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
CONTENTS
Page
1.
1.1
1.2
1.3
INTRODUCTION AND SCOPE OF WORK
GENERAL
SURVEY SPECIFICATIONS
PROJECT DATUM
1-4
1-4
1-5
1-5
2.
2.1
MOBILIZATION AND DATA ACQUISITION
AIRBORNE SURVEY
2.1.1
AIRCRAFT MOBILIZATION
2.1.2
POSITIONING
2.1.3
SENSOR ORIENTATION
2.1.4
LIDAR SYSTEM
GROUND CONTROL
CHALLENGES ENCOUNTERED
2.3.1
ENVIRONMENTAL
2.3.2
TECHNICAL
SUMMARY OF SURVEY ACTIVITIES
2-7
2-7
2-8
2-9
2-9
2-10
2-10
2-12
2-13
2-13
2-14
3.3
3.4
3.5
3.6
DATA PROCESSING
KGPS PROCESSING
SHOALS GCS PROCESSING
3.2.1
AUTO PROCESSING
3.2.2
DATA VISUALIZATION & EDITING
TIDAL DATUM DEPTH REDUCTION
TOTAL PROPAGATED UNCERTAINTY
REFLECTANCE
ORTHO-MOSAIC IMAGERY
3-17
3-18
3-18
3-18
3-19
3-22
3-22
3-24
3-24
4.
4.1
4.2
4.3
4.4
QUALITY CONTROL
GROUND TRUTH CHECK (RUNWAY)
KGPS QUALITY
DYNAMIC NAVIGATION CHECKS
CROSSLINE ANALYSIS
4-25
4-25
4-25
4-28
4-29
5.
DATA DELIVERABLES
5-30
6.
APPENDICES DESCRIPTION
6-31
2.2
2.3
2.4
3.
3.1
3.2
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TABLES
Table 1-1 Project Geodetic and Projection Parameters
Table 2-1 Aircraft Technical Specifications
Table 2-2 Control Points for ground control (NAD83 (CSRS) 2002)
Table 2-3 Environmental Operational Limits
Table 3-1 Total Propagated Uncertainty values for LiDAR data
Table 4-1 LIDAR data overall vertical difference over runway surface
Table 4-2 Dynamic Navigation Checks Summary
Table 4-3 Crossline Results
Page
1-6
2-7
2-11
2-12
3-23
4-25
4-28
4-29
FIGURES
Page
Figure 1-1 LiDAR Survey Area
Figure 2-1 Beechcraft King Air A90
Figure 2-2 Aircraft Offset Calculations, 08 May 2011
Figure 2-3 Control GPS stations showing 70 Km (YHK1) and 50 km (MIKI) baseline radii.
Figure 3-1 LiDAR data processing flowchart
Figure 3-2 Fledermaus PFM View
Figure 3-3 Fledermaus 3D Editor LiDAR point cloud
Figure 3-4 Waveform Viewer
Figure 3-5 Downlook digital Imagery
Figure 4-1 KGPS processing, PDOP quality plot
Figure 4-2 KGPS processing, RMS quality plot
Figure 4-3 KGPS processing, Processing Mode quality plot
Figure 4-4 KGPS processing, RMS plots of problematic solutions: a) spikes, b) over tolerance.
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1-4
2-7
2-8
2-12
3-17
3-19
3-19
3-20
3-21
4-26
4-26
4-27
4-28
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ABBREVIATIONS
ABS
CHS
CMP
CSRS-PPP
DAVIS
DGPS
FPI
GCS
GRS80
HHWLT
HOF
Hz
IHO
IMU
INH
IR
KGPS
LLWLT
LPTT
PDOP
PFM
POS AV
PPK
RMS
SBET
SHOALS
SWA
UTC
UTM
WMO
SHOALS-1000T Airborne System
Canadian Hydrographic Service
Common Measuring Point
Canadian Spatial Reference System – Precise Point Positioning
Download, Auto Processing, and Visualization Software
Differential Global Positioning System
Fugro Pelagos, Inc.
SHOALS Ground Control System
Geodetic Reference System of 1980
Higher High Water Large Tide
Optech’s bathymetric LiDAR data format
Hertz
International Hydrographic Organization
Inertial Measurement Unit
Optech’s bathymetric LiDAR waveform data format
Infrared
Kinematic Global Positioning System
Lower Low Water Large Tide
Laser Power Timing Test
Position Dilution of Precision
IVS Fledermaus 3D Surface grid processing reference file format
Position Orientation System, Airborne Vehicle (Applanix)
Post-processed kinematic GPS
Root Mean Square
Smoothed Best Estimated Trajectory
Scanning Hydrographic Operational Airborne LiDAR Survey
Shallow Water Algorithm
Universal Time Coordinated
Universal Transverse Mercator
World Meteorological Organization
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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
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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.
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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
1
Inverse Flattening:
/f = 298.25722210100002
Project Datum Geodetic Parameters
Datum:
NAD83 (CSRS) 2002
Spheroid:
GRS80
Semi major axis:
a = 6 378 137.000 m
1
Inverse Flattening:
/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
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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
Registration Number
Owner
Wing Span
Length
Gross Weight
Typical Empty Weight
Survey Mode Duration
Engines
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BEECHCRAFT KING AIR A90
N89F
Dynamic Aviation
14.6 m
10.8 m
4,377 kg
2,336 kg
~4-5 hours
PT6A-20 (Turboprop)
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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
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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
1
swath. The SHOALS system calibration was fully geometrically calibrated on 8 August 2009 and
2
calibration verification prior to the start of this project were performed on 18 Aug 2011 and 21 Aug 2011 .
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
2
th
SHOALS-1000T System Calibration Report. Full Geometric. August 8 , 2009
rd
SHOALS-1000T System Calibration Verification, August 23 , 2011
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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
3
bathymetric LiDAR can be found in Guenther, et al. (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 postprocess 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.
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The GPS base station data was processed to derived accurate geodetic position using Natural Resources
4
Canada (NRC) online GPS processing CSRS-PPP . 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
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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
Cloud Ceiling
Precipitation
Wind Speed
WMO Sea State
Aircraft Cabin Temperature
Topography
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Limitation
>400m
Data are not collected during periods of heavy rainfall.
Head Wind
< 40 kts / 74 km/h
Tail Wind
< 20 kts / 37 km/h
Cross Wind
< 40 kts / 74 km/h
1–4
o
< 40 C (system will shut off automatically if this limit is
breached and data collection will stop)
< 200m
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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.
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2.4
SUMMARY OF SURVEY ACTIVITIES
Date
19 Aug 2011
Daily Summary
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.
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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
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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.
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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
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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 postprocessed 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.
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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
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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).
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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.
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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
5
corner of the survey area (Leyzack ). 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,
6
2008 ). 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
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Topo elevations (negative sign convention) over -1.5 m show a value of 0.538 which is the propagated
7
error resulting from the quadratic sum 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
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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.
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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:






4.1
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
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
4.2
Number
of lines
Average # Points
Compared
Mean Difference
(m)
Difference Std.
Dev. (m)
5
7333
-0.114
0.077
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.
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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
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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)
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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
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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
0.163
Points
within
±0.5 m
18474
Point %
within
±0.5 m
98.7
0.265
60658
93.9
Crossline Flight
Line #
Points
Analyzed
Difference
mean (m)
Difference
std. dev. (m)
20110822-Flight1
68-1
18709
0.007
20110822-Flight2
70_2
64612
0.043
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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)
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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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