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Realities of Conducting Natural Resource Surveys Interagency Cooperation in Natural Resource Surveys ____________________________________________________________.

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Presentation on theme: "Realities of Conducting Natural Resource Surveys Interagency Cooperation in Natural Resource Surveys ____________________________________________________________."— Presentation transcript:

1 Realities of Conducting Natural Resource Surveys Interagency Cooperation in Natural Resource Surveys ____________________________________________________________ 1.Introduction 2.Northern Oregon Demonstration Project 3.Annualized Interagency Inventory & Monitoring Initiative (AIIMI) 4.Other Interagency Efforts 5.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-1990s vs. Earlier Will focus mostly on FIA & NRI Quick overview of programs Historical endeavors Ft. Collins project (early 1980s); Lund (1986); Leech (1998) Realities of conducting natural resource surveys 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-1990s vs. Earlier Will focus mostly on FIA & NRI Quick overview of programs Historical endeavors Ft. Collins project (early 1980s); Lund (1986); Leech (1998) Realities of conducting natural resource surveys

3 Northern Oregon Demonstration Project – Overview Inter-agency demonstration project conducted in mid- 1990s 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]

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5 Northern Oregon Demonstration Project – Introduction Support from Under Secretarys 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 1.Ascertain if sampling frames give proper coverage 2.Determine best frame; investigate statistical & operational difficulties of constructing joint data base 3.Explain discrepancies in forest & range (area) estimates

7 Northern Oregon Demonstration Project – Objectives 4.Investigate collecting common information on common samples with joint FIA/NRI data collection teams 5.Explore data collection methodology for vegetation & soil attributes in integrated survey context 6.Determine whether sampling for animal abundance can be included in survey design 7.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 Class Cover % Estimate Estimate Estimate Estimate Timberland ,517 36, , ,972 Oak Woodland ,036 3, ,358 30,358 Unclassified Woodland ,361 6,361 Juniper Woodland ,403 98, ,912 43,912 Chaparral 3,036 3,036 Desert Shrub 169, ,548 Grass/Herbaceous 392, , Total (Phase I) 928, , , ,272 45% 36% 27% 37% Total – Regression 793, , , ,913 Estimator 39% 34% 30% 35%

16 Repeatability of Selected Measurements CorrelationMeasurement error as (r) % of plot variance Average # of plant species per plot % Average DBH % Total basal area % Number of species % Number of trees % % of total shrubs as seedlings % % of total shrubs as mature % Total count, shrubs %

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) 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 1. 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 2. 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 1. 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 2. 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)

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21 AIIMI - Products (cont.) 3. 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 4. 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 3. 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 4. 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.) 5. Image-based detection of land cover change Used integrated set of FIA and NRI data for 10-county area as training data for classification 6. 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 5. Image-based detection of land cover change Used integrated set of FIA and NRI data for 10-county area as training data for classification 6. 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 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 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 to enrich the soils data bases [productivity; biomass] 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 to enrich 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) 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 programs 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 1990s 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 1)Joint press conference Jan. 2001, Secretaries of Interior & Agriculture 2)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 Councils 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 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 Councils 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 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 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 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] 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!

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