United States

Department of 

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-521

September 2001

Fuzzy Logic Knowledge

Bases in Integrated

Landscape Assessment:

Examples and Possibilities

Keith M. Reynolds
Author Keith M. Reynolds is a research forester, Forestry Sciences Laboratory, 3200 SW

