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Evaluating precision gain for timber and non-timber attributes via Landsat-based stratification on California’s North PowerPoint Presentation
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Evaluating precision gain for timber and non-timber attributes via Landsat-based stratification on California’s North Coast. Antti Kaartinen, Jeremy Fried & Paul Dunham. Other collaborators: Michael Lefsky, Dale Weyermann Dave Azuma. Portland. Why stratify?.

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Evaluating precision gain for timber and non-timber attributes via Landsat-based stratification on California’s North


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slide1

Evaluating precision gain for timber and non-timber attributes via Landsat-based stratification on California’s North Coast

Antti Kaartinen, Jeremy Fried & Paul Dunham

Other collaborators: Michael Lefsky, Dale Weyermann Dave Azuma

Portland

why stratify
Why stratify?

Increase precision of inventory estimates by reducing sampling error (std err of estimate/estimate).

How does it work?

  • Divides area “population” into strata such that:
    • variability of plots within strata < variability of plots within the population as a whole, and
    • Strata with high variability make up a relatively small proportion of the population.
  • Then, sample from the strata using stratified random sampling or double-sampling
standards of precision
Standards of precision
  • Forest Survey Handbook reliability standards:
    • Timberland area: 3% sampling error per million acres
    • Growing stock volume: 10% sampling error per billion cu ft
    • Where sampling error = std error / estimate
  • Are these standards or targets?
two phase sampling
Two-phase sampling
  • Phase 1
    • Collect data for stratification
    • Photo-interpretation for Forest Land Strata (FLS)
  • Phase 2
    • 1/16 of Phase 1 plots are designated field plots
    • Install/measure field plots
  • Efficient strategy for sampling error reduction, but
    • Phase 1 not really cheap
      • ~$2 million for CA, OR, WA
why evaluate more automated methods
Why evaluate more automated methods?
  • Save time and money?
  • Responsive to national mandate!
  • Standardization could facilitate interpretation
  • Timely- several PIs now 20 years old
  • How current does Phase 1 need to be?
how does fia stratify elsewhere
How does FIA stratify elsewhere?
  • Photo-interpretation (PI) in most areas
  • North Central: NLCD + Edge classes
  • Rocky Mountain: AVHRR
  • Northeast: NLCD+5X5 pixel moving window filter
pnw s stratification testing
PNW’s Stratification Testing
  • Tested 3 LANDSAT-TM based stratification methods
  • Compared with PI & Simple Random Sampling
  • Location criterion: availability of recent PI
  • Assembled multi-institutional strike team:

Antti Kaartinen, Helsinki University

Michael Lefsky, Oregon State University

Dale Weyermann, PNW-FIA, Inv. Reporting & Mapping

Paul Dunham, PNW-FIA, Inv. Reporting & Mapping

Jeremy Fried, PNW-FIA, Environmental Analysis & Research

Dave Azuma, PNW-FIA, Environmental Analysis & Research

stratification sources all based on tm
Stratification sources- all based on TM
  • Existing GIS layers
    • NLCD
    • CALVEG
  • Customized system for generating a new GIS layer
    • FIASCO-TM
nlcd n ational l and c over d ataset
NLCD:National Land Cover Dataset
  • Developed at EROS from LANDSAT 5 TM imagery circa 1992 by MRLC
  • Covers lower 48 states
  • Used leaf on/off imagery
  • Built on unsupervised classification, census & National Wetlands Inventory data, and digital terrain models
  • Intended update cycle is 5-10 years
calveg c lassification and a ssessment with l andsat of v isible e cological g roupings
CALVEG:Classification and Assessment with Landsat of Visible Ecological Groupings
  • Developed by USFS R5 RSL, Sacramento & CDF
  • LANDSAT-TM data used for life form
  • Other inputs vary by location and include
    • Field observations
    • DEMs
    • Local knowledge
  • Classified polygons include life form, tree cover species and stage of stand development
slide12
FIASCO-TM: Forest Inventory and Analysis Stratification with Classification of Thematic Mapper
  • Developed in cooperation with Michael Lefsky, Oregon State University Dept of Forest Science
  • TM scenes trained by a 20% intensity phase 1 PI
  • Semi-automated, supervised classification
  • Uses spectral signature of pixels overlaying a PI point as a basis for classifying other pixels
  • Produces a map of Forest Land Strata (FLS)
image processing
Image Processing
  • Reprojection
  • Masking
  • Image correction
  • Image mosaic
image processing16
Image Processing
  • Reprojection
  • Masking
  • Image correction
  • Image mosaic
  • Classify/Recode
recode cross walk nlcd
Stratification crosswalks

Forest/nonforest (fnf)

fnf + other forest (fofnf)

