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Realities of Conducting Natural Resource Surveys Interagency Cooperation in Natural Resource Surveys ____________________________________________________________. Introduction Northern Oregon Demonstration Project Annualized Interagency Inventory & Monitoring Initiative (AIIMI)

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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
introductory comments
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”
northern oregon demonstration project overview
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]
northern oregon demonstration project introduction
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
northern oregon demonstration project objectives
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
northern oregon demonstration project objectives7
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]
northern oregon demonstration project data collection design methods
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
data collection phase i
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)
data collection phase ii
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

Plot design

was similar to FIA/FHM


data collection phase iii
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
measurement repeatability study data collection
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
some of the lessons learned
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”
forest and rangeland estimates in ha using usfs and nrcs definitions
Forest and rangeland estimates (in ha.) using USFS and NRCS definitions
  • Forest Land Rangeland
  • 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%
repeatability of selected measurements
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 %
interagency inventory monitoring initiative aiimi
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)
aiimi products
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
aiimi products cont
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
aiimi products cont22
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
aiimi discussion findings
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
aiimi discussion findings cont
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
aiimi suggestions
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]
aiimi suggestions26
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)


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

other inter agency efforts
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
status trends of wetlands
Status & Trends of Wetlands

National estimates produced through 2 separate natural

resource surveys [both with legislative mandates]

  • Status & Trends – USFWS, Dept. of Interior
  • 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
assessment of rangelands
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
development of rangeland protocols
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
current rangeland protocols
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
north american carbon project
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]

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 ???
agricultural statistics
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
resource inventory and monitoring focus area work group fawg
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
national land cover characterization nlcd 2001
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]
the realities of conducting natural resource surveys lessons learned
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?
the realities of conducting natural resource surveys lessons learned41
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?
the realities of conducting natural resource surveys lessons learned42
The Realities of Conducting Natural Resource Surveys – Lessons Learned
  • Who pays the bills? What pays the bills?
  • 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!