The document summarizes a study that used LIDAR data from 2005 and 2009 to analyze changes in elevation of gravel bars and the migration of channels in a section of the North Fork of the Nooksack River in Washington. Bare earth layers were generated from the LIDAR data and analyzed in ArcGIS to detect changes. Results showed gravel bars gained up to 4 meters and lost up to 3 meters in elevation over the 4 year period, indicating significant river migration. While LIDAR only measures surface elevations, the analysis demonstrated it can effectively monitor short-term stream changes and the method has various applications for research and conservation efforts.

1. Using LIDAR Data to Monitor Channel Migration in the Nooksack River
Washington
Drew T. Carey
03/15/2013

2. Abstract:
The purpose of this particular lab was to further explore the uses of the USFS Fusion program by
developing our own bare earth layers from all returns LIDAR data for a GIS change analysis. The study
area for the analysis is a small 21.5 square mile section of the North Fork of the Nooksack River in
Whatcom County Washington. A bare earth layer was generated from all returns LIDAR data collected
by the Nooksack Tribe for the river in 2005 and 2009. These bare earth layers were then imported into the
ArcGIS software program to conduct a change detection analysis to monitor stream migration of the
gravel bars within the river. Overall analysis indicated that gravel bars rose up to 4 meters and also
diminished up to 3 meters in elevation. Based on this analysis it has become quite clear that the Nooksack
river migrates at such a past pace that a change detection can be conducted for as short a time interval as
four years.
Methods:
Analysis methods written by Dr. Wallin 2013 were followed for processing the data. LIDAR data
from 2005 & 2009 was acquired by the Nooksack tribe and obtained for analysis through Dr. Wallin and
Western Washington University. The basic process for the analysis used two software programs; the first
was the USFS program Fusion and the second is ESRI’s ArcGIS program. Within the Fusion program all
returns LIDAR data from 2005 & 2009 was first imported in the form of ASCII text files and then
converted to LAS files. Once this was complete a series of commands were executed using the command
prompt dialog box to filter through the all returns data and generating a proper bare earth layer for both
years to use for the analysis. The first of these commands is known as the Groundfilter Command.
“GroundFilter is designed to filter a cloud of LIDAR returns to identify those returns that
lie on the probable ground surface (bare-earth points). GroundFilter does not produce a
perfect set of bare-earth returns in that it does not completely remove returns from large,
relatively flat, elevated surface such as building roofs. Most vegetation returns are

3. removed with the appropriate coefficients for the weight function and sufficient
iterations. Experimentation with GroundFilter has shown that the default coefficients for
the weight function produce good results in high-density point clouds (> 4 returns/sq m)”
(McGaughey 2013).
Once ground filter was run the next command to create the bare earth surface is grid surface
create.
GridSurfaceCreate creates a gridded surface model using collections of random points.
The surface model is stored in PLANS DTM format using floating point elevation values.
Individual cell elevations are calculated using the average elevation of all points within
the cell. GridSurfaceCreate is most often used with bare-earth point sets obtained from
LIDAR vendors or by using the GroundFilter program (McGaughey 2013).
The grid surface create command produces a .las file that was then converted into ASCII format for
import into ArcGIS for further analysis.
The ASCII text files for both the 2005 & 2009 layers was imported into the Arcmap program by
first converting it to a raster and then defining the projections for the two rasters. The projection used for
the analysis was Universal Transverse Mercator North American Datum 1983 zone 10 north. Once both
rasters were in the same projection I used the tool raster calculator to conduct the final change analysis by
subtracting the 2005 layers values from the 2009 layer to view the changes in elevation between 2005 and
2009 in the Nooksack river. I then classified the image using the density slicing method within arcmap to
give it a visually interpretable color scheme.

4. Results:
Overall results from the change detection analysis indicated that gravel bars in the Nooksack river
gained up to 4 meters in elevation as well as lost up to 5 meters in elevation, causing the river to migrate
it’s course by several meters in just a 4 year interval. The final output from the change analysis conducted
in ArcGIS was a bare earth layer of the Nooksack River with associated histogram of values indication
the gain or loss in elevation of the rivers gravel bars. A visual analysis of the image (figure 1) shows the
creation and destruction of several cut-banks and point bars within the river. Observation of the image
also depicts the migration of the main river channel over the four year time interval.
Discussion:
Overall the method of using all returns LIDAR data with the Fusion software program to generate
bare earth layers for a change detection analysis proved to be quite efficient and effective. However the
only drawback of this method is the data itself. LIDAR only measures the surface of the water and gravel
bars and thus we can only monitor stream and gravel bar migration to a certain extent. As the data only
measures the surface of the water we cannot monitor changes in the actual depth of the river and its
channels. Sediment within the water body will also affect the reflectance values obtained as heavily
sedimented water will reflect more light than clear water. This can cause confusion in the data is one
image was acquired when the river was clear and one when it had an abundance of sediment.
Overall however results indicating stream change and migration can now be utilized as an
effective tool by scientists and researchers to monitor different aspects of the river. One example of the
effective use of similar data was a study conducted by The Department of the Interior to monitor the
effects of land surface disturbances and stream migration on archaeological sites in the American Falls
Archaeological district, Idaho. By conducting a change detection analysis on LIDAR data collected in
2003, 2009 and 2011 researchers were able to identify key sites that land surface disturbances were
impacting and should be targeted for conservation.

5. An additional use for this data is to aid in creating a predictive model for stream migration and its
potential impacts on nearby houses and roads. However the original use of this data was to aid fisheries
biologists in determining the impacts of stream change and channel migration on the spawning of salmon
in the river. By monitoring stream change and its impacts on salmon spawning grounds biologists can
utilize this data to create a comprehensive plan for the mitigation, protection and restoration of the
species. Thusly we have seen how LIDAR data can be an effective tool aiding many different fields of
research benefitting both the private and the public sectors.

6. Figure 1: The Final change detection analysis image for the Nooksack River. Image shows
the elevation changes within the river between 2005 and 2009.

7. Sources Cited:
 Hyatt, Tim. “Lower North Fork Nooksack Reach Assessment; Ecological Findings and
Restoration Recommendations.” Nooksack Tribe Natural Resources Department. June 1, 2005.
 Hyatt, Tim. “Project Report; Nooksack North Fork .” Terrapoint. May 5, 2005.
 Huang, Jennifer. “The Applications of Light Detection and Ranging (LIDAR) Technology to
Improve the Management and Protection of Heritage Assets in the American Falls Archaeological
District, Idaho.” U.S. Department of the Interior. September 2012.
 McGaughey, Robert J. “FUSION/LDV: Software for LIDAR Data Analysis and Visualization.”
United States Department of Agriculture & Forest Service. February 2013.
 Wallin, David. “Lab X: Using LIDAR Data to Monitor Channel Migration.”
http://faculty.wwu.edu/wallin/envr442/442_lidar_nooksack.htm. March 15, 2013.