Introduction Northern Oregon Demonstration Project

1. Realities of Conducting Natural Resource SurveysInteragency Cooperation in Natural Resource Surveys____________________________________________________________ • Introduction • Northern Oregon Demonstration Project • Annualized Interagency Inventory & Monitoring Initiative (AIIMI) • Other Interagency Efforts • Further Considerations

2. Introductory Comments • Several U.S. Federal agencies conduct national-scale periodic surveys to monitor status & trends of natural resources • Most are conducted by U.S. Department of Agriculture (USDA) or Department of Interior (DOI) • The setting: Current vs. Mid-1990’s vs. Earlier • Will focus mostly on FIA & NRI • Quick overview of programs • Historical endeavors • Ft. Collins project (early 1980’s); Lund (1986); Leech (1998) • “Realities of conducting natural resource surveys”

3. Northern Oregon Demonstration Project – Overview • Inter-agency demonstration project conducted in mid-1990’s to examine feasibility of combining/integrating Federal environmental surveys • Focused on 6-county area of Oregon that contains diversity of land cover & use, and ownerships • Scientists from 6 agencies were responsible for funding, design, implementation, management, analysis [USFS, NRCS, NASS, USGS/NBS, BLM, EPA]

5. Northern Oregon Demonstration Project – Introduction • Support from Under Secretary’s office, Federal Geographic Data Committee (FGDC), and White House (CEQ) – but “hands off” approach • The project goal was to study broad topic of integrating natural resource surveys – but specific focus was on NRI, FIA, FHM, and NFS survey procedures • Goebel, Schreuder, House, Geissler, Olsen, and Williams (1998); House et al (1998) • Many issues and concerns were identified, but project focused on 7 objectives

6. Northern Oregon Demonstration Project – Objectives • Ascertain if sampling frames give proper coverage • Determine “best” frame; investigate statistical & operational difficulties of constructing joint data base • Explain discrepancies in forest & range (area) estimates

7. Northern Oregon Demonstration Project – Objectives • Investigate collecting common information on common samples with joint FIA/NRI data collection teams • Explore data collection methodology for vegetation & soil attributes in integrated survey context • Determine whether sampling for animal abundance can be included in survey design • Analyze measurement errors associated with collection of different variables [most important for new protocols]

8. Northern Oregon Demonstration Project – Data Collection Design & Methods • Data collection portion conducted in 3 phases • Included selection of important existing measurements from NRI, FIA, FHM, and NFS Region 6 surveys • Also included several experimental variables associated with soil quality, range and forest health, wildlife habitat, and animal relative abundance

9. Data Collection – Phase I • Carried out in office by experienced USFS, BLM, and NRCS personnel • Used aerial photos, GIS data layers, hard-copy ancillary materials • Sample consisted of 613 sample points: 337 FIA/NFS sites and 276 from NRI • samples selected independently from two complete frames, so • used straight-forward multiple-frame estimation procedures • Data elements: several cover & use, classifications, evidence of disturbance, soils, site characteristics ownership category, geographic delineations (e.g., HU)

10. Data Collection – Phase II • Carried out by joint 2- and 3-person field crews • USFS personnel were FIA inventory specialists NRCS: soil scientists, soil conservationists, & range conservationists [with some NRI experience] • Sample consisted of 91 sample points selected from the 613 Phase I sample sites [unable to sample 13 sites] • Data elements: site characteristics; veg. structure; ground cover; herbaceous veg. species freq.; shrub canopy cover; shrub density; tree tallies; woody debris; soil characteristics • Soil samples collected & analyzed at soil laboratory • All variables collected for each sample but various protocols used to obtain different measurements

11. Plot design was similar to FIA/FHM design

12. Data Collection – Phase III • Carried out by specialized 3-person USGS field crew [National Biological Survey staff] • Sample consisted of 14 Phase II sample sites occurring on particular portions of 3 national forests • Various protocols used to observe diurnal breeding birds, amphibians, ground insects, and flying insects • Each site visited 3 times within 5-week period

