Remote Sensing of Forest Structure Van R. Kane School of Forest Resources

Lecture 18 – Forest remote sensing Wednesday’s lecture Mars spectroscopy Today’s lecture: Forest remote sensing LECTURES Jan 05 1. Intro Jan 07 2. Images Jan 12 3. Photointerpretation Jan 14 4. Color theory Jan 19 5. Radiative transfer Jan 21 6. Atmospheric scattering Jan 26 7. Lambert’s Law Jan 28 8. Volume interactions Feb 02 9. Spectroscopy Feb 04 10. Satellites & Review Feb 09 11. Midterm Feb 11 12. Image processing Feb 16 13. Spectral mixing Feb 18 14. Classification Feb 23 15. Radar & Lidar Feb 25 16. Thermal infrared Mar 02 17. Mars spectroscopy (Matt Smith) previous Mar 04 18. Forest remote sensing (Van Kane) Mar 09 19. Thermal modeling (Iryna Danilina) Mar 11 20. Review Mar 16 21. Final Exam

Today’s Topics Physical measurements Change detection Approaches Problems Spectral and LiDAR Change detection

“It is, perhaps, time to draw the conclusion that current satellite sensors are not in general suitable for forestry planning since they contain little relevant information…” -- Holmgren and Thuresson (1998)

Did Someone Miss the Memo? Remote Sensing of Environment 1990: 5 papers on forest remote sensing (7%) 2000: 32 papers (25%) 2010: 89 papers (36%) Understates trend – forest ecology papers using remote sensing increasingly common in mainstream ecological journals

What Are They Studying? Research goals Map by Biomass (where’s the carbon?) Wood volume (when can we take it to the bank?) 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?

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) ~ 50 years ~ 125 years ~ 300 years ~ 50 years ~ 125 years ~ 300 years Robert Van Pelt

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

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

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

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

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

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

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 Airborne laser scanning systems have four major hardware components: a) a laser emitter-receiver scanning unit, b) GPS (aircraft and ground units), c) a highly sensitive inertial measurement unit (IMU) attached to the scanning unit, and of course, d) a computer to control the system and store data from the first three components. Laser scanners designed for terrain mapping emit near-infrared (NIR) laser pulses at a high frequency (typically 25,000 to 100,000 pulses per second). The position and attitude of the laser scanner unit at the time each pulse is emitted are determined from flight data collected by the GPS and IMU units. The range or distance between the scanner and an object that reflects the pulse is computed using the time it takes for the pulse to travel from the scanner, to the object, and back to the scanner. A precise coordinate is computed for each reflection point using the position and attitude of the scanner and the direction and distance traveled by the pulse from the scanner to the object. A swath of terrain under the aircraft is surveyed through the lateral deflection of the laser pulses and the forward movement of the aircraft. The scanning pattern within the swath is established by an oscillating mirror or rotating prism which causes the pulses to sweep across in a consistent pattern below the aircraft (See slide). Large areas are surveyed with a series of swaths that often overlap one another by 20 percent or more. The final pattern of pulse reflection points on the ground and the scanned swath width depend on the scanning mechanism settings and design (e.g. pulse rate, returns per pulse, scanning angle), flying height and speed, and the shape of the topography.

Samples of LiDAR Data Point Cloud Canopy Surface Model 400 x 400 ft 400 x 10 ft Canopy Surface Model High-density LIDAR data covering a portion of Capitol State Forest located near Olympia, Washington. Douglas-fir is dominant over the area with some Western Hemlock and Red Alder. Return data have been color-coded according to the height above ground. Old-growth stand Cedar River Watershed

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 vegetation structure 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

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

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!

Reality Check #!@^% Trees! But the variation! 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!

Still no beer, but had my dissertation and post-doc funding Another Year Later… Still no beer, but had my dissertation and post-doc funding

Remotely Sensing Forest Attributes - 1 m spectral - 24 m hyperspectral - 30 m spectral - LiDAR From Lefsky et al. 2001 Match your question to the instrument best suited to answer it

Change Detection - Fire Severity Green Vegetation Burned Vegetation A B A B B G R Landsat TM B4 Landsat TM B7 Landsat TM B4 Landsat TM B7 (1984 – 2005) Landsat MSS B2 Landsat MSS B4 Landsat MSS B2 Landsat MSS B4 (1974 – 1983) van Wagtendonk et al. 2004

Differenced Normalized Burn Ratio Pre-fire Post-fire High severity Moderate severity Low severity No fire effect dNBR Key et al. 2002, Key and Benson 1999, 2002, 2004, 2005, van Wagtendonk 2004, Miller and Fites 2006, Miller 2007

Change Detection – Regional Monitoring LandTrendr Courtesy Robert Kennedy, OSU

Local Detection, Regional Monitoring Courtesy Robert Kennedy, OSU

Some Remote Sensing Thoughts Remote sensing rarely gives answers Remote sensing provides data that must be interpreted with intimate understanding of the target system Interpretation is almost always local 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

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