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://www.fas.org/irp/imint/docs/rst/Sect8/Sect8_1.html http://southport.jpl.nasa.gov/index.html

Tuesday, 2 March 2010

Remote Sensing of Forest Remote Sensing of Forest StructureStructure

Van R. Kane

College of Forest Resources

Today’s Topic

How do you pull measurements of physical world out of remote sensing data?

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 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

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

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

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

Mo

del

ed s

elf-

shad

ow

ing

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 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

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 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

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

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5Class

8 7 6 5 4 3 2 1

Ru

mp

le In

dex

0 6 12 18 24 30 36 42 48 54 60 660.0

0.2

0.4

0.6

0.8

1.0

Ca

no

py

Den

sity

95th Percentile Height (m)

1

2

3

1

2

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!

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…

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 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

But…

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

Surface roughness from space

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