The Nationwide Forest Imputation Study (NaFIS): Challenges, results and recommendations from the western United States Matt Gregory 1, Emilie Grossmann.

The Nationwide Forest Imputation Study

(NaFIS): Challenges, results and

recommendations from the

western United States

Matt Gregory1, Emilie Grossmann2,

Janet Ohmann3, Heather Roberts1

1Forest Ecosystems and Society, Oregon State University2 Institute for Natural Resources, Oregon State University

3PNW Research Station, USDA Forest Service

The Genesis of NaFIS Loose affiliation of researchers from USFS and

universities split into eastern and western teams Core objective: Assess the utility of nearest-

neighbors mapping as a basis for nationwide: resource estimation landscape scenario/ecosystem modeling forest threats assessment and forest health monitoring

Develop tools and software to aid mapping Funding from FHTET, WWETAC and FIA

Why Use Nearest Neighbor Techniques

in Forest Mapping? Spatially explicit forest assessments for simulation

modeling, e.g. studies require tree lists for growth and yield modeling multivariate maps for habitat capability modeling

Small area estimation for national level inventories Role of forest inventories expanding from not only

answering “How much?” but also “Where?” (McRoberts and Tomppo, 2007)

7

19

28

43

51

54

63

NaFIS Pilot Areas

7

19

28

43

51

54

63

Oregon (7) Montana (19) Colorado (28)

Plot count 1468 1179 1787

Year range 2001 – 2006 2003 – 2007 2002 – 2007

Total area (mi. ha) 9.39 10.82 10.28*

Forest percentage estimate

69.2 45.5 60.1

NaFIS West Pilot AreasOregon Montana Colorado

Plot Database

NaFIS data and methodology concepts Geospatial datasetsFIA Annual plot data

ID Species 1 BA

Species 2 BA

Species 3 BA

1 12.2 5.7 4.3

2 1.4 4.4 1.4

3 0.0 13.4 1.4

4 10.5 0.0 0.0Response variables | Species matrix | Y matrix

1

2

Topography

ClimateLandsat TM

Plot Database

NaFIS data and methodology concepts Geospatial datasetsFIA Annual plot data

ID Species 1 BA

Species 2 BA

Species 3 BA

1 12.2 5.7 4.3

2 1.4 4.4 1.4

3 0.0 13.4 1.4

4 10.5 0.0 0.0Response variables | Species matrix | Y matrix

1

2

ID ANNPRE TM4 DEM

1 741.0 2808.4 200.6

2 767.7 4263.3 385.0

3 724.5 3516.2 341.2

4 698.3 3216.8 271.4

Environmental variables | Covariates | X matrix

Design Choices for Nearest Neighbors Mapping

Distance metric to use to determine neighbor plotsEuclidean (EUC), CCorA (MSN), CCA (GNN),

Random Forest NN (RFNN) Number of neighbors (k) used in prediction With k>1, weighting of neighbor distances

none, inverse distance, inverse squared distance

Distance metrics – Euclidean (k=1)

Environment matrix

(X)

Species matrix(Y)

geographic space

X1

X2

gradient/feature space

plot

number

Distance metrics – MSN, GNN (k=1)

geographic space

LC1

LC2

gradient/feature space

plot

number

Direct ordination(CCorA for MSN, CCA for

GNN)

Environment matrix

(X)

Species matrix(Y)

Distance metrics – RFNN (k=1)geographic

space

gradient/feature space

Random foresttrees

Environment matrix

(X)

Species matrix(Y)

?

Distance metrics – RFNN (k=1)

|

August maximum temp < 23.24

PSME TSHEPSME THPL

ABAM TSME PSME PIPO

High elevation (> 1244)High August temperature (> 23.24°C)High reflectance in TM Band 5 (> 24)

Elevation < 1625

TM Band 5 < 24

August maximum temp < 25.60

Summer meantemp < 12.79

Season temperaturedifference <

12.79

Elevation < 1244

Simple classification tree for dominant species

Distance metrics – RFNN (k=1)

|| |

|| |

20 43 32 16 40 31 23 25 13 42 38 16 4 12 22 28

27 23 31 19 18 47 3 12

8 14

13 22

7

16 12

31 3

2714 32

20 25

8

Distance = number of trees minus number of times a plot was picked

Random Forest - A “Forest” of classification trees Each tree is built from a random subset of plots

and variables

Values of kgeographic

spacegradient/feature space

Axis 1

Axis

2

k=5

(Weighted) average value of attribute

Absent

Nearest Neighbor Map Examples

Color composite of Landsat TM 4|

5|3Quad. mean

diameter of trees >= 3cm

Basal area of trees >=

100cm

Presence of Thuja plicata

Low High Low High Present

Map Assessment Protocols McRoberts (2009)

Tailored for nearest neighbors mapping Homoscedasticity, RMSE, bias, outlier determination, mapped

extrapolations, reference set distribution in feature space, maintenance of covariance

