Remote Sensing of Forest Remote Sensing of Forest StructureStructure

Van R. Kane

School of Forest Resources

Lecture 18 – Forest remote sensingLecture 18 – Forest remote sensing

LECTURESJan 05 1. IntroJan 07 2. ImagesJan 12 3. PhotointerpretationJan 14 4. Color theoryJan 19 5. Radiative transferJan 21 6. Atmospheric scatteringJan 26 7. Lambert’s LawJan 28 8. Volume interactionsFeb 02 9. SpectroscopyFeb 04 10. Satellites & ReviewFeb 09 11. MidtermFeb 11 12. Image processingFeb 16 13. Spectral mixingFeb 18 14. ClassificationFeb 23 15. Radar & LidarFeb 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. ReviewMar 16 21. Final Exam

Wednesday’s lecture

Mars spectroscopy

Today’s lecture:

Forest remote sensing

Today’s Topics

Physical measurements 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 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)

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 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

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

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 had my dissertation and post-doc funding

Remotely Sensing Forest AttributesRemotely 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 SeverityChange Detection - Fire Severity

van Wagtendonk et al. 2004

Landsat TM B4 Landsat TM B7 (1984 – 2005)Landsat TM B4Landsat TM B7

Green Vegetation Burned Vegetation

B G R

Landsat MSS B2 Landsat MSS B2Landsat MSS B4 Landsat MSS B4 (1974 – 1983)

A

A

B

B

Differenced Normalized Burn RatioDifferenced Normalized Burn Ratio

Pre-fire Post-fire

74

741000

BB

BBNBR

POSTPRE NBRNBRdNBR dNBR

High severity

Moderate severity

Low severity

No fire effect

Key et al. 2002, Key and Benson 1999, 2002, 2004, 2005, van Wagtendonk 2004, Miller and Fites 2006, Miller 2007

Change Detection – Regional MonitoringChange Detection – Regional Monitoring

Courtesy Robert Kennedy, OSU

LandTrendr

Local Detection, Regional MonitoringLocal 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