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FUGRO PELAGOS, INC.

ARCTIC CHARTING AND MAPPING PILOT PROJECT

AIRBORNE LIDAR BATHYMETRIC SURVEYS

ALEXANDRA STRAIT, NUNAVUT

REPORT OF SURVEY

Submitted to:

Canadian Hydrographic Service

Department of Fisheries and Oceans

867 Lakeshore Boulevard

P.O. Box 5050

Burlington, Ontario

L7R 4A6

Canada

Prepared by:

Fugro Pelagos, Inc.

3574 Ruffin Road

San Diego, CA 92123

USA

Telephone: +1 858 292-8922

Facsimile: +1 858 292-5308

Project Number: 23.00002009

0 Issued JM/JC RB MM 14 March 2012

Rev Description Prepared Checked Approved Date

Arctic Charting and Mapping – Alexandra Strait 23.00002009

Report No. FP2009-RPT-01-00.docx Page i

CONTENTS

Page

1. INTRODUCTION AND SCOPE OF WORK 1-4 1.1 GENERAL 1-4 1.2 SURVEY SPECIFICATIONS 1-5 1.3 PROJECT DATUM 1-5

2. MOBILIZATION AND DATA ACQUISITION 2-7 2.1 AIRBORNE SURVEY 2-7

2.1.1 AIRCRAFT MOBILIZATION 2-8 2.1.2 POSITIONING 2-9 2.1.3 SENSOR ORIENTATION 2-9 2.1.4 LIDAR SYSTEM 2-10

2.2 GROUND CONTROL 2-10 2.3 CHALLENGES ENCOUNTERED 2-12

2.3.1 ENVIRONMENTAL 2-13 2.3.2 TECHNICAL 2-13

2.4 SUMMARY OF SURVEY ACTIVITIES 2-14

3. DATA PROCESSING 3-17 3.1 KGPS PROCESSING 3-18 3.2 SHOALS GCS PROCESSING 3-18

3.2.1 AUTO PROCESSING 3-18 3.2.2 DATA VISUALIZATION & EDITING 3-19

3.3 TIDAL DATUM DEPTH REDUCTION 3-22 3.4 TOTAL PROPAGATED UNCERTAINTY 3-22 3.5 REFLECTANCE 3-24 3.6 ORTHO-MOSAIC IMAGERY 3-24

4. QUALITY CONTROL 4-25 4.1 GROUND TRUTH CHECK (RUNWAY) 4-25 4.2 KGPS QUALITY 4-25 4.3 DYNAMIC NAVIGATION CHECKS 4-28 4.4 CROSSLINE ANALYSIS 4-29

5. DATA DELIVERABLES 5-30

6. APPENDICES DESCRIPTION 6-31


Report No. FP2009-RPT-01-00.docx Page ii

TABLES

Page

Table 1-1 Project Geodetic and Projection Parameters 1-6 Table 2-1 Aircraft Technical Specifications 2-7 Table 2-2 Control Points for ground control (NAD83 (CSRS) 2002) 2-11 Table 2-3 Environmental Operational Limits 2-12 Table 3-1 Total Propagated Uncertainty values for LiDAR data 3-23 Table 4-1 LIDAR data overall vertical difference over runway surface 4-25 Table 4-2 Dynamic Navigation Checks Summary 4-28 Table 4-3 Crossline Results 4-29

FIGURES

Page

Figure 1-1 LiDAR Survey Area 1-4 Figure 2-1 Beechcraft King Air A90 2-7 Figure 2-2 Aircraft Offset Calculations, 08 May 2011 2-8 Figure 2-3 Control GPS stations showing 70 Km (YHK1) and 50 km (MIKI) baseline radii. 2-12 Figure 3-1 LiDAR data processing flowchart 3-17 Figure 3-2 Fledermaus PFM View 3-19 Figure 3-3 Fledermaus 3D Editor LiDAR point cloud 3-19 Figure 3-4 Waveform Viewer 3-20 Figure 3-5 Downlook digital Imagery 3-21 Figure 4-1 KGPS processing, PDOP quality plot 4-26 Figure 4-2 KGPS processing, RMS quality plot 4-26 Figure 4-3 KGPS processing, Processing Mode quality plot 4-27 Figure 4-4 KGPS processing, RMS plots of problematic solutions: a) spikes, b) over tolerance. 4-28


Report No. FP2009-RPT-01-00.docx Page iii

ABBREVIATIONS

ABS SHOALS-1000T Airborne System

CHS Canadian Hydrographic Service

CMP Common Measuring Point

CSRS-PPP Canadian Spatial Reference System – Precise Point Positioning

DAVIS Download, Auto Processing, and Visualization Software

DGPS Differential Global Positioning System

FPI Fugro Pelagos, Inc.

GCS SHOALS Ground Control System

GRS80 Geodetic Reference System of 1980

HHWLT Higher High Water Large Tide

HOF Optech’s bathymetric LiDAR data format

Hz Hertz

IHO International Hydrographic Organization

IMU Inertial Measurement Unit

INH Optech’s bathymetric LiDAR waveform data format

IR Infrared

KGPS Kinematic Global Positioning System

LLWLT Lower Low Water Large Tide

LPTT Laser Power Timing Test

PDOP Position Dilution of Precision

PFM IVS Fledermaus 3D Surface grid processing reference file format

POS AV Position Orientation System, Airborne Vehicle (Applanix)

PPK Post-processed kinematic GPS

RMS Root Mean Square

SBET Smoothed Best Estimated Trajectory

SHOALS Scanning Hydrographic Operational Airborne LiDAR Survey

SWA Shallow Water Algorithm

UTC Universal Time Coordinated

UTM Universal Transverse Mercator

WMO World Meteorological Organization


Report No. FP2009-RPT-01-00.docx Page 1-4

1. INTRODUCTION AND SCOPE OF WORK

1.1 GENERAL

Fugro Pelagos Inc. (FPI) was contracted on 09 August 2011 by the Canadian Hydrographic Service (CHS) to conduct an airborne bathymetric LiDAR survey in specific areas of the Canadian Arctic at various times and locations and deliver fully processed and verified hydrographic survey data. The goal of the project was to investigate the feasibility of the implementation of bathymetric LiDAR into the hydrographic survey program in Canada. This report of survey describes the survey activity and processing efforts for data collected over Alexandra Strait, Nunavut province. The airborne LiDAR survey was conducted with the SHOALS-1000T system for data collection to the extents provided by the CHS. The final project area is depicted in Figure 1-1.

