Remote Sensing of ForestStructureRemote Sensing of ForestStructure
Van R. Kane
College of Forest Resources
Book Keeping Stuff
Reading assignment:
Ch. 8.21 - LiDAR (p. 714 - 726)
Next lecture – Radar
Radar tutorials:
Today’s Topic
How do you pull measurements ofphysical world out of remote sensingdata?
Approaches
Problems
Spectral and LiDAR
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?
Goal: Map Forest Structure
What is structure?
Vertical and horizontalarrange of trees and canopy
Why structure?
Reflects growth,disturbance, maturation
Surrogate for maturity,habitat, biomass…
We’ll look at just twoattributes
Tree size (height or girth)
Canopy surface roughness(rumple)
Robert Van Pelt
~ 50 years
~ 125 years
~ 300 years
~ 50 years
~ 125 years
~ 300 years
Spectral Mixture Analysis
Each pixel’s spectra dominated by a mixture ofspectra from dominant material within pixel area
Sabol et al. 2002
Roberts et al. 2004
Endmember Images
NPV
(lighter = more)
Original
Landsat 5
image
(Tiger Mountain S.F.)
Shade
(darker = more)
Conifer
(deciduous is ~ inversefor forested areas)
Lighter = more
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”
Test Relationship
Rumple
Modeled self-shadowing
Kane et al. (2008)
Beer time!
Reality Check
Kane et al. (2008)
Topography sucks
#!@^% Trees!
One Year Later…
No beer… but Chapter 1 of dissertation
New Instrument - LiDAR Systems
Scanning laseremitter-receiver unittied to GPS & inertialmeasurement unit(IMU)
Pulse footprint 20 –40 cm diameter
Pulse density 0.5 – 30pulses/m2
1 – 4 returns perpulse
Samples of LiDAR Data
400 x 400 ft
400 x 10 ft
Point Cloud
Canopy Surface Model
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 point cloud not specifictree heights
Canopy density
Hits in canopy / all hits
Shape complexity
Canopy surface model
Intensity (brightness) of return
Near-IR wavelength typically used, photosynthetically activematerial are good reflectors
Physical Model
Height
(95th percentile)
Canopy density
(# canopy hits/# all pulses)
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 classeswith forest development
•Class 8 old growth
•Class 3 early closed canopy
Kane et al. (in review)
Beer time!
Reality Check
#!@^% Trees!
•Older stands more likely in morecomplex classes and vice versa
•But the variation!
•Young and older forests insame classes
•Wide range of classes withinage ranges
•Possible Explanations:
•Multiple forest zones, presenceor absence of disturbance, siteproductivity, conditions ofinitiation…
Another Year Later…
Still no beer, but have 2nd chapter of dissertation…
Some Remote Sensing Thoughts
Remote sensing rarely gives answers
Remote sensing provides data that must be interpretedwith intimate understanding of the target system
Data must be tied to a physical model of thetarget system
The more directly the measurement is tied to thephysical properties of the system, the easier it is tointerpret and apply
In many ways harder than research that collectsfield data because you must be familiar withboth the technical methods of remote sensingand intimately familiar with the target system
You’ll read twice as many papers at a minimum
But…
Remote sensing can open up avenues ofresearch at scales impossible with fieldwork alone
