Airborne and terrestrial laser scanning for landslide monitoring

Norbert Pfeifer, Andreas Roncat, Sajid Ghuffar, Balazs Szekely

norbert.pfeifer@geo.tuwien.ac.at

Research Group Photogrammetry

Department of Geodesy and Geoinformation Vienna University of Technology

www.ipf.tuwien.ac.at

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Laser scanning landslides

Theory Lidar equation Full waveform lidar Terrain modeling from point clouds Range flow for monitoring Application Doren site ALS and TLS data

comparison (lidar equation) 3D deformation

monitoring at Doren Conclusions

Lidar equation

Light detection and ranging, the distance(++) measurement of laser scanning Equivalent to RADAR equation

• microwave RS, laser scanning (pulse round trip and phase based), electronic distance measurement (total station), Time Of Flight cameras (ToF, RIM)

• does not apply for very short distances (beam widening model) Relates transmitted power to received power

Target characteristics: area, reflectivity, solid angle of backscatter System characteristics: aperture diameter, beam divergence, system effectivity

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BKSYSATM42

2

σ

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PAR

DPP TR +×

Ω×= ηηρπ

πβ

A

Backscatter cross section σ

Backscatter cross section σ [m²]: combines all relevant object parameters • Isotrop Ω=4π σ = ρA • Lambertian Ω=π σ = 4ρA (orthogonal incidence)

σ = 4ρAcosα • Retro reflection Ω=const and small • General:

AρπσΩ

=4

BKSYSATM42

2

σ

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PAR

DPP TR +×

Ω×= ηηρπ

πβ

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Target area, multiple echoes

Target characterisic: area A A > laser footprint: extended target

A=R2β 2π/4 PR ∝ 1/R2

Example: open terrain

A < laser footprint PR ∝ 1/R3 - 1/R4

Example: leaf of vegetation, corner reflector

Multiple echoes from targets not covering the entire footprint

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Dynamic lidar equation

Introduce time, allows ranging Introduce shape of emitted pulse – as function of time Record shape of echo (echoes) – as function of time

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𝑃𝑃𝑅𝑅,𝑖𝑖 𝑡𝑡 ≈𝐷𝐷2

4𝜋𝜋𝛽𝛽2𝑅𝑅𝑖𝑖4 𝑃𝑃𝑇𝑇 𝑡𝑡 −

2𝑅𝑅𝑐𝑐𝑔𝑔

𝜎𝜎𝑖𝑖 𝑅𝑅 𝑑𝑑𝑅𝑅𝑅𝑅𝑖𝑖+𝛿𝛿

𝑅𝑅𝑖𝑖−𝛿𝛿

𝜎𝜎 𝑅𝑅

𝑃𝑃𝑅𝑅 𝑡𝑡 𝑃𝑃𝑇𝑇 𝑡𝑡

Full waveform recording

Sample/digitize 𝑃𝑃𝑅𝑅 𝑡𝑡 Model waveform (e.g. Sum of Gaussians) to

• range of echo • amplitude of echo • echo width • differential cross section 𝜎𝜎 𝑅𝑅

Exploit echo parameters (or differential cross section parameters) ? Contrary: in discrete return systems, 𝑃𝑃𝑅𝑅,𝑖𝑖 𝑡𝑡 is

processed electronically to infer range

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Georeferencing

From ranges and angles to points ALS: direct georeferencing

• Trajectory: full exterior orientation (6 DoF) • Ranges • Scan angle

TLS: indirect georeferencing • Targets measured with GNSS or

total station in superior reference frame • Between scans: targets or ICP

Point clouds

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TLS point cloud shadows from self occlusion

Terrain modeling from point clouds

1. Step : identify ground points Classification task: points on terrain (land slide) surface vs. other points Available information: XYZ, additionally: FWF attributes # points: 10.000 – 100.000 – 1.000.000 – ... : automation required Methods available

• Mathematical morphology • Surface based filters (TIN densification, robust interpolation, ...)

ALS + TLS point cloud „filtering“ • Consider only last or single echoes • Consider only echoes with narrow echo width (esp. ALS) • Apply surface based filters to remove surface trend, especially in mountaineous

terrain

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Filtered points clouds: ALS + TLS

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Buildings removed

Points on water surface

Vegetation removed Varying density

Concentration on area of interest

Terrain modeling from point clouds

2. Step: interpolate terrain surface Ground points to surface

• avoid extrapolation • TLS: areas not visible (shadow) • ALS + TLS: areas without ground points (vegetation)

Methods available • Triangulation • Kriging • Moving Least Squares

Terrain model • Interpolate regular grid • Mask areas with low point density

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Terrain model

ALS + TLS terrain model TLS model provides more

detail in well coverd areas ALS model better below

vegetation

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Terrain model

ALS terrain model from 2 epochs

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Tracking changes

Monument based: not part of the deforming/landslide surface Reflectors tracked with total stations GNSS receivers placed on object Feature based Identify corresponding features manually in models Identify corresponding features automatically: SIFT, curvature extremes, ... Area based Photographic images: LSM Point clouds: ICP Terrain models: LSM Terrain models with small changes: Range Flow

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Range flow equation

Z = f (X,Y,t)

U V W : 3 components of motion ZX ZY Zt : computed from terrain models 3 unkonwns in 1 equation Apply to window assuming constant U V W within the window

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X(x)

Z(x) t1 t2

(U,W)

𝑍𝑍𝑥𝑥𝑈𝑈 + 𝑍𝑍𝑦𝑦𝑉𝑉 −𝑊𝑊 + 𝑍𝑍𝑡𝑡 = 0

Range flow result

In each window center (each pixel) U V W are estimated Normal equation matrix singular, if ZX ZY 1 are linear dependent

• Planar surface within the window: 2 singularities • 2 planar surfaces intersecting

in an edge: 1 singularity • In range flow known as

„aperture problem“ • Small windows:

assumption of constant U V W holds better

• Large windows: aperture problem reduced

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Landslide Doren (Vorarlberg, Austria)

Length: ~1km; material tranported away by Weißach river; above: settlement

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Data acquisition missions

ALS campaigns: 2003, 2006, 2007 by Landesvermessungsamt Vorarlberg • Optech ALS 2050, 3100; leaf off state

TLS campaigns: 2008, 9, 10, 11, 12, 13 by GEO • Riegl LPM-321, LMS-420i, VZ400; late summer/early autumn

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TLS acquisition (autumn 2009)

Georeferencing based on reflectors

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Point density

Measure: #points in sphere with 1m radius (measure for each point) ALS 2007, TLS 2009

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Range

ALS: 800-1100m, TLS: 2-1000m : PR ∝ 1/R2 : 1:1.9 vs 1:250000

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Incidence angle

ALS and TLS: flipped incidence angle distributions

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90° 0°

Terrain models

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DTM grid width 1m

Range flow results

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Details: scarp

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1st to last epoch flow vs. epoch wise additive flow

In areas of low local relief, local deformation dominates and detection of motion becomes impossible

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Comparison to geodetic observations

Reflectors mounted on poles and trunks For epochs 2010-11 and 11-12 (but only approx. same measuremen time) Agreement typically within 3dm (1m terrain model grid width)

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Landslide and tracking

Complex motion pattern • Manual counter measures, e.g. artificial drainage • Different stability, e.g. due to roots • Local incision, e.g. due to surface runoff • Temporal motion not uniform (therefore no change rates given)

Therefore • Area coverage advantageous (vs. few points from tracking) • Shape deformation limits accuracy of tracking

Landslide processes • Movement of material vs. morphologic changes

e.g. scarp retreats backwards vs. material transport downwards

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Tracking movement by range flow

Provides area wise 3D motion vectors Manual input limited (surface interpolation parameters, window size) Embedding in least squares adjustment provides precision Surface modeling makes independent of acquisition method

lidar vs. photo – airborne vs. terrestrial Basically equivalent to least squares matching

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Instrumental development

Constant acquisition time throughout TLS campaigns: 1 day Increase in measurement rate (ongoing!) as key improvement in TLS

Long range capability at Doren offers hardly advantages for TLS opposite side forested, limited area with steep slopes

Low vegetation especially problematic for TLS • identification of low vegetation easier with FWF • no improvement w.r.t. number of ground points

FWF speeds up classifiction of ground points and increases reliability (ALS+TLS)

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ALS vs. TLS

ALS provides more homogeneous • sampling/point density • ranges • angles of incidence

Airborne position better for vegetation penetration • max 1 tree along line of sight • sunlight triggers leaf growth parallel to ALS viewing direction • TLS viewing direction parallel to ground, orthogonal to growing direction

TLS • Easier deployment • Sampling

characteristics and ranges controlled by surveyor

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Further improvements

Close range aerial data acquisition • UAV for larger areas required • Lidar advantageous for vegetation penetration • Lidar on UAV challenges: weight, data storage

Higher temporal sampling • Shape deformations smaller for reduced temporal baseline

Maintaine FWF (and therefore also multi target capability) • Identification of esp. low vegetation

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References and acknowledgement

Austrian Academy of Sciences: ÖAW – Program GdE : Geophysik der Erdkruste FFG Bridge: AirborneGeoAnalysis Support in field: Drexel (Local Authority), Molnar (Budapest)

Remote Sensing, Special Issue: Deformation Monitoring

Ghuffar et al., 2013 Landslide displacement monitoring using 3D range flow on airborne and terrestrial LiDAR data

COGeo, Proceedings 2010 Roncat et al., 2010 Influences of the Acquisition Geometry of different Lidar Techniques in High-Resolution Outlining of microtopographic Landforms DOI: 10.5242/cogeo.2010.0000

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