2004 Western Forest Mensurationists Conference
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2004 Western Forest Mensurationists Conference Kah-Nee-Ta Resort, Warm Springs, OR June 20-22, 2004. 2004 Western Forest Mensurationists Conference Kah-Nee-Ta Resort, Warm Springs, OR June 20-22, 2004. [email protected] [email protected]

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2004 Western Forest Mensurationists Conference

Kah-Nee-Ta Resort, Warm Springs, OR

June 20-22, 2004

2004 Western Forest Mensurationists Conference

Kah-Nee-Ta Resort, Warm Springs, OR

June 20-22, 2004

[email protected]

[email protected]

Bootstrap Operation for Generating Hi-Resolution Inventory Estimates Using Incompatible Multi-Source Data

Roger Lowe, Chris Cieszewski, Kim Iles

Lowe, 04


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I have inserted running commentary throughout the slides in these blue text boxes. Maybe they’ll help you understand (somewhat) what we’re trying to do.

Lowe, 04


What’s the Problem?

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How can we create a spatially explicit inventory if the data of interest are not sufficiently spatially explicit?

Lowe, 04


What’s the Problem?

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How can we create a spatially explicit inventory if the data of interest are not sufficiently spatially explicit?

Lowe, 04


For Example…

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Total Conifer Volume per County (mil. cuft.)

Lowe, 04


What’s the Problem?

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How can we create a spatially explicit inventory if the data of interest are not sufficiently spatially explicit?

How can simulations that incorporate adjacency constraints be run using ground information summarized at the county-level?

Lowe, 04


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Instead of running simulations at the county resolution,

Lowe, 04


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…can we run them at a finer spatial resolution?

Lowe, 04


Data

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USFS FIA plot-level tabular data (no locations)

Landsat 5 Thematic Mapper satellite data

Forest industry inventory data (tabular, spatial)

Other GIS data (rivers, roads, DEMs, etc.)

Lowe, 04


Approach

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Create forested “stands” from the LTM imagery to populate with inventory information

Lowe, 04


Approach

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Create forested “stands” from the LTM imagery to populate with inventory information

Somehow rank those polygons according to amount of timber out there

Lowe, 04


Approach

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[email protected]

Create forested “stands” from the LTM imagery to populate with inventory information

Somehow rank those polygons according to amount of timber out there

Rank the FIA data similarly

Lowe, 04


Approach

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Distribute FIA information to LTM-generated polygons

Lowe, 04


Approach

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Distribute FIA information to LTM-generated polygons

Scale distributed information back to the unbiased FIA totals

Lowe, 04


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Create Forested “Stands”

Group similar pixels to create the forest polygons

  • Used Euclidean spectral distance

  • to group similar pixels

  • Initial minimum group size of 5

  • pixels (~1 acre)

  • Done separately for the 8 scenes (and fractions of scenes) required for complete LTM imagery coverage of Georgia

Lowe, 04


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Create Forested “Stands”

Lowe, 04


P19R38 ED VS Pine BA

160

140

120

100

R2 = 0.63

RMSE = 23.2

80

PineBA

60

40

Observed

20

Predicted

Poly. (Observed)

0

0.000

10.000

20.000

30.000

40.000

50.000

60.000

-20

EDistance

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Rank Data

Basal area – Euclidean spectral distance model

  • Nonlinear regression models relating Euclidean spectral distance and basal area

  • Populated LTM-generated polygons with est. basal area from these models

  • Ranked LTM-generated polygons using these estimated values

  • Ranked FIA data using their ba measures

Lowe, 04


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Rank Data

Basal area – Euclidean spectral distance model

  • Have 2 sorted lists

    • polygon list sorted by LTM-estimated basal area

    • FIA condition-level list sorted by ground-measured basal area

Lowe, 04


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Distribute FIA Information

LTM polygon area scaled to the area represented by the FIA plots (for data distribution)

  • Polygon area equals the area represented by the FIA plots

  • This aides the distribution process

Lowe, 04


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Distribute FIA Information

LTM polygon area scaled to the area represented by the FIA plots (for data distribution)

Info from highly ranked FIA plots distributed to highly ranked LTM-polygons

- Poly ac / FIA ac => volume

- All others

  • Trying to put information from similar FIA plots into LTM-generated polygons with similar characteristic(s)

  • Volume was scaled by the ratio of polygon acreage to FIA acreage

  • Other information was transferred as well (tpa, age, si, etc.)

Lowe, 04


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Distribute FIA Information

LTM polygon area scaled to the area represented by the FIA plots (for data distribution)

Info from highly ranked FIA plots distributed to highly ranked LTM-polygons

LTM polygon areas recalculated, total volume calculated

  • Volume per acre recalculated using correct polygon acreage

Lowe, 04


Scale Distributed Info Back to FIA Totals

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Polygons currently contain LTM-estimated basal area, and FIA plot information

Lowe, 04


Scale Distributed Info Back to FIA Totals

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Polygons currently contain LTM-estimated basal area, and FIA plot information

LTM area-weighted vol/ac scaled up/down to match FIA area-weighted vol/ac

  • Yields an unbiased volume per acre estimate for each scene processed

Lowe, 04


Scale Distributed Info Back to FIA Totals

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Polygons currently contain LTM-estimated basal area, and FIA plot information

LTM area-weighted vol/ac scaled up/down to match FIA area-weighted vol/ac

Differences in sum totals due to differences in land area

Lowe, 04


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Now, What Have We Got?

~ 1.5 million polygons populated with FIA data

Lowe, 04


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Now, What Have We Got?

~ 1.5 million polygons populated with FIA data

Riparian zone and urban buffer information included

Lowe, 04


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Now, What Have We Got?

~ 1.5 million polygons populated with FIA data

Riparian zone and urban buffer information included

Enough information to run spatially explicit fiber supply simulations

Lowe, 04


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Thanks

Lowe, 04


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