Figure 1-1 LiDAR Survey Area



The survey acquisition operations collected data from the following sources: • Bathymetric LiDAR data from the SHOALS-1000T system. • Digital Aerial Photography from the SHOALS-1000T. • Dual frequency GPS data at several ground control stations SHOALS-1000T acquisition operations took place from 22 August 2011 to 26 August 2011, inclusive. This included vertical and horizontal verification flights over ground truth locations in Gjoa Haven. GPS base stations (manned by FPI) were installed at the Gjoa Haven airport and M’Clintock Island in the vicinity of the survey area (manned by CHS). Base station data were typically collected every day from each station when a flight mission was planned. This covered a period from 21 August 2011 to 01 September 2011, inclusive. Back-up secondary stations at each location where set up for redundancy purposes.

1.2 SURVEY SPECIFICATIONS

The airborne bathymetric LiDAR survey was planned to achieve IHO SP-44 Order 1b category of survey coverage and accuracy. This was accomplished by combining a 5 m x 5 m spot spacing (flying at 400 m altitude and speed-over-ground of approximately 160 knots) with a 100% coverage plan. Planned line spacing provided 30 m of sidelap. The survey was flown with sufficient options, made available to the airborne operator, to devise a best ‘plan of the day’ for climatic and water quality considerations, such that successful data collection was possible in both shallow and deep regions of the area. Operator assessments included reconnaissance of areas for water turbidity issues and wind direction and strength affecting survey parameters.

1.3 PROJECT DATUM

The survey real-time positioning datum was defined by the Omnistar DGPS service operating in the NAD83 reference system. During the KGPS post processing, the horizontal and vertical control were referenced to NAD83 (CSRS) 2002. Therefore, initially, all bathymetric LiDAR data and derived deliverable products were vertically referenced to NAD83 (CSRS) 2002 datum in meters. However, bathymetric depths were, later, reduced to tidal datum (LLWLT) with observed tides provided by CHS from tide station 6213 at Racon Island. Table 1-1 (next page) presents the geodetic details of project datum and projection parameters.



Table 1-1 Project Geodetic and Projection Parameters

Positioning System Geodetic Parameters

Datum: NAD83

Spheroid: GRS80

Semi major axis: a = 6 378 137.000 m

Inverse Flattening: 1/f = 298.25722210100002

Project Datum Geodetic Parameters

Datum: NAD83 (CSRS) 2002

Spheroid: GRS80

Semi major axis: a = 6 378 137.000 m

Inverse Flattening: 1/f = 298.25722210100002

Local Projection Parameters

Map Projection: Universal Transverse Mercator

Grid System: UTM Zone 14N

Central Meridian: 99° W

Latitude of Origin: 0° 00’ 00”

False Easting: 500 000 m

False Northing: 0 m

Scale factor on C.M.: 0.9996

Units: meters

Notes: Omnistar positioning services uses the NAD83 datum for geodetic positioning in North America



2. MOBILIZATION AND DATA ACQUISITION

On 13 August 2011, the SHOALS-1000T LiDAR sensor was installed in the survey aircraft and the offset verification survey was performed in San Diego, CA before departing for Canada. The aircraft commenced transit toward survey area on 17 August. By 19 August, the field office was set up and GPS ground control station locations were identified. The aircraft arrived on-site and ready for data collection on 20 August. The first mission took place on 21 August to begin the production effort, thus ending the mobilization phase. Airborne data acquisition was intermittent throughout the project. The main factors, affecting collection continuity, included delays imposed by environmental factors such as fog, low cloud and weather systems moving in the area. Airborne logs, describing the mission flight activities, are included in Appendix A

2.1 AIRBORNE SURVEY

A Beechcraft King Air A90, tail number N89F, equipped with the SHOALS-1000T system was used for the project (Figure 2-1). Technical specifications for the aircraft are located in Table 2-1. Detailed equipment specifications for the SHOALS-1000T are available in Appendix B.

Figure 2-1 Beechcraft King Air A90

Table 2-1 Aircraft Technical Specifications

AIRCRAFT BEECHCRAFT KING AIR A90

Registration Number N89F

Owner Dynamic Aviation

Wing Span 14.6 m

Length 10.8 m

Gross Weight 4,377 kg

Typical Empty Weight 2,336 kg

Survey Mode Duration ~4-5 hours

Engines PT6A-20 (Turboprop)



2.1.1 AIRCRAFT MOBILIZATION

The airborne components of the SHOALS-1000T consist of two separate modules. The laser and camera sources are contained in a single housing bolted to a flange above the aircraft camera door. An equipment rack, containing the system cooler and power supplies is usually installed aft of the laser. All hardware was located on the starboard side of the N89F aircraft. The system is controlled thru a laptop by the Airborne Operator and a separate Pilot Console provides navigation and track guidance information to the flight crew. The SHOALS-1000T system is regularly verified for valid calibration parameters to ensure vertical and horizontal accuracies are maintained throughout the operational service of the system. A report for the Calibration Verification issued for this survey can be found in Appendix C, which describes in detail the procedures taken and results achieved. 2.1.1.1 OFFSET MEASUREMENTS

The only offset measurement required during system mobilization was from the POS/AV Inertial Measurement Unit (IMU) to the POS AV GPS antenna. The IMU is completely enclosed within the laser housing. The offsets from the IMU to the common measuring point (CMP) on the outside of the housing are known constants. Offsets were measured using a total station and a baseline along the port side of the aircraft. Ranges and bearings were measured from the total station to the CMP on the top of the laser housing. Additional measurements were made to the sides and top of the housing to determine its orientation. A final measurement was made to the center of the POS/AV GPS antenna. The IMU to POS/AV GPS offsets were calculated using the known IMU to CMP offsets. A summary of the offset measurements made on 8 May 2011 during an earlier system installation on aircraft N89F are found in Figure 2-2 (and Appendix C). The offsets from the IMU to the POS AV GPS antenna are entered into the POS/AV console prior to survey.

Figure 2-2 Aircraft Offset Calculations, 08 May 2011



2.1.1.2 LIDAR CALIBRATION

A LiDAR in-flight calibration was performed in post-processing using data acquired at the onset of this project. A “raster pattern” calibration is used in the determination of the small offsets of the scanner mirror frame relative to the optical axes of the system. The raster pattern calibration required flying reciprocal straight lines over a relatively calm water surface for at least 5 minutes, into and against the waves. To calculate the angular offsets, an average of the water surface is derived by the system. The resulting Scanner Azimuth versus Wave Height plots are used to confirm a flat water surface across the LiDAR swath. The SHOALS system calibration was fully geometrically calibrated on 8 August 2009

1 and

calibration verification prior to the start of this project were performed on 18 Aug 2011 and 21 Aug 20112.

Reports of these calibrations and verifications are found in Appendix C.

2.1.2 POSITIONING

Aircraft positioning was determined in real time using a Omnistar DGPS system. However, final LiDAR point positions were determined using a post-processed Kinematic GPS solution (see Section 3.1). The primary position GPS antenna was a Trimble GNSS & L Band (AeroAntenna), which was connected to the POS AV computer. The same antenna provided GPS to both the POS AV and the Omnistar topside computers. The differential GPS corrections were acquired from the Omnistar service using an Omnistar 3100LM receiver.

2.1.3 SENSOR ORIENTATION

The SHOALS-1000T utilizes an Applanix POS AV 510 to measure position and sensor orientation (roll, pitch, and heading). The system consists of a ruggedized POS computer with a Trimble VBS 690 GPS card, an Inertial Measurement Unit (IMU), and one Trimble GNSS & L Band (AeroAntenna) GPS antenna mounted externally on the aircraft. The IMU is permanently mounted within the sensor. It uses a series of linear accelerometers and angular rate sensors (gyroscopes) that work in tandem to determine orientation.

1 SHOALS-1000T System Calibration Report. Full Geometric. August 8

th, 2009

2 SHOALS-1000T System Calibration Verification, August 23

rd, 2011



The orientation information is used in post-processing to refine the aircraft position and better determine position of the laser spots. However, analog data from the POS AV is also used during acquisition to maintain a consistent laser scan pattern as the aircraft pitches and rolls in flight.

2.1.4 LIDAR SYSTEM

The SHOALS-1000T acquired bathymetric and topographic at a rate of 1 kHz. Background theory on bathymetric LiDAR can be found in Guenther, et al.

3 (Appendix B). In general, the laser output is infrared

(1064nm) with a frequency doubled green wavelength (532nm) in a single beam. The infrared wavelength is used to detect the water surface and does not penetrate the air/water interface. The green wavelength penetrates through the water and detects the seafloor. The green wavelength also generates red energy (645nm) in the water column. This by-product is known as Raman scattering and is used to detect the sea surface. Distances from the surface and seafloor are calculated using the speed of light, index of refraction in water, and the times of the laser pulse returns recorded by the receivers. Data received by the airborne system were continually monitored for data quality during acquisition operations. Display windows show coverage and information about the system status. In addition, center waveforms at 5 Hz were shown. All of this information allowed the airborne operator to assess the quality of data being collected. In addition to LiDAR data, a DuncanTech DT4000 digital camera was also used to acquire one 24-bit, 4 megapixel color photo per second. The camera, mounted in a bracket at the rear of the sensor, captures imagery of the area being over flown, and can be used during post-processing.

2.2 GROUND CONTROL

Dual-frequency GPS data were collected at 1-second sampling rate on each ground control point to post-process a kinematic GPS (KGPS) solution for the aircraft. Novatel DL-V3 or Novatel DL-5 GPS systems were used at the control point in Gjoa Haven airport. Thales Z-Max and Trimble receivers were used for CHS control points in M’Clintock Island. Detailed specifications for all ground control observations may be found in Appendix D, along with all the station descriptions.

3 Guenther, G.C., A.G. Cunningham, P.E. LaRocque and D.J. Reid. 2000. Meeting the Accuracy Challenge in Airborne LiDAR Bathymetry. Proceedings of EARSeL-SIG-Workshop LiDAR, Dresden, Germany, June 16-17, 2000. 27pp.



The GPS base station data was processed to derived accurate geodetic position using Natural Resources Canada (NRC) online GPS processing CSRS-PPP

4. On each control point two or more static sessions (>

6 hrs) were processed and resulting coordinates averaged to produce the final coordinates (Ground Control Summaries, Appendix D). Final control point coordinates used in kinematic GPS processing are presented in Table 2-2.

