stud
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4.1 MCAS CHERRY POINT Marine Corps
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2006 the primary eBirdRad unit from
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(directional WiFi) to the Internet.
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• Large (several thousand) flocks
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location for demonstrating the capa
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5.1 CONCEPTUAL TEST DESIGN 5 TEST D
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5.3 DESIGN AND LAYOUT OF TECHNOLOGY
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Assuming these lines evidence confi
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that dataset [Section 6.5.1.3]. 5.5
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6 PERFORMANCE ASSESSMENT The Perfor
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ecord the time, Track ID, and other
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Figure 6-1. Percentage of targets c
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Comparing the performance of dish v
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Figure 6-4. The basic sensor elemen
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Figure 6-6. The polygon around the
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Figure 6-8. Diagram of sampling “
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• It takes several scans (nominal
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Session Correla tions 600 500 y = 1
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To evaluate the capabilities of the
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Figure 6-15. Runway 3 at SEA, showi
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comparisons between the RCH-GPS dat
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Figure 6-18. Plot of the northing c
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Figure 6-20. Plot of the easting co
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Conclusion In the quantitative perf
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Table 6-8. Column headings of the T
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Table 6-9 demonstrates quantitative
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Table 6-10. The parameters of targe
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Table 6-11. The position of Visual
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Table 6-12. Solitary targets at MCA
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Figure 6-23. The track of target T2
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intended to demonstrate these syste
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Figure 6-34. Screen capture of bird
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6.1.2 Qualitative Performance Crite
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upper right have completely converg
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Figure 6-38. The progression of the
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The slight difference in alignment
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Figure 6-42. eBirdRad display based
Page 159 and 160: Figure 6-44. A partial history of t
Page 161 and 162: 6.2 SAMPLING PROTOCOL 6.2.1 Quantit
Page 163 and 164: Figure 6-46. Image from the track h
Page 165 and 166: Table 6-14. Dates, times and names
Page 167 and 168: Figure 6-48. Fifteen-minute (06:04-
Page 169 and 170: Figure 6-50. Fifteen-minute (02:24-
Page 171 and 172: We used a TVW to process the plots
Page 173 and 174: Figure 6-53. In an example of night
Page 175 and 176: 6.2.1.4 Efficiently Stores Bird Tra
Page 177 and 178: Figure 6-56 shows sites 13-24 in th
Page 179 and 180: We extracted the radar data for thi
Page 181 and 182: Figure 6-57. Digital image of the d
Page 183 and 184: Table 6-17. Altitudinal distributio
Page 185 and 186: hours apart, showed considerable va
Page 187 and 188: • The sampling event time period
Page 189 and 190: underwent a 4-minute initialization
Page 191 and 192: Figure 6-62. DRP Live application l
Page 193 and 194: commands we issued to the radar; th
Page 195 and 196: 4. Range Decrease (x7) 5. Range Inc
Page 197 and 198: Figure 6-65, Figure 6-66, and Figur
Page 199 and 200: 6.2.2.4 Provides Spatial Distributi
Page 201 and 202: Figure 6-69. A plot of all track ac
Page 203 and 204: Figure 6-71. Twelve 2-hour historie
Page 205 and 206: Figure 6-72. TVW application displa
Page 207 and 208: Conclusion Figure 6-74. Oblique vie
Page 209: Archived Data to a GIS We used the
Page 213 and 214: Figure 6-78. Daily, unfiltered, tra
Page 215 and 216: Figure 6-79. Time periods selected
Page 217 and 218: Figure 6-82. Hourly track count ove
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Page 221 and 222: As can be seen in Figure 6-83, the
Page 223 and 224: Figure 6-84 clearly demonstrates th
Page 225 and 226: Conclusion We have successfully dem
Page 227 and 228: LAN within a facility to the operat
Page 229 and 230: All reference times were recorded i
Page 231 and 232: Description of Remote Display - As
Page 233 and 234: mounted laptop computer (Figure 6-8
Page 235 and 236: very good because the system was sa
Page 237 and 238: operations personnel or their contr
Page 239 and 240: Figure 6-90. Alarm status pane and
Page 241 and 242: Figure 6-92. Alarm triggered as a f
Page 243 and 244: Figure 6-95. Screen capture of the
Page 245 and 246: Figure 6-96. An example from a data
Page 247 and 248: Table 6-29. Differences found betwe
Page 249 and 250: 6.4 DATA INTEGRATION 6.4.1 Quantita
Page 251 and 252: Figure 6-100. Screen captures for S
Page 253 and 254: We used a screen-capture program at
Page 255 and 256: Results Table 6-32 records the time
Page 257 and 258: We acquired the appropriate plots a
Page 259 and 260: Figure 6-105. Track histories of ta
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synchronized, both spatially and te
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0.004687 seconds ahead of the NTP t
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Figure 6-108: COP display showing f
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Figure 6-110: TVW display showing t
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The next example from NASWI involve
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Figure 6-114: TVW display showing t
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Figure 6-116: COP display showing a
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Fusion processing was enabled in th
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processing associated with this dat
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Figure 6-122: Track IDs shown for b
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Conclusion We successfully demonstr
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Of course, many other factors contr
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one, Jim Swift, had been exposed to
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Methods The US military 27 designat
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Natural R.esoan:es Br.mch 22541Jolm
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Given these considerations, the IVA
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Results No maintenance was required
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acquired and is testing JRC S-band
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areas of overlap. Data fusion algor
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6.8 FUNCTIONAL REQUIREMENTS AND PER
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Table 7-1. Cost Model for a Standal
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additional processors account of th
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Installation Site Assessment - Rada
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e incurred, depending on what infor
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eal time, avian radars and can dete
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Network Connectivity. The US Depart
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Airport Improvement Program (AIP) f
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9 REFERENCES Anderson, C., and S. O
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APPENDIX A. POINTS OF CONTACT POINT
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POINT OF CONTACT Name Ryan King Mat
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APPENDIX B. ANALYTICAL METHODS SUPP
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METHOD #2: VALIDATION BY IMAGE COMP
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The pattern of yellows and blues in
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Radar Team (RT) Site Selection Sinc
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will measured the magnetic bearing
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Figure B-6. Using a GPS to record s
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• Visual Observer - observes bird
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Figure B-9. Flowchart of target con
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VT: “Radar, Team 2-Alpha copies T
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eam and the VTs have difficulty loc
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why it was changed, who made the ch
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METHOD #4: CONFIRMATION OF BIRD TAR
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Figure B-12. Duplexed image showing
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Table B-2. Dates, times, and antenn
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For the analysis of the simultaneou
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Table B-3. Altitude of the radar be
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Figure B-17. Diagram of sampling
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METHOD #6: DEMONSTRATING REAL-TIME
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APPENDIX C. DATA QUALITY ASSURANCE/
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APPENDIX D. SUPPORTING DATA D.1 Par
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Table D-1 (cont.). Date Track ID Up
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D.2 Listing of One-Hour Track Histo
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Table D-2 (cont.). 07:00-08:00 WI_A
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APPENDIX E. SURVEY OF AIRPORT PERSO
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used as the real-time target becaus
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Table E-1. Remote radar display lat
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7. Please rate the following where
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