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Landsat and Vegetation Change: Towards 50 Years of Observation and Characterization. Warren B. Cohen USDA Forest Service, PNW Research Station, Corvallis, OR. Collaborators: S. Goward, C. Haung, S. Healey, R. Kennedy, J. Masek, G. Moisen, S. Powell, T. Schroeder, Z. Yang.

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slide1

Landsat and Vegetation Change: Towards 50 Years of Observation and Characterization

Warren B. Cohen

USDA Forest Service, PNW Research Station, Corvallis, OR

Collaborators: S. Goward, C. Haung, S. Healey, R. Kennedy, J. Masek, G. Moisen, S. Powell, T. Schroeder, Z. Yang

LDCM Science Team Meeting, 9-11 January 2007

slide2

Two Main Studies

  • 1. NACP/USFS FIA integration
    • Initial focus on providing sample-based estimation of forest disturbance and succession dynamics for the entire conterminous US since 1972
    • Accelerate the operational use of Landsat data by the Forest Service in the nation’s ongoing forest census
slide3

Chosen run

Probability of inclusion

National-level estimation of forest dynamics

  • Random sample constrained by several criteria, e.g.,
    • Dispersion of scenes
    • Capture of all major forest types
    • Preference for high forest-area in each selected scene
    • Inclusion of certain fixed scenes
slide4

Take advantage of temporal richness of Landsat data

Longer-term trends emerge above the noise of year-to-year variation and may be the most reliable signal

Each sample scene consists of ~ 2-year interval data cube

1972

2006

slide5

1972

LEDAPS processing

Continuous change in biomass

Biomass (lbs/acre)

2006

0

300,000

Arizona

Predicted

Observed

FIA plot data; e.g., biomass

fit curves to trends
Fit Curves to Trends

Parameters of best-fitting trajectory define its shape, e.g. using spectral band

  • P(0) Year of disturbance
  • P(2) Intensity of disturbance
  • P(3) Rate of recovery

Kennedy, Cohen et al., in review

slide7

Regrowth

Disturbance

Intensity of disturbance = biomass at beginning of disturbance segment – biomass at the end

Recovery rate = slope of regrowth segment in units of biomass/yr; 2 disturbance segments

Regrowth

Disturbance

Disturbance

slide8

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

One disturbance two recovery segments (both subtle)

slide9

Studies, cont.

  • 2. NPS Inventory & Monitoring
    • I&M protocols, based on integration of limited field data and low cost remote sensing (i.e., Landsat, MODIS, …)
    • Broad array of vegetation types, change agents, and questions
    • Diversity in emphasis and modest funding requires robust, generally applicable strategy
slide10

NCCN

SWAN

NCPN/SCPN

NPS I&M network research

slide11

Time 2 spectral data

Spectral contrast data

Time 2 baseline / updated map

Change Map

Baseline map updating over n2-date intervals

Time 1 spectral data

Time 1 baseline map

idealized distributions of basic physiognomic types in landsat spectral space
Idealized Distributions of Basic Physiognomic Types in Landsat Spectral Space

Dense broadleaf/ grass

Conifer/Broad-leaf Mix

Closed-canopy conifer

Broadleaf tree/shrub

Water/Deep shade

Increasing TC Greenness

Mixed

Open: Bright

Snow and ice

Open: Dark

Increasing TC Brightness

change detection 2 date change in pom

Pixel starts here…

… and ends here

Open: Bright

Change detection: 2-date change in POM

Dense broadleaf/ grass

Conifer/Broad-leaf Mix

Closed-canopy conifer

Broadleaf tree/shrub

Water/Deep shade

Increasing TC Greenness

Mixed

Snow and ice

Open: Dark

Increasing TC Brightness

We measure high POM difference & (1) have confidence meaningful change has occurred and (2) be relatively certain what type of change has occurred

more subtle change in pom

… and ends here

Pixel starts here…

More subtle change in POM

Dense broadleaf/ grass

Conifer/Broad-leaf Mix

Closed-canopy conifer

Broadleaf tree/shrub

Water/Deep shade

Increasing TC Greenness

Mixed

Open: Bright

Snow and ice

Open: Dark

Increasing TC Brightness

Low POM difference—we have lower confidence that change has occurred & also not certain what type of change (e.g., class change or within class change)

slide16

Summary

  • Focus on two research projects relying on Landsat for change detection
  • One uses Landsat for time-series analyses to quantify historic disturbance and succession processes nationally
  • Other establishes protocols for long-term, interval-based monitoring of all vegetation types across several park networks