Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection

Randolph H. Wynne, Jared P. Wayman, Christine Blinn

Virginia Polytechnic Institute and State University

Blacksburg, Virginia

John A. Scrivani, Rebecca F. MusyVirginia Department of Forestry

Charlottesville, VirginiaSupport from

Southern Research Station FIANCASI

Goals

• Phase I forest area estimation

• Production of forest/non-forest maps

• of acceptable resolution and accuracy• enabling stratification and spatial analyses

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

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

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)

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”

Study Areas

Mountains 2,435,000 ac

Piedmont 732,000 ac

Coastal Plain 1,499,000 ac

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

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

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

• 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

Raw TM Image

After First Iteration

After Pure Classes Removed

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

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

of P

ure

Spe

ctra

l C

lass

es

Coastal Plain

Piedmont

Ridge and Valley

Cumulative Pure Spectral Classes

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

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

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)

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%

1992

68.1%

71.2%

75.1%

72.8%

63.4%

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

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

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

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

Forest From 2-ft Orthophotography

IGSCR Classification

Recoded MRLC Classification

Recoded Virginia GAP Classification

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

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)

Spectral Class Representation100 ISODATA Classes - 30,000 acres reference data

0500

100015002000250030003500400045005000

1 10 19 28 37 46 55 64 73 82 91 100

Class rank order

Nu

mb

er o

f p

ixel

s

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

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

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

Thank you, Questions ?