Table 2-2 Control Points for ground control (NAD83 (CSRS) 2002)

Point ID Latitude (N) Longitude (W) Ellipsoid Height (m)

YHK1 68° 37’ 59.42270” 95° 51’ 01.92014” 10.318

MIKI 69° 04’ 02.38580” 100° 02’ 29.09400” 9.743

CLIN 69° 04’ 2.80390” 100° 02’ 28.81210” 9.854

A baseline length of 70 km was used for GPS stations within 10Km of the airport of operations, whereas a baseline of 50 km was used for base stations more remotely located. The difference in baseline length was due to the method used for system initialization. Static hold initialization (on-the-ground, no movement – pre/post flight) was performed at the airport, while in-air alignments (system initialization by flying over the station) were performed at the remote GPS stations. Although YHK1 base station is situated beyond 70 km baseline from the survey area, as seen in Figure 1-1Figure 2-3, it was used for the airborne system KGPS initialization on the ground. Once on the air, local stations MIKI and CLIN were over flown to re-initialize the system’s positioning. In fact, station MIKI was the primary ground control station for the LiDAR acquisition.

4 NRCan Geodetic Survey Division http://www.geod.nrcan.gc.ca/online_data_e.php



Figure 2-3 Control GPS stations showing 70 Km (YHK1) and 50 km (MIKI) baseline radii.

2.3 CHALLENGES ENCOUNTERED

Challenges encountered on this survey were of both environmental and technical nature. The system performed according to specifications and within the accuracies verified before the survey was performed. Table 2-3 describes the standard environmental operational limits for a SHOALS-1000T LiDAR survey.

Table 2-3 Environmental Operational Limits

Restriction Limitation

Cloud Ceiling >400m

Precipitation Data are not collected during periods of heavy rainfall.

Wind Speed

Head Wind < 40 kts / 74 km/h

Tail Wind < 20 kts / 37 km/h

Cross Wind < 40 kts / 74 km/h

WMO Sea State 1 – 4

Aircraft Cabin Temperature < 40

oC (system will shut off automatically if this limit is

breached and data collection will stop)

Topography < 200m



2.3.1 ENVIRONMENTAL

Water turbidity caused by wind, sea swell, and tidal flow are common factors affecting the bottom detection capability of the system and were accounted for in the initial assessment of survey. The real challenge was to utilize optimal weather periods to maximize mission flights achieving good bottom detection. After a weather system or extreme tides cycles have affected the survey area, water conditions remain with high turbidity that require some time to settle. Weather conditions also tested airborne logistics, with respect to aircraft operations safety. The aircraft pilot had to practice his best abilities and experience to assess the conditions at take-off and forecast conditions for eventual return from the survey area. Safety was the first priority and at times conflicted with survey operations. Fog and low clouds were the most common cause for flight delay and postponement. The continued delays depleted the allocated stand-by time allowance for the project. By 2 Sep 2011 the decision to end survey operations was given to the survey crew, though the original flight plan was partially incomplete.

2.3.2 TECHNICAL

On 26 August, the POS/AV 510 top console had to be replaced due to conflicting electrical interference with aircraft’s avionics. An earlier POS/AV 410 model replaced the 510 model as it is less sensitive to interference with radio signals emitted by aircraft communications system. The interference caused cycle slips in the GPS signals which in turn challenged the accuracy of KGPS during post-processing. The POS/AV model swap helped to mitigate KGPS positioning issues.



2.4 SUMMARY OF SURVEY ACTIVITIES

Date Daily Summary

19 Aug 2011 FPI Personnel arrived in Gjoa Haven 18 August. Office set up, temporary rental vehicle acquired. Met with airport personnel, obtained permission and set the primary and secondary control points. Tentative times scheduled for acquisition of ground truth data of runway surface. Obtained permission and use of ladder for collection of four corner points on the Dynamic Navigation Check building (Northern Grocery). Completed building survey. Aircraft and final FPI personnel to arrive 20 August.

20 Aug 2011 Processed building coordinates for Dynamic Navigation Check building. Created shapefile of building and began GT mission planning, using an estimated shape location for runway area. K. Kline arrived in afternoon. Aircraft arrived at 17:45 local time. Airport supervisor was concerned about permission to perform runway survey, prefers we speak with Cambridge Bay control center for access. Secchi disk reports from surrounding areas show 10.3 m depth.

21 Aug 2011 Began collection on Gjoa Haven airport secondary GPS stations to establish coordinates.

No problems with aircraft or ABS on morning test flight, however, determined that an old system parameters file was used on the plan, created new mission plan with the most up to date parameters file and re-flew test lines. Attempted to reach the CHS representative and meteorologist on the CCGS Sir Wilfrid Laurier to check survey area weather and to confirm setup of primary GPS stations. Previous communication stated stations would be set today. Waiting for confirmation.

22 Aug 2011 Morning weather update from ship showed 1300' scattered, CHS representative was

attempting to service the GPS units. He was unable to reach the primary stations due to polar bear activity. Third station was set to the south as another backup. Afternoon weather report had the cloud ceiling down to 900', but report from the Coast Guard helicopter confirmed the ceiling was 1600'. One flight completed in the survey area. Deepest reported depths around 22 m, solid coverage in the 12 m to 15 m range. Received approval for runway survey.

23 Aug 2011 Positive weather report from ship given in the morning, but conditions at Gjoa Haven

were too windy and overcast. Trough of weather at Gjoa moved towards survey area, cancelled morning mission. Performed runway surface survey. Afternoon flight collected 9 lines, flight shortened due to the addition of the second station requiring overflight before beginning collection – a loss of 30 to 40 minutes of production. Inquiry sent to CHS representative regarding the need for both stations. Difficulty processing GPS due to interference from SkyConnect tracking system, working with San Diego to improve the solution. Switching to the electric heaters in aircraft while on the ground to prevent wear on our air conditioner.

