MAPPING CURRENT AND
FUTURE POTENTIAL SITE
PRODUCTIVITY: ARE
PROCESS-BASED MODELS
NEEDED?
Aaron Weiskittel, University of Maine School of Forest Resources
Nick Crookston, US Forest Service Rocky Mountain Research Station
Phil Radtke, Virginia Tech
INTRODUCTION
• Measures of site productivity are needed:
– Growth and yield projections
– Management regimes
– Landscape classification
• Most common means of quantifying in
forestry is site index
– Applicable to forested places
• Other quantitative measures have been
used with varying degrees of success:
– Maximum MAI
– Maximum basal area
– Yield
INTRODUCTION
• Site index has often found to
have a limited relationship with
soil, climate, and physiographic
variables (Carmean 1975)
• Monserud et al (1990) offered
several reasons for this:
– Number of samples often
low
– High within- and betweenstand variability in estimates
– Failure to measure the true
causes of site productivity
INTRODUCTION
• Process-based models and
remote sensing tools offered as
a more effective tool for
assessing site productivity
(Swenson et al. 2005; Waring et
al. 2006, etc.)
– Mechanistic
– Integrate soil and climate
– Can map productivity across
large regions and all biotic
communities
– Predict future productivity
INTRODUCTION
Latta et al. (2010)
Coops et al. (2010)
Both empirical and process-based models predict a change in
future site productivity, but differ in the level of change
RESEARCH QUESTION
• How to better represent
potential changes in future
site productivity in an
empirical growth and yield
model like FVS?
– Use a process-based model
– Re-fit equations to include
climate
– Relate site index to climate
RESEARCH
OBJECTIVES
1. What is the relationship between
climate and several measures of
site productivity in the Western
US?
2. What are driving variables
influencing each measure of site
productivity?
3. Does it matter what climate
model used to make the
assessment?
4. What is the future forecast for
changing productivity?
METHODS
• Individual tree height-age data obtained
from USFS FIA
– n=83,016
– 61 different species
– Douglas-fir most common (38%)
• Estimates of site index standardized using
Monserud (1984) equation
– Flexible model form
– Avoids species and regional differences
– Strong correlation between
standardized and observed site index
• Tree-level estimates averaged for each plot
(n=21,554)
METHODS
• Climate data obtained for each location based on
fuzzed lat/long
– USFS Moscow lab
(http://forest.moscowfsl.wsu.edu/climate/;
Rehfeldt 2006)
• 1961 to 1990
• Monthly resolution
• Developed using ANUSPLINE
• Point estimates
– DAYMET (http://www.daymet.org/)
• 1980 to 2003
• Daily resolution
• Truncated Gaussian weighting
filter(Thornton et al. 1997)
• Grid estimates (1 km2)
METHODS
• Climate variables used to
develop different
temperature and moisture
indices identified by
Rehfeldt et al. (2006)
METHODS
• Estimates of gross primary production
(GPP) obtained from 2 sources based on
fuzzed lat/long
– 3-PG (Nightingale et al. 2007)
• Relatively simple process-based
model
• Relies on monthly climate and soils
(held constant) data
– MODIS (Running et al. 2004)
• Driven by a NASA satellite sensor
• Daily estimates of current APAR,
LAI, and surface climate
• ε*(APAR/PAR)*PAR
METHODS
• Variable importance assessed using
RandomForests
– Nonparametric technique
– Iteratively ran so that the least
influential was dropped until only 2
left (~35 initial variables)
• Maps of current and future site index
generated from RandomForests model
• Multiple models developed
– Climate only
– Climate and GPP
– Climate, GPP, and physiographic
variables (lat, long, elev)
RESULTS
Limited relationship between site index and
GPP, but MODIS GPP was a significant
improvement over 3-PG
RESULTS
• Both site index and MODIS
GPP highly related to
climate variables
• Generally, 7 variables was
most effective
• 2 variables explained
between 70-78% of
variation
Results of RandomForests Fit
RESULTS
Climate
Climate/Physiographic
Variable
%IncMSE
Variable
%IncMSE
prdd5
49.0
elev
83.7
mmax
48.3
mmax
77.1
mmindd0
36.0
sdi
72.6
maptd
33.7
prdd5
71.7
sdi
33.0
gsptd
60.0
gsptd
32.1
lat
59.1
tdgsp
25.7
maptd
55.6
RMSE (m)
4.59
RMSE (m)
3.75
• Relatively little
difference between
DAYMET and Moscow
• Neither MODIS or 3-PG
GPP in top 10 of
influential variables for
site index
• Results dependent on
whether physiographic
variables included
RESULTS
RESULTS
mindd0
gsp
RESULTS Distributions of site index for future climate change scenarios
Climate
Mean change -6.3 m
Climate/Physiographic
Mean change +4.5 m
RESULTS
Climate
Climate/Physiographic
RESULTS
Climate
Climate/Physiographic
RESULTS
Climate
Climate/Physiographic
DISCUSSION
• Process-model output significantly
related to derived climate
variables
– Is the complexity necessary?
• What’s elevation a proxy for?
– Imprecise climate estimates
– Solar radiation
– Soils*Climate interaction
– Gas concentrations
• Is there a better measure of
potential productivity?
LIMITATIONS
• Assumes a static relationship
between site index and climate
– Genetics?
– Influence of CO2?
• Role of past and potential future
disturbances not considered
• Uncertainty in stand-level
estimates of site index not
addressed
– Only 1-4 sample trees/plot
• Climate an effective predictor of current
site index across western US
CONCLUSIONS
• Site index influenced by both
temperature and moisture limitations
– Elevation is an important variable in
addition to climate
• PNW Coast Range most vulnerable to a
changing climate
• Site index an imperfect measure, but
allows relatively easy modification of FVS
predictions
•
•
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REFERENCES
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