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Integrating time series of Landsat-based information into FIA's estimation process

RMRS: Gretchen Moisen, Todd Schroeder, Sean Healey, Ray CzaplewskiPNW: Warren B. Cohen

WO: Ken BrewerUMD: Sam Goward, Karen Schleeweis

FIA Nat’l User Group Meeting— 7-8 March2012

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Status: How much is out there now, ….and where is it?

Change: What just happened?

Trend: What’s happening?

Some Simple Questions

Status:

Change:

Trend:

How’s FIA Doing?

A-

I

A …for effortI …for accomplishment

Outline

3. NAFD Phase 3

4. How can we integrate Landsat time series into FIA’s estimation processes?

1. Forest disturbance and monitoring

2. History of the North American Forest

Dynamics (NAFD) Project

• Impacts ~ 1-3% of a forest area per year• Occurs at different spatial scales, temporal scales, and intensities• Can impact canopy, understory and forest floor • Climate change and growing human population may alter the

frequency and severity of future disturbance regimes• Monitoring has taken on renewed importance

FireClearcut

1987

1989

1990

1991

1993

1994

1995

1997

1998

1999

2001

2002

2004

2006

2008

0

0.5

1

1.5

2

0

2

4

6

8

10

12ClearcutFire

Year

Annu

al R

ate

of C

lear

cutti

ng Annual Rate of Fire

Spatial Temporal

Forest Disturbance

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Disturbance and Time

(Brewer, 2009)

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Disturbance and Space

(Brewer, 2009)

Monitoring Through Plots

Unbiased estimates at broad scales

Sampling error is well understood

Measurement error can be assumed to be negligible for many variables

Results are not spatially explicit at the local level

Revisit frequency may miss disturbance events

Post-dating is problematic

Difficult to see upper canopy disturbances from the ground

Monitoring Through Landsat Time Series

16-day repeat cycle and 40-year historical archive allows development of dense image times series which can be used to detect changes in forest cover over large areas.

Spatial grain (30m) and variety of spectral bands allows detection and causal attribution of most natural and anthropogenic disturbances.

Can be used for mapping forest change and for collecting human interpreted reference data (e.g. Timesync).

There is no sampling error BUT measurement error is variable and often poorly understood.

Different monitoring methods are appropriate for different purposes

Joining traditional forest inventory data with temporally dense satellite data results in new information for monitoring change and trend

Outline

3. NAFD Phase 3

4. How can we integrate Landsat time series into FIA’s estimation processes?

1. Forest disturbance and monitoring

2. History of the North American Forest

Dynamics (NAFD) Project

North American Forest Dynamics (NAFD)(UMD, NASA-Goddard, FIA, PNW, NRS, CFS, CONAFOR)

North American Forest Dynamics

• NASA-funded project designed to characterize disturbance patterns and recovery rates of forests across the continent

• Goal: Determine the role of forest dynamics in North American carbon balance

Phase I & II Sample Sites

Eastern Stratum

Western Stratum

Phase IPhase II

Phase IPhase II

Processed time series (1985-2008) of Landsat satellite imagery using FIA inventory data for validation and training

Vegetation Change Tracker

Year Disturbed1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

Forest

Nonforest

Lake Anna, VA, 60 km NW of Richmond, VA

Major disturbance

0

5

10

15

20

25

1985 1990 1995 2000

Year

FI

Minor disturbance

0

5

10

15

20

25

1985 1990 1995 2000

Year

FI

(Huang et al. 2006, 2008)

NAFD “Science” (NASA, PNW, UMD, CONAFOR, CFS, and others)

Characterizing disturbance and regrowth patterns on US forests by analyzing a biennial time series of Landsat imagery over a sample of Landsat data cubes spread across US forests. Objectives include:

1. Produce nationwide estimates of forest dynamics for NACP2. Convert data cube reflectance to data cube biomass3. Develop nationwide maps predictions of forest dynamics4. Begin trials in Canada and Mexico5. Quantify forest component of woody encroachment nationally

NAFD “Applications” (NASA, PNW, UMD, all FIA units)

Illustrate how FIA data can be combined with temporal disturbance and biomass products to answer management questions relevant to FIA users. • Developed FIA monitoring

products that take advantage of satellite-derived disturbance and biomass data (storm-related loss, harvest rates across time and ownerships, fragmentation, carbon considerations)

• Note studies by Sean Healey, Mark Nelson, Randy Morin, Hobie Perry, Andy Lister, John Coulston, and others

Detour: A Model for Collaboration

• Pre-proposal communication with FIA• Engagement of FIA scientists and managers• Common problem identification• Memorandum of Understanding• Sensitivity to logistical and political constraints• Patience

Outline

3. NAFD Phase 3

4. How can we integrate Landsat time series into FIA’s estimation processes?

1. Forest disturbance and monitoring

2. History of the North American Forest

Dynamics (NAFD) Project

NAFD Phase 3(Goward, Huang,Cohen, Masek, Moisen, Nemani)

