evaluating precision gain for timber and non-timber attributes via landsat-based stratification on...
TRANSCRIPT
N
EW
S
70 0 70 140 Miles
Evaluating precision gain for timber and non-timber attributes via
Landsat-based stratification on California’s North Coast
Antti Kaartinen, Jeremy Fried & Paul Dunham
Portland
Other collaborators: Michael Lefsky,
Dale WeyermannDave Azuma
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, andStrata 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
Forest Survey Handbook reliability standards:Timberland area: 3% sampling error per million acres Growing stock volume: 10% sampling error per billion cu ftWhere sampling error = std error / estimate
Are these standards or targets?
Two-phase sampling
Phase 1Collect data for stratificationPhoto-interpretation for Forest Land Strata (FLS)
Phase 21/16 of Phase 1 plots are designated field plotsInstall/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?Save time and money?Responsive to national mandate!Standardization could facilitate interpretationTimely- several PIs now 20 years oldHow current does Phase 1 need to be?
How does FIA stratify elsewhere?Photo-interpretation (PI) in most areasNorth Central: NLCD + Edge classesRocky Mountain: AVHRRNortheast: NLCD+5X5 pixel moving window filter
PNW’s Stratification TestingTested 3 LANDSAT-TM based stratification methodsCompared with PI & Simple Random SamplingLocation criterion: availability of recent PIAssembled multi-institutional strike team:Antti Kaartinen, Helsinki UniversityMichael Lefsky, Oregon State UniversityDale Weyermann, PNW-FIA, Inv. Reporting & MappingPaul Dunham, PNW-FIA, Inv. Reporting & MappingJeremy Fried, PNW-FIA, Environmental Analysis & ResearchDave Azuma, PNW-FIA, Environmental Analysis & Research
Study Area
N
EW
S
70 0 70 140 Miles
Water
Forest ServiceNational Park Service
North Coast Survey Unit
Private or not reserved
N
20 0 20 40 60 Kilometers
Land Owner Groups
Included in Study:
Excluded from Study:
State of California (state parks)
Stratification sources- all based on TMExisting GIS layers
NLCDCALVEG
Customized system for generating a new GIS layerFIASCO-TM
NLCD:National Land Cover Dataset
Developed at EROS from LANDSAT 5 TM imagery circa 1992 by MRLCCovers lower 48 statesUsed leaf on/off imageryBuilt on unsupervised classification, census & National Wetlands Inventory data, and digital terrain modelsIntended update cycle is 5-10 years
CALVEG:Classification and Assessment with Landsat of Visible Ecological Groupings
Developed by USFS R5 RSL, Sacramento & CDFLANDSAT-TM data used for life formOther inputs vary by location and include
Field observationsDEMsLocal knowledge
Classified polygons include life form, tree cover species and stage of stand development
FIASCO-TM: Forest Inventory and Analysis Stratification with Classification of Thematic Mapper
Developed in cooperation with Michael Lefsky, Oregon State University Dept of Forest ScienceTM scenes trained by a 20% intensity phase 1 PISemi-automated, supervised classificationUses spectral signature of pixels overlaying a PI point as a basis for classifying other pixelsProduces a map of Forest Land Strata (FLS)
How the class definitions affect the resulting classified image
Image ProcessingReprojectionMaskingImage correctionImage mosaic
46
45
31
32
33
N
20 0 20 40 60 Kilometers
Landsat scenes from raw images to georeferenced and normalized mosaic
Image ProcessingReprojectionMaskingImage correctionImage mosaicClassify/Recode
Recode/cross-walk: NLCDStratification crosswalks
Forest/nonforest (fnf)fnf + other forest (fofnf)Deciduous, evergreen, mixed, other forest, non-forest (DEMON)
ForestDeciduous ForestEvergreen ForestMixed Forest
Other forestBare/transitionalShrublandWoody wetland
NonforestEverything else
Recode/cross-walk: CALVEG9 cover typesSeveral stand size class, density and species attributes; 100s of combinationsUltimately aggregated to eight strata
Constructed strata1. non-stocked2. hardwood3. low-volume conifer4. medium-volume conifer5. high-volume conifer6. other-forest7. non-forest8. unclassified
Image ProcessingReprojectionMaskingImage correctionImage mosaicClassify/RecodePost-processing
Filtering via clump & sieve
Original classified image After clump & sieveClumps of pixels, that wereSmaller than the threshold Value (4 pixels) are removed
After neighborhood analysisMajority function in 3*3 pixelwindow defines a new value forEach ‘empty’ cell
Steps in filtering a classified image file
Evergreen & mixed forest
Nonstocked forest
Deciduous forest
Nonproductive forest
Nonforest
30-METER PIXELS
How filtering changes the image
Image ProcessingReprojectionMaskingImage correctionImage mosaicClassify/RecodePost-processing
Filtering via clump & sieveEdge class generation
Edge classesEdges created around every typeAddresses issues of misregistration-induced incorrect assignments of plots to strata
Such incorrectly assigned plots comprise a smaller strata, thus having less impact on overall varianceExperimented with edge widths of 2-4 pixels