13. Measurement Repeatability Study(Data Collection) • Each Phase II sample site was visited by 2 different crews • Subplots 1 & 2 sampled by both crews; only one crew sampled subplots 3 & 4 • Plot data collected independently by the 2 crews • Visits by the 2 crews made at same time • Operational efficiency • Limited accessibility to private property • Ensured that measurements made at same locations

14. Some of the Lessons Learned • Agencies can work together; have complementary skills • Uniform land classification is achievable • Many basic inventory needs can be met with the same protocols • Sensitivity of access to private lands • Efficiencies of doing things only once is achievable • Plant identification to species level = large workload • Must have mobile GPS units and CASI (Computer Assisted Survey Instrument) – more than just a data recorder • Developed an “Integrated Inventory Vision”

15. Forest and rangeland estimates (in ha.) using USFS and NRCS definitions • Forest Land Rangeland • Crown USFS NRCS USFS NRCS • Land ClassCover % Estimate Estimate EstimateEstimate • Timberland 10-24 36,517 36,517 • 25 + 706,972 706,972 • Oak • Woodland 10-24 3,036 3,036 • 25 + 30,358 30,358 • Unclassified • Woodland 10-24 • 25 + 6,361 6,361 • Juniper • Woodland 10-24 98,403 98,403 • 25 + 43,912 43,912 • Chaparral 3,036 3,036 • Desert Shrub 169,548 169,548 • Grass/Herbaceous 392,820 392,820 • ---------------------------- ------------- ------------ ------------ ----------- • Total (Phase I) 928,595 743,691 562,368 747,272 • 45% 36% 27% 37% • Total – Regression 793,246 700,043 613,710 706,913 • Estimator 39% 34% 30% 35%

16. Repeatability of Selected Measurements • Correlation Measurement error as • (r) % of plot variance • Average # of plant • species per plot 0.89 6.1 % • Average DBH 0.90 5.6 % • Total basal area 0.97 1.5 % • Number of species 0.96 2.1 % • Number of trees 0.99 + 0.4 % • % of total shrubs as seedlings 0.27 73.0 % • % of total shrubs as mature 0.52 32.4 % • Total count, shrubs 0.93 3.8 %

17. Interagency Inventory & Monitoring Initiative (AIIMI) • Follow-up to Northern Oregon Demonstration Project • Study area = Minnesota; initiated in 1999 • Further explored feasibility and limitations of integration (of FIA and NRI) • Featured assimilation & use of data rather than new data collection • Further examined differences in focus & design of inventories when combining data in a common framework • Collaborators: Minnesota DNR; USFS; NRCS • Also USGS EROS Data Center for one project • NRCS Statistician co-located with FIA in St. Paul • Czaplewski et al (2002); Rack et al (2002)

18. AIIMI - Products • GIS Test Data Base • GIS test-bed provided a statewide integrated coverage of FIA, FHM, NRI, and variety of other (ancillary) spatial data • Huge task; quite valuable • Ancillary data included: STATSGO soils data; 1990 Census data; Digital Elevation Model (DEM) data; Digital Raster Graphics (DRG) data; supplemental digital aerial photography; Landsat TM imagery; Digital Ortho Photo quads; wetlands and ecological zone mapping • Intranet Application for Retrieving and Viewing Plot-level Imagery and GIS Data • Navigational capabilities enable data collection and QA specialists to view plot locations in a landscape context

19. (Nelson et. al. 2004)

21. AIIMI - Products (cont.) • Comparison of FIA and NRI Estimates • Investigated land cover/use classification and area estimates to discover types and reasons for similarities and differences in estimates • Mapping Changes in Land Cover/Use • Based upon both FIA & NRI plot data • Geospatial representation of change • Provides insight and perspectives not available through commonly reported summary statistics

