**USING COUPLED MODELS AND GIS FOR UNDERSTANDING**

**NORTHERN FOREST ECOSYSTEM DYNAMICS**

K.J. RANSON1, G. SUN2, E.R. LEVINE1, R.G. KNOX1, J. F.WEISHAMPEL3 and D.
L. WILLIAMS1

1NASA/Goddard Space Flight Center, Code 923, Greenbelt, Maryland 20771
USA

2University of Maryland, Dept. of Geography, College Park, MD 20742

3Univ. of Central Florida, Dept. of Biology, Orlando, FL 32816 USA

**ABSTRACT**

The Forest Ecosystem Dynamics (FED) Project involves the development and
integration of models to understand soil, vegetation, and radiation
dynamics in northern forest ecosystems. Through the use of simulation
models, remote sensing, field investigation, and GIS, the vegetation,
soil, and energy components within the northern forest are being
investigated, In addition, responses to global change or other
disturbance are being explored and quantified. In the first phases of
the FED project, data collection and model development has focused on
the International Paper\'s Northern Experimental Forest (NEF) at
Howland, Maine. Intensive field measurements coinciding with aircraft
and satellite overflights, as well as ancillary data ,were obtained and
incorporated into the FED GIS. In this paper the use of models of soil
physics , forest growth developed and linked using a modeling
environment are discussed. The GIS was used to provide driving variables
for the models, to validate model predictions, and to identify areas in
the field requiring more intensive study.

1\. INTRODUCTION

The circumpolar boreal forest is one of the earth\'s major vegetative
ecosystems, accounting for nearly 20% of the terrestrial plant carbon
and covering one-sixth of the Earth\'s land surface (Bolin 1986). The
northern and southern margins are especially sensitive to climate change
as evidenced by the northward migration of boreal species since the end
of the Wisconsin Ice Age. There has also been a notable decline in the
health and vigor of spruce-fir forests throughout the northern
hemisphere during the past two decades (e.g., Van Deusen et al. 1991).
In addition, increasing harvesting pressures coupled with the ever
present impacts of forest fires are dramatically changing the face of
the boreal forest. The nature and extent of the impacts of these
changes, as well as the feedbacks on global climate are not well
understood, but may be addressed through modeling the interactions of
the vegetation, soil, and energy components of the boreal ecosystem
(Pastor and Post 1988). The boreal forest is immense in extent and,
although similar in structure across the biome, has significant regional
and local variability (Larsen 1980). This is especially evident in the
hydrology and soils of the biome due to the effects of glacial activity
during the Wisconsin Ice Age (Rourke et al. 1978). The use of combined
ecosystem and remote sensing models presents an especially efficient and
tractable method to study regional and global environmental changes.

The Forest Ecosystem Dynamics (FED) Project at GSFC involves the
development and integration of models to understand soil, vegetation,
and radiation dynamics in northern forest ecosystems. The overall
objective of this study was to use models of forest dynamics and soil
processes along with remotely sensed data to understand northern forest
dynamics. Through the use of simulation models, remote sensing, field
investigation, and GIS, the vegetation, soil, and energy components
within northern forests are being investigated, and their responses to
global change or other disturbance are being explored and quantified.
Data collection and model development has focused on northern forests of
North America. Intensive field measurements coinciding with aircraft and
satellite overflights, as well as ancillary data, were obtained and
incorporated into a GIS. The GIS is used to provide driving variables
for the models, initialize model runs, validate model predictions, and
identify areas in the field requiring more intensive study. The project
also incorporates a significant remote sensing modeling component to
simulate expected signature response for a variety of forest simulations
(see Ranson et al., 1997).

This paper discusses methods and results using remote sensing images and
ancillary data in a GIS to initialize and exercise a coupled forest
ecosystem model. Forest succession model (ZELIG) was coupled to the soil
physics model (FroST) and run for 10 types of soil conditions found at
our study site over 250 years. Remote sensing data were used to map
current forest type and biomass level. Individual pixels from these two
maps along with a soil type map were compared with the simulation
results of forest model to determine the age of the forest. This age and
forest conditions were used to initiate forest model for predicting
forest type and biomass level certain years late. This paper is
organized into 4 sections. Section 2 (background) provides brief
descriptions of FED modeling components and environment, the remote
sensing and field data, and GIS data structure. In section 3 (methods),
the implementation of forest modeling, the analysis of remote sensing
data, and the prediction of forest successions were described. Section 4
(results) presents the results of this study.

