New 3D Mapping Technique Improves Landslide Hazard Prediction
Landslides cause loss of life and billions of dollars in damage each year. The ability to predict them accurately can reduce both.
Slow-moving
landslides, places where the land creeps sluggishly downhill over long periods
of time, are relatively stable - until they aren't. When they become unstable,
which can happen for a variety of reasons - including heavy rain, snowmelt,
earthquakes and volcanic activity - landslides can quickly turn catastrophic,
especially in populated areas.
"It's
important to understand how landslides work and how they respond to
environmental changes so that we can better predict when they might transition
from this gradual motion to a more rapid, catastrophic failure," said NASA
Jet Propulsion Laboratory scientist Eric Fielding, coauthor of a new study focused
on just that.
Because
landslides are often inaccessible and don't respond uniformly to changes, they
can be difficult to predict. But the study team, which includes collaborators
from the University of California, Berkeley and the U.S. Geological Survey (USGS),
has developed a new technique to make prediction both easier and more accurate.
"By
combining multiple datasets from the subsurface, ground surface, air and space,
we constructed a mechanical framework to quantify different features and
movements of the landslide," said lead author Xie Hu of UC Berkeley.
"High-resolution synthetic aperture radar data from JPL's airborne UAVSAR instrument was particularly
important in developing this framework."
UAVSAR, or Uninhabited
Aerial Vehicle Synthetic Aperture Radar, is attached to the bottom of an
airplane. When the plane flies over a specific area, the instrument measures
the ground level with extreme accuracy. When it flies over that same area
again, scientists can glean how much and in which direction the land has moved
since the previous flyover. Because it's attached to a plane, rather than a
satellite like some similar instruments, scientists can design flight plans to perform
multiple passes precisely over the same area in a short amount of time.
The study team
centered their research on the 2.5-mile-long (4-kilometer-long) Slumgullion
landslide in southwestern Colorado. In motion for well over a century, this
landslide provides an ideal natural laboratory for studying the dynamics of
slow-moving landslides. They acquired data from multiple UAVSAR flights over
Slumgullion each year between 2011 and 2018 and incorporated this data into
their new mapping framework.
"By
flying over this landslide multiple times, in different directions at
perpendicular angles, we had sufficient data to reconstruct the full
three-dimensional motion in very clear detail as well as how it varies over the
years," said Fielding.
Although
UAVSAR doesn't measure deformation at depth directly, the study team integrated
the instrument's 3D data into their new mapping process, which enabled them to
model the various depths and movements of the slide. Depth is a significant
factor in determining when and where a landslide is likely to become unstable.
According
to coauthor Bill Schulz of the USGS, most of the action happens at the bottom
of the landslide, with everything sliding on a thin shear zone that may be only
a few centimeters thick. Because the depth can vary greatly across a single
landslide, different parts of the slide will respond to changes in pressure at
different times.
"Groundwater
pressure - from rain and snowmelt, for example - changes first right at the
ground surface and last at the bottom of a landslide," said Schulz.
"So if one area of a landslide is half as deep as another, the area that's
half as deep will respond first to the change in pressure." The new
framework takes this critical depth information into account.
In addition to
its bigger-picture implications for improving the forecasting of catastrophic
landslide hazards, the study provided insights specific to Slumgullion.
"We found
that the central part of this landslide moves quickly - about an inch per day,
every day. But in observing all of the data over time, we see that the top and
bottom parts are moving, too, just far more slowly," said Fielding.
"We also found that the uppermost part of the landslide responds most
quickly to spring snowmelt and that the central part responds significantly to
annual variation - drought years versus wet years."
Overall, the
new framework can be used to improve the accuracy of landslide forecasting and
in turn, enable land managers and relevant authorities to better mitigate
landslide risks, potentially saving lives.
The study was
published Wednesday in Nature Communications.
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