Mapping Current Vegetation in the Pacific Coast States with

Gradient Nearest Neighbor Imputation

Landscape Ecology, Modeling, Mapping, and Analysis (LEMMA) team (www.fsl.orst.edu/lemma):

Janet Ohmann1, Matt Gregory2, Ken Pierce1, Tim Holt2, Heather May2, Emilie Grossmann2

Collaborators:Jeremy Fried3, Jimmy Kagan4, Ken Brewer5, Miles Hemstrom6, Melinda Moeur7, Tom DeMeo7, Gary Lettman8, Mike Wimberly9

1USDA FS, PNW, Ecosystem Processes; 2Oregon State University, Forest Science Department; 3USDA FS, PNW, Forest Inventory and Analysis; 4Oregon State University,

Institute of Natural Resources; 5USDA FS, Remote Sensing and Applications Center; 6USDA FS, PNW, Focused Science Delivery; 7USDA FS, Region 6; 8Oregon Department of Forestry;

9South Dakota State University

Outline...

• Motivations for GNN

• Key attributes of GNN

– Canonical correspondence analysis

• Overview of GNN applications (or focus on GAP Ecological Systems mapping???)

• Areas of research interest

Primary motivation (GNN niche): supply missing data for analysis and modeling of forest ecosystems at the regional

level(not estimation of sub-population totals)

-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.

Criteria for maps of current vegetation in the Pacific Northwest• Spatially complete (spatial pattern, small geographic areas)

• Consistent across large, multi-ownership regions

• Rich in detail on species composition and forest structure

– Reasonable covariance structure among attributes

• Suitable for input to stand and landscape simulation models

• Flexibility to meet a variety of analytical needs

– Too expensive to develop multiple vegetation maps at the regional scale

– Advantages in using consistent information for multiple analyses, by multiple agencies

COLA

CLAMS,GNNfire

GNNFire

GNNFire

GNN projects

GNN mapping in the Pacific Coast States• Analysis of forest policy effects

via landscape modeling and scenario analysis (CLAMS, COLA, IMAP)

• Regional risk assessments (WWETAC, RSAC 250-m study)

• Assessing fire hazard, planning fuel management, modeling fire behavior and effects (GNNFire)

• Regional inventory and monitoring (FIA, NWFP Effectiveness Monitoring)

• National Forest planning, BLM cumulative effects analysis

• Conservation planning (GAP)

• 3-D visualization using computer gaming technology (JFSP)

GAP

Overview of Gradient Nearest Neighbor Imputation

(GNN)

Components of GNN Imputation (Current Incarnation)• Statistical model:

– direct gradient analysis (canonical correspondence analysis)

– explanatory variables: satellite imagery, other GIS layers

– response variables = abundance (basal area, importance value, cover, etc.) of ‘species’ on plots (FIA, CVS, etc.)

– other vegetation variables retained with plot-map link

• Similarity measure:

– Euclidean distance in n-dimensional gradient space, where n = first n axes (usually 8) from CCA

– Axes weighted by their explanatory power (eigenvalues)

• Imputation method:

– Single nearest neighbor (k=1), to maintain covariance structure

– Summary statistics of multiple neighbors

Why Canonical Correspondence Analysis???(ter Braak 1986)

• Multivariate predictors and multivariate response

– Results in a weight for each of many spatial variables, based on its relationship with the multiple response variables

• Grounded in ecological theory, widely used in practice

– Heuristic mathematical approximation to a Gaussian response curve of species along environment gradients, but robust to violations (Palmer 1993)

– Used primarily for exploratory and descriptive studies, not prediction or hypothesis testing

• Robust to sparse data matrices (species on plots), common across regions with long gradients and high species turnover (Palmer 1993)

• Robust to multicollinearity among explanatory variables (Palmer 1993)

• Alternative models can be specified, depending on objectives

Canonical Correspondence Analysis

(CCA) Algorithms• LC = linear

combination site scores (used in GNN)

• WA scores = weighted averaging site scores

• End result: ordering of plots (LC scores) along n orthogonal axes such that most similar plots are nearest one another

Assign species scores as weighted average of LC scores

Arbitrarily assign LC scores

Assign WA scores as weighted average of species scores

Create LC scores as predicted values from

multiple regression

Start

Stop

Any change in scores?Yes

No

(adapted from Palmer 1993)

• Guru #1: yes

• Guru #2: no

• Guru #3: both

• Guru #4: dumb question

• Guru #5: why do you want to know?

Ask the ordination guru...

“Is CCA non-parametric?”