Edge class effectiveness explored for each data source
Forest / Nonforest with 4-pixel edge strata
Forest
Forest Edge
Non Forest
Non Forest Edge
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 GenerationPopulation estimates & sampling errors for
Timberland areaTimberland growing stock volumeCoarse woody debris volumeArea of vegetation cover classes
Processed via SAS scripts designed to handleDouble samplingStratified random sampling Simple random sampling
Also conventional PI and random (no Phase 1)
min
ima
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k=1 k=0.83 k=0.67 k=0.50 k=0.25
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Variance with stratificationVariance with simple random sampling
Design effect k= after Särndal et al. 1992
METHODTIMBERLAND
AREA
SAMPLING ERROR / 1,000,000
DESIGN EFFECT
Production PI 2,924,100 2.9% 30%
Optimal PI 2,911,300 3.0% 31%
CALVEG Edge 2,854,800 3.5% 41%
NLCD fnf 2,911,700 3.7% 48%
FIASCO_post 2,809,200 4.2% 58%
Random Sample 2,847,300 5.4% 100%
Timberland area
Timberland area
0%
1%
2%
3%
4%
5%
6%
Photo-interpretation
NLCD CALVEG FIASCO Random Sample
Sampling error per
1 million acres
METHOD FT3 VOLUMESAMPLING
ERROR / 1,000,000,000
DESIGN EFFECT
Optimal PI 10,490,000,000 12.9% 58%
Production PI 10,618,000,000 14.3% 71%
CALVEG FLS 10,297,000,000 14.7% 73%
NLCD fofnf 10,410,000,000 14.7% 74%
FIASCO_post 10,050,000,000 14.7% 72%
Random Sample 10,245,000,000 17.2% 100%
Volume on timberland
Volume on timberland
0%
5%
10%
15%
Photo-interpretation
NLCD CALVEG FIASCO Random Sample
Sampling error per
1 billion cubic feet
METHOD CWD VOLUMESAMPLING
ERROR / 1,000,000,000
DESIGN EFFECT
Production PI 5,692,500,000 14.6% 78%
FIASCO_post 5,386,000,000 15.8% 87%
CALVEG density 5,392,200,000 15.9% 88%
NLCD fnf 5,525,300,000 16.0% 92%
Random Sample 5,393,400,000 16.9% 100%
Coarse Woody Debris
Sampling error per1 billion
cubic feet
Coarse woody debris
0%
5%
10%
15%
Photo-interpretation
NLCD CALVEG FIASCO Random Sample
Understory vegetation cover classes
MethodArea of
schrubcover class 1
Sampling error /
1,000,000
Design effect (k)
Production PI 3,128,600 3.49% 63%CALVEG Edge 3,177,500 3.55% 66%Optimal PI 3,161,400 3.64% 69%FIASCO_post 3,187,700 3.74% 73%NLCD fofnf 3,148,900 3.75% 73%Random Sample 3,183,300 4.37% 100%
MethodArea of
schrubcover class 2
Sampling error /
1,000,000
Design effect (k)
Production PI 1,313,500 4.54% 74%Optimal PI 1,299,400 4.66% 77%CALVEG Edge 1,283,700 4.71% 78%NLCD fofnf 1,310,300 4.81% 83%FIASCO_post 1,278,300 4.89% 84%Random Sample 1,282,700 5.34% 100%
MethodArea of
schrubcover class 3
Sampling error /
1,000,000
Design effect (k)
FIASCO_post 551,520 5.51% 85%NLCD fofnf 565,050 5.60% 90%Optimal PI 564,070 5.73% 95%CALVEG Edge 564,870 5.78% 96%Production PI 582,520 5.71% 97%Random Sample 558,650 5.92% 100%
Class 1:(0% shrub cover)
Class 2:(0 – 40 % shrub cover)
Class 3:( >= 40 % shrub cover)
Cost ($) per million acres
ComponentTraditional
PI FIASCO NLCDCALVEG
CACALVEG
outside CA
Photo acquisition 1,945 778Photo Setup 14,140 5,998Photo Interpretation 2,203 441Landsat scenes 36 0 0 0Reproject, mosaic & mask 251 251 251 251Classify/post-process filtering/edging 503 503 1,715 1,715Administration/coordination 251 101 251 251CALVEG Creation 80,000Total 18,288 8,259 854 2,218 82,218
Method cost per million acres
California
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
0 5000 10000 15000 20000
Cost ($) per million acres
Sa
mp
ling
Err
pe
r m
illio
n a
cre
s
Area
Volume
rnd
pi
nlcd calveg fiasco
PI- advantagesGenerally high precisionOpportunities for ancillary studiesEasy to fine tune
For areas of interestTo fit FIA definitions
Opportunities for year-round employment of some data collection staff
NLCD - advantagesCould standardize in lower 48Development costs shared among agenciesPre-rectified/classified imagery huge savingsPrecision nearly as good as PI for this study area
FIASCO-TM - advantagesEasily fine tuned to local conditions/needsCurrent version gives good precision; may be amenable to improvementGenerates a wall-to-wall FLS map which may be useful to some clients
CALVEG - advantagesPolygons have many attributes, facilitating customizationData may be useful for other purposesPrecision performance good
CaveatsCost comparisons don’t consider
value of maps produced incidental to the stratificationcapacity to conduct ancillary studiesself-sufficiency wrt phase 1 production
We don’t yet know true costs for NLCD 2000
Sparse forest extension Forest Cover Thresholds
NLCD = 25% FIA = 10%
Test aging of phase 1 Scheduled for Winter 2002 in 4 Central OR
counties
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LAKE
HARNEY
MALHEUR
KLAMATH
GRANT
BAKER
CROOK
UMATILLA
UNION
WASCO
WALLOW A
DESCHUTES
MORROW
WHEELER
GILLIAM
JEFFERSON
SHERMAN
Sparse forest extension1981 and 2001 PINLCD 1992FIASCO-TM
Built on 1981 PIBuilt on 2001 PI
Thank you for your patience…
Questions????