1. Lecture 17 – Forest remote sensing  Reading assignment:  Ch 4.7, 8.23,  Kane et al., 2008. Interpretation and topographic correction of conifer forest canopy self-shadowing using spectral mixture analysis. Remote Sensing of Environment 112(10), 3820-3832 (class website) Next lecture – Radar Radar tutorials:  http://satftp.soest.hawaii.edu/space/hawaii/vfts/kilauea/radar _ex/intro.html 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://www.fas.org/irp/imint/docs/rst/Sect8/Sect8_1.html  http://southport.jpl.nasa.gov/index.html http://southport.jpl.nasa.gov/index.html Tuesday, 2 March 2010

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

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. Goal: Map Forest Structure  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) Robert Van Pelt ~ 50 years ~ 125 years ~ 300 years ~ 50 years ~ 125 years ~ 300 years

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

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

8. Physical Model 1)More structurally complex forests produce more shadow 2)We can model self-shadowing 3)Use self-shadowing to determine structure Measure “rumple”

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

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

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/m 2  1 – 4 returns per pulse

13. Samples of LiDAR Data 400 x 400 ft 400 x 10 ft Point Cloud 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 Height (95 th percentile) Canopy density (# canopy hits/# all pulses) 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 Kane et al. (in review) Beer time!

17. Reality Check #!@^% Trees! 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…

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

21.  Surface roughness from space Next lecture: