Rejection Sensitivity and the Rejection–Hostility Link in ...
Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection
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Transcript of Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection
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Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection
Randolph H. Wynne, Jared P. Wayman, Christine BlinnVirginia Polytechnic Institute and State University
Blacksburg, Virginia
John A. Scrivani, Rebecca F. MusyVirginia Department of Forestry
Charlottesville, VirginiaSupport from
Southern Research Station FIANCASI
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Goals
• Phase I forest area estimation • Production of forest/non-forest
maps• of acceptable resolution and accuracy• enabling stratification and spatial analyses
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Criteria for Classification Methodology
• Objective and repeatable– across operators, regions and time
• Quick and low-cost– repeatable at 3-5 years
• Provides a binary forest/non-forest landcover/landuse classification
• Usable to estimate Phase 1 forest land use – adjusting map marginals with ground truth
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Virginia Work To Date
• Iterative Guided Spectral Class Rejection method developed
• Applied to three physiographic provinces• Compared to MRLC• Comparison with GAP in progress• IGSCR for areas of more rapid change in
progress
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Traditional PI Method
• Operational SAFIS program used for comparison
• Phase 1 PI– 1994 NAPP photography
(1991-96)– ground truth performed
late 1997-2000• Estimates via Li, Schreuder,
Van Hooser & Brink (1992)
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Phase I Estimates from Image Classifications
• Phase II (and III) FIA ground plots and intensification points used as “small sample”, or ground truth, to adjust map marginal proportions
• Standard errors estimated via Card (1982) formulae
• Map marginal proportions from classification used as large “sample”
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Study Areas
Mountains 2,435,000 ac
Piedmont 732,000 ac
Coastal Plain 1,499,000 ac
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Imagery used in IGSCR Landsat TM
Coastal (Path 14 Row 33) 5/14/98
Piedmont (Path 16 Row 34) 9/26/98
Mountains (Path 18 Row 34) 11/11/98
Registration
Used GCP’s from Virginia DOT roads coverage
Obtained ~15 m RMSE
However, subjective adjustments needed, especially in Mountains
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Reference Data for IGSCR• Any source of know forest and non-forest can be used
• Need to sample range of spectral variability
• Need to sample “confused” spectral classes
• Need to sample proportionally within confused classes
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Iterative Guided Spectral Class
Rejection• unsupervised ISODATA
clustering into 100-500 spectral classes
• reference data used to “reject” relatively “pure” spectral classes (e.g. 90% pure)
• “pure” classes removed from image and remaining pixels enter into next iteration
ISODATAClustering
RawImage
Apply Rejection Criteria
ReducedImage
RemovePixels
More pure classes?yes
no
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• Iterations continue until no further pure spectral classes are extracted
• Identified “pure” spectral classes used as signatures for a ML classification
• 3x3 scan majority filter used to assign final pixel classification
Signature Filefrom “pure” classes
MLClassification
3x3 ScanMajority
Iterative Guided Spectral Class Rejection
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Raw TM Image
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After First Iteration
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After Pure Classes Removed
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Coastal Plain Piedmont Ridge and Valley
Iteration Max # ofClasses
PureClasse
s
Max # ofClasses
PureClasses
Max # ofClasses
PureClasses
Max # ofClasses*
Pure Classes*
1 500 246 500 239 500 290 191 10
2 752 55 553 30 1139 127 90 1
3 619 19 486 19 894 29 90 1
4 580 8 457 8 849 18 89 3
5 563 6 447 8 820 9 85 1
6 554 6 438 6 804 9 84 1
7 537 6 431 3 791 10
8 527 2 428 1 771 4
9 519 1 418 1 767 3
10 411 2 763 4
11 409 2 759 4
12 406 5 722 2
13 401 2
14 397 4
15 391 4
16 383 1
17 381 2
Total 349 337 509 17* Iterations carried out on pixels that were either shadow or water
IGSCRIterationSummary
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150
200
250
300
350
400
450
500
550
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Iteration Number
