Introduction Northern Oregon Demonstration Project Annualized Interagency Inventory & Monitoring Initiative (AIIMI) - PowerPoint PPT Presentation

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

• 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”

• 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]

• 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

• 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

• 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]

• 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

• 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)

• 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

design

• 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

• 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

• 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 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%

• 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 %

• 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)

• 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

(Nelson et. al. 2004)

• 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

• 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

• 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

• 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

• 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]

• 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

• 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

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

• 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

• 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

• 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

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 ???

2-mile cells

(4 sq.

• 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

• 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

• 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]

• 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?

• 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?

• 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!