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Generating Hi-Resolution Inventory Estimates with Incompatible Multi-Source Data

This presentation discusses the challenges of creating spatially explicit inventories when the data of interest are not sufficiently spatially explicit. The author proposes a bootstrap operation approach to generate high-resolution inventory estimates using incompatible multi-source data. The approach involves creating forested "stands" from satellite imagery and ranking these polygons based on timber amount. The presentation also covers the distribution of ground data to the ranked polygons and the scaling of distributed information back to unbiased ground totals.

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Generating Hi-Resolution Inventory Estimates with Incompatible Multi-Source Data

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  1. 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 rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Bootstrap Operation for Generating Hi-Resolution Inventory Estimates Using Incompatible Multi-Source Data Roger Lowe, Chris Cieszewski, Kim Iles Lowe, 04

  2. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  3. What’s the Problem? rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu How can we create a spatially explicit inventory if the data of interest are not sufficiently spatially explicit? Lowe, 04

  4. What’s the Problem? rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu How can we create a spatially explicit inventory if the data of interest are not sufficiently spatially explicit? Lowe, 04

  5. For Example… rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Total Conifer Volume per County (mil. cuft.) Lowe, 04

  6. What’s the Problem? rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  7. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Instead of running simulations at the county resolution, Lowe, 04

  8. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu …can we run them at a finer spatial resolution? Lowe, 04

  9. Data rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  10. Approach rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Create forested “stands” from the LTM imagery to populate with inventory information Lowe, 04

  11. Approach rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  12. Approach rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  13. Approach rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Distribute FIA information to LTM-generated polygons Lowe, 04

  14. Approach rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Distribute FIA information to LTM-generated polygons Scale distributed information back to the unbiased FIA totals Lowe, 04

  15. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  16. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Create Forested “Stands” Lowe, 04

  17. 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 rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  18. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  19. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  20. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  21. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  22. Scale Distributed Info Back to FIA Totals rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Polygons currently contain LTM-estimated basal area, and FIA plot information Lowe, 04

  23. Scale Distributed Info Back to FIA Totals rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  24. Scale Distributed Info Back to FIA Totals rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  25. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Now, What Have We Got? ~ 1.5 million polygons populated with FIA data Lowe, 04

  26. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Now, What Have We Got? ~ 1.5 million polygons populated with FIA data Riparian zone and urban buffer information included Lowe, 04

  27. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu 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

  28. rcl7820@owl.forestry.uga.edu rcl7820@owl.forestry.uga.edu Thanks Lowe, 04

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