1) Conduct an annual, wall-to-wall analysis of US disturbance history between1985-2010

2) Undertake a detailed validation of the resultant national disturbance map

3) Examine variation in post-disturbance forest recovery trajectories, using repeat measurements from FIA plot data,

4) Determine disturbance causal agents ***

Cause of Disturbance Maps

Disturbance Year Disturbance Type

GeoDatabaseForest Change

Processes

User Community

Change Agent Forestry Suburbanization/Urbanization

Pests and Pathogens

Hurricanes/Tornadoes Fires Conversion

Data Source Timber Treatment & Removals

Decadal Census – # new housing units

Digitized Aerial sketches of insect

damage

Ground measurements-wind

speedLandsat NDVI

changeLandsat change

detection

Reference UFSF FIA (Smith et al. 2009) (Theobald 2004)

US Forest Health Program

http://www.fs.fed.us/r3/resources/health/fid_surveys.shtml

U.S. National Hurricane Center

(Jarvinen et al. 1984)

MTBS (Eidenshenk et

al. 2007)

NLCD Retrofit Data Set (Fry et al. 2009)

SpatialGrain County polygons

or > 100m grid polygon <1 ha to county lines 30m grid 30m

Extent sampled - national national sampled - national national national National

Temporal Grain 5-10 year cycles decadal annual annual annual decadal

Extent varies by region 1940-2030 varies by region 1851-2008 1984-2007 1992-2001

Web Browser/DistributionDatabase of Forest Change Processes (Scleeweis et al., In Review)

Incorporating Textural Metrics

19871984

Patch level spatial metricsContinuous Discrete

•Homogeneity •Edge Contrast•Heterogeneity•Texture•Range/Mean

Harvest Fire Suburbanization

Different disturbance processes result in different patterns of landscape structure and fragmentation that are visible in Landsat Imagery.

•Shape•Direction•Fractal dimension•Area•Compactness

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150.05

0.10

0.15

0.20

0.25

0.30

0.35Forest StdevForest AvgCCFire

# of Years Post-disturbance

B5 R

eflec

tanc

e

Spectral-Temporal Patterns of Disturbance

0 1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

# of Years Post-disturbanceN

DVI

Green Leaf AreaStructure

(Schroeder et. al, 2010)

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Pilot Phase for Attributing Cause of DisturbanceTen sample scenes were identified as good candidates for testing, representing a range of causal agents and varying forest types and prevalence.

Outline

3. NAFD Phase 3

4. How can we integrate Landsat time series into FIA’s estimation processes?

1. Forest disturbance and monitoring

2. History of the North American Forest

Dynamics (NAFD) Project

Post-stratification

Disturbance Year Disturbance Type

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Alternatives to the Moving Average0

1

0 5 10 15Year

true trend = 50% between years 6-7

(Czaplewski, 2008)

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Endogenous Post-stratification(Breidt and Opsomer 2008; Dahlke et. al In Press, Tipton et. Al In Prep)

Using FIA as training data to make maps

Then using those maps to post-stratification that same FIA data

Mapping Plot Attributes Through Time

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Strategic Timing of Ground Observation

Before After

AREBA: Accelerated Remeasurement and Evaluation of Burned Areas (RSAC 2009)

1994 2000 2002

Trend

Anomaly

LandTrendr (Kennedy et al.)TimeSync (Cohen et al.)

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Plot History: Clearcut and recovery

Andy Gray

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Plot History: Defoliation, delayed mortality, recovery, salvage, and recovery

Andy Gray

Very few disturbances are detected by both Timesync and FIA.

FIA records lots of disturbances which are undetectable by Landsat (e.g. animal damage).

82% of disturbances detected only by Timesync fall outside FIA’s observation window (i.e. disturbance date is > 5 yrs before or is after plot measurement date).

Disturbance is less common thus overall accuracy is inflated by high proportion of undisturbed plots.

A Utah Example: Comparing FIA and Timesync Observations of Disturbance

(Schroeder et. al, In Prep.)

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Integrating Landsat time series into FIA’s estimation processes?

Integrating Landsat time series into FIA’s estimation processes?

1. Make best use of (endogenous) post-stratification2. Incorporate Landsat (photo)-based “observations” on

field plots3. Consider alternative sampling frequency for disturbed

strata 4. Develop alternatives to the MA and make best use of

RS data through model-assisted or model-based methods

5. Ensure compatibility between status maps and status estimates

6. Ensure compatibility in maps through time7. Explore ways to reduce costs through these processes

38FIA Nat’l User Group Meeting— 7-8 March2012

Third phase of NAFD is providing annual maps of forest disturbance along with attribution, validation, and re-growth analyses nationwide

We need to keep pushing our statistical tools beyond post-stratification and moving average into more integrated ground and RS approaches