Deciduous, evergreen, mixed, other forest, non-forest (DEMON)

Forest

Deciduous Forest

Evergreen Forest

Mixed Forest

Other forest

Bare/transitional

Shrubland

Woody wetland

Nonforest

Everything else

Recode/cross-walk: NLCD
recode cross walk calveg
9 cover types

Several stand size class, density and species attributes; 100s of combinations

Ultimately aggregated to eight strata

Constructed strata

non-stocked

hardwood

low-volume conifer

medium-volume conifer

high-volume conifer

other-forest

non-forest

unclassified

Recode/cross-walk: CALVEG
image processing19
Image Processing
  • Reprojection
  • Masking
  • Image correction
  • Image mosaic
  • Classify/Recode
  • Post-processing
    • Filtering via clump & sieve
steps in filtering a classified image file
Steps in filtering a classified image file

Original classified image

After clump & sieve

Clumps of pixels, that were

Smaller than the threshold

Value (4 pixels) are removed

After neighborhood analysis

Majority function in 3*3 pixel

window defines a new value for

Each ‘empty’ cell

Evergreen & mixed forest

Nonstocked forest

Deciduous forest

30-METER PIXELS

Nonproductive forest

Nonforest

image processing22
Image Processing
  • Reprojection
  • Masking
  • Image correction
  • Image mosaic
  • Classify/Recode
  • Post-processing
    • Filtering via clump & sieve
    • Edge class generation
edge classes
Edge classes
  • Edges created around every type
  • Addresses issues of misregistration-induced incorrect assignments of plots to strata
    • Such incorrectly assigned plots comprise a smaller strata, thus having less impact on overall variance
    • Experimented with edge widths of 2-4 pixels
  • Edge class effectiveness explored for each data source
forest nonforest with 4 pixel edge strata
Forest / Nonforest with 4-pixel edge strata

Forest

Forest Edge

Non Forest

Non Forest Edge

demon with 4 pixel strata
DEMON with 4-pixel strata

Evergreen Forest

Evergreen Forest Edge

Deciduous Forest

Deciduous Forest Edge

Other Forest

Other Forest Edge

Non Forest

Non Forest Edge

Mixed Forest &

Mixed Forest Edge

table generation
Table Generation
  • Population estimates & sampling errors for
    • Timberland area
    • Timberland growing stock volume
    • Coarse woody debris volume
    • Area of vegetation cover classes
  • Processed via SAS scripts designed to handle
    • Double sampling
    • Stratified random sampling
    • Simple random sampling
  • Also conventional PI and random (no Phase 1)
slide27

Variance with stratification

Variance with simple random sampling

Design effect k=

Relative confidence intervals

at different levels of statistical efficiency

moderate

minimal

substantial

excellent

k=0.25

k=0.50

k=0.67

k=0.83

k=1

after Särndal et al. 1992

timberland area
Timberland area

Sampling error per

1 million acres

volume on timberland
Volume on timberland

Sampling error per

1 billion cubic feet

coarse woody debris
Coarse Woody Debris

Sampling error per

1 billion cubic feet

slide31

Understory vegetation

cover classes

Class 1:

(0% shrub cover)

Class 2:

(0 – 40 % shrub cover)

Class 3:

( >= 40 % shrub cover)

pi advantages
Generally high precision

Opportunities for ancillary studies

Easy to fine tune

For areas of interest

To fit FIA definitions

Opportunities for year-round employment of some data collection staff

PI- advantages
nlcd advantages
Could standardize in lower 48

Development costs shared among agencies

Pre-rectified/classified imagery huge savings

Precision nearly as good as PI for this study area

NLCD - advantages
fiasco tm advantages
Easily fine tuned to local conditions/needs

Current version gives good precision; may be amenable to improvement

Generates a wall-to-wall FLS map which may be useful to some clients

FIASCO-TM - advantages
calveg advantages
Polygons have many attributes, facilitating customization

Data may be useful for other purposes

Precision performance good

CALVEG - advantages
caveats
Caveats
  • Cost comparisons don’t consider
    • value of maps produced incidental to the stratification
    • capacity to conduct ancillary studies
    • self-sufficiency wrt phase 1 production
  • We don’t yet know true costs for NLCD 2000
sparse forest extension
Sparse forest extension
  • Forest Cover Thresholds
    • NLCD = 25%
    • FIA = 10%
  • Test aging of phase 1
  • Scheduled for Winter 2002 in 4 Central OR counties
sparse forest extension40
Sparse forest extension
  • 1981 and 2001 PI
  • NLCD 1992
  • FIASCO-TM
    • Built on 1981 PI
    • Built on 2001 PI