Quantitative geomorphic analysis of LiDAR datasets – application to the San Gabriel Mountains, CA
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Transcript of Quantitative geomorphic analysis of LiDAR datasets – application to the San Gabriel Mountains, CA
Quantitative geomorphic analysis of LiDAR datasets –
application to the San Gabriel Mountains, CA
Roman DiBiaseLiDAR short course, May 1,
2008
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
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
Alternatives to LiDAR
• Total station surveys– Time consuming!!
• Photogrammetry– Tree cover– Expensive
Field Area: San Gabriel Mountains, CA
modified from Blythe et al., 2000
30 km
NSAF = San Andreas FaultSMF = Sierra Madre FaultCF = Cucamonga FaultSGF = San Gabriel Fault = igneous/metamorphic rocks
10m NED Elevation (sea level – 3000 m)
30 km
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
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…
10Be is produced in quartz grains through the interaction of cosmic rays
with oxygen nuclei
Quartz grains accumulate 10Be proportional to the time they spend within the top meter or so of Earth’s surface.
Quartz grains accumulate 10Be during their path from bedrock to stream sand
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
Catchment-averaged sample location map
So far, erosion rates range from ~10 – 1000 m/My
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?
Which processes are acting to lower the landscape?
• Hillslope processes
• Channel incision
• Debris flow scour
• Bedrock landsliding
Which processes are acting to lower the landscape?
• Hillslope processes
• Channel incision
• Debris flow scour
• Bedrock landsliding
Most understood!
What can channels tell us about erosion rates?
Channel long profile analysis
• Well-adjusted channel profiles tend to follow a power-law relationship between slope and drainage area
S = ks A-
– ks = channel steepness index: varies with uplift, climate, lithology
= concavity index: independent of uplift rate
elev
atio
n
distance log A
log
S
Duvall, Kirby, and Burbank, 2004
Cattle Creek
Slope-area plots extracted from 10m DEMs
Debris flow regime?
Fluvial regime S=ksnA-0.45
Cattle Creek
Slope-area plots extracted from 10m DEMs
Channel steepness index, ksn
Slope-area plots extracted from 10m DEMs
Cattle Creek
Spatial variations in erosion rates
red = high uplift zoneblue = low uplift zone
Temporal variations in erosion rates
Bear Creek
Temporal variations in erosion rates
knickpoint
knickpoint
Bear Creek
Temporal variations in erosion rates
knickpoint
knickpoint
ksn = 86ksn = 192
Bear Creek
Map of channel steepness index variation
Green = low channel steepness Red = high channel steepness
Channel steepness vs. cosmogenic erosion rate
NCALM seed project LiDAR coverage
30 km
200m
200m
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
100m
100m
1350
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1550
6400 6600 6800 7000 7200 7400 7600 7800
distance (m)
ele
va
tio
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m)
2m Lidar
field survey
Channel profile extraction, comparison with field surveys
Channel profile extraction, comparison with field surveys
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distance (m)
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m)
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field survey
1450
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7100 7150 7200 7250 7300 7350 7400 7450 7500
distance (m)
ele
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m) 2m Lidar
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Pretty darn good, though there are some funny offsets
~12m offset
~25m offset
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
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
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
Hillslope angle vs. cosmogenic erosion rate
Determining the soil production function
• Use 10Be 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
prod
uctio
n
soil thickness
ssrs qt
e
t
h ~
accumulation
production
transport
Soil continuity equation
Heimsath et al., Nature 388, pp 358-361 (1997)
The Soil Production Function
ssrs qt
e
t
h ~
zqs ~
zt
e
r
s 2
0t
hassume
start with continuity equation
K is constant
soil production topographic curvature
(from LiDAR)
Slope dependent transport processes
tree throw
burrowing
http://wdfw.wa.gov/wlm/living/gophers.htm
rain splash
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 35-40 degrees, and mass wasting dominates
• How do we deal with this transition?
zqs ~
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
cs S
zzq 1~
(Roering et al. 1999)
zKqs ~cSz 0
Transport model comparison
Roering et al., 1999; WRR 35, p. 853-870
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
Dimensionless relief
C
HHT
c
Hsr
c
S
LC
KS
LEE
S
SR
2)/(2*
*
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
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
Point cloud data from bedrock erosion monitoring on Colorado
Plateau(photo-derived color)
Images courtesy Steve DeLong
Images courtesy Steve DeLong
8 scans merged together
Images courtesy Steve DeLong
8 scans merged together (photo-derived color)
Images courtesy Steve DeLong