OPPORTUNITIES TO DEVELOP A 30-METER SPATIAL DATABASE FOR THE USA

1. OPPORTUNITIES TO DEVELOP A 30-METER SPATIAL DATABASE FOR THE USA JOINT MEETING OF THE MIDWEST FOREST MENSURATIONISTS AND THE ANNUAL FIA SCIENCE SYMPOSIUM Ray Czaplewski Forest Inventory and Analysis Program Fort Collins, CO

2. 1992 1995 1997 1998 1999 2000 2001 FIA’s Technical Vision for Remote Sensing 1. Chronology 2. Implementation

3. First Blue Ribbon Panel on FIA FIA should … have a preeminent position in all federal efforts to inventory and monitorforest resource conditions at the regional and national levels.'‘ • “FIA (should) implement … remote sensing to accomplish inventory in a more cost-effective and efficient manner; … ” 1992

4. MRLC 1992 Data Buy • MMulti-Resolution Land Characterization (MRLC) consortium • USGS builds a consortium of federal programs to acquire standardized Landsat-5 imagery for the USA • UUSGS, EPA, NOAA, BLM, NASA, NPS, USDA, Forest Service 1992

5. 2000 1995 National Land Cover Data 1992 • USGS EROS Data Center begins first successful effort to map the forest and land cover of the USA with Landsat satellite data from MRLC 1992 1992

6. 1992 2000 1995 National Land Cover Data 1992 • Forested Upland • Deciduous Forest • Evergreen Forest • Mixed Forest • 14 categories for other types of land cover • 2 water classes • 3 barren classes • 3 urban classes • 5 agricultural classes • 1 other wetland class Woody Wetlands Non-native Woody Shrubland Grasslands

7. 1992 2000 1995 MRLC v. NLCD • MRLC: Multi-Resolution Land Characterization • Consortium to buy Landsatdata and post-process into standardized national imagery • NLCD: National Land Cover Data • Consortium to produce national land cover map using standardized MRLC imagery

8. 1992 2000 1995 MRLC 1992 & NLCD 1992 Forest Service joins MRLC 1992 to buy Landsat data FIA data from periodic surveys too out-of-date in many States to use as training data for Landsat classifications. FIA does not join NLCD 1992

9. 1995 1997 Office of Science and Technology Policy • “Increased availability and affordability of remotely sensed data requires improvedcollaboration andcommunication between agencies and programs …” 1992

10. 1995 1997 1998 Second Blue Ribbon Panel on FIA FIA should “(i)mprove … efficiency … through cooperation with agencies which have the remote sensing expertise not available within the FIA organization.” • Panel reemphasizes that “FIA should … have a preeminent position in all federal efforts to inventory and monitor forest resource conditions at the regional and national levels.” 1992

11. 1992 1995 1997 1998 1998 Farm Bill • “The Secretary shall … submit to Congress a strategic plan … that shall describe in detail … the process for employing … remote sensing … , and (its) subsequent use.”

12. 1999 Rand Corporation policy analysis • “Decentralized forest monitoring efforts greatly complicate the data delivery process.” “Integrat(e) … FIA … and Multi-Resolution Land Characterization (MRLC)” consortium. 1992 1997 1998

13. “Adopting an annual inventory system: user perspectives” • “More ambitious applications of remote sensing are … hindered by … lack of a far-reaching technical vision (that)might lead to radical changes in the FIA system and yield significant improvements in information quality and cost-effectiveness.” December, 1999 97(12)11-14 1992 1997 1998 1999

14. 1995 1997 1998 1999 2000 FIA Vision for Remote Sensing • FIA adopts performance standard to “go … operational with (satellite) remote sensing … by the end of … 2003.” 1992

15. Million of acres U.S.A North South West Timber land 504 159 201 143 Reserved 52 8 4 40 Other forest 191 3 9 179 1992 1995 1997 1998 1999 2000 FIA Vision for Remote Sensing • Traditional FIAstatistics remain essential for strategic analyses

16. 1992 1995 1997 1998 1999 2000 FIA Vision for Remote Sensing “At the national to regional scale …, distinguishing among 15 to 20 forest cover types is important. • However, “key analytical outputs today are maps, map layers, or other spatial representations of information and complex models.”

