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° Slope Colluvial 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 900000 750000 600000 150000 300000 450000

SfM surface density (number of neighbors divided by neighborhood surface) N / (Pi.R2)

TDP

0 2800 2400 2000 1600 400 800 1200 3200

TDP surface density (number of neighbors divided by neighborhood surface) N / (Pi.R2)

60 240 180 120 300 360 420 0

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

Ele

va

tio

n (

m)

Ele

va

tio

n (

m)

Reflectance (dB) Reflectance (dB)

7A 7B

Seepage in TLS

Light sediment

Light sediment

Light sediment