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Gradient Nearest Neighbor Imputation Maps for Landscape Analysis in the Pacific
NorthwestJanet L. Ohmann
Pacific Northwest Research StationUSDA Forest Service
Corvallis, Oregon
ww.fsl.orst.edu/lemma
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
COLA
CLAMS,GNNfire
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
Gradient Nearest Neighbor MethodPlot data
ClimateGeologyTopographyOwnership
Satelliteimagery
PredictionSpatial data
Plot locations
Direct gradient analysis
Plot assigned
to each pixel
Statistical model
Imputation
Pixel
Plot #
PSME (m2/ha
)
CanCov (%)
Snags >=50 cm
(trees/ha)
Old-growth index
Etc...
1 12 11 3 7.4 0.27 ...
2 793 79 97 2.1 0.82 ...
Post-classificatio
n
Landsat TM Bands, transformations, texture
Climate Means, seasonal variability
Topography Elevation, slope, aspect, solar
DisturbancePast fires, harvest, insects &
disease
Location X and Y coordinates
OwnershipFS, BLM, forest industry, other
private
EasternWashingto
n
CoastalOregon
Explanatory Variables
Inventory plots used in GNN mapping for Central Oregon Landscape Analysis (3.4
million acres)
Source
n
FIA 158
BLM 12
CVS1,38
1
Total1,55
1
1
2 3 4
5 6 7* 8 9
10 11 12
13
Plot layout (~1 ha)
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
GNN model specification
SpeciesSpecies
+ structure
Structure
Image segments
(polygons), watersheds
(imagery not used)Median-
filtered√ √
Unfiltered √ √
Coarsegrain
Finegrain
Model response variables
Spatial grainof Landsatvariables
Emphasison speciescompositi
on
Emphasis on forest structure
‘Tuning’ of GNN models(the ‘art’ of
GNN)
Factors Associated with Vegetation Gradients (Coastal Oregon)
Subset of explanator
y variables
Explained variation (% of total inertia)
Species model (tree
species)
Structure model (tree species
and size-class)
Topography
2.5 3.0
Climate 8.0 8.6
Landsat TM
-- 12.8
Ownership -- 5.5
Location 5.0 4.9
Full model 10.0 23.9
Goal: develop a map of current vegetation to support landscape modeling and analysis
-Gradient Nearest
Neighbor Method
Satelliteimagery
GISdata
Landscape vegetation map
Fuelmodels,wildlifemodels,
etc.
Fuel maps
Fieldplots
Predicted future
landscapes
Stand and landscape simulators (FVS-FFE,VDDT, TELSA, etc.)
Fire behavior models
(FARSITE, FLAMMAP)
Fire effectsmodels(FOFEM,
CONSUME)
Habitat maps
Etc.
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
Paci
fic
Oce
an
(Ohmann et al., in press, Ecological Applications)
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)
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)
IDNO TREE # SPECIES DBHCM HTM CC BHAGE TPHPLT
41034020 101 TSHE 39.116 24.384 4 83 2.617
41034020 116 CHLA 109.728 32.309 3 136 2.617
41034020 123 TSHE 55.880 39.319 3 103 2.617
41034020 129 PSME 200.152 58.826 3 913 1.000
41034020 133 PSME 66.802 40.843 3 99 2.617
41034020 316 TSHE 57.404 40.234 3 80 2.617
41034020 319 CHLA 105.664 45.110 3 244 2.617
41034020 320 CHLA 80.518 42.062 4 349 2.617
1996 Vegetation (GNN) and Land Cover (GAP)
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)
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)
FLAMMAP InputsFLAMMAP Inputs
Canopy bulk densityCanopy bulk density
Fuel Fuel modelmodel
Moderate Moderate Fuel Moisture,Fuel Moisture,10 mph Wind10 mph Wind
Very Low Fuel Very Low Fuel MoistureMoisture25 mph Wind25 mph Wind
FLAMMAP OutputsFLAMMAP Outputs
(www.fsl.orst.edu/lemma/gnnfire)
Summary: strengths and limitations of GNN mapsAdvantages:
• 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