LIDAR Based Stand Delineation In Natural Stands

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LIDAR Based Stand Delineation In Natural Stands

• Alicia Sullivan
• Precision Forestry Cooperative
• University of Washington
• Seattle, WA

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Project background

• Funded by the Bureau of Land Management.
• Federal Agency.
• Approximately 2 million acres of forest land in
  western Oregon.
• Generates income for Counties through harvesting
  activities.

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Background contd

• First step in a LIDAR based inventory.
• Out of date inventory.
• Challenges with large ownership area.
• Time for re-inventory 10 years.
• Cost for field crews.
• LIDAR will be flown statewide for terrain
  mapping.
• Fewer resources available as industry changes.

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Background Contd

• Current stand delineation achieved through manual
  photo interpretation.
• Time and labor intensive.
• Hard to consistently reproduce.
• Often based on local foresters knowledge,
  disappears when they retire.

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Project Objectives

• Enable the BLM to respond to the current
  situation, and improve forest management
  practices.
• Develop a repeatable method for forest stand
  delineation from LIDAR data.
• Assess the accuracy of the method.

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What is LIDAR?

• Active sensor that emits laser pulses.

• GPS and inertial system to accurately identify
  plane location.
• Records X, Y, and Z coordinates for returned
  pulses.

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What does that look like?
Raw LIDAR Data
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Urban Application
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Example of a hillshade from LiDAR data From
quake.usgs.gov/.../geology/lidar/example1.html
Most common application is terrain modeling.
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Method Development

• Variation on a common technique.
• Object based image classification, with a twist.
• Image bands were LIDAR derived raster datasets.

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Software

• FUSION
• Developed by the USFS for viewing and analysis of
  LIDAR data.
• SPRING
• Developed by Brazilian Govt. Object based image
  classification.
• ESRI ArcMap-
• Viewing of final products.

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Data sets and LIDAR

• Study area-
• Blue Ridge study site, Capitol Forest. Southwest
  of Seattle.
• LIDAR datasets-
• 1999 helicopter platform, 4 returns per square
  meter.
• 2003 airplane platform, 4 returns per square
  meter.

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Percent Cover- 0 to 100
Stem Density- per pixel
Average Height- per pixel
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Classification Results
Classes Mature Clearcut/Road Thinned Intermediate
Young 1 Young 2
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Accuracy Assessment

• Used an error matrix to evaluate classification.
• Reports users accuracy, producers accuracy,
  overall accuracy.
• KHAT and Z-statistics were calculated for each
  classification.
• Congalton and Green 1999.

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1999 Error Matrix
Visual Classification
Computer Classification
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Accuracy Assessment 1999
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2003 Error Matrix
Visual Classification
Computer Classification
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Accuracy Assessment 2003
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Importance of Results

• Demonstrates stand delineation with LIDAR is
  possible.
• Accuracies are similar to photo interpretation.
• Potential for significant savings in time to
  delineate stands over large areas.
• Potential savings in cost for ownerships with
  LIDAR coverage.

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Thank you to

• U.S. Bureau of Land Management
• Precision Forestry Cooperative
• Robert J. McGaughey
• Dr. Peter Schiess, Dr. L.M. Moskal, Dr. David
  Briggs, Dr. Hans-Erik Andersen
• Akira Kato

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• Questions?
• Contact Info
• as14_at_u.washington.edu
• http//students.washington.edu/as14