17. 1992 1995 1997 1998 1999 2000 FIA Vision for Remote Sensing

18. 1992 1995 1997 1998 1999 2000 FIA Vision for Remote Sensing • “Reduc(e) … human intervention … (to achieve) the cheapest and fastest way to produce (the remote sensing) product.” The “process must provide … linkages to other spatial data sets, such as census demographics or digital elevation models.” “(D)ata from various … programs can be combined more effectively to answer a much broader range of questionsthan any agency could tackle alone.”

19. 1992 1995 1997 1998 1999 2000 FIA joins MRLC 2000 Consortium • FIA contributes $200,000 in a federal partnership to build a Landsat 7 image-set for the entire USA.

20. 1992 1995 1997 1998 1999 2000 FIA joins MRLC 2000 Consortium • Three seasons of Landsat 7 imagery 2000-2001 • Early season (green up) • Peak greenness (summer) • Late season (brown up)

21. 1992 1995 1997 1998 1999 2000 FIA joins MRLC 2000 Consortium • Terrain corrected, one pixel spatial accuracy Radiometric calibration for seamless data across scenes

22. 1992 1995 1997 1998 1999 2000 FIA joins MRLC 2000 Consortium • $45 per CD, including 3 dates of calibrated Landsat 7 data and Digital Elevation Model (DEM) used for terrain correction

23. 1992 1995 1997 1998 1999 2000 National Land Cover Data (NLCD 2000) • FIA considers working with USGS EROS Data Center to replace NLCD 1992 forest cover map of USA using MRLC 2000 new Landsat 7 ETM+ data Annualized FIA data more valuable as training data for classification of Landsat data

24. 1995 1997 1998 1999 2000 2001 FIA partnership with USGS • FIA and USGS conduct pilot studies on highly automated digital classification of forest types with MRLC 2000 database and FIA plot data. 1992

25. NLCD 2000 Classification Process MRLC Input Database Land cover classification rule-sets Slope Brightness Greeness Elevation Wetness Position CART model to predict land cover type from Landsat and geospatial data Regression equation predicting crown cover from Landsat Training data FIA NRI IKONOS % Tree Canopy Aspect Texture Shape Soils NLCD 2000 Land Cover Apply CART model to wall MRLC Input Database 30-m Digital Elevation Model for each pixel in the USA Derivatives of Digital Elevation Model 3 seasons of state of the art Landsat 7 ETM+ data Tassel Cap Transformation to compress data quantity Coarse resolution STATSGO soils data from NRCS E.g. soil texture and soil depth If bright > 30 & green <45 & wet > 30 & texture <25 & shape = 3 & slope >5 & elevation > 4500 & position < 3 & canopy>10 & aspect = north & soil = 2 Then Deciduous Forest Variability within a 5x5 moving window over 30-m Landsat pixels Image segmentation (digital grouping of adjacent pixels into “polygons”) and FRAGSTATS indices of polygon shape assigned to each pixel E.g. a square polygon is more likely a corn field than a forest stand

26. 1995 1997 1998 1999 2000 2001 FIA partnership with USGS • Pilot study using FIA plots from NE and SRS FIA Units • 1100+ non-forest plots • 535 forested plots for training digital classifier • 134 forested plots plots for validation 1992

27. 1995 1997 1998 1999 2000 2001 FIA partnership with USGS Preliminary accuracy results (Chesapeake Bay) 1992

28. 1995 1997 1998 1999 2000 2001 FIA partnership with USGS ??????????????????????? • FIA decides to station an FIA scientist at USGS EROS Data Center in Sioux Falls SD to • Assure FIA has preeminent role in national mapping of forest cover • Improve coordination on NLCD 2000 database • Guard confidentiality of FIA plot locations 1992

29. 1995 1997 1998 1999 2000 2001 Western Governors’ Association • 10-year comprehensive strategy to improve prevention and suppression of wildfires, and reduce hazardous fuels 1992

30. 1995 1997 1998 1999 2000 2001 Western Governors’ Association • Produce a geospatial database for large-area assessments of • Communities at risk • Current vegetative conditions • Likelihood of severe wildland fire • Threats to • Key habitats • Water quality, such as post-fire erosion • Air quality • Local economies 1992

