1010 
models require inputs that are specific to each species and canopy layer (e.g., LAI, spectral and dielectric 
properties, canopy architecture). Also each model requires some representation of processes or properties 
simulated by the others. 
Modeling Framework 
Although scientists from a wide range of disciplines have studied forested ecosystems, it has been difficult to 
relate and contrast models of forested ecosystems from such disparate fields as biogeochemistry, ecophysiology, 
land surface climatology, pedology, plant demography, remote sensing science, and soil physics. Integrated 
approaches spanning more than two or three of these disciplines have proved unsatisfactory to specialists, yet 
questions of how forested ecosystems respond to global change require integrated approaches. Our approach is to 
develop the modeling tools and data to construct a virtual workbench or laboratory (Figure 2) for collaborative 
modeling and testing ideas that cross disciplines, using the spatial context provided by data from remote sensing. 
Key differences from previous integrated models stem from our efforts to keep critical elements accessible to 
specialists in the various disciplines and from providing the type of graphical interface previously associated 
with applied science and engineering models or teaching environments. 
We encapsulate existing models to be more "object like" so they can collaborate in an object 
oriented environment. (See Figure 2.) The environment supports a standard protocol for passing information 
among models and for responding to interactive requests for information or graphical display of intermediate 
results. Time is incremented by a single clock process, but models executing the same time step may run 
concurrently. The user selects which models to use for a particular simulation, configures them, and may create 
multiple instances of a single type of model. Models need not run on the same computer as the graphical 
interface or other models. This extends the framework described by Levine et al. (1993) by allowing greater 
flexibility in selecting which models participate in any single simulation and requiring fewer changes to the 
simulation code of existing models. The combination of an assortment of encapsulated models, a common 
graphical interface, and tools for scheduling and interprocess communication, establishes a framework for model 
integration. Rather than building a single integrated model of a particular ecosystem, we plan to extend and 
improve the framework as understanding improves and new data become available. 
CONCLUSION 
Our approach in the second phase of the FED Project has been to develop a modeling workbench that can link 
individual submodels of forest physiology, growth and succession, soil processes, and the radiation regime 
within and external to the forest-soil complex. These linked models are to be used in combination with ground- 
based, airborne and satellite observations, to better understand the dynamics of forest ecosystem evolution. By 
the end of Phase II, we will be able to predict multi-spectral response (optical and microwave) from simulated 
forest ecosystems for a variety of conditions, and as such, have a sensitive indicator of both direction and 
magnitude of ecosystem change. The approach allows us to test hypotheses about process interactions, spatial 
and temporal scaling, related to global change. This hypothesis testing occurs as we: (a) complete the logical 
and functional integration of the various process sub-models using an object oriented approach; (b) analyze the 
extensive ground-based, airborne, and satellite data sets which have been acquired in order to derive the products 
that are needed as inputs to the models and are useful in improving our understanding of ecosystem processes; 
and (c) rigorously validate both the sub-models and integrated models by comparing model-derived results with 
ground-based and remote observations. 
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