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

1. 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

2. 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

3. 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?

4. 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

5. 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?

6. 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

7. 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

8. Study Area

9. Stratification sources- all based on TM • Existing GIS layers • NLCD • CALVEG • Customized system for generating a new GIS layer • FIASCO-TM

10. 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

11. 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

12. 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)

13. How the class definitions affect the resulting classified image

14. Image Processing • Reprojection • Masking • Image correction • Image mosaic

15. Landsat scenes from raw images to georeferenced and normalized mosaic

16. Image Processing • Reprojection • Masking • Image correction • Image mosaic • Classify/Recode

17. 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

18. 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

19. Image Processing • Reprojection • Masking • Image correction • Image mosaic • Classify/Recode • Post-processing • Filtering via clump & sieve

20. 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

21. How filtering changes the image

22. Image Processing • Reprojection • Masking • Image correction • Image mosaic • Classify/Recode • Post-processing • Filtering via clump & sieve • Edge class generation

23. 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

24. Forest / Nonforest with 4-pixel edge strata Forest Forest Edge Non Forest Non Forest Edge

25. 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

26. 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)

27. 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

28. Timberland area Sampling error per 1 million acres

29. Volume on timberland Sampling error per 1 billion cubic feet

30. Coarse Woody Debris Sampling error per 1 billion cubic feet

31. Understory vegetation cover classes Class 1: (0% shrub cover) Class 2: (0 – 40 % shrub cover) Class 3: ( >= 40 % shrub cover)

32. Method cost per million acres

34. 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

35. 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

36. 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

37. Polygons have many attributes, facilitating customization Data may be useful for other purposes Precision performance good CALVEG - advantages

38. 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

39. Sparse forest extension • Forest Cover Thresholds • NLCD = 25% • FIA = 10% • Test aging of phase 1 • Scheduled for Winter 2002 in 4 Central OR counties

40. Sparse forest extension • 1981 and 2001 PI • NLCD 1992 • FIASCO-TM • Built on 1981 PI • Built on 2001 PI

41. Thank you for your patience… Questions????