24 Aug 2011 No flights. Stand-by day for weather. Unable to obtain site weather as the CCG ship had

to depart the area for SAR operations. Morning METAR at Gjoa was 1000’ broken with poor conditions to the West of the survey area in Cambridge Bay, 200’ with light rain. Afternoon, Gjoa ceiling dropped to 800’ with light rain for the rest of the day. Confirmed with CHS representative that the original GPS stations have been taken down and the new station on M’Clintock is now the primary. Downloaded GPS data from the FTP site provided by CHS and ran data through CSRS-PPP to establish a coordinate. Completed GPS processing of GT data with good results. Interference causing problems with the first flight GPS processing. Due to concerns with the interference, FPI personnel will hand carry the POS AV 410 to Gjoa to swap with the POS 510.



25 Aug 2011 No flights. Stand-by day for weather, no further weather days remain in budget. Ship

returned previous evening from SAR. Morning weather report was 500’ broken ceiling in both YHK and survey area, with light rain in Gjoa. Improving late morning only to 800’ at YHK – below VFR conditions for departure. Confirmed with local ATC that we can file a composite IFR-VFR-IFR flight plan to depart YHK. By afternoon, conditions had improved above acceptable VFR limits at the survey area to 2000’ with few clouds at 1000’ nearby. However, conditions at YHK were now 600’ with light rain, and a forecast down to 400’ with similar conditions at the alternate airport. No go decision was made at 2300Z for concern of not being able to return. When final forecast was released at 0000Z, the ceiling was predicted to be at 1000’. M. Minton arrived from SD with POS 410 unit to use in place of the POS 510 to eliminate the SkyConnect interference.

26 Aug 2011 Two flights, good weather ceiling throughout day. Slight improvement in water quality, but

overall turbid areas remain consistent. Morning flight slightly delayed to review POS AV 410 installation. Completed mid-project Dynamic Nav Check and Topo Groundtruth on runway at end of second mission. Difficulty downloading CHS data from FTP site for the 23 August mission – large files, limited hotel internet bandwidth. San Diego personnel downloaded the data from the two new stations (MIKI & TOCK) and split them to more manageable portions for us to download in the field. M. Minton departed.

27 Aug 2011 One flight, aborted for weather. Replaced sensor POS AV unit in AM. Silicone sealant for

the POS antenna purchased 26 August is corrosive to aluminum and cannot be used on the aircraft. Gjoa stores did not open until 1100 and 1300 local, and no alternatives found after checking around town. Morning flight cancelled, although plane is still airworthy. PM flight aborted after an hour on site because the aircraft could not align over GPS station due to 400’ ceiling. Lines of low cloud and precipitation seen in the middle of survey area, despite being clear over the ship to the North. Upon return to YHK area, flew GT and Dynamic Nav building due to POS swap. Attempting to DL 26 August data uploaded by CHS - operation times out. Will request assistance from SD. Informed by CHS that the ship will be departing area for about a week, leaving the GPS units with 3-4 days of battery and plenty of storage space to collect data.

28 Aug 2011 Two flights, both aborted once onsite due to low clouds (400-200’) at the GPS station

and in the Southeast section of survey area. Waited for conditions to improve above IFR mins in AM, and levels reported better than 1600’ at the ship position, continued improving at both in the PM. POS AV 410 is performing within spec, but requiring occasional reboots before take-off. DA main office is requiring crew rest day 29 August, per regulations. CCG Wilfrid Laurier

will depart the area 29 August. Seeing good results with the KGPS application, identified a few lines that will require refly. Unable to pull CHS GPS data from 26 August off their FTP site. SD personnel downloaded and sent to field via SFT.

29 Aug 2011 No flights, aircrew rest day per Dynamic Aviation safety regulations. CCG Wilfrid Laurier

has departed the area - currently in Simpson Strait, 60 km SE of the survey area, south of King William Island. Good results of KGPS processing confirmed in SD. Attempting to determine if refly is needed for some lines or if POSPac PPP can resolve the RMS spikes. Delivered coverage review to CHS – detailed complete coverage areas, contours at 10 m and 15 m, and provided an ArcGIS shape encompassing areas where we have solid coverage with LiDAR data. Requested response regarding areas of interest.

30 Aug 2011 Morning flight cancelled due to questionable weather and possible icing conditions.

Weather improved into the afternoon, attempted flight. Mission was aborted once arriving in the survey area as the cloud level was down to 300’. GPS stations have roughly a day



left of battery power. CHS has stated they cannot cover any additional stand-by days, however, we will attempt an additional day to complete the final lines remaining. Initial results of the PPP processing were poor. Four original lines with standard KGPS identified for refly, few others are borderline, but still within spec.

31 Aug 2011 Confirmed with CHS that the GPS units will still have battery power until 2200 local. Two

flight attempts, both aborted for low cloud in area. CHS helicopter sortie near area also found cloud height at 800’ and worsening. PPP processing were poor for complete datasets, but found they fixed some lines that were slated for refly. Due to concern with occasional DGPS dropouts and possible reboots, we have swapped back to the POS 510. If GPS stations are unavailable, CHS is comfortable with us using DGPS for collection and later applying their tide model, however, priority is to replenish GPS batteries as soon as possible.

1 Sep 2011 CHS was able to replenish the battery supply at the GPS stations. Two flight attempts,

both flights aborted for weather. Excellent conditions in Gjoa Haven and ship, but cloud level at 1200’ or lower in survey area. Weather expected to deteriorate significantly as the local high pressure system moves on. Reviewing exit strategy.

2 Sep 2011 Heavy morning fog restricted any flight attempts. Decision made to depart Gjoa Haven.

N89F departed YHK at 1430. FPI crew packed office and departed at 1715. This is the final DPR for P2009.

Complete daily project reports can be found in Appendix E.



3. DATA PROCESSING

During the field acquisition period, all data were initially checked for coverage and quality at the temporary field office. These initial steps ensured that no time was spent on trying to process data which did not meet Fugro’s standards, and also to guarantee that any such data was identified at an early stage so that preparations for reflight could take place in a timely manner. At the conclusion of field operations, the survey data package was transferred to FPI Datacenter in San Diego, where final processing and product assembly took place. Data processing flow is summarized in Figure 3-1.

