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