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. Musy Virginia Department of Forestry Charlottesville, Virginia Support from Southern Research Station FIA NCASI

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 ( ) –ground truth performed late 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 Mountains2,435,000 ac Piedmont 732,000 ac Coastal Plain1,499,000 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 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 ISODATA Clustering Raw Image Apply Rejection Criteria Reduced Image Remove Pixels 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 File from “pure” classes ML Classification 3x3 Scan Majority Iterative Guided Spectral Class Rejection

Raw TM Image

After First Iteration

After Pure Classes Removed

IGSCR Iteration Summary

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) multi-date TM imagery +/- 1 pixel registration Collasped 4, or 5, classes into forest Applied same validation and estimation method

Phase I Estimates - Piedmont 1.91% 2.55% 2.73% 2.88% % 71.2% 75.1% 72.8% 63.4%

Results Summary

Accuracy Statistics - Piedmont

Accuracy Statistics - Coastal

Accuracy Statistics - Mountains

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 Representation 100 ISODATA Classes - 30,000 acres reference data

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 ?