Figure 3-1 LiDAR data processing flowchart



3.1 KGPS PROCESSING

For each flight, a KGPS navigation solution was processed in Applanix POSPac v5.3 software package. GPS data from the airplane and ground control base stations were input in a POSPac project and post-processed until an optimal KGPS solution was found. In general, a best possible KGPS solution should present a small separation difference between forward and reverse solutions when combined, ideally <0.10 m and remain fixed throughout the flight period. The KGPS solution is combined and smoothed with the orientation data to create a smoothed trajectory solution (SBET), which is then used by the GCS during LiDAR auto processing. Additional post-processing techniques in POSPac were employed due to the problems encountered with electrical interference on the aircraft (see section 2.3.2). Precise Point Positioning processing (PPP) was also used, in attempts to improve the vertical accuracy of LiDAR depths. PPP processing results were mixed, but provided an option to select the SBET solutions that minimized vertical misties in the data.

3.2 SHOALS GCS PROCESSING

All SHOALS-1000T data was processed using the Optech SHOALS Ground Control System v6.32 (GCS) on Windows 7 workstations. GCS includes links IVS Fledermaus software for data visualization and 3D editing. GCS program’s DAViS (Download, Auto-processing and Visualization Software) module was used to download raw SHOALS sensor data, apply the inertially-aided KGPS solution, auto-process waveforms with specialized algorithms for surface/bottom detection and depth determination, perform waveform analysis for reflectance generation, and make an initial assessment of data quality.

3.2.1 AUTO PROCESSING

Once calibration values were set, environmental parameters selected, KGPS zones defined and KGPS data processed, the LiDAR data are processed using the GCS. The Auto Processing routine contains a waveform analysis algorithm that detects and selects surface and bottom returns from the raw data. Land surfaces are also detected from the bathymetric laser. Raw LiDAR depths are initially referenced by DGPS to the sea surface, when processing with KGPS, all data points were accorded an elevation relative to the ellipsoid. The Auto Processing algorithms obtained inputs from the raw data and calculated a height, position and confidence for each laser pulse. This process, using the default environmental parameters, also performed an automated first cleaning of the data, rejecting poor land and seafloor detections. Questionable soundings were flagged as suspect, with associated warning information. In addition to the hardware values, some default environmental parameters were also set relative to assumed water quality conditions of the survey area. Surface detection method (surface logic) was set as Infrared-Raman-Green. This prompted surface detection to use the IR channel initially. If no IR pick was found then the Raman channel would be used. The bottom detection mode was also assessed as, where initially planned to use the first pulse logic, it was then determined to use the strongest pulse bottom logic as there were extensive fliers due to water clarity.

Data were then imported into a Fledermaus project in PFM format file to allow data inspection and editing in a 3-D environment.



3.2.2 DATA VISUALIZATION & EDITING

Data visualization and editing was done using IVS Fledermaus. Fledermaus displays a gridded and shaded 3-D surface (PFM) of each project block (Figure 3-2). Smaller sections are then reviewed using the 3D area-based editor. The 3D Editor opens up a smaller subset of data, displaying each individual sounding point clouds in 3D (Figure 3-3).

Figure 3-2 Fledermaus PFM View

Figure 3-3 Fledermaus 3D Editor LiDAR point cloud



Gross outliers were manually rejected. Other data of uncertain quality, requiring more examination, were reviewed using the waveform window, which displays shallow and deep channel bottom selections, and IR and Raman surface picks (Figure 3-4). Data coverage had a high priority for CHS, therefore criteria for the validation of the depths at extinction or bottom detection limits were broadened, providing CHS with the final decision on marginal data.

Figure 3-4 Waveform Viewer

Other metadata such as confidence values and laser shot warnings are also incorporated into the waveform viewer. In addition, the down look camera image associated with the laser pulse was also displayed (Figure 3-5).



Figure 3-5 Downlook digital Imagery

Other SHOALS specific tools, such as swapping a sounding that was falsely recognized as land to water, were used inside Fledermaus by experienced Data Analysts. In the shallower nearshore margins the Shallow Water Algorithm (SWA) for bottom detection was used to recover very shallow (<1.5m) bathymetry and to allow, where valid returns permitted, a seamless join with the topographic data obtained on the specific missions that these data were collected.



3.3 TIDAL DATUM DEPTH REDUCTION

Once the LiDAR datasets were edited and validated in KGPS mode, observed tide level was applied to retained depth points. The application of tides to SHOALS data involves the water surface level measured by LiDAR at the time of acquisition, such that the instantaneous water level measured is correlated to the tide level recorded at the tide station to reduce water depths to tidal datum. The CHS provided the observed tidal records from station 6213 located at Racon Island, on the North corner of the survey area (Leyzack

5). No tide zones or other correctors were applied.

Not all LiDAR points could be reduced by tides, particularly data collected over land (topographic). The adjacent topo LiDAR data to the coastline can usually be reduced to tides due to proximity, however data collected over larger extensions cannot. In order to preserve the maximum topographic coverage reduced to tidal datum, the offset between the ellipsoidal height and the tidal reduced depths was calculated by differencing each point in the two vertical reference determinations. The ellipsoidal-tidal vertical offset approximation was calculated to be 31.897 m, however due to the difficulties experienced processing KGPS solutions, the standard deviation of the offset determination was 0.442 m. This level of accuracy only affects topographic data offset to tidal datum but it does not apply to any bathymetric depths or topographic data approximately 1.5 m over chart datum. The standard deviation determined will be carried on to the propagated uncertainty estimation.