2\. BACKGROUND

**Forest Dynamics Modeling**

Mathematical models that simulate forest dynamics have gained widespread
acceptance and use over the past two decades. The most successful models
(in terms of general applicability to diverse forest types) are
individual tree-based models called gap models (Shugart et al. 1992,
Botkin 1993). The strength of these models lies in their versatility to
predict qualitative successional patterns related to species composition
and forest structure.

ZELIG (Urban, 1990) is an individual tree simulator that simulates the
establishment, annual diameter growth, and mortality of each tree on an
array of model plots. Model states are recorded in a tally of all trees
on a plot, with each tree labeled by species, size (diameter), height to
base of live crowns, and vigor (based on recent growth history). The
competitive environment of the plot is defined by the height, leaf area,
and woody biomass of each individual tree determined by allometric
relationships with diameter. Plot size is defined by the primary zone of
influence of a single canopy-dominant tree. The plot is considered
homogeneous horizontally, but vertical heterogeneity (canopy height and
height to base of crown) is simulated in some detail. Adjacent cells
interact through light interception at low sun angles. Establishment and
annual diameter growth is first computed under optimal (nonlimiting)
conditions, and then reduced based on the constraints of available
light, soil moisture, soil fertility, and temperature. Climate effects
are summed across simulated months. Seedling establishment, mortality,
and regeneration are computed stochastically, while the growth stage is
largely deterministic. Simulations can start or stop at any point within
the life cycle of a forest.

**Soil Process Modeling**

The goal of simulating the soil system beneath the forest is to
understand the controls and feedbacks that operate within the soil as
well as between the soil and the rest of the forest environment. This
includes physical, biological, chemical, and mineralogical
characteristics and mechanisms that vary at short-, medium-, and
long-term temporal scales within soils. At present, a model including
the physical processes occurring within the soil is being used with the
FED modeling framework. FroST (Frozen Soil Temperatures) is a simulation
model of soil properties which produces estimates of water content,
matric potential, temperature, and ice content within each soil horizon.
FroST was developed from the Residue model of Bidlake et al. (1992)
which couples surface residue to the soil-atmosphere system, and uses
network analysis to describe heat and moisture transfer, and phase
changes in water. Short-wave and long-wave radiative transfer, changes
in energy status, rainfall interception, infiltration, redistribution,
evaporation, and drainage are all accounted for. Climate input
requirements include global short-wave radiation, air temperatures,
average wind speed, and precipitation. General site, canopy, and soil
characteristics for individual horizons are also needed. Enhancements to
the Residue model to produce FroST included algorithms for calculating
surface runoff, transpiration, Penman demand, and a simple snow model.
In FroST, surface residue from the Residue model is configured to
reflect above ground characteristics of forested sites. Snow is
simulated by changing the characteristics of the surface soil node from
soil characteristics to snow characteristics. Precipitation increases
the node\'s thickness, and a simple melt factor is used to melt off the
snow. Once the snow has melted off, node characteristics are reset to
that of soil.

**Modeling Environment**

To interactively use the forest succession and soil processes they were
incorporated within a framework that supports interactive configuration,
manages the transfer of variables among models, and dynamically displays
results (Levine et al. 1993, Knox et al. 1996, Knox and Friend, 1997).
The FED modeling environment, WISE (for Workbench for interactive
Simulation of Ecosystems) allows multiple process models to be coupled
using a generic query-response system where parameter values from
detailed models in one discipline can be provided to drive models of
other disciplines. Models are encapsulated and then run synchronously
from a common external clock. Database-like features added while
encapsulating each model allow models to query one another while
running. Each encapsulated model also has X windows panels defining a
model-specific graphical \"sub-interface\" and a version of a
configuration tool to check parameter values entered interactively
against rule sets defining allowable combinations of values. (Example
panels may be viewed over the Internet via World Wide Web
http://fedwww.gsfc.nasa.gov). Currently, several models are encapsulated
including the forest dynamics model ZELIG (see below), soils physics
model, and a energy dynamics model are encapsulated for use in this
framework. With this modeling tool, scaling parameters from detailed
models can be derived to improve values used in simpler models for the
same parameter.