22. AIIMI - Products (cont.) • Image-based detection of land cover change • Used integrated set of FIA and NRI data for 10-county area as training data for classification • Landsat classification utilizing NRI and FIA plot data • Conducted in cooperation with USGS Data Center • To determine if FIA and NRI data would help in development of National Land Cover Data (NLCD) mapping

23. AIIMI - Discussion; Findings • GIS Data • It takes considerable work to “align” geospatial data • Mostly manual work rather than automatic • Differing standards, scales, etc • Cover and Use Data • Classification systems vary between programs • NRI and FIA oriented toward use; satellite data – cover • For plots giving heterogeneous signatures – difficult to correlate satellite and survey plot data

24. AIIMI - Discussion; Findings (cont.) • Maps – Geospatial Displays of Data • Very useful in supplementing area statistics [for example, where are the losses of forest land to urban development] • Requires spatial and temporal consistency • Annual Inventories • Both FIA and NRI migrated to Annual Inventory system during the period that AIIMI was being conducted • Both surveys being “annual” should help collaborative efforts • But both programs were too pre-occupied with implementation (including funding issues) to seriously investigate integration

25. AIIMI - Suggestions • Use GIS to develop common “Universe of Interest” • NRI & FIA should have same Total Surface Area & Census Water • Develop common “cover” classification system • Would allow USDA to have “common reporting system” • But also – FIA and NRI need to keep their current/historical systems [needed for Agency programs & have huge investment] • Soils Data • Add NRCS soils data base information to FIA, geospatially [would have characteristics and interpretations for each sample site] • FIA would then supply plot information to NRCS toenrich the soils data bases [productivity; biomass]

26. AIIMI - Suggestions • Further linkage of FIA and NRI data • Statistical • geospatial • Survey Integration • Czaplewski et al (2002)] • Limited budgets; Accountability; OMB • Do NOT start from scratch • Utilize strengths of each system • NRI: land use change; soil; cost/ plot; site condition (general) • FIA: volume; veg. composition change; site condition (specific)

27. . FIA/NRI Integration – should take advantage of each program’s strengths & not start from scratch

28. Other Inter-Agency Efforts • Status and Trends of Wetlands • Assessment of Rangelands • North American Carbon Project • Agricultural Statistics • Resource Inventory & Monitoring, Focus Area Work Group (FAWG), NASA/USDA • National Land Cover Characterization, NLCD 2001

29. Status & Trends of Wetlands National estimates produced through 2 separate natural resource surveys [both with legislative mandates] • Status & Trends – USFWS, Dept. of Interior • NRI – NRCS, USDA • Considerable pressure during the 1990’s to develop a single report by year-2000 [Clean Water Act] • Currently not possible to produce statistically reliable results by combining USFWS and NRI data [Dahl (2000)] • Accomplishments • Joint press conference Jan. 2001, Secretaries of Interior & Agriculture • Statistics on trend (Quantities & types of loss) are “consistent” due to field work by USFWS & NRCS, and subsequent report modifications

30. Assessment of Rangelands • National Research Council (1994) • Called for development & utilization of new methods to classify, inventory, and monitor rangeland • Placed emphasis on rangeland healths • Cooperative work during 1995 – 2002 to develop field protocols that attempt to address Council’s call • NRCS, ARS, BLM, & USGS have been most active, with limited participation by USFS • What about “Criteria & Indicators for Sustainable Rangeland” [Sustainable Rangeland Roundtable]? • Protocols meant to help detect long-term changes in conditions & to monitor short-term impacts

31. Development of Rangeland Protocols • Limited trial studies started in 1996 in 2 regions • BLM conducted field test in Colorado, 1997 & 1998 • Limited field test conducted on private lands in 7 states in 1999 • Collected valuable cost/time data • Current protocols include combination of quantitative and qualitative measurements • NRCS utilizing these as part of NRI for 2003 – 2005 • NRCS expects that a subset of these will be “permanent” • Research activities (with ARS) – reduce replications; incorporate remote sensing; make 100%quantitative

