Airborne and terrestrial laser scanning for landslide monitoring

Transcription

1 Airborne and terrestrial laser scanning for landslide monitoring Norbert Pfeifer, Andreas Roncat, Sajid Ghuffar, Balazs Szekely Research Group Photogrammetry Department of Geodesy and Geoinformation Vienna University of Technology

2 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 2

3 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 P P D 2 4 A T R = π ρ η ATM SYS 4πβ R Ω η σ Target characteristics: area, reflectivity, solid angle of backscatter System characteristics: aperture diameter, beam divergence, system effectivity P BK 3

4 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: P P D 4πβ R 4πAρ Ω σ 2 T R = η 2 4 ATMη SYS + π σ = 4 ρ A Ω P BK 4 A

5 Target area, multiple echoes Target characterisic: area A A > laser footprint: extended target A=R 2 β 2 π/4 P R 1/R 2 Example: open terrain A < laser footprint P R 1/R 3-1/R 4 Example: leaf of vegetation, corner reflector Multiple echoes from targets not covering the entire footprint 5

6 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 PP RR,ii tt DD2 RR ii +δδ 4ππββ 2 4 RR PP TT tt 2RR ii RR ii δδ cc gg σσ ii RR dddd PP TT tt PP RR tt σσ RR 6

7 Full waveform recording Sample/digitize PP RR 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, PP RR,ii tt is processed electronically to infer range 7

8 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 TLS point cloud shadows from self occlusion 8

9 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: : 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 9

10 Filtered points clouds: ALS + TLS Buildings removed Concentration on area of interest Vegetation removed Varying density Points on water surface 10

11 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 11

12 Terrain model ALS + TLS terrain model TLS model provides more detail in well coverd areas ALS model better below vegetation 12

13 Terrain model ALS terrain model from 2 epochs 13

14 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 14

15 Range flow equation Z = f (X,Y,t) Z(x) t 1 t 2 (U,W) X(x) U V W : 3 components of motion Z X Z Y Z t : computed from terrain models 3 unkonwns in 1 equation Apply to window assuming constant U V W within the window ZZ xx UU + ZZ yy VV WW + ZZ tt = 0 15

16 Range flow result In each window center (each pixel) U V W are estimated Normal equation matrix singular, if Z X Z Y 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 16

17 Landslide Doren (Vorarlberg, Austria) Length: ~1km; material tranported away by Weißach river; above: settlement 17

18 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 18

19 TLS acquisition (autumn 2009) Georeferencing based on reflectors 19

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

21 Range ALS: m, TLS: m : P R 1/R 2 : 1:1.9 vs 1:

22 Incidence angle 90 0 ALS and TLS: flipped incidence angle distributions 22

23 Terrain models DTM grid width 1m 23

24 Range flow results 24

25 25

26 Details: scarp 26

27 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 27

28 Comparison to geodetic observations Reflectors mounted on poles and trunks For epochs and (but only approx. same measuremen time) Agreement typically within 3dm (1m terrain model grid width) 28

29 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 29

30 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 30

31 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) 31

32 ALS vs. TLS ALS provides more homogeneous sampling/point density ranges angles of incidence Airborne position better for vegetation penetration TLS 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 Easier deployment Sampling characteristics and ranges controlled by surveyor 32

33 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 33

34 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: /cogeo