Sampling Design for Intensive Landscape Scale Measurements

David Hollinger USDA Forest Service

Intensive Landscape-Scale Measurement of Carbon for Reference Sites in the Americas Workshop

September 13-17, 2010

Outline of talk: •  History – the North American Carbon Program

–  Intermediate tier needed for NACP •  Sample Design

–  Link with National Inventory System –  Ground truth for remote sensing –  Estimate parameters for carbon cycle modeling

•  Test of the Sample Design at Experimental Forests

•  Lessons Learned •  Alternative Designs

The North American Carbon Program (NACP)

Scientific Motivation: To reduce the large uncertainties in future

projections of climate, we need a much better understanding of the carbon cycle.

1.  Strong evidence for a substantial Northern Hemisphere Terrestrial C sink. ~2 gT

2.  Evidence that North America was a substantial sink

3.  Lack of agreement between atmospheric & inventory estimates

NACP Questions 1.  What is the carbon balance of North America

and adjacent oceans? How is the balance changing over time? (“Diagnosis”)

2.  What processes control the sources and sinks of CO2, CH4, and CO, and how do the controls change with time? (“Attribution/Processes”)

3.  Are there potential surprises (could sources increase or sinks disappear)? (“Prediction”)

4.  How can we enhance and manage long-lived carbon sinks ("sequestration"), and provide resources to support decision makers? (“Decision support”)

9/16/10 5

Where is the carbon going?

How will this change?

NACP Features a Hierarchy of Terrestrial Measurements

1.  “Wall-to-wall” remote sensing and GIS

2.  Extensive inventories (FIA and NRI) More than 170,000 sites at 5-10 yr intervals Complementary networks in Canada & Mexico

3.  Intermediate intensity sampling at many sites, to facilitate scaling from local data to regional modeling with Remote Sensing/GIS

4.  Very intensive investigation of processes AmeriFlux, LTER, ~100 sites

NACP Tier-3 Workshop June 25-7, 2003 Portsmouth, N.H.

Sponsored by Ruth Reck (NIGEC/DOE) and Rich Birdsey (NGC/USDA-FS)

Objectives of Landscape Monitoring in the NACP

•  Augment coverage of the land surface by intensive monitoring sites –  Natural disturbances and

management –  Edges/fragmented landscapes –  Mountain terrain

•  Facilitate scaling from intensive sites to landscapes

•  Model parameterization or validation

NACP Main Products from Forest Landscape Monitoring

•  Direct measurements of landscape-level carbon sequestration (and its uncertainty) –  Application: “ground truth” – reference cases for

carbon management and accounting •  High spatial/temporal resolution estimates of

CO2 flux for modeling forest carbon dynamics –  Application: parameterize, calibrate, or validate

models •  Map products: vegetation structure; carbon

stocks; NPP/NEP estimates –  Application: analysis for policy and land

management

Outline of talk:

Year Two

Year Three

Year Four

Year Five

Five-Year Panel:

Year One

USDA Forest Service Forest Inventory and Analysis (FIA) Plots

•  6000 acre grid cells •  1 plot per grid cell •  each plot visited every 5 (east) or 10 (west) years

Condition B = Seedling-Sapling Stand

Tree Variables

Species: Yellow Poplar DBH: 12.9 inches Tree history: Live Tree Class: Growing Stock

Sample-Location Variables

Location ID: St, Un, Co, # Date: Mo, Day, Yr Sample Kind: Remeas. 1/5th

GPS: Lat-Long.

Condition Variables

Slope & Aspect: Pct & Degrees Land Use: Forest Stand Origin: Natural Owner Class: Individual

Condition A = Mature Stand

Old 1/5-acre plot

FIA Plot Measurements

1 plot/6,000 acres (1/96,000 phase 3)

Phase 2 verses Phase 3 plots

Outline of talk:

MODIS 500 m

Landsat SPOT 30 – 15 m

77 m

Remote Sensing and Inventory Plots:

•  FIA plots small compared to spatial resolution of traditional remote sensing satellites •  need to be accurately located for lidar studies

The next generation (2013-15) of remote sensing satellites may be used for more broad-scale carbon inventories & studies

1000

10 (3 km ) 35 25

•  SMAP •  ICESat-II •  DESDyni

16

Measurement Variables: Frequency, Angle, Polarization Observations: high spatial resolution, seasonal to annual revisit time,

all time capability

(H,V)

(H,V)

Radar Backscatter Measurements

Soil Moisture Profile

Outline of talk:

Modeling Annual Forest/Atmosphere Carbon Exchange for Large Areas

•  Ecosystem C Cycle models (IBIS, Orchidee, CLM)

•  Demographic models (ED, ED2) •  All models need estimates of C

pools and parameters –  Biomass equations (ρ) –  Productivity equations –  Forest floor dynamics

(CWD, litter, humus) –  Soil C dynamics –  Tree physiology – response to climate

Landscape monitoring

systematic sampling was chosen for forest and tree data collection. Systematic sampling in forest inventory is: (i) easily planned, (ii) faster in execution and mostly cheaper (iii)  it gives better estimates of the mean than unrestricted random sampling

To link the landscape to the FIA system, FIA plots were located within a 1 km2 area.

3 Choices: locate plots randomly, systematically, or stratify?

Landscape-scale sample design:

Systematic arrangement of FIA plots across 1 km2

Idealized Sampling Scheme for a Landscape

Monitoring Site

3 x 3 km grid of inventory plots surrounding a special research installation • Meteorological tower or other installation at center • Inventory plots follow FIA protocol • Other measures (litterfall, respiration) take place at inventory plots

Niwot Ridge LTER, Colorado

•  Central km2 has intensive biometric measurements •  Larger area for remote sensing; will include range of disturbance

Outline of talk:

Landscape Carbon Monitoring and Analysis at a Network of Experimental Forests and other Research Sites

FS Flux Tower Other Flux Tower Potential New FS Flux Tower Potential New FS Biometric Site

Pilot test location

AmeriFlux landscape monitoring sites

Figure 1. Location of a.) regional forest ecosystem monitoring sites in the northeastern U.S.; b.) the Bartlett Experimental Forest (BEF) within the White Mountain National Forest; and c.) long-term and eddy covariance monitoring sites at the BEF in relation to forest species distribution and topographic relief.

Forest Service Tier 2 Activities •  2003 Workshop •  Field Manual & Overview •  Pilot Studies

•  Fraser •  Niwot Ridge •  GLEES •  Bartlett •  Marcel

Outline of talk:

Lessons Learned: •  Evaluate sampling design and intensity for each

measurement –

–  Researchers should consider how variability in space and time influences sampling intensity and frequency and balance these requirements against the impact of the process to total carbon storage and balance. Focus on the big and changeable stocks (stems, litter, CWD.)

Cost versus benefits of measuring forest Carbon pools and fluxes:

Lessons Learned:

•  Standardization facilitates comparison – –  The consistent plot layout and sampling strategy

that we employed enabled very straightforward comparisons between sites. Consistency is good!

•  Stratify the landscape – –  Sites were diverse – stratification would improve

estimates.

•  Carefully consider plot layout – –  Plot layout must efficiently represent the landscape

while minimizing bias. The FIA design is slow to implement.

“Stratification based on forest types and land use classes is discarded as strata may change over time and render stratified sampling irrelevant with permanent samples.”

It was felt that the FIA design was inefficient in the field. Is there an alternative to the FIA plot?

