1. Thermal Imaging Results
Infrared Thermography (IRT)
• IRT data were collected during daylight hours in December,
thus, both reflectance and emissivity were recorded.
• Data were processed in ResearchIR using a temperature range
of -5 to 30°C (23 to 86°F).
11 12
Introduction
We are using multiple remote sensing methods to document erosion
of the Point Grey sea cliffs in Vancouver, British Columbia. The
cliffs, which are up to 65 m high, are formed in Quadra Sand, a
horizontally stratified, well-sorted, late Pleistocene sand and silt
outwash unit overlain by till (Clague, 1976). Retreat of the sea
cliffs is driven by wave erosion, groundwater seepage, and
gravitational collapse of the loose Quadra sediments (Clague and
Bornhold, 1980). Despite drainage installation, boulder protection
and revegetation of the slope, failure still occurs.
Instrumentation
Allison M. Westin, Mirko Francioni, Ryan Kremsater, Doug Stead, John J. Clague
Simon Fraser University, British Columbia, Canada
Literature Cited
Clague, J.J. 1976. Quadra Sand and its relation to the late Wisconsin glaciation of southwest British
Columbia. Canadian Journal of Earth Sciences, 13(6): 803-815.
Clague, J.J. and Bornhold, B.D. 1980. Morphology and littoral processes of the Pacific coast of Canada; in
The Coastline of Canada, S.B. McCann, editor; Geological Survey of Canada, Paper 80-10: 339-380.
The process of SfM matches similar features between
photographs. Pictures can be taken from any location, such
that no base-length ratio is required between the object and
photo stations. Features were matched in VisualSfM using the
same photographs as conventional photogrammetry to
maintain identical resolution and layout of the model for
comparison with conventional photogrammetry. SfM provided
almost 14 times more data points than conventional
photogrammetry for the same photographs.
Fig. 4.
Riegl VZ 4000
(TLS)
Fig. 3.
FLIR SC7650
50 mm lens
(Thermal imaging)
Terrestrial LiDAR and Photogrammetry Results
Fig. 2. Point Grey sea cliff in 1975.
Conclusions
• TLS provided higher resolution and faster post processing.
• Photogrammetry models were improved by manually editing
photographs to create more distinct points and features.
• SfM creates more points from the same images than TDP.
• The quality and density of point clouds differ with the TLS,
TDP and SfM methods.
• Point density is not necessarily an indicator of data quality.
• Thermal imaging identifies seepage as higher temperature
zones in the upper unit that are potential areas of groundwater
sapping.
• Further work is needed to optimize methods to complement
each other.
• Remote sensing is an excellent format for fine-scale geological
mapping, characterizing slope stability, monitoring movement,
and detecting other slope changes.
NH43A-1867
Image fans at 12 photo stations were spaced 5 m
apart, 40 m from the cliff to provide a base-length
ratio of 1:8. We georeferenced stations to the
photographs to produce a 3D model with a
ground error of < 1 mm for a σ = 1 model.
Matching of points between photos was done
using CalibCam.
Visible features in the model include slabbing
erosion and stratification in Quadra Sand, but
vegetation limits interpretation.
Fig. 1. Regional Quaternary map
Unit 9: Till
Unit 10: Quadra Sand in cliffs
From Geomap Vancouver,
Geological Survey of Canada 1998.
Further Information
© Copyright Allison Westin
For further information please contact Allison at
allison.westin@gmail.com or via LinkedIn:
We created a point cloud with a point spacing of 1 cm by
merging two scans taken from different angles. The point
clouds were georeferenced using reflective targets. The
standard deviation (σ ) of tie points for merged scans was 2 cm.
Visible features included slabs of failed sediment, cobbles and
boulders in till, and dry rills (potential evidence of previous
groundwater sapping). We analyzed scans in RiSCAN PRO.
Fig. 12
Fig. 9
Fig.
11 Fig. 7A
Fig.
7B
Fig. 8C
Upper unit
Lower unit
77° SlopeColluvial cone
35° Slope
Colluvial blanket
Colluvium
Fig. 6. Overview figure of the study area and locations of select figures sections.
Fig. 5.
Canon EOS 50D
35 mm lens
(TDP and SfM)
Fig. 12. Higher temperatures observed in
upper unit, inferred seepage.
Fig. 11. Temperature lenses
within units, inferred seepage.
Fig. 8. TDP and TLS of the same section of the slope
(C in Fig. 6) provide similar profiles. Points from TDP
were derived from the mesh, as the raw points were
too few to create a profile. The increase in density
using a mesh smoothened the slope profile.
Erosion occurred between data acquisition dates,
which slightly altered the slope profile.
9
Fig. 9. TDP-produced section of the model with
visible slabbing and ripple marks.
Fig.10. Comparison of surface density distribution between
models in Cloud Compare using a sphere radius of 2 m. We
compared monitoring scans (1 % file size of the full scans) to
TDP and SfM. Poor density areas are blue; higher density
regions are yellow, orange and red. Densities are relative to
each model. The TLS model created 194840767 points,
whereas the TDP and SfM created 677720 and 9298005
points, respectively
TLS provides an even distribution of points, whereas TDP
and SfM have larger areas of sparse density, a limitation of
the survey points. The break between the upper and lower
units is visible in TDP and SfM models, which is likely due to
the light color of the upper unit.
.
Conventional Photogrammetry (TDP)Terrestrial Laser Scanning (TLS)
Structures from Motion (SfM)
Logistical Challenges
• Tides
• Weather
• Daylight hours
• Occlusion
• Slope aspect
• Section size
10 m
Points derived from TDP mesh cause
smoothing
Profile differences due to 1 day of
erosion and deposition
8C
Legend
TLS 2 July 2015
TDP 3 July 2015
SfM
0 900000750000600000150000 300000 450000
SfM surface density (number of neighbors divided by neighborhood surface)
N / (Pi.R2)
TDP
0 2800240020001600400 800 1200 3200
TDP surface density (number of neighbors divided by neighborhood surface)
N / (Pi.R2)
60 240180120 300 360 4200
TLS – monitoring scans surface density (number of neighbors divided by neighborhood surface)
N / (Pi.R2)
Fig. 7. Reflectivity profiles generated from TLS data. Greater variability in
reflectivity of upper unit aquifer (A) than lower unit aquitard (B). Reflective
peaks in 7B located in areas of lighter color sediments. Locations of scan lines
A and B are indicated in Fig. 6.
Lower unit
Upper unit
Lower unit
Upper unit
Lower unit
Upper unit
Lower unit
Upper unit
Upper unit
2 m 2 m
Section A Reflectance Section B Reflectance
Elevation(m)
Elevation(m)
Reflectance (dB) Reflectance (dB)
7A 7B
Seepage in TLS
Light sediment
Light sediment
Light sediment