Grossmann et al. (2009) Community composition dissimilarity metrics (Bray-Curtis, binomial) Diversity measures (Shannon-weaver, beta) Determination of unrealistic species assemblages

Riemann et al. (2010) Diagnostics tailored for any continuous geospatial data Useful across many spatial scales

Accuracy Assessment Local (plot/pixel) scale

Normalized RMSE, categorical kappa statistics, individual species kappa statistics

Dissimilarity metrics, species richness, unlikely species co-occurrence

Regional (whole map) scaleArea comparison of design-based (plots) vs.

model-based (map) estimates

Accuracy Assessment – Distance metric

Normalized RMSEBAA_GE_3

Basal area per hectare of trees >= 2.5 cmBAA_GE_100

Basal area per hectare of trees >= 100 cmQMDA_GE_3

Quadratic mean diameter of trees >= 2.5 cmQMDA_GE_13

Quadratic mean diameter of trees >= 12.5 cmVPH_GE_3

Volume per hectare of trees >= 2.5 cmForest type kappa statisticsFOR_TYPE_AN

Forest type as determined by FIAFOR_TYPE_GR

Forest type group as determined by FIA

From Oregon models with k=1 neighbor

Accuracy Assessment – Distance metric

From Oregon models with k=1 neighbor

Species presence-absence kappa for five most common species

Species richness

Bray-Curtis dissimilarity

Binomial dissimilarity

Accuracy Assessment – Distance metric

From Oregon models with k=1 neighbor

Area comparison of design-based (plots) vs. model-based (map) estimates

Spatial pattern – Distance metric

Low

Quad

. m

ean

dia

mete

r of

trees

>=

3cm

High

Basa

l are

a p

er

ha. of

trees

>=

1

00

cm

Low

High

Thuja

plic

ata

p

rese

nce

EUC MSN GNN RFNN

Abse

nt

Pre

sen

t

Accuracy Assessment – Values of k

Normalized RMSEBAA_GE_3

Basal area per hectare of trees >= 2.5 cmBAA_GE_100

Basal area per hectare of trees >= 100 cmQMDA_GE_3

Quadratic mean diameter of trees >= 2.5 cmQMDA_GE_13

Quadratic mean diameter of trees >= 12.5 cmVPH_GE_3

Volume per hectare of trees >= 2.5 cmForest type kappa statisticsFOR_TYPE_AN

Forest type as determined by FIAFOR_TYPE_GR

Forest type group as determined by FIA

From Oregon RFNN models

Accuracy Assessment – Values of k

From Oregon RFNN models

Species presence-absence kappa for five most common species

Species richness

Bray-Curtis dissimilarity

Binomial dissimilarity

Accuracy Assessment – Values of k

From Oregon RFNN models

Area comparison of design-based (plots) vs. model-based (map) estimates

Accuracy Assessment – Values of kErrors of species

omissionErrors of species commission

Areal extent of common species

From Oregon RFNN models

Spatial pattern – Values of k

k = 1 k = 5 k = 10 k = 20

Nonforest Both species absent

Both species present

Tsuga heterophylla

Pinus ponderosa

From Oregon RFNN models

Percent overlap of unlikely co-occurring species

Spatial pattern – Values of k

Low

Quad

. m

ean

dia

mete

r of

trees

>=

3cm

High

Basa

l are

a p

er

ha. of

trees

>=

1

00

cm

Low

High

Thuja

plic

ata

p

rese

nce

k = 1 k = 5 k = 10 k = 20

Abse

nt

Pre

sen

t

Key Findings - Accuracy Assessment

Accuracy varied little across distance metrics, although RFNN slightly better with categorical variables (such as forest type or forest type group)

Accuracy varied substantially across values of k RMSE, forest type kappa improve with higher k Area distributions, species community metrics degrade

with higher k New assessment protocols will help guide users on

appropriate uses of nearest neighbors maps

The “k conundrum” Need for structural attribute accuracy must be

weighed against need for reasonable forest community composition

Possible approaches:Two step modeling where candidate neighbors

must come from appropriate composition classes (McRoberts, 2009)

Hierarchical nearest neighbor modeling – iterative neighbor finding based on spatial patterning grains

NaFIS implementation challenges

Consistency/currency of plot data (greatly eased with FIA annual design)

Mapping nonforest areas (some preliminary products have been developed)

Currency of mapped information – how best to account for disturbance

Incorporating emerging science into a production mapping environment

For more information

NaFIS products and software http://blue.for.msu.edu/NAFIS/

NaFIS west final report http://www.fsl.orst.edu/lemma/pubs/ Track me down for PDF

NaFIS collaborators (in alphabetical order) Jerry Beatty (WWETAC), Ken Brewer (formerly

RSAC), Mark Finco (RSAC), Andy Finley (MSU), Matt Gregory (OSU), Emilie Grossmann (OSU), Ron McRoberts (NRS), Janet Ohmann (PNWRS), Heather Roberts (OSU), Frank Sapio (FHTET), Eric Smith (FHTET), and Brian Roberts (MSU)