Num
ber o
f Pur
e Sp
ectra
l Cla
sses
Coastal Plain
Piedmont
Ridge and Valley
Cumulative Pure Spectral Classes
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Validation • Used FIA permanent plots located with
DGPS, Phase 1-3, 5-10 meter accuracy• Intensification plots digitized from SPOT
10m pan imagery• Since FIA plot locations only used for
validation, confidentiality maintained
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MRLC Comparison• EPA Region 3 MRLC
classification (1996)• 1991-93 multi-date TM
imagery • +/- 1 pixel registration• Collasped 4, or 5,
classes into forest • Applied same
validation and estimation method
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Collapsed Class MRLC Classes
Forest Conifer, mixed,deciduous forest, woodywetland (7-10)
Non-Forest Water (1), developed,(2-3) agriculture, (4-6)barren (11-14)
Confused Barren, transitional (15)
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Phase I Estimates - Piedmont
1.91% 2.55% 2.73% 2.88%
67.70%
69.49%68.67%
69.53%
60.04%59.28%
57.75% 58.02%
63.87% 64.38%63.21% 63.77%
55%
60%
65%
70%
75%
Traditional FIAPI
IGSCR (3x3filter)
MRLC-15for MRLC-15non
SE =1.91% 2.55% 2.73% 2.88%
199268.1%
71.2%
75.1%
72.8%
63.4%
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Results Summary
Unadjusted Adjusted Std 1992Method % Forest % Forest Error n Estimate
CoastalTraditional FIA PI 67.27% 66.06% 1.08% 260 69.99%IGSCR (3x3 filter) 68.38% 67.14% 3.07% 121
MRLC-15for 68.40% 69.84% 3.15% 121 MRLC-15non 66.70% 72.14% 3.36% 121
MountainTraditional FIA PI 65.75% 69.74% 1.22% 493 67.17%IGSCR (3x3 filter) 76.87% 69.68% 2.44% 241
MRLC-15for 77.28% 70.53% 2.52% 240 MRLC-15non 77.17% 70.70% 2.50% 240
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Accuracy Statistics - Piedmont
IGSCR MRLC-15f
MRLC-15n
Overall Acc. 85.4 82.8 80.7Kappa .676 .605 .565Users - For 85.0 80.5 80.4Users - Non 86.4 89.6 81.5Producers - For 93.4 95.9 91.7Producers -Non 71.8 60.6 62.0
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Accuracy Statistics - Coastal
IGSCR MRLC-15f
MRLC-15n
Overall Acc. 94.8 86.8 81.8Kappa .855 .667 .566Users - For 94.7 90.9 90.2Users - Non 95.2 75.8 64.1Producers - For 98.6 90.9 84.1Producers -Non 83.3 75.8 75.7
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Accuracy Statistics - Mountains
IGSCR MRLC-15f
MRLC-15n
Overall Acc. 82.6 80.8 81.2Kappa .552 .508 .521Users - For 84.0 83.2 83.7Users - Non 77.78 72.7 73.2Producers - For 92.9 91.1 91.1Producers -Non 58.3 56.3 57.7
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Forest From 2-ft Orthophotography
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IGSCR Classification
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Recoded MRLC Classification
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Recoded Virginia GAP Classification
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Reference Data• Any source of know forest and non-forest can be used
• Need to sample range of spectral variability
• Need to sample “confused” spectral classes
• Need to sample proportionally within confused classes
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Possible Reference Data Protocol
• Training reference data– 500-1,5000 acre maplet at each Phase 3 plot
from high resolution imagery (photo, video, IKONOS)
– would provide 40-80,000 acres per TM scene• Validation reference data
– Phase 2 plots– Larger intensification samples (e.g. 16 pt
cluster at each Phase 2 plot)
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Spectral Class Representation100 ISODATA Classes - 30,000 acres reference data
0500
100015002000250030003500400045005000
1 10 19 28 37 46 55 64 73 82 91 100Class rank order
Num
ber o
f pix
els
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Conclusions• IGSCR is an objective, repeatable and
low cost process for a given rectified imagery and reference data
• Accuracy is as good or better than MRLC
• Further work needed on standardized rectification, reference data protocols, and optimal iteration parameters
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Conclusions
• Acceptable Phase I estimates can be obtained from either ISGSCR or MRLC
• Lower accuracy of satellite image classifications requires larger ground truth sample compared to traditional PI method
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Further work• Standardized rectification• IGSCR Parameters
• ISODATA parameters• Number of ISODATA classes• Minimum Pixel Count Per Class• Homogeneity Criteria• Stopping Criteria
• Multinomial classifications• Reference data protocol• Trials in other regions
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Thank you, Questions ?