1. Gradient Nearest Neighbor Imputation Maps for Landscape Analysis in the Pacific Northwest Janet L. Ohmann Pacific Northwest Research Station USDA Forest Service Corvallis, Oregon ww.fsl.orst.edu/lemma

2. Mapping Ecological Systems of Map Zones 8 & 9 Nonforest lands: –J. Kagan and J. Hak (Oregon Natural Heritage Program, Oregon State University) Forest lands mapped using Gradient Nearest Neighbor (GNN): –J. Ohmann and J. Fried (PNW Research Station, USDA Forest Service), M. Gregory (Oregon State University) 8 9

3. COLA CLAMS, GNNfire GNNFire Landscape simulations to assess forest policy and natural disturbance effects on biophysical and socio-economic responses across large, multi-ownership regions. Extended to map fuels; emphasis on forest structure. Quantify environmental and disturbance factors controlling regional variation in forest communities Integrate inventory plot, imagery, and other spatial data to develop detailed maps of forest composition and structure. Completed GNN projects Background: GNN vegetation mapping

4. Gradient Nearest Neighbor Method Plot data Climate Geology Topography Ownership Satellite imagery PredictionSpatial data Plot locations Direct gradient analysis Plot assigned to each pixel Statistical model Imputation Pixel Plot # PSME (m 2 /ha) CanCov (%) Snags >=50 cm (trees/ha) Old-growth index Etc... 1121137.40.27... 279379972.10.82... Post- classification

5. Landsat TMBands, transformations, texture ClimateMeans, seasonal variability TopographyElevation, slope, aspect, solar DisturbancePast fires, harvest, insects & disease LocationX and Y coordinates Ownership FS, BLM, forest industry, other private Eastern Washington Coastal Oregon Explanatory Variables

6. Inventory plots used in GNN mapping for Central Oregon Landscape Analysis (3.4 million acres) Sourcen FIA158 BLM12 CVS1,381 Total1,551 1 234 56 7* 89 101112 13 Plot layout (~1 ha)

7. Accuracy assessment (‘obsessive transparency’) Local accuracy (cross-validation for plot locations): –Confusion matrices –Kappa statistics –Correlation statistics Regional accuracy: –distribution of forest conditions in map vs. plot sample –range of variation in map vs. plot sample Spatial depictions (unique to imputation): –Natural variation (among k nearest neighbors) –Sampling sufficiency (distance to nearest neighbor(s)) Accuracy for individual variables or classifications

8. GNN model specification Species Species + structure Structure Image segments (polygons), watersheds (imagery not used) Median-filtered√√ Unfiltered√√ Coarse grain Fine grain Model response variables Spatial grain of Landsat variables Emphasis on species composition Emphasis on forest structure ‘Tuning’ of GNN models (the ‘art’ of GNN)

9. Factors Associated with Vegetation Gradients (Coastal Oregon) Subset of explanatory variables Explained variation (% of total inertia) Species model (tree species) Structure model (tree species and size-class) Topography2.53.0 Climate8.08.6 Landsat TM--12.8 Ownership--5.5 Location5.04.9 Full model10.023.9

10. Goal: develop a map of current vegetation to support landscape modeling and analysis - Gradient Nearest Neighbor Method Satellite imagery GIS data Landscape vegetation map Fuel models, wildlife models, etc. Fuel maps Field plots Predicted future landscapes Stand and landscape simulators (FVS-FFE, VDDT, TELSA, etc.) Fire behavior models (FARSITE, FLAMMAP) Fire effects models (FOFEM, CONSUME) Habitat maps Etc.

11. Species Gradients (Linked to Environment) CCA axis 1 (climate) CCA axis 2 (elevation) Maritime Interior (Valley) Forest Vegetation Types Picea sitchensis Tsuga heterophylla Quercus woodlands Abies amabilis/ procera Dry T. heterophylla/ mixed evergreen High Low Pacific Ocean (Ohmann et al., in press, Ecological Applications)

12. GNN-predicted occurrence of Juniperus occidentalis in the Central Oregon Cascades Species model (tree species) (n=1415, kappa=0.72) Structure model (tree species and size-class) (n=1408, kappa=0.62)

13. Forest Structure (Linked to Disturbance and Ownership) Young forests, open canopies, hardwoods private lands Old forests, closed canopies, public lands Very young (0-25 cm) Young to middle-aged (25-50 cm) Mature (>50 cm) Old growth (OGHI >75) (Ohmann et al., in press) CCA axis 1 (Landsat, ownership)

15. 1996 Vegetation (GNN) and Land Cover (GAP)

16. Northern Spotted Owl Habitat Capability Index Nesting capability (patch level) –Trees/ha >100 cm dbh –Diameter Diversity Index Foraging capability (patch/landscape level) –Canopy height –Diameter Diversity Index –Habitat availability within 2.2 km 1996 (GNN) 2096 projected (base policy) (McComb et al. 2002)

17. Western Bluebird Habitat Capability Index Snags/ha 25-50 cm Snags/ha >50 cm Canopy closure 1996 (GNN) 2096 projected (base policy) (McGrath and Vesely, unpublished)

18. FLAMMAP Inputs Canopy bulk density Fuel model Moderate Fuel Moisture, 10 mph Wind Very Low Fuel Moisture 25 mph Wind FLAMMAP Outputs (www.fsl.orst.edu/lemma/gnnfire)

19. Summary: strengths and limitations of GNN maps Advantages: Regional in extent and rich in detail (continuous variables, 30-m grain) Analytical flexibility: –Post-mapping classification, analysis, modeling –User-defined geographic regions Models can be ‘tuned’ to meet different objectives Maintains multi-attribute covariance (classification and simulation modeling) Recaptures variation in plot data Excellent accuracy at regional and mid-scales Limitations: Map values are constrained to those at sampled locations Natural variability may reduce local prediction accuracy vs. other methods Forest structure accuracy is better for westside forests Lack of data for GNN-mapping of nonforest