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

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Realities of Conducting Natural Resource Surveys
Interagency Cooperation in Natural Resource
Surveys__________________________________________
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1. Introduction
2. Northern Oregon Demonstration Project
3. Annualized Interagency Inventory Monitoring
   Initiative (AIIMI)
4. Other Interagency Efforts
5. Further Considerations

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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

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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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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

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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

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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

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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

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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)

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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

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Plot design was similar to FIA/FHM design
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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

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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

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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

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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

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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

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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)

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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

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(Nelson et. al. 2004)
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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

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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

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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

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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

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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

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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)

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FIA/NRI Integration should take advantage of
each programs strengths not start from scratch
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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

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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

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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

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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

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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

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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

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Soil carbon in forested lands of the North
Central region
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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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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

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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

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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

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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?

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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?

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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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