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

Realities of Conducting Natural Resource Surveys

Interagency Cooperation in Natural Resource Surveys

____________________________________________________________

1. Introduction2. Northern Oregon Demonstration Project3. Annualized Interagency Inventory &

Monitoring Initiative (AIIMI)4. Other Interagency Efforts5. Further Considerations

Introductory CommentsIntroductory 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”

• 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

• 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

• 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

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

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]

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

• 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

• 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

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)

• 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 timeOperational efficiencyLimited accessibility to private propertyEnsured that measurements made at same locations

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 Land Rangeland Crown USFS NRCS USFS NRCS Land Class Cover % Estimate Estimate Estimate Estimate

Timberland 10-24 36,517 36,517 25 + 706,972 706,972OakWoodland 10-24 3,036 3,036 25 + 30,358 30,358UnclassifiedWoodland 10-24 25 + 6,361 6,361Juniper 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

Correlation Measurement error as

(r) % of plot variance

Average # of plantspecies 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)

• 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 - ProductsAIIMI - 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

(Nelson et. al. 2004)

AIIMI - Products (cont.)

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

AIIMI - Products (cont.)

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

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

• 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

• 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 - SuggestionsAIIMI - 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]

AIIMI - SuggestionsAIIMI - 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)

..

FIA/NRI Integration – should take

advantage of each program’s strengths

& not start from scratch

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

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 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

Assessment of RangelandsAssessment 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

• 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

• 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 ProtocolsCurrent 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

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]

Soil carbon in forested lands of the North Central regionSoil carbon in forested lands of the North Central region

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 ???

2-mile cells(4 sq.

Agricultural StatisticsAgricultural 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

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]

• 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

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 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 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!