Gradient Nearest Neighbor MethodPlot data

ClimateGeologyTopographyOwnership

Remotesensing

PredictionSpatial data

Plot locations

Direct gradient analysis

Plot assigned to each pixel

Statistical model

Imputation

PixelPSME

(m2/ha)CanCov (%)

Snags >50 cm

(trees/ha)

Old-growth index

Etc...

1 11 3 7.4 0.27 ...

2 79 97 2.1 0.82 ...

Regional Plot Data

Source n (OR) n (WA)

FIA (nonfederal) 385 445

BLM (BLM) 99 --

CVS (Natl. Forest) 279 1,596

Ecology (Natl. Park) -- 52

Total 763 2,093

CoastalOregon

EasternWashington

1

2 3 4

5 6 7* 8 9

10 11 12

13 Plotlayout

(~1 ha)

Vegetation data:

• Live trees

• Snags

• Down wood

• Understory vegetation

Landsat TMBands, transformations,

texture

Climate Means, seasonal variability

Topography

Elevation, slope, aspect, solar

Disturbance

Past fires, harvest, I&D

Location X, Y

Ownership Public, private

EasternWashingto

n

CoastalOregon

Explanatory Variables

(2) calculate

axis scores of pixel from mapped data

layers

(3) find nearest-

neighbor plot in

gradient space

Axis 2(climate)

gradient space geographic space

Axis 1(Landsat)

(1)conductgradient

analysis ofplot data

field plots study

area(4)

impute nearest

neighbor’s grounddata to

mapped pixel

The imputation component of GNN

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

Accuracy assessment (‘obsessive transparency’)• Local-scale accuracy (at plot locations) via cross-validation:

– Confusion matrices

– Kappa statistics

– Correlation statistics

• Regional-scale accuracy:

– distribution of forest conditions in map vs. plot sample

– range of variation in map vs. plot sample

• Spatial depictions:

– Variation among k nearest neighbors

– Distance to nearest neighbor(s) (sampling sufficiency)

• General findings re. GNN map accuracy:

– Excellent for regional patterns and amounts, imperfect for local sites (mid-scales???)

– Appropriate for regional planning and policy analysis

Results:

What factors are associated with regional gradients in forest composition

and structure?

What factors are associated with vegetation gradients?

(variation explained in GNN models, % of total inertia)

Subset of explanatory variables

Species composition(‘species’ model)

Stand structure(‘species-size’ model)

Coastal

Oregon

CentralOrego

n

Calif.Sierra

Coastal

Oregon

CentralOrego

n

Calif.Sierra

Topography* 2.5 11.1 8.2 3.0 6.7 7.8

Climate 8.0 19.3 13.2 8.6 11.8 12.2

Location 5.0 9.9 5.1 4.9 5.8 4.5

Disturbance:

Landsat -- -- -- 12.8 12.1 9.5

Ownership

-- -- -- 5.5 3.3 ?

Full model 10.0 22.7 16.5 23.9 19.5 18.2

* Includes elevation.

Dominant Gradients in Forest Structure (coastal Oregon)

Explanatory Variables:yellow=climate, pink = topography, white =

location, blue = Landsat TM, red = disturbance

Dominant Gradients in Forest Structure (California)

Explanatory Variables:yellow=climate, pink = topography, white =

location, blue = Landsat TM, red = disturbance

ANNPRE

ANNSWANNVP

AUGMAXTCONTPRE

DECMINT

SMRTP X

Y

TC1

TC2 TC3

TC4

TC5

ADTC1

ADTC3ADTC4

DEM

PRR

SLP CHG FIRE

Axis 1

Axis 2

QUCH2

Tree Species Positions Along Dominant Gradients (California)

red = hardwoodsblue = high-elevation

Axis 1

Axis 2

low precip.,

southeast

high precip.,

northwest

low elevation, warm, high

moisture stress

high elevation, cold, low

moisture stress

ABCO

ABMA

ACMA3

AECA

ALRH2ARMECADE27

CONU4

FRLA

JUGLA

JUOC

JUOS

LIDE3

PIAL

PIAT

PIBAPICO

PICO3

PIJE PILA

PIMO

PIMO3

PIPO

PISA2

PLRA

POBAT

POFR2

POTR5

PSME

QUDO

QUGA4

QUKE

QULO

QUWI2

SALIX

SEGI2

TABR2

TOCA

TSME

UMCA

Structure

Axis 1

Coastal Oregon: Dominant Gradients in Vegetation and Environment

low elevation Young forests, open canopies,hardwoods, private lands

Old forests, closed canopies, public lands

Species

Axis 1

Species

Axis 2interior climate

high elevationmaritime climate

Dominant Gradients in California (Scores on CCA Axes)

Axis 1,species and structure

Axis 2, species

high elevation,cold, low moisture stress

low elevation, warm, high moisture stress

high precip.,

northwest

low precip., southeast

Axis 2, structure

high greenness, high wetness, high precip.

low greenness, low wetness, low precip.