3.4 TOTAL PROPAGATED UNCERTAINTY

Fugro has developed methodology to determine vertical and horizontal uncertainty (TPU) for the SHOALS-1000T sensor using spatial variance from direct observation of surveyed data (Lockhart et al, 2008

6). Ideally, data collected over a reference bathymetric surface within or near the survey area provide

the closest results by principle, however, when the required reference surface cannot be determined on the survey area due to logistics or operational constraints, the uncertainty modeling can be produced from survey data collected elsewhere. For this project, uncertainty analysis performed during an acquisition period between October-November 2011 was used to estimate TPU. Table 3-1 shows the vertical and horizontal TPU values by depth range to be applied to each sounding.

5 Leyzack, Andrew, 2011. Tid files for Victoria Strait. Email communication with data attachment. 02

December 2011. 6 Lockhart, C., D. Lockhart, J. Martinez, 2008. Comparing LIDAR and Acoustic Bathymetry Using Total

Propagated Uncertainty (TPU) and the Combined Uncertainty and Bathymetry Estimator (CUBE) Algorithm, ILMF 2008. http://www.fugro-pelagos.com/papers.asp



Topo elevations (negative sign convention) over -1.5 m show a value of 0.538 which is the propagated error resulting from the quadratic sum

7 of the original 0.307 uncertainty value and the 0.442 estimated

ellipsoidal-tidal shift as reviewed in section 3.3. The elevations over -1.5 m is the approximated range where topographic elevations required the tidal offset shift.

Table 3-1 Total Propagated Uncertainty values for LiDAR data

Depth (m) vTPU (m) hTPU (m)

-20.0 0.538 4.499

-2.5 0.538 4.499

-1.5 0.307 4.499

0.0 0.307 4.499

2.5 0.318 4.499

5.0 0.330 4.499

7.5 0.342 4.499

10.0 0.354 4.499

12.5 0.365 4.499

15.0 0.377 4.499

17.5 0.389 4.499

20.0 0.401 4.499

22.5 0.412 4.499

25.0 0.424 4.499

27.5 0.436 4.499

30.0 0.448 4.499

32.5 0.459 4.499

35.0 0.471 4.499

37.5 0.483 4.499

40.0 0.495 4.499

7



3.5 REFLECTANCE

During the auto processing of each flight dataset, raw bottom reflectance data (BRF) were produced for each line. After completion of the editing work, the BRF files were updated to reflect the validated bottom returns. Then, these BRF files were taken to Optech’s REA software that runs as an add-in module in ENVI software v4.7. REA processes exclusively SHOALS data to produce reflectance imagery. Imagery was exported as 32-bit geotiff files for display and analysis on common GIS software. The REA processing is further described in the following paragraphs. Each imagery dataset was created based on a min/max water attenuation coefficient and shallow water segment threshold. Using a radiative transfer equation, the measured LiDAR signal is expressed as a function of the transmitted energy, imaging geometry, and physical environment. This equation is inverted to solve for seafloor reflectance for each pulse. This procedure yields an estimate of reflectance at each location where the depth is measured. Images are produced from the point cloud by rasterizing the reflectance values into the same grid used to generate the digital surface model of the seafloor, normally at the data density collected or a little smaller, in this case, a 4 m cell size. In this way, the reflectance image is perfectly registered to the 3D model of the seafloor. The resulting dataset images were brought in ArcGIS to build balanced gray-scale mosaics. Final rendering is preserved when converting mosaic imagery to 8-bit geotiff. Note that the reflectance imagery coverage could extend beyond the valid depths coverage. This is because the valid waveform signal from which both datasets are derived, are analyzed with different algorithms. The algorithms that determine valid depths are more stringent that those evaluating the backscatter intensity; in the end more laser pulses are accepted for reflectance extraction than for valid bottom detection.

3.6 ORTHO-MOSAIC IMAGERY

Digital RGB images were exported from their raw packaged format in GCS into individual frame images in jpeg format. During exportation, each 1600 x 1200 pixels frame was provided with timestamp, position and orientation information from the SBET KGPS solution. This information was used to create the rotation matrices required in the rectification process conducted in ERDAS software v9.3. No DEM was used in the rectification procedure; instead a constant elevation simulating the sea surface was supplied. The rectified imagery was analyzed for general image quality and further enhancements. Common situations where imagery required additional processing included: • Dark imagery due to existing conditions at time of survey (twilight, clouds) • Bad timestamps that produced wrong geo-registration of individual frames • Reversing order of overlapping line imagery to minimize sun glint FPI in-house software was used for the final mosaic creation. Feathering on the frame overlap was applied but no color correction, balancing or other procedures were used in the mosaic production in order to provide as much original and unaltered image description of surface conditions.



4. QUALITY CONTROL Throughout the data acquisition and processing procedures a number of quality control checks are conducted at regular instances. The Airborne Operator continually monitors the data collected in real-time to ensure all navigation and laser system quality parameters are within acceptable tolerances. The Data Analysts inspect the data throughout the entire processing flow to ensure collected data are within project accuracy specifications. These checks include:

Vertical accuracy checks over ground truth surface locations

Laser power measurements and system timing tests (LPTT) for each flight, collected before takeoff, during flight, and post mission.

KGPS accuracy checks, such as RMS values of forward/reverse SBET solution separation and PDOP values.

Visual inspection of the auto-processed LiDAR data.

Dynamic navigation check (horizontal accuracy) over ground truth positions

Crosscheck analysis

4.1 GROUND TRUTH CHECK (RUNWAY)

The runway at Gjoa Haven airfield was surveyed to generate a ground truth surface to analyze SHOALS LiDAR data vertical accuracy. A runway survey was carried out with a rover GPS receiver, mounted on a vehicle, collecting continuous data, while driving over regular transects along the runway. GPS data were post-process to obtain a kinematic solution (PPK) for each epoch. Data points were exported and gridded into a regular 5 m cell size raster DEM. LiDAR data points were analyzed against the runways surface and difference statistics generated. Table 4-1 presents the overall results of these analyses, from flights conducted on August 21, 26, and 27. Complete logged results are included in Appendix F (Quality Control – Ground truth).

