1. Extensive, Strategic Assessment of Southeast Alaska’s Vegetative Resources Willem van Hees, Bert Mead Pacific Northwest Research Station, Forest Inventory and Analysis, Anchorage, AK

2. FIA and R10 – Historic perspective Roles of FIA inventory Inventory description Analysis of inventory data

3. FIA and R10 – Historic perspective Roles of FIA inventory Inventory description Analysis of inventory data

4. 1950’s - 60’s PNW – Alaska Forest survey Unit, Juneau, inventory, some remeasurement 1970’s PNW, Forest Inventory & Analysis, Juneau, reinventory; FIA/R10 MOU 1978 PNW FIA moves to Anchorage 1980’s R10, reinventory 1995 - 2001 PNW FIA, Anchorage, hex-based sample, annual 1/10 remeasurement

5. Roles of FIA inventory FIA and R10 – Historic perspective Inventory description Analysis of inventory data

6. Strategic scale; spatial nature Information base for resource management in landscape/owner context Can serve NFS regional planning needs Independent data set to validate and adjust allowable harvest ratesIndependent data set to validate and adjust allowable harvest rates Spatial data to model coverages of derived products unattainable elsewhere.Spatial data to model coverages of derived products unattainable elsewhere. Nationally consistent core data and analyses across political, administrative, and land ownerships boundaries. Internal Agency information needs include Resource Planning Act (RPA) assessmentsResource Planning Act (RPA) assessments Status reports on Criteria and IndicatorsStatus reports on Criteria and Indicators Special assessments such as the UN - FAO Forest Resource Assessment.Special assessments such as the UN - FAO Forest Resource Assessment.

7. Roles of FIA inventory FIA and R10 – Historic perspective Inventory description Analysis of inventory data

8. Field Plot distribution

9. 423 1 Microplot: 6.8 ft. radius Subplot: 24.0 ft. radius 120 ft. between subplot centers Field Plot design

10. Mapped Plot 423 1 Forest Nonforest

11. Field Plot (re)measurement schedule 2003 2003 initiate annual remeasurement of 1/10 of plots with forested conditions 1995-2001 1995-2001 baseline measurement, all vegetated

12. Also in 2003: Initiate measurement for Forest Health Monitoring

13. Forest Health Monitoring 5 indicators of forest health: Ozone: Plant ozone injury used to adjust ozone emission standards Lichens: Sensitivity to pollutants used as an indicator of changing air quality Soil: Evaluation of soil physical and chemical properties, erosion and compaction. Vegetation: Assessment of abundance and spatial arrangement of all trees, shrubs, herbs, grasses, ferns for biodiversity changes Coarse woody debris: Estimates biomass of coarse woody debris, fine woody debris, duff, litter, slash, and fuelbed depths for carbon sequestration, fire models….

14. Roles of FIA inventory FIA and R10 – Historic perspective Inventory description Analysis of inventory data

15. Analysis of inventory data – Woodpile descriptors Nonforest vegetation characterization Spatial analyses

16. Woodpile descriptors: Area of forest/nonforest Owner TotalForest Nonforest NFS 16.99.47.6 Other federal 4.40.63.8 State & local 0.80.40.4 Private 0.70.60.1 All 22.911.011.9 -----------------million acres------------------

17. Woodpile descriptors: Area of timberland/other forest Owner TotalTimberland Other forest All 10,995 4,096 6,898 ---------------------thousand acres----------------------- Other federal 618 6 612 Other federal 618 6 612 NFS9.355 3,423 5,932 State & local 390 272 118 Private 632 396 236

18. Woodpile descriptors: Forest type distribution

19. Woodpile descriptors: volume/ac on timberland

20. Woodpile descriptors: growth and mortality on timberland

21. Woodpile descriptors: Mortality per acre Cubic-foot mortality, per acre, Western redcedar/hemlock forest type 20-40 41-60 61-85

22. Nonforest vegetation characterization

23. Nonforest vegetation characterization: shrub & herbaceous types

24. Nonforest vegetation characterization: area of low shrub

25. Cooperative Research Centre for Biodiversity & Conservation Schools of Biology & Geography, University of Leeds William Kunin, Steve Carver, Jack Lennon, Simon Corne, Michael Pocock, Naomi van der Velden & Crewenna Dymond Spatial analyses

26. Research Program Development & testing of New Spatial Analytic Methods Analysis and mapping of SEAK Forest Data Basic research in Macro- ecology Research in Forest Biology & Manage- ment

27. Research Program Development & testing of New Spatial Analytic Methods Analysis and mapping of SEAK Forest Data Basic research in Macro- ecology Research in Forest Biology & Manage- ment

28. Macroecology research Cross-scale analyses of species distributions; extrapolating across scales Species-area relationships and their basis in individual species range structure Climate & tree spp effects on understory plant spp diversity

29. Research Program Development & testing of New Spatial Analytic Methods Analysis and mapping of SEAK Forest Data Basic research in Macro- ecology Research in Forest Biology & Manage- ment

30. Forest Biology & Management Biology of core & marginal tree populations Testing human impacts on vegetation – – Changes in forest composition after harvest

31. Core vs. marginal populations Developed methods for determining the degree of “marginality” of SEAK tree populations Results suggest marginal populations become more specialised in habitat tolerances (Global Ecology & Biogeography 11: 103-114)

32. Human impacts on vegetation Analyses of clearcut sites suggest change in forest species composition Edge effects of clearcuts difficult to measure (due to position errors)

33. Research Program Development & testing of New Spatial Analytic Methods Analysis and mapping of SEAK Forest Data Basic research in Macro- ecology Research in Forest Biology & Manage- ment

34. Analysis & Mapping Neural net analyses of SEAK data estimating forest characteristics Species maps including marginality index Biodiversity maps (of trees, understory spp)

35. Neural Net models Models trained with SEAK surveys Proven potential to outperform commercial LANDSAT image analysis in predicting: – –Crown closure – –Land cover type – –Size/structure Application software being developed

36. Land Cover Type Compared to commercially available post-processed images purchased from Pacific Meridian Resources

37. Summary comparison (PNN model)

38. Research Program Development & testing of New Spatial Analytic Methods Analysis and mapping of SEAK Forest Data Basic research in Macro- ecology Research in Forest Biology & Manage- ment

39. New methods in spatial analysis Tests of relative effectiveness of various Neural Net models. Land cover fragmentation using fractal measures Novel methods for testing associations between autocorrelated variables

40. Future directions Neural net analyses could be applied easily to other regions or other issues (e.g. fire risk): a cost-efficient way to generalise from field survey data Species interactions at range margins: growth, competition, pathogens