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Quantitative geomorphic analysis of LiDAR datasets – application to the San Gabriel Mountains, CA Roman DiBiase LiDAR short course, May 1, 2008.

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Presentation on theme: "Quantitative geomorphic analysis of LiDAR datasets – application to the San Gabriel Mountains, CA Roman DiBiase LiDAR short course, May 1, 2008."— Presentation transcript:

1 Quantitative geomorphic analysis of LiDAR datasets – application to the San Gabriel Mountains, CA Roman DiBiase LiDAR short course, May 1, 2008

2 Quantitative analysis of topography using LiDAR Airborne laser swath mapping (ALSM) consistently provides data good enough to produce 1m digital elevation models (DEMs) Ground-based systems can be used for finer scale analysis of millimeter to centimeter scale features These datasets are more than just pretty pictures; many important research questions have become testable as a result of this technology

3 There are many cases where detailed terrain modeling is needed Geomorphic mapping –fault scarps, landslides, stream terraces Geomorphic process studies –soil production rates, soil transport model testing –knickpoint form, channel geometry/morphology Landscape monitoring –repeat scans using ground-based LiDAR

4 Alternatives to LiDAR Total station surveys –Time consuming!! Photogrammetry –Tree cover –Expensive

5 Field Area: San Gabriel Mountains, CA modified from Blythe et al., km N SAF = San Andreas Fault SMF = Sierra Madre Fault CF = Cucamonga Fault SGF = San Gabriel Fault = igneous/metamorphic rocks

6 10m NED Elevation (sea level – 3000 m) 30 km

7 Local relief (1km radius) West-to-east gradient in uplift rate from low to high can be inferred from topography, quaternary slip rates, and low- temperature thermochronometry work

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11 How do we obtain appropriate erosion rates? Thermochron cooling ages range from 3-60 Ma Even using geologic constraints, the inferred erosion rates are averaged over millions of years We need more geomorphically appropriate rates, on the order of landform development…

12 10 Be is produced in quartz grains through the interaction of cosmic rays with oxygen nuclei

13 Quartz grains accumulate 10 Be proportional to the time they spend within the top meter or so of Earth’s surface. Quartz grains accumulate 10 Be during their path from bedrock to stream sand

14 By analyzing a bag of sand (~1 kg) in bulk we are in effect averaging over the entire area draining to the sample Alluvial sand samples average exposure ages of millions of grains

15 Catchment-averaged sample location map So far, erosion rates range from ~10 – 1000 m/My

16 OK, now that we have erosion rates… There are a few main questions we can tackle now –How does hillslope form vary with erosion rate? –What is the erosion rate threshold for hillslope sensitivity? –How does channel steepness vary with erosion rate? –Do channels have a similar threshold? –Does channel width vary with erosion rate? –How are conditions different across transition zones (knickpoints)? –How replicable are basin cosmo rates in bedrock landscapes?

17 Which processes are acting to lower the landscape? Hillslope processes Channel incision Debris flow scour Bedrock landsliding

18 Which processes are acting to lower the landscape? Hillslope processes Channel incision Debris flow scour Bedrock landsliding Most understood!

19 What can channels tell us about erosion rates?

20 Channel long profile analysis Well-adjusted channel profiles tend to follow a power-law relationship between slope and drainage area S = k s A -  –k s = channel steepness index: varies with uplift, climate, lithology –  = concavity index: independent of uplift rate elevation distancelog A log S

21 Duvall, Kirby, and Burbank, 2004

22 Cattle Creek Slope-area plots extracted from 10m DEMs

23 Debris flow regime? Fluvial regime S=k sn A Cattle Creek Slope-area plots extracted from 10m DEMs

24 Channel steepness index, k sn Slope-area plots extracted from 10m DEMs Cattle Creek

25 Spatial variations in erosion rates red = high uplift zone blue = low uplift zone

26 Temporal variations in erosion rates Bear Creek

27 Temporal variations in erosion rates knickpoint Bear Creek

28 Temporal variations in erosion rates knickpoint k sn = 86 k sn = 192 Bear Creek

29 Map of channel steepness index variation Green = low channel steepness Red = high channel steepness

30 Channel steepness vs. cosmogenic erosion rate

31 NCALM seed project LiDAR coverage 30 km

32 200m

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34 Tutorial Channel network extraction –How do we define a channel? Scale issues –What problems do we run into when using high-resolution elevation data? –Resampling high-resolution data Techniques to probe datasets –Extracting elevation profiles, slope profiles

35 100m

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45 Channel profile extraction, comparison with field surveys

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47 Pretty darn good, though there are some funny offsets ~12m offset ~25m offset

48 Stream extracted from 2m LiDAR DEM follows a tortuous path around large boulders, etc. Channel is much wider than 1 pixel! At high flow,channel is ~15-20m wide 100m

49 LiDAR contributions to understanding channel processes Flow paths are often wrong with high-res data, meaning drainage areas are troublesome to determine Local channel slope is underestimated in some cases due to critical jump in scale to less than channel width Despite this, lidar data contain valuable information concerning knickpoint form, width variation, and potentially bed roughness

50 What can hillslopes tell us about erosion rates? Hypothesis: With increasing erosion rates, slopes steepen, soil thickness decreases, but once maximum soil production rate is exceeded, threshold, landsliding slopes dominate

51 Hillslope angle vs. cosmogenic erosion rate

52 Determining the soil production function Use 10 Be to measure soil production rate Exp. relationship between soil thickness and production Does this relationship vary with erosion rate? Does max soil production rate vary? maximum soil production rate log soil production soil thickness

53 accumulation production transport Soil continuity equation Heimsath et al., Nature 388, pp (1997)

54 The Soil Production Function assume start with continuity equation K is constant soil production topographic curvature (from LiDAR)

55 Slope dependent transport processes tree throw burrowing rain splash

56 Soil transport models In a simplified view, we can think of the previous processes as acting linearly with slope However, slopes reach a threshold near degrees, and mass wasting dominates How do we deal with this transition?

57 Non-linear soil transport One way to think about this is to have linear transport with a threshold… Field data suggest a more gradual transition to threshold slopes (Roering et al. 1999)

58 Transport model comparison Roering et al., 1999; WRR 35, p

59 LiDAR contributions to understanding hillslope processes High resolution topography is needed to characterize curvature (second derivative!!) We can use this to guide fieldwork and the construction of soil production functions calibrated with cosmogenics Differences in transport models are subtle, definitely not distinguishable at 10m, but may be resolved at 1m

60 Dimensionless relief Dimensionless erosion Even with high resolution topography, nature is still messy! How can we best extract information from high- resolution DEMs? Monte-carlo methods? Hand picking ‘representative’ hillslopes? following Roering et al. 2007

61 Ground-based scanning LiDAR Up to millimeter scale resolution Allows for extremely detailed monitoring studies, using repeat scans Potential geomorphic applications include studies of bedrock erosion, sediment transport, and bed roughness modeling Measuring bedrock erosion in the Henry Mountains, UT

62 Point cloud data from bedrock erosion monitoring on Colorado Plateau (photo-derived color) Images courtesy Steve DeLong

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64 8 scans merged together Images courtesy Steve DeLong

65 8 scans merged together (photo-derived color) Images courtesy Steve DeLong


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