Outline of talk:

FAO System is in use in many parts of the world. Systematic Sampling Design (“Tract” location)

Sample region

Tract Plot Subplot

Sampling Intensity: FAO: 1°, 0.5°, or 0.25° •  at equator, 1°=111.2 km •  at 10°, 1°=109.5 km •  at 20°, 1°=104.5 km •  at 45°, 1°=78.6 km

FAO Tract with delineation of forest types and land use classes. Plots have dimensions of 20 x 250m (0.5 ha). More area sampled compared to FIA design

12 Subplots for saplings, soils, etc.

Summary •  To answer questions about carbon stocks and

how they are changing across a landscape, a sample design was proposed and tested

•  The landscape-scale design consists of inventory plots systematically arranged across a 1 km2 area –  Ideally with a meteorological tower in the center –  Additional plots are located in adjacent 1 km2 areas

•  Conclusions from testing of the design: –  The consistent design facilitated comparisons

between sites –  Researchers had some concerns about efficiency

•  Alternative Designs include FAO plots

Thank you !

Useful papers (pdfs available):

An excellent discussion of size and shape of plots for forest research: Curtis & Marshall, 2005. Permanent-Plot Procedures for Silvicultural and Yield Research (pages 9 – 22) pnw_gtr634.pdf

Woodall et al. 2010. The Forest Inventory and Analysis Database Version 4.0: Database Description and Users Manual for Phase 3. gtr_nrs61.pdf

Many of you have probably seen this excellent manual: Phillips et al. RAINFOR field manual for plot establishment and remeasurement.

Other important papers:

For estimating change (such as change in C stocks) in a survey;

(general info) Olsen et al. 1999. Statistical issues for monitoring ecological and natural resources in the United States. Environmental Monitoring and Assessment 54:1-45.

(good statistical treatment) Fuller 1999. Environmental surveys over time. Journal of Agricultural, Biological, and Environmental Statistics 4:331-345.

Additional Information: 1) Question about what was the desired precision of the estimates and how many plots were needed:

The goal was within 10% of the mean 95% of the time. The number of plots necessary depends on how clustered or homogeneous the population (sample variance). The best way to determine sampling variance is a 2-step design; install some plots to determine variance of your sample then determine the number of sample plots N. Preliminary data suggested 16 – 25 FIA plots would provide the necessary precision.

For details about determining N, see a statistics book e.g. Cochran & Cox, Experimental Designs also Lackey 1981 (see pdf) is a useful guide.

Results from power analysis using a sample plot size equal to 1 FIA subplot for carbon pools (left) or fluxes (right) at 3 sites. AGlive refers to aboveground live C, FF to forest floor. From Bradford et al. 2010 (pdf available).

Not enough plots were installed to meet the precision goals !

The test of the design moved or omitted plots that fell on roads or in different vegetation types. For a landscape–scale estimate, plot locations should not be moved from the grid. (from Bradford et al. 2010)

Question about diameter limits of plots:

•  The minimum stem diameter (dbh) measured at a FIA plot is 5 inches (not 4 inches). In a microplot (for saplings) all stems that reach dbh and are <5 inches are measured.

•  For the FAO big plots (20 X 250 m) all stems >30 cm dbh are measured. For the 10 X 20 m subplots at the ends and middle of the big plot, stems >= 10 cm are measured and in the circular (50 m2) subplotsa stems <10 cm at dbh are measured.

•  For intensive plots it is a good idea to set lower dbh cutoffs (for example 2 inches for fia plots or 15 cm for the big FAO plots). This provides better accuracy and knowledge and is still compatible with the inventory system as long as each tree is tracked.

FAO Sample Design •  i) Stratification based on forest types and land use classes

is discarded as strata may change over time and render stratified sampling irrelevant with permanent samples. But stratification on basis of relatively stable strata such as ecological zoning may be applied to countries with pronounced differences in ecosystems such as humid and dry forests (Cameroon), desertic and non-desertic climate (Algeria), etc.

•  ii) The sampling is designed to include tree resources outside forest and an array of biophysical and management/uses parameters of forest and TOF.

•  iii) The approach foresees establishing sample plots to monitor changes over time between forests and other land use classes and the processes within the forests;

•  iv) Biophysical data and the management and use information will be geo-referenced and integrated to enable assessing the size and state of the resources in relation to their geographic location in the country and their social context;

•  v) Data quality is guaranteed and measurement methods are harmonised