**Remote Sensing**

The remote sensing work in the FED Project has two major components. The
first is to understand the sensitivities of remote sensing observations
to forest structural attributes such as successional stage, biomass, LAI
and forest spatial characteristics such as patch-to-patch variation,
texture, and patch size, shape, and number. The second component is to
use remote sensing models to examine the expected signatures from forest
stands with a range of the above parameters. These objectives require
detailed sets of both remote sensing and field data and robust canopy
reflectance and scattering models. We are fortunate to have both
requirements well-in-hand through our earlier FED and collaborative
efforts (e.g., Sun and Ranson, 1995, Ranson et al., 1997a, b). In this
paper the work involved with producing maps of forest types and biomass
using synthetic aperture radar data (and described in detail by Ranson
and Sun 1994a, b, 1997a, b) will be discussed.

**GIS**

The Forest Ecosystem Dynamics project has been conducting research and
collecting data at the International Paper Northern Experimental Forest
(NEF) near Howland, Maine, USA. The site is located at approximately 45o
15\' N latitude and 68o 45\' W longitude. The area comprises
approximately 7000 ha containing several intensive experimental sites,
where detailed ecological and mensuration measurements have been
obtained. It contains an assortment of small plantations,
multi-generation clearings, and large natural boreal-northern hardwood
transition forest stands consisting of hemlock-spruce-fir, aspen-birch,
and hemlock-hardwood mixtures. Topographically, the region varies from
flat to gently rolling, with a maximum elevation change of less than 135
m within a 10 by 10 km study area. Due to the region\'s glacial history,
soil drainage classes within a small area may vary widely, from
excessively drained to poorly drained. This site was the focus of a NASA
Multi-sensor Aircraft Campaign (MAC) for the Forest Ecosystem Dynamics
project at GSFC (e.g., Goward et al. 1994) and was a backup Supersite
for the recent SIR-C/XSAR missions (Ranson et al. 1995; Stofan et al.
1995).

The FED project has collected a wide variety of imagery, map data, and
point data at the NEF and surrounding areas within a Geographic
Information System (GIS) for research purposes. These data include
field, tower, aircraft and satellite based measurements and are
described in distributed from the FED projects\' GIS database available
via World Wide Web at http://fedwww.gsfc.nasa.gov.

![](media/image1.wmf){width="4.055555555555555in"
height="2.7222222222222223in"}

Figure 1. Location of study site in the north-eastern USA.

3\. METHODS

In this paper we demonstrate that parameter maps developed from remotely
sensed data can be used to initialize and test a forest succession
model. First a forest model (ZELIG) was coupled to the soil physics
model (FroST) and run for a range of soil conditions found at the site
over 250 years (see Weishampel et al. 1997). Remote sensing data were
analyzed for forest type and biomass levels and maps were developed.
Individual pixels from these maps along with a soil type map are
compared with the results of the forest model to determine the age of
the forest represented by the pixel. This age and forest conditions are
used for initialization and the model is run for a period of certain
years. Forest type maps and biomass maps can be generated for any
simulation.

**Forest Model Implementation**

The forest model ZELIG (Urban 1990) adapted as described in Levine et
al. (1993), was used to simulate the successional dynamics of the
southern boreal/northern hardwood forest transition zone found at the
NEF. Because soil moisture is considered to be of primary importance in
determining the structure (e.g., biomass and species composition) of
these forests (Bonan and Shugart 1989), waterlogging effects (adapted
from Botkin 1993) were included. This required connecting the ZELIG
model to a soil physics model that simulates depth to saturated soils
(see Weishampel et al. 1997).

To implement the model, site parameters (e.g., soil fertility and
monthly values of temperature and precipitation) and autecological
parameters (e.g., height and diameter maxima and growth tolerances) were
derived from empirical data and published sources (e.g., Pastor and Post
1985, Botkin 1993). Forest succession on ten soil types (see Table 1)
found at the NEF were simulated starting from bare ground. Depth to
water saturated soil and water holding capacity of the soil were derived
using the FroST model (Levine et al. 1995). The spatial scale of the
ZELIG simulations were performed at a resolution (30 x 30 m) to
correspond to the scale of typical remotely sensed data. Diameter at
breast height (dbh), height, height to base of crown, and foliage
density were recorded for each individual tree in nine (10 x 10 m) ZELIG
plots.

Model results were recorded at five year intervals up to 100 years and
at 50 year intervals through 250 years. Because gap models possess
underlying stochasticity in their regeneration, mortality, and weather
routines, fifteen separate runs were performed to generate a range of
stand responses from which stand averages were calculated.

**Remote Sensing Analysis**

*Image Registration -* Image registration was required to use multidate
images. The SAR images were converted from slant range to ground range.
Since our research area is quite flat (maximum change in elevation of
145 m over 10 km), and the flight or orbit directions and incidence
angles of pairs of images were similar, the registration was relatively
easy. A linear interpolation with about 10 control points yielded
results superior to a cubic polynomial interpolation with 20 control
points. The AIRSAR image of April 15, 1994 was registered to the AIRSAR
image of October 7, 1994. Nine channels (C-, L- and P-band with HH, VV
and VH polarizations) were used for each date.

*Forest Type Classification* - Ranson and Sun (1994a) produced a forest
type map of the Howland area using AIRSAR images. They found that
combining summer (September 2, 1989) and winter (March 30,1990) images
produced better results than using data from a single date. They used
principal components analysis to reduce the number of channels used with
a maximum likelihood classifier. Here, we used all available channels
with a supervised minimum distance classifier.

A parallelepiped classifier (Moik, 1980), which approximates the
hyperellipsoid decision boundaries of Bayesian classifier by
parallelepipeds, was used in this study (see Ranson and Sun 1997a) Nine
land cover classes were selected from a generalized cover type map
provided by International Paper: water, bog, wetland, grassland,
clearing, regeneration, mixed forest, hardwood forest, and softwood
forest. The latter five classes represent the state of the forest stands
in the area from cutting through regrowth (regeneration) to mature
\"pure\" or mixed stands. The classifier was trained for the nine
classes by locating areas identified from airphotos, forest cover maps
and field observations on the SAR imagery.

*Above Ground Biomass Mapping* - Forest stands, measured during 1992 and
1994 were located on AIRSAR images and the average backscatter was
calculated. The field biomass data was acquired from stands of
predominantly spruce and hemlock and mixtures of hemlock and hardwood
species. The measured biomass ranged from about zero for clearings to 37
kg/m2 for a mature mixed forest stand.

An earlier analysis of AIRSAR data over the Maine study area estimated
above ground standing biomass from a combination of radar channels
(Ranson and Sun 1994b). A combination of P-band HV and C-band HV (i.e.
PHV-CHV dB) was found to have the best sensitivity to total above ground
biomass. Briefly, the procedure involves developing a linear regression
equation with biomass and SAR backscatter. We used a cube root
transformation for the dependent variable (biomass) and combined SAR
channels as the independent variable. The cube root transformation
equalizes the variance and produces a normal distribution of the biomass
data (e.g. Ranson et al., 1997c). The relationships between SAR image
ratio values and cube root of forest biomass were determined using
linear regression and measurements from homogeneous stands large enough
to provide representative radar signatures. 28 additional stands were
used for testing. The equation using a combination of AIRSAR bands
(i.e., PHV-CHV)was:

b1/3 = 2.186 0.259 (PHV-CHV) , r2 = 0.78 (1)

This relationship was then used to produce images of the predicted
biomass from the AIRSAR images. To reduce the effects of speckle the
average backscatter value from an array of points were used as the
center pixel backscatter value from which the biomass was calculated.

**Prediction of Forest Dynamics**

Forest types and biomass maps were incorporated into FED GIS database.
With an additional soil map, these parameters were used to initiate
forest modeling environment to predict the forest dynamics as briefly
illustrated in Figure 2. The present age of forest of a pixel in the map
was determined from its soil type, forest class and biomass level. The
forest model was initiated and than run to the age to be predicted.

![](media/image2.pct){width="434.0in" height="330.0in"}

> Figure 2. Conceptualized diagram for using GIS data to initialize a
> forest succession model. Forest model can then be run to determine
> likely future forest type and biomass.

4\. RESULTS

Figure 3 presents the average biomass trajectories simulated from
fifteen 30 x 30 m (900 m2) stands growing on three of the ten NEF soil
types used in the simulation over the 250 years. The range of biomass
simulations illustrate the importance of considering soil types in our
study area. Generally, better drained or mesic soils produced higher
biomass values whereas poorly drained and wet soils produced lower
biomass values. Because of the stochastic timing of tree birth and death
replicate simulation can exhibit considerable variation about these
averages. The simulated biomass trends and the underlying patterns of
dominance by needle leaf (conifer) and broadleaf (deciduous) species
were consistent with field measurements reported by Ranson and Sun
(1994) and Levine et al. (1994). A total of 3450 simulations were
conducted (10 soil types X 15 replications X 23 time steps).

![](media/image3.wmf){width="5.06875in" height="3.513888888888889in"}

Figure 3. Mean biomass of forest stands growing over different soil
types for a 250 year simulation.

Forest type classification results for the AIRSAR data were very good
when compared with field information (Ranson and Sun, 1997a). Briefly,
all non-forest classed were 100% correctly identified. Forest type
classification for softwood was 94% correct. Hardwood showed 86.5%
correct classification and mixed forest had 84.0% correct
classifications.

Biomass mapping results were also consistent with ground observations.
Comparing biomass predicted with equation 1 against the 28 field
measurements resulted in the equation:

Predicted biomass = 1.954 + Measured biomass \*1.064, r2 = 0.87.

We found that the biomass method worked best for biomass values below 15
kg/m2. Figure 4 presents the classification and biomass maps for the
test site.

Table 1. Average biomass and % forest type for initialization and years
25 and 50 simulations.

  --------------------- ---------------- ---------------- ----------------
                                         Simulation Year  

                        0                25               50

  Average Biomass       13.1 (8.9)       14.6 (7.6)       15.6 (6.8)
  (kg/m2)                                                 

  Percent Forest Type                                     

  Softwood              67.8             73.5             76.4

  Mixed Forest          10.1             7.2              8.5

  Hardwood              21.3             18.5             14.3
  --------------------- ---------------- ---------------- ----------------

Using the forest type, biomass and soil maps to initialize the forest
succession model produced the images shown in Figure 4. Maps of forest
type and biomass produced from model runs from 25 and 50 years are also
shown in Figure 4. The initialization results are consistent with the
remote sensing images (not shown) indicating that this is a viable
method for simulating forest dynamics in this study area. The predictive
images can be used to assess forest dynamics as the change in forest
type and biomass over time. Assuming no areas of forest are harvested
(although this can included in the analysis) the forest can be expected
as shown in Table 1.

FOREST COVER

YEAR 0 YEAR 25 YEAR 50

![](media/image4.wmf){width="1.9736111111111112in"
height="1.398611111111111in"} ![](media/image5.wmf)

BIOMASS

YEAR 0 YEAR 25 YEAR 50

![](media/image6.wmf){width="2.152083333333333in"
height="1.4645833333333333in"}![](media/image7.wmf){width="2.0548611111111112in"
height="1.4534722222222223in"}![](media/image8.wmf){width="2.1243055555555554in"
height="1.4645833333333333in"}

Figure 4. Forest type classification map (Up-Left) , Red = Hardwood,
Green = softwood, yellow = mixed forest. Biomass map (Low-Left), gray
levels from black to white represent range from 0 to \>15kg/m2.

SUMMARY

This paper describes a procedure to use forest type and biomass maps
developed from remote sensing data to initialize a forest growth model
for a northern forest in Maine, USA. The remotely sensed forest
attributes were used along with a soils map to determine the stand age
to begin model simulation runs. This approach enabled the development of
predictive maps of forest type and biomass for 25 and 50 years in the
future.

The results obtained are reasonable, given the uncertainties of the
actual soil type for a pixel and errors in the remote sensing maps, and
demonstrate the potential for using remote sensing in combination with
ecosystem models to simulate forest dynamics. Such results may be useful
for understanding the effects of management practices on forests and the
response of forest dynamics to changing climate.

ACKNOWLEDGMENTS

This work is supported in part by NASA Mission to Planet Earth through
the Terrestrial Ecology Program at NASA Headquarters. Special thanks to
Steve Fifer for assistance with the FED GIS.

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