The Origin, Evolution and Metrics of Topography

1. The Origin, Evolution and Metrics of Topography (Philippines 2006; Stephen Evans)

2. Metrics of Topography • Types of data • Points, Lines, Polygons • Raster data • Creating data • Ground survey (GPS, Total Station, Auto-Level and Stadia Rod) • Transition of legacy historic data (contours) through interpolation to DEMs • Photogrametery: stereo imagery translated to DEMs (airborne or satellite) • Structure from Motion: many oblique images used to create a point cloud • Ground Based or Airborne LiDAR • Preparing data for analysis • Filling holes • Smoothing • Accuracy assessment • Resources • http://gis4geomorphology.com/ • http://hydrology.usu.edu/taudem/taudem5/ • http://www.opentopography.org/

3. Best DEM for Chile? • ASTER Satellite • Collected global stereo imagery • Automated processing • V1, 2009; V2, 2011 • ~30m resolution • Limitations with mountains, clouds, ice • http://gdem.ersdac.jspacesystems.or.jp/search.jsp • http://asterweb.jpl.nasa.gov/gdem.asp

4. ASTER satellite in orbit.

5. Satellite carries many valuable remote sensing tools.

7. Ground Based LiDAR Airborne LiDAR

8. Metrics of Topography • Metrics • Length • Horizontal, Vertical and Slope distances • Area • Plan-view or surface area • Volume • Differences between two surfaces • Slope • Steepest Descent • Neighborhood averaging • Curvature • Issues with raw data analysis: Noise • Increasing smoothing simplifies surfaces

9. Metrics of Topography • Metrics • Length • Horizontal, Vertical and Slope distances • Area • Plan-view or surface area • Volume • Differences between two surfaces • Slope • Steepest Descent • Neighborhood averaging • Curvature • Issues with raw data analysis: Noise • Increasing smoothing simplifies surfaces

11. Metrics of Topography • Other Metrics • Relief • Roughness • Cross section • Longitudinal section • Swath Profile • Change detection • Subsurface topography • Spectral Analysis: • 2-dimensional Discrete Fourier Transform • Continuous Wavelet Analysis • Specifically, variations in spectral power within spatial frequency bands characteristic of deep-seated landslides allow us to efficiently produce accurate kilometer-scale, deep-seated landslide maps.

12. Metrics of Topography • Other Metrics • Relief • Roughness • Cross section • Longitudinal section • Swath Profile • Change detection • Subsurface topography • Spectral Analysis: • 2-dimensional Discrete Fourier Transform • Continuous Wavelet Analysis • Specifically, variations in spectral power within spatial frequency bands characteristic of deep-seated landslides allow us to efficiently produce accurate kilometer-scale, deep-seated landslide maps.

13. Types of Relief

14. The functional relationship between erosion rate and topography is central to understanding both controls on global sediment flux and the potential for feedback between tectonics, climate, and erosion in shaping topography. • These analyses support the emerging view that erosion rates adjust to high rates of tectonically driven rock uplift primarily through changes in the frequency of landslidingrather than hillslope steepness, and imply that changes in local relief play a minor role in controlling landscape-scale erosion rates in tectonically active mountain ranges.

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16. Montgomery and Brandon, 2002 Do hillslopes just get steeper as rock uplift rate (which drives erosion rate) increases?

17. Montgomery and Brandon, 2002 Tectonically Inactive Tectonically Active (compiledfrom Summer￠eld and Hulton [19] and Pazzagliaand Brandon [18], which include data updated from Ahnert [17]).

18. Montgomery and Brandon, 2002 OR

19. Metrics of Topography • Other Metrics • Relief • Roughness • Cross section • Longitudinal section • Swath Profile • Change detection • Subsurface topography • Spectral Analysis: • 2-dimensional Discrete Fourier Transform • Continuous Wavelet Analysis • Specifically, variations in spectral power within spatial frequency bands characteristic of deep-seated landslides allow us to efficiently produce accurate kilometer-scale, deep-seated landslide maps.

20. http://www.watershedsciences.com/about/news/automated-landslide-detectionhttp://www.watershedsciences.com/about/news/automated-landslide-detection

21. How do they do that? • WSI’s landslide detection relies on a detailed bare-earth DEM derived from a LiDAR ground model. Ground-classified LiDAR points are first processed to create a variety of raster images (≤ 1-meter cell size), which reveal different landform features unique to landslides and faults. These images are then used as inputs for a series of semi-automated classification steps. The various algorithms include a combination of: • iso-clustering, • principal component analysis, and • support vector machines, • which result in an initial landslide classification. http://www.watershedsciences.com/about/news/automated-landslide-detection

22. Metrics of Topography • Other Metrics • Relief • Roughness • Cross section • Longitudinal section • Swath Profile • Change detection • Subsurface topography • Spectral Analysis: • 2-dimensional Discrete Fourier Transform • Continuous Wavelet Analysis • Specifically, variations in spectral power within spatial frequency bands can characteristic of deep-seated landslides allow us to efficiently produce accurate kilometer-scale, deep-seated landslide maps.

23. (slides from Jim McKean, USFS)

24. Many signals in the earth sciences look like this.

25. Periodic Signals are Everywhere

29. Wavelet Fitting

30. Wavelet Requirements Wikipedia, 2013

43. Unfailed Terrain Failed Terrain

44. Fourier Transforms Continuous Wavlet Analysis

47. Relevant Timescales Korup et al, 2010

48. A very important website • http://blogs.agu.org/landslideblog/