Table 4-1 LIDAR data overall vertical difference over runway surface

Number of lines

Average # Points Compared

Mean Difference (m)

Difference Std. Dev. (m)

5 7333 -0.114 0.077

4.2 KGPS QUALITY

The quality of post-processed KGPS solutions for each mission flight was analyzed by reviewing the results of POSPac processing, in particular the resulting PDOP, Processing Mode and RMS plots. For PDOP, which is a measure representing the geometry of the available GPS satellite baselines. In a good quality plot (Figure 4-1), the vertical range remains below a value of three.



Figure 4-1 KGPS processing, PDOP quality plot

For RMS (root mean square), which represents the relative positional accuracy (sample to sample), the tolerances for a good quality solution are to a maximum of 0.1 m vertically (down position). The example in Figure 4-2 shows the RMS values for down, east and north positions, all of them within 0.05 m:

Figure 4-2 KGPS processing, RMS quality plot



The Processing mode plot, indicates the quality of the GPS state for each sample. In KGPS processing the ideal state is Fixed (0, 1), however depending on trajectory, duration and baseline, a Float (2) mode could be acceptable. Example in Figure 4-3 shows the settling of good GPS state toward fixed solution:

Figure 4-3 KGPS processing, Processing Mode quality plot

During the Alexandra Strait data acquisition operations a couple of difficulties were experienced on the processing of good KGPS solutions, mainly caused by electrical interference in the aircraft to GPS signals and periods of high PDOP, sometimes unpredicted. Figure 4-4 shows examples of quality plots presenting processing difficulties.

a)



b)

Figure 4-4 KGPS processing, RMS plots of problematic solutions: a) spikes, b) over tolerance.

The RMS values in both examples present spikes up to 0.7 m at times or over 0.1 m throughout the flight. All KGPS processing plots for flights producing the final data coverage are found in Appendix F (Quality Control – KGPS Plots) The standard procedure to utilize KGPS solutions to derive LiDAR depths and elevations have to be put aside in favor of a better vertical reduction, which came with application of tides water level. Nevertheless KGPS horizontal accuracies were never compromised and still offered better accuracies that standard code DGPS solutions (< 1.0 m RMS).

4.3 DYNAMIC NAVIGATION CHECKS

LiDAR data were compared against the corners of a building selected in the vicinity of Gjoa Haven airport, which was coordinated with ground KGPS survey at the beginning of the survey. This provided a gross error check on dynamic horizontal positioning. Due to the LiDAR data spot spacing, compared points may not fall exactly on the targeted corner, therefore some of the distance error is attributed to the scanning pattern and not only to the navigation solution (KGPS). A summary of these analyses is presented in Table 4-2. Complete results are included in Appendix F (Quality Control – Dynamic Navigation Check)

Table 4-2 Dynamic Navigation Checks Summary

Bldg Pt. #

LiDAR point distances (m) – flight dates Average

Distance (m)

21-Aug 26-Aug 27-Aug 30-Aug

1 2.685 0.511 1.696 0.819 1.428

2 3.685 1.813 3.790 0.153 2.360

3 3.732 2.068 4.318 0.235 2.588

4 1.833 1.764 2.045 1.045 1.672

Overall Avg 2.012



4.4 CROSSLINE ANALYSIS

A difference analysis between the cross lines and the main survey lines was performed using the Crosscheck program within Fledermaus. A surface grid was created from the production lines at approximated 5 m bin size, then the cross line points were compared against the surface. The approximated vertical accuracy result for this analysis is ±0.5 m (95% c.l.). More specifically, the accuracy specification is in line to IHO SP-44 Order 1b:

Where, a=0.5 and b=0.013, d=depth The cross line check analysis results are presented in Table 4-3. Complete results are included in Appendix F (Quality Control – Crossline Analysis).

Table 4-3 Crossline Results

Crossline Flight Line # Points

Analyzed Difference mean (m)

Difference std. dev. (m)

Points within ±0.5 m

Point % within ±0.5 m

20110822-Flight1 68-1 18709 0.007 0.163 18474 98.7

20110822-Flight2 70_2 64612 0.043 0.265 60658 93.9



5. DATA DELIVERABLES The following are the data deliverables produced:

LiDAR data in CARIS HDCS format, including:

Processed XYZ data reduced to LLWLT tidal datum (HOF files)

Raw waveforms (INH files)

Applied uncertainty values for SHOALS sensor and reductions

Reflectance (bottom reflectivity) in TIFF format (8-bit)

Integrated bathymetry/topography DEM in TIFF format (32-bit)

Digital imagery ortho-mosaics in TIFF format (8-bit RGB)

Metadata file for each deliverable product type

LiDAR data in LAS format

GPS and positioning data (raw base station and SBET solutions)



6. APPENDICES DESCRIPTION Contents of the Appendices of this report are documents produced digitally. Please refer to accompanying directory structure when looking for referenced information. Following is the content descriptions of each Appendix.

Appendix A – Airborne Collection Logs

SHOALS-1000T Airborne Log sheets

Appendix B – SHOALS Equipment Specs

Gunther, G. et al. “Meeting the Accuracy Challenge in Airborne LIDAR Bathymetry” white paper SHOALS-1000T System Specifications

Appendix C – Sensor Calibration Reports

SHOALS-1000T Calibration Report (8 August 2009) SHOALS-1000T Calibration Verification Report (23 August 2011) Sensor Installation, Antenna Offset Survey Log

Appendix D – Ground Control

CSRS Processing Summaries Ground Control Summaries Station Descriptions

Appendix E – Daily Project Reports

Daily Project Reports

Appendix F – Quality Control

Groundtruth Results KGPS Plots Dynamic Navigation Checks Crossline Analysis Results and Summary

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