Remote Sensing of Forest Structure Van R. Kane College of Forest Resources.
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Transcript of Remote Sensing of Forest Structure Van R. Kane College of Forest Resources.
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Remote Sensing of Forest Remote Sensing of Forest StructureStructure
Van R. Kane College of Forest Resources
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Book Keeping StuffReading 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
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Today’s Topic How do you pull measurements of
physical world out of remote sensing data?
Approaches Problems Spectral and LiDAR
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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?
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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
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Spectral Mixture Analysis
Each pixel’s spectra dominated by a mixture of spectra from dominant material within pixel area
Sabol et al. 2002Roberts et al. 2004
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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
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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”
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Test Relationship
Rumple
Mod
eled
sel
f-sha
dow
ing
Kane et al. (2008)
Beer time!
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Reality Check
Kane et al. (2008)Topography sucks #!@^% Trees!
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One Year Later…
No beer… but Chapter 1 of dissertation
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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
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Samples of LiDAR Data
400 x 400 ft 400 x 10 ft
Point Cloud
Canopy Surface Model
Old-growth stand Cedar River Watershed
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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
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Physical Model
Height(95th percentile)
Canopy density(# canopy hits/# all pulses)
Rumple(area canopy surface/area ground surface)
Calculate for 30 m grid cells
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Classify Sites by Using LiDAR Metrics
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5Class
8 7 6 5 4 3 2 1
Rum
ple
Inde
x
0 6 12 18 24 30 36 42 48 54 60 660.0
0.2
0.4
0.6
0.8
1.0
Can
opy
Den
sity
95th Percentile Height (m)
1
2
3
12
3
1 – Closure2 – Low complexity3 – High complexity
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!
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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…
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Another Year Later…
Still no beer, but have 2nd chapter of dissertation…
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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
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But … Remote sensing can open up avenues of
research at scales impossible with field work alone