31. 1995 1997 1998 1999 2000 2001 LANDFIRE Database • Produce higher-resolution maps to support more cost-effective implementation of the National Fire Plan and Western Governors’10-year Comprehensive Strategy. • Better prepare for and allocate firefighting resources 1992

32. 1995 1997 1998 1999 2000 2001 LANDFIRE Database • Map and prioritize areas for fuel reduction efforts • Determine potential impacts of these fire treatments on wildlife, fish and riparian areas. 1992

33. 1995 1997 1998 1999 2000 2001 LANDFIRE Database Develop geospatial database at the 30-m scale for the entire USA • Vegetation type • Forest structural-stage • Stand density • Fire history • Fuel loadings • Wildlife habitats 1992

34. 1995 1997 1998 1999 2000 2001 LANDFIRE Database • Proposal to develop 30-m database with MRLC and FIA partnership as its foundation • LANDFIRE could fund considerably more detail on forest conditions in 30-m geospatial database for the USA 1992

35. NLCD 2000 becomes a 30-m database for USA • Landsat imagery and derived indices • Land cover and forest type classification • Digital Elevation Models • Coarse-scale geospatial data • Ecoregions • Climate • Soils (STATSGO)

36. GIS themes in 30-m database Map more detailed types (15-20) of forest cover using • NLCD data base • Landforms • FIA training data(?)

37. GIS themes in 30-m database Coarse-scale (e.g., 1-km) information to geospatial database • Bailey’s Ecoregion Sub-sections • Climate (DAYMET) daily means • Precipitation • Minimum and maximum surface air temperature • Surface air humidity • Incident shortwave radiation • Land ownership • Bureau of the Census population and housing density

38. GIS themes in 30-m database • Soils (STATSGO) • Available water capacity • Soil organic carbon • Soil suitability for agriculture • Soil Texture • Soil Depth

39. GIS themes in 30-m database Land-use • Probable urban areas • Buffered road network • Nighttime Lights of the World • Protected Areas Database • Urban/wildland interface zones

40. GIS themes in 30-m database Biophysical Settings Model • 30-m version of Potential Natural Vegetation • 30-m Digital Elevation Model • Soil depth and texture (STATSGO) • Better identify areas with similar • Fire frequencies • Climatic regimes • Geological and topographical characteristics

41. GIS themes in 30-m database Historical Natural Fire Regimes • GIS model predicting • Fire frequency • Fire severity • Inputs • Digital Elevation Model • Biophysical Settings

42. GIS themes in 30-m database Subdivide general forest cover types into additional 30-50 detailed forest types • Separate spectrally similar forest types using Biophysical Settings model (PNV) • >65% accuracy

43. GIS themes in 30-m database • Remotely sensed estimates of tree/shrub cover • Structural stages • Open stands, small trees • Open stands, large trees • Closed stands, small trees • Closed stands, large trees • >70% accuracy

44. GIS themes in 30-m database Model predictions of fuel loading • Climatic regime • % tree/shrub cover • Detailed forest type • Structural stage (open v. closed; large- v. small trees) • Potential Natural Vegetation • 30-m Digital Elevation Model

45. GIS themes in 30-m database Value-added products from other partner programs using same database • GIS analyses of risk from insects and diseases • GAP wildlife habitat mapping (USGS BRD) • % imperviousness surfaces (EPA)

46. Caveat Intended for large assessment areas • National (e.g., fuel treatment priorities) • Regional (e.g., multiple States) • Large-areas • River basins • Ecological Provinces

47. Caveat • National 30-m spatial database is a starting point for priority setting among smaller geographical areas • Local datasets are usually more accurate for local analyses

48. Conclusion • FIA mission “Make and keep current a comprehensive inventory and analysis of the present and prospective conditions of and requirements for the renewable resources of the forest and rangelands of the United States." Partnerships among agencies and USFS programs for remote sensing and geospatial databases provide cost-effective support to the FIA mission and users of FIA data.

49. Poster Session • A comparison of stratification effectiveness between the National Land Cover Data set (NLCD 1992) and photo-interpretation in western Oregonby Paul Dunham, Dale Weyermann and David Azuma (PNW-FIA) • Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mappingby Chengquan Huang (USGS EROS Data Center) and Andrew Lister (NE-FIA)