Species Gradients(Linked to Environment)

CCA axis 1(climate)

CCA axis 2(elevation)

Maritime

Interior(Valley)

Forest Types

Picea sitchensis

Tsuga heterophylla

Quercus woodlands

Abies amabilis/procera

Dry T. heterophylla/

mixed evergreen

High

Low

Paci

fic

Oce

an

(Ohmann and Gregory, in press)

Forest Structural Conditions

Young

Old

• Coast: linked to disturbance history and ownership

• Cascades: confounding of environment, disturbance, ownership

GNN Kappa Statistics for Species Presence/Absence

Tree speciesCoastal OR

Central OR

Casc.

East. WA

CA Sierr

a

OR Blue Mtns.

Abies amabilis -- 0.60 0.67 -- --

Abies concolor/grandis 0.39 0.54 0.57 0.51 0.45

Abies procera 0.34 0.50 -- -- --

Juniperus occidentalis -- 0.72 -- 0.40 0.43

Pinus contorta 0.60 0.77 -- 0.60 0.44

Pinus ponderosa -- 0.74 0.53 0.56 0.52

Pseudotsuga menzieseii 0.22 0.81 0.43 0.59 0.45

Thuja plicata 0.11 0.57 0.53 -- --

Tsuga heterophylla 0.27 0.68 0.49 -- --

Tsuga menzieseii -- 0.69 0.58 0.53 0.43

Acer macrophyllum 0.25 0.56 0.24 0.27 --

Alnus rubra 0.32 0.31 -- -- --

Example applications of GNN maps

-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.

Coastal Landscape Analysis and Modeling Study (CLAMS): a simulation approach

Current policy

Alt A

Alt B

Alt C

Natural Processes

Landowner Behavior

t =1BiophysicalResponse

t =n

Landscape/ Watershed Condition

t =1t =n

Socio-economicResponse t =1

Coast Range Ecosystem

t =n

Conceptual model

Map of current vegetation needed for:

• Stand and landscape simulation models

• Response models for wildlife, aquatic, timber

• Requires:

– tree list for each pixel

– species and structure

– reasonable covariance structure

– fine-scale pattern

100-Year Change in Vegetation Classes

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

1996 2046 2096

Mil

lio

n a

cre

s

Open Forest Remnants

Broadleaf Small Mixed/ Conifer

Medium Mixed/ Conifer Large Mixed/ Conifer

Very Large Mixed/ Conifer

1996(GNN)

2096 projected(base policy)

(Spies et al., in press)

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)

* Wimberly and Ohmann (2004)

Coastal Oregon:

Change in Large Conifer Forest, 1936-1996 *

1936 1996 1996 1996

% change

Shrub/Tree Regeneration0 – 25 yearsOpen Mid-

heightShrub

Grass/Forb1 – 15 years

Closed Herbland

Interior Ponderosa Pine150 years or more

Late-Seral, Single-Layer Forest

Interior ponderosa pine150 years or more

Late-Seral, Multi-layer Forest

Interior Ponderosa Pine40 – 85 years

Stem Exclusion Forest

Interior Ponderosa Pine

20 – 60 years Forest Stand

Initiation

Interior Ponderosa Pine

75 – 175 yearsForest Understory

Reinitiation

Crown Fire

MixedSeverityFire

Insects or Disease

GroundFire

GroundFire

Ground Fire Insects or

Disease

Growth and Development

Growth and Development

Growth and Development

Growth and Development

Growth and Development

Growth and Development

VDDT state-and-transition model for warm, dry ponderosa pine forest

100-year change in ‘Giant Tree’ forest, Central Oregon

Cascades• Pixel data aggregated to strata defined by 5th-field

HUC, owner class, potential vegetation type, cover type, structure class

• Aggregated data input to VDDT state-and-transition models for scenario analysis

• Approach adopted by IMAP across Pacific Northwest

(Hemstrom et al.)

