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Remote Sensing of Forest Structure

Remote Sensing of Forest Structure. Van R. Kane College of Forest Resources. Book Keeping Stuff. Reading assignment: Ch. 8.21 - LiDAR (p. 714 - 726) Next lecture – Radar Radar tutorials: http://satftp.soest.hawaii.edu/space/hawaii/vfts/kilauea/radar_ex/intro.html

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Remote Sensing of Forest Structure

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  1. Remote Sensing of Forest Structure Van R. Kane College of Forest Resources

  2. Book Keeping Stuff • Reading assignment: • Ch. 8.21 - LiDAR (p. 714 - 726) • Next lecture – Radar • Radar tutorials: • http://satftp.soest.hawaii.edu/space/hawaii/vfts/kilauea/radar_ex/intro.html • http://www.fas.org/irp/imint/docs/rst/Sect8/Sect8_1.html • http://southport.jpl.nasa.gov/index.html

  3. Today’s Topic • How do you pull measurements of physical world out of remote sensing data? • Approaches • Problems • Spectral and LiDAR

  4. Forests and Remote Sensing • Remote Sensing of Environment - 2008 • 117 papers on forest remote sensing (35%) • Research goals • Biomass (where’s the carbon?) • Presence (has something removed it?) • Productivity (how much biological activity?) • Fire mapping (where? how bad?) • Map habitat (where can critters live?) • Composition (what kinds of trees?) • Structure (what condition? how old?) • Map by • Space – where? • Time – change?

  5. What is structure? Vertical and horizontal arrange of trees and canopy Why structure? Reflects growth, disturbance, maturation Surrogate for maturity, habitat, biomass… We’ll look at just two attributes Tree size (height or girth) Canopy surface roughness (rumple) ~ 50 years ~ 50 years ~ 125 years ~ 125 years ~ 300 years ~ 300 years Goal: Map Forest Structure Robert Van Pelt

  6. Spectral Mixture Analysis Sabol et al. 2002 Roberts et al. 2004 Each pixel’s spectra dominated by a mixture of spectra from dominant material within pixel area

  7. Endmember Images Original Landsat 5 image (Tiger Mountain S.F.) Shade (darker = more) Conifer (deciduous is ~ inverse for forested areas) Lighter = more NPV (lighter = more)

  8. Physical Model Measure “rumple” • More structurally complex forests produce more shadow • We can model self-shadowing • Use self-shadowing to determine structure

  9. Test Relationship Modeled self-shadowing Rumple Beer time! Kane et al. (2008)

  10. Reality Check #!@^% Trees! Topography sucks Kane et al. (2008)

  11. One Year Later… No beer… but Chapter 1 of dissertation

  12. New Instrument - LiDAR Systems • Scanning laser emitter-receiver unit tied to GPS & inertial measurement unit (IMU) • Pulse footprint 20 – 40 cm diameter • Pulse density 0.5 – 30 pulses/m2 • 1 – 4 returns per pulse

  13. Samples of LiDAR Data Point Cloud 400 x 10 ft 400 x 400 ft Canopy Surface Model Old-growth stand Cedar River Watershed

  14. What LiDAR Measures • x, y, z coordinates of each significant reflection • Accuracies to ~10-15 cm • Height measurements • Max, mean, standard deviation, profiles • Measures significant reflections in point cloud not specific tree heights • Canopy density • Hits in canopy / all hits • Shape complexity • Canopy surface model • Intensity (brightness) of return • Near-IR wavelength typically used, photosynthetically active material are good reflectors

  15. Physical Model Canopy density (# canopy hits/# all pulses) Height (95th percentile) Rumple (area canopy surface/area ground surface) Calculate for 30 m grid cells

  16. Classify Sites by Using LiDAR Metrics • Statistically distinct classes • Distinct groupings of height, rumple, density values • Easy to associate classes with forest development • Class 8 old growth • Class 3 early closed canopy Beer time! Kane et al. (in review)

  17. Reality Check • Older stands more likely in more complex classes and vice versa • But the variation! • Young and older forests in same classes • Wide range of classes within age ranges • Possible Explanations: • Multiple forest zones, presence or absence of disturbance, site productivity, conditions of initiation… #!@^% Trees!

  18. Another Year Later… Still no beer, but have 2nd chapter of dissertation…

  19. Some Remote Sensing Thoughts • Remote sensing rarely gives answers • Remote sensing provides data that must be interpreted with intimate understanding of the target system • Data must be tied to a physical model of the target system • The more directly the measurement is tied to the physical properties of the system, the easier it is to interpret and apply • In many ways harder than research that collects field data because you must be familiar with both the technical methods of remote sensing and intimately familiar with the target system • You’ll read twice as many papers at a minimum

  20. But … • Remote sensing can open up avenues of research at scales impossible with field work alone

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