32. Current Rangeland Protocols • Ecological site information; soils; landscape • Line point transects for cover composition • Line intersect transects for basal & canopy cover • Cover density & height [wildlife habitat] • Disturbance indicators; conservation practices & treatment needs • Noxious weeds & invasive/alien plants • Soil stability test • Species composition by weight • Rangeland Health

33. North American Carbon Project Need complete accounting for carbon • Involves many Agencies, Universities, etc. • Science-based approach • For both domestic and international reporting • Need to reconcile models [& calibrate & improve] • “Top down” approach [Atmospheric scientists] • “Bottom up” approach [Agricultural & forestry scientists]

34. Soil carbon in forested lands of the North Central region

35. Opportunity As part of the North American Carbon Project, there appears to be a need to build a comprehensive FIA/NRI Data Base • Reconcile FIA & NRI data for use in C models & elsewhere • One “proposal” is to create geospatial (tesellated) data base with land use, land management, land use history, soils [maybe something equivalent to 10-km. grid ??] • Would include measures of “uncertainty” • Would need protection of confidentiality • Should also investigate incorporation of NASS crop maps, MODIS data, and ???

36. 2-mile cells (4 sq.

37. Agricultural Statistics • NASS & NRCS currently cooperating on several survey activities • Reconciliation of NRI and Census of Agriculture acreage figures – showing how to properly align categories • Conservation Effects Assessment Project (NRI-CEAP), where NASS conducting 0n-farm interviews for NRI sample sites; Farm Services Agency (FSA) also cooperating • Investigating integration of Agricultural Resource Management Survey (ARMS) & NRI-CEAP, collaboratively with Economic Research Service (ERS) • NRI needs NRI-CEAP type data on an annual basis for many uses (including C modeling) – part of Continuous NRI concept introduced in 1998 • NASS crop maps

38. Resource Inventory and Monitoring, Focus Area Work Group (FAWG) • One of 8 focus areas identified by NASA and USDA in May 2003 MOU • Objective is to identify projects for collaborative development to enable USDA operating units to incorporate NASA earth observations, modeling, and systems engineering capabilities • NRI and FIA serving as co-chair

39. National Land Cover Characterization (NLCD), 2001 • Land cover data base being developed by region/zone • Cooperative mapping effort of Multi-Resolution Land Characteristics (MRLC) 2001 consortium • USGS EROS Data Center collaborating with EPA, USFS, NOAA, NASA, NPS, USFWS, BLM, NRCS (NASS?) • Utilizes Landsat TM data from 3 time periods, plus ancillary data from Digital Elevation Model (DEM) • Zone 41 (much of Minnesota) – developed as part of AIIMI • Produces “objective” data layers for each time period • Decision tree approach – rules developed to transform objective data into themes [cover; imperviousness; trees]

40. The Realities of Conducting Natural Resource Surveys – Lessons Learned • Who pays the bills? What pays the bills? • What is expected of your survey program? • When do we get “burned”? • How do we maintain “credibility” with Policy Makers, other scientists, the public? Perception is almost everything. Cooperating with an independent entity like Iowa State University is good business & good science!! • “Keeping NRI going” is a large challenge. Therefore, inter-agency is even greater challenge?

41. The Realities of Conducting Natural Resource Surveys – Lessons Learned • Who pays the bills? What pays the bills? • “MONITORING” – conducting a longitudinal survey properly for natural resources rather than for people issues [health; economics] – are the scientific and operational challenges fully realized • New (& great) technologies come along that affect your “favorite reporting indicator”, like soil erosion for NRI. What do you do? • Are you sampling farms or fields or forests or trees? What happens with departures and new arrivals into your universe of interest?

42. The Realities of Conducting Natural Resource Surveys – Lessons Learned • Who pays the bills? What pays the bills? • “MONITORING” • Indicators [condensing complicated science into useful factoids] – collect the “most basic factors” and not the “Indicator” itself • OMB/USDA Quality of Information standards • Realistic – must use Computer Assisted Survey Instruments & modern supporting systems • Make sure that you can deliver – No excuses!