Biometrics Working Group Meeting – New Orleans, LA. 04 March, 2009

Forest Inventory Components of Change (Growth, Removals & Mortality):

Christopher M. OswaltSouthern Research Station FIA

OutlineOutline

• Brief introduction of the topic

• Walk through the components of change

• Examples

• Recent regional trends

• Challenges

• Summary

IntroductionIntroduction

• Development informs utilization

• Of interest:– A population as recruitment occurs into,

through, and eventually exiting some expressed standard

Inventory Then

Inventory Now

G

RM

GG

M

G G

Conceptually Simple

GG

G

MG

G RR R

G G G

Operationally ComplexOperationally Complex

Private Timberland

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Select

white

oak

s

Select

red

oaks

Other

whit

e oa

ks

Other

red

oaks

Hickor

y

Yellow

birc

h

Hard

map

le

Soft m

aple

Beech

Sweetg

um

Tupelo

and

bla

ckgu

mAsh

Cotto

nwoo

d an

d as

pen

Bassw

ood

Yellow

-pop

lar

Black

walnu

t

Other

eas

tern

soft

hard

woods

Other

eas

tern

har

d ha

rdwoo

ds

Easte

rn n

onco

mm

ercia

l har

dwoo

ds

Mill

ion

tre

es (n

o.)

1990

2003

Components of ChangeComponents of Change

• Survivor growth• Ingrowth• Mortality• Cut• Reversions• Diversions• Cull increment

(minus)• Cull decrement

• Growth on ingrowth• Mortality growth• Cut growth• Reversion growth• Diversion growth• Cull increment growth• Cull decrement

growth

AssumptionsAssumptions

• Time of change is between then and now, but exactly when is unknown

• Time of change is estimated to be midway between t (time 1) and t+1 (time 2)

• Growth model is used to predict midpoint tree size– Usually based on past attributes

– Based on current attributes when there are no past attributes

T1 T2

Components of GRMComponents of GRM

• Survivor growth– Trees alive and measured time 1 and time 2– Diameter >= threshold (e.g. 5.0”) both times– No change in tree class (growing stock, cull,

etc.)– Contributes to G

Threshold DBH

Components of GRMComponents of GRM

• Ingrowth– Tree below threshold time 1 (may or may not

have been measured)– Tree grows across threshold before time 2– Contributes to G

Threshold DBH

Components of GRMComponents of GRM

• Mortality– Tree alive time 1– Tree dead time 2– Predicted midpoint diameter used to compute

volume– Contributes to M

Components of GRMComponents of GRM

• Cut– Tree alive time 1– Tree cut before time 2– Predicted midpoint diameter used to compute

volume– Contributes to R

Components of GRMComponents of GRM

• Cull increment– Growing stock time 1– Cull time 2– Predicted midpoint diameter used to compute

volume– Reduces G

Components of GRMComponents of GRM

• Cull decrement– Cull time 1– Growing stock time 2– Predicted midpoint diameter used to compute

volume– Contributes to G

Components of GRMComponents of GRM

• Reversion– Nonforest (or non-timberland) time 1– Forest (or timberland) time 2– Predicted midpoint diameter used to compute

volume– Contributes to G

Components of GRMComponents of GRM

• Diversion– Forest (or timberland) time 1– Nonforest (or non-timberland) time 2– Predicted midpoint diameter used to compute

volume– Contributes to R

GRM’s Defined (Scott et. al 2005)GRM’s Defined (Scott et. al 2005)

• Growth– Gross ingrowth = Ingrowth + Reversion– Gross growth = Gross ingrowth + accretion

• Accretion = GS+GI+GR+GM+GC+GD

– Net growth = Gross growth - mortality

• Removals– Cut stems and/or Diversions(forest – nonforest or timberland-reserved)

• Mortality– Death from natural causes

Growth & Removal TrendsGrowth & Removal Trends

Year

1940 1950 1960 1970 1980 1990 2000 2010

Ft3

(th

ousa

nds)

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

South

North

Pacific Coast

Rocky Mountain

Year

1940 1950 1960 1970 1980 1990 2000 2010

Ft3

(th

ousa

nds)

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

South

North

Pacific Coast

Rocky Mountain

Growing Stock on Timberland

Solid line - Growth Broken line - Removals

Growth & Removal TrendsGrowth & Removal Trends

Year

1970 1975 1980 1985 1990 1995 2000 2005 2010

G:R

0

1

2

3

4

5

NorthSouthRocky MountainPacific Coast

Year

1970 1975 1980 1985 1990 1995 2000 2005 2010

G:R

0

1

2

3

4

5

NorthSouthRocky MountainPacific Coast

Growing Stock on Timberland

ChallengesChallenges

• Historical reporting biases and naming conventions– Not all FIA reported removals reflect “removed” trees– Change and growth not the same thing

• Address through – additional tables or definitional changes?

• Historic variability in interpreting reserve status– GRM estimates are highly sensitive to changes in reserve status

• Address from what angle - data acquisition or compilation/processing?

• Not applicable on forest land

• Regional inconsistencies – Current estimation procedures– Mapped plot to mapped plot implementation

Additional DetailAdditional Detail

Total AAR – AAR(diversions) = More informative picture of actual removals

ChallengesChallenges

• Historical reporting biases– Not all FIA reported removals reflect “removed” trees

• Address through – additional tables or definitional changes?

• Historic variability in interpreting reserve status– GRM estimates are highly sensitive to changes in

reserve status• Address from what angle - data acquisition or

compilation/processing?• Not applicable on forest land

• Regional inconsistencies – Current estimation procedures– Mapped plot to mapped plot implementation

Currently… Not all there yet.Currently… Not all there yet.

SouthPrimarily annual to annual

NorthPrimarily annual to annual

Rocky MountainRemeasurement has not started

Pacific NorthwestTesting in California National Forests

Similar plot footprints (periodic to annual) in Alaska (testing)

ChallengesChallenges

• GRM labeling conventions to facilitate proper interpretation– FIDO II

• Tennessee: 2007 GRM - Tennessee (47) -- Mortality :: growing-stock on timberland by U.S. Counties and Major species group (in cuft/year)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

ChallengesChallenges

• Lack of access to previous condition when using external data tools– Internal – uses previous condition (correct)

– External – uses current condition (wrong)

Source Total Large Medium Small Nonstocked

External 437.6 152.2 48.1 167.8 4.1

Internal(published)

437.6 341.1 85.8 10.3 .3

Absolute Difference

0 188.9 37.7 157.5 3.8

Million ft3 year-1

Example – TN(2007) – Removals of live trees on forest land by stand size class

SummarySummary

• GRM is conceptually simple• GRM analysis is complicated by the fact that the

same tree can contribute to different components of GRM

• GRM analysis will become simpler and more precise in the future

• Additional analytical techniques needed• Refinements in methodology potentially

necessary• Updates to some external tools needed

AcknowledgementsAcknowledgements

• Ray Sheffield

• Gary Brand

• Bill Burkman

• John Coulston