Combining Airborne LIDAR and Satellite RADAR for a Dynamic DEM. Ramon Hanssen, Delft University of Technology

Transcription

1 Combining Airborne LIDAR and Satellite RADAR for a Dynamic DEM Ramon Hanssen, Delft University of Technology 1

2 Release 27 September 2

3 Land surface elevation H(t) = H(t 0 ) + dh(dt) dt Elevation at time epoch t = Elevation at initial time t 0, plus Elevation change as a function of time (t-t 0 ) 3

4 <2001: GTOPO30 Different sources, 1 km resolution Vertical accuracy (meters) Source L.E. at 90% RMSE Estimation method DTED product specification DCW calculated vs. DTED USGS DEM product specification AMS maps estimated from 500-meter interval IMW maps estimated from 100-meter interval Peru map estimated from 1,000-meter interval N.Z. DEM 15 9 estimated from 100-foot interval ADD highly variable wide range of scales and intervals 4

5 Shuttle Radar Topographic Mission (2000) 90 m resolution ~8 m vertical RMSE 5

6 Parameter Specification HRTI-3 definition DTED-2 Relative vertical accuracy 90% linear point-to-point error over a 1º x 1º cell 2 m (slope 20%) 4 m (slope 20%) 12 m (slope < 20%) 15 m (slope > 20%) Absolute vertical accuracy 90% linear error 10 m 18 m Relative horizontal accuracy 90% circular error 3 m 15 m Horizontal accuracy 90% circular error 10 m 23 m Spatial resolution Independent pixels 12 m (1 arcsec) 30 m (1 arcsec) 6

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8 AHN-1/2 Lidar DEM ~4 pts/m2 ~5 cm noise ~5 cm systematic 8

9 AHN; Probably one of the best, nationwide, digital elevation models in the world but is it stable? 9

10 Delta committee ( Plan Veerman ) Sustainability of Netherlands against climate change consequences: water safety Sea level rise assumptions are cm up to 2100 and 200 tot 400 cm up to including land subsidence but no clear subsidence data available Cost /year Delta program 1,2-1,5 x 10 9 Euro 10

11 The Dynamic DEM Definition: Elevation for an (x,y) location as a function of time (t) estimate the best (absolute) time series of elevation, of a predefined signal, w.r.t. ETRS89, given all available data, for a given location Determine the signal of interest: Dynamic DEM is a DTM (no houses/vegetation) explicitly define expectations of the spatio-temporal signal Dynamic DEM needs to be: defined in a common geodetic datum, and accompanied with appropriate quality metrics 11

12 Two parts of the Dynamic DEM 1. Contemporary part (based on quantitative geodetic observations, including quality metrics) 2. Prospective part (based on (geo)physical understanding of the driving mechanisms, including quality metrics) 12

13 Key technology: Radar interferometry 13

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16 November 19,

17 Interferometry: deformation sensitivity 18

18 Time-Series approach: Persistent Scatterers Pixels with strong and consistent (coherent) reflections in time. Multi-pass InSAR time series necessary. First realized by Politecnico di Milano 19

19 The zero-problem of Satellite Radar Interferometry The Reference point 20

20 Vertical datum connection We want this: But we measure this with InSAR: Bottom line: This is all because we need absolute data in stead of relative data, or better: data in a properly defined geodetic datum *assuming linear deformation of the reference point 21

21 How do we get it? Mathematically: height as a function of time h(t) at location (x,y) in 1 st order approximation h(x,y,t) = h(x,y,t 0 ) + v(x,y) (t-t 0 ) h(x,y,t 0 ), estimated from given input DEM (Lidar), v(x,y), e.g. estimated from InSAR, GPS, leveling, abs.gravity This is under the assumption of a common geodetic datum 22

22 Dutch nation wide subsidence map(-ping) 23

23 Data Combination of Absolute gravimetry Leveling GPS Radar interferometry (InSAR) 24

24 Absolute gravimetry 5 stations around the Netherlands Observations between 1991 and 2011 Conversion from μgal/y to mm/y 25

25 Leveling Historic heights Minimal 3 heights per benchmark Deformation rate estimates [mm/y] with outlier removal benchmarks selected, measured on average every 5 years 26

26 GPS 15 continuous GPS stations in and around the Netherlands used Linear vertical deformaton rates [mm/y] estimated taking a periodic effect with period of 1 year into account 27

27 Radar interferometry Radar images ascending orbit 10 tracks 200km 330km 28

28 Radar interferometry Radar images descending orbit 8 tracks 200km 330km 29

29 Radar interferometry ERS-1/2 radar images, , 6 ascending and 5 descending tracks Envisat radar images, , 5 ascending and 3 descending tracks 600 images 6 million PS detected On average 100 PS/km 2 30

30 Total set 31

31 Data combination Estimated rates referenced to the same reference frame. We select leveling reference system (the Dutch Datum known as NAP) We apply a quadtree decomposition to reduce the amount of data (especially the number of Persistent Scatterers) The quadtree enables a joint estimation of the final deformation model, including error propagation 32

32 Quadtree decomposition 33

33 Joint estimation Example deep deformation 34

34 Deformation deep processes 35

35 Deformation shallow processes 36

36 Total deformation 37

37 Dynamic DEM Elevation + Velocity Summary Combining Lidar and Radar Centimeters versus millimeters per year Allows for planning for the next century 38