Fuel Models in Yosemite (GNN species-size model)

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

Mapping Ecological Systems (ESs) for Gap Analysis Program

• Relatively new national classification of existing vegetation, based on floristics (Comer et al. 2003)

• Use GNN ‘species model’ to map plant communities

• Classify plots into ESs

– Guidance from qualitative descriptions and LANDFIRE ‘sequence table’ rules based on species relative abundance

– Species often are site indicators (mesic vs. dry) and/or understory species, inconsistently measured on inventory plots

– Sensitive to minor shifts in relative abundance, often caused by disturbance and/or localized microsites (within-plot variability)

• ‘Mask’ GNN (forest) model with nonforest maps from other sources

Forested Ecological Systems of the Blue Mountains Ecoregion, Eastern

Oregon

Kappa statistics for 18 forest Ecological Systems

• % correct: 52%, % ‘fuzzy’’ correct: 79%

• Many ESs are rare (only 4 are >5% of forest area)

• Many ESs are similar (mixed-conifer species)

• Confusion with nonforest ESs not shown

Ecological SystemKapp

aFuzzykappa

NRM W. larch 0.03 0.81

RM aspen 0.00 0.60

CP w. juniper 0.60 0.88

EC mesic montane mixed-conifer 0.67 0.86

NP mountain hemlock 0.00 0.00

NRM dry-mesic montane mixed-conifer 0.33 0.68

NRM subalpine 0.26 0.66

NRM mesic montane mixed conifer 0.21 0.86

RM lodgepole pine 0.05 0.61

NRM ponderosa pine 0.42 0.58

RM subalpine dry-mesic spruce-fir 0.38 0.59

RM subalpine mesic spruce-fir 0.14 0.43

RM subalpine-montane limber-bristlecone pine 0.62 1.00

MRM montane Douglas-fir 0.33 0.88

RM poor site lodgepole pine 0.08 0.68

EC oak-ponderosa pine 0.83 0.85

IMB mountain mahogany 0.00 0.00

CP = Columbia PlateauEC = East CascadesIMB = Inter-mountain basinsNP = North PacificNRM = northern Rocky MountainRM = Rocky Mountain

Area of Ecological Systems from plot-based estimates and GNN prediction

(Blue Mtns.)

0

5

10

15

20

25

30

35

Nor ther n Rocky

Mountain

Wester n Lar ch

Savanna

Rocky Mountain

Aspen For est and

Woodland

Columbia P lateau

Wester n J uniper

Woodland and

Savanna

East Cascades

Mesic Montane

Mixed-Conif er

For est and

Woodland

Nor th P acifi c

Mountain

Hemlock For est

Nor ther n Rocky

Mountain Dr y-

Mesic Montane

Mixed Conif er

For est

Nor ther n Rocky

Mountain

Subalpine

Woodland and

P ar kland

Nor ther n Rocky

Mountain Mesic

Montane Mixed

Conif er For est

Rocky Mountain

Lodgepole P ine

For est

Nor ther n Rocky

Mountain

P onder osa P ine

Woodland and

Savanna

Rocky Mountain

Subalpine Dr y-

Mesic Spr uce-Fir

For est and

Woodland

Rocky Mountain

Subalpine Mesic

Spr uce-Fir For est

and Woodland

Rocky Mountain

Subalpine-

Montane Limber -

Br istlecone P ine

Woodland

Middle Rocky

Mountain

Montane Douglas-

fi r For est and

Woodland

Rocky Mountain

P oor Site

Lodgepole P ine

For est

East Cascades

Oak-P onder osa

P ine For est and

Woodland

Inter -Mountain

Basins Mountain

Mahogany

Woodland and

Shr ubland

Columbia Basin

Foothi l l Ripar ian

Woodland and

Shr ubland

Notaly tr ees

Perc

ent

of

Fore

ste

d A

rea

OBSERVED FROM PLOTS PREDICTED FROM GNN

GNN-predicted occurrence of Juniperus occidentalis in the Central Oregon Cascades

Species model (tree species)(n=1415, kappa=0.72)

Ongoing areas of research for gradient modeling...

• More and better data (e.g., LIDAR, nonforest vegetation)

• Map evaluation tools (spatial error depictions, assessing spatial pattern)

• Comparison of alternative statistical models (imputation, other)

• Isses of spatial scale and pattern

– Fine-scale heterogeneity and pattern: What is real and what is useful? Optimal spatial resolution? Pixels vs. polygons?

– Multi-scale and hierarchical modeling approaches, e.g. partial bivariate scaling (Thompson and McGarigal 2002), hierarchical variance partitioning (Cushman and McGarigal 2003, 2004)

• Ecological research

– Ecological characterization

– Linkages to stand and landscape models for ecological analysis

• Technology transfer:

– Database and software systems to support imputation mapping

– Tools for serving and interacting with maps (web server, visualization using computing gaming software)