Remote Sensing Tools Advance Avalanche Research

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The USGS Snow and Avalanche Project (SNAP) uses remotely sensed technologies to understand snowpack changes  that influence water storage, recreation, avalanche hazard and acts as a driver of landscape change.  Satellites, uninhabited aerial systems (UAS), and structure-from-motion (SfM) photogrammetry are some of the tools scientists use to collect high resolution imagery that  supports ongoing snow science research and provides practical new tools that advance avalanche forecasting and benefit resource management. 

Researchers prepare to fly drone in snowy mountain terrain

USGS scientists prepare to fly a drone for avalanche studies in Glacier National Park. (Credit: USGS. Public domain.)

Remote Sensing  Provides New Perspective 

Scientists from diverse disciplines utilize remote sensing tools to provide a birds-eye view of the landscape and efficiently collect valuable high resolution data. For snow scientists and avalanche forecasters, knowing the distribution of snow across the landscape in relation to weather events is critically important, but challenging to obtain due to complex terrain, inherent risks, and spatial limitations. Remote sensing tools provide  comprehensive data and a safer alternative to collecting snow depth and distribution information over a variety of spatial scales. The USGS Snow and Avalanche Project (SNAP) employs a variety of remote sensing techniques to support studies that focus on how avalanches act as both a hazard and a driver of landscape change.  Ongoing research include these remote sensed applications:


One Photo and one Structure from Motion elevation model showing avalanches

Pair of images: Left - Heaven's peak avalanches and Right - Structure from Motion output showing change in surface elevation where avalanche have occured. (Credit: USGS. Public domain.)

  • Uninhabited Aerial Systems (UAS), commonly known as “drones,” are used to photograph complex mountain terrain as the first step in creating high resolution maps of snow surface elevation. A map of snow depth is created by comparing the snow surface elevation at different times, known as “differencing.” Data collected this way in short intervals, such as before and after storm or wind events, provides avalanche specialists with detailed snow depth information critical for avalanche forecasting. At longer intervals and larger spatial scales, such as over a winter season in a mountain range, differencing maps created from UAS provides resource managers with detailed estimates of landscape scale snowpack as an indicator of water storage and flood risk. Scientists also use UAS to capture imagery of the spatial extent of avalanche activity over a given area.


  • Structure from Motion (SfM) Photogrammetry  is a method that approximates three dimensional structure from high resolution photography and location information to create digital elevation or surface models. Images captured by UAS are processed using SfM to provide scientists with high spatial resolution mapping of complex terrain to study snow distribution and avalanche processes.
Satellite and Lidar image of avalanche path

Satellite image with avalanche study paths outlined (left panel; credit, Google Earth) and Lidar output showing relative canopy height (right panel; credit:USGS). (Click image to see full Google Earth image attribution)


  • Lidar which stands for Light Detection and Ranging, is a technique that uses a pulsed laser to measure distances to create high resolution three dimensional elevation maps to compare snow depth change and detailed vegetation mapping in Pair of images: Google Earth satellite image with avalanche study paths outlined and Lidar output showing relative canopy height.avalanche paths to assess changes in vegetation associated with different avalanche occurrence intervals.


Satellite images over time (1990 and 2014)

By comparing vegetation patterns over a time sequence of satellite images, scientists create a chronology of avalanche events for specific paths. (Click image to see full Google Earth image attribution)

  • Satellite imagery, which now spans decades, provides scientists with a record of landscape change. USGS scientists evaluate the efficacy of using satellite imagery to create a regional chronology of avalanche disturbance to complement research on avalanche frequency and magnitude. The use of pattern recognition techniques applied to remotely sensed products can be used to examine changes in vegetation within and around avalanche paths to provide a measure of avalanche frequency.


The application of these technologies to snow science provides researchers with spatial data at multiple scales, from a single slope or known hazard zone, to a full watershed scale where snowpack data are used in regional hydrologic analyses. Scientists working on the USGS SNAP  continue to explore innovative ways to use these rich sources of data to expand the understanding of snow on the landscape and advance avalanche forecasting.


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