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

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Title: Realities of Conducting Natural Resource Surveys Interagency Cooperation in Natural Resource Surveys ____________________________________________________________


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

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

4
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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
  1. Investigate collecting common information on
    common samples with joint FIA/NRI data collection
    teams
  2. Explore data collection methodology for
    vegetation soil attributes in integrated survey
    context
  3. Determine whether sampling for animal abundance
    can be included in survey design
  4. 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 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

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

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)
20
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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 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)

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
  • 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 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
    100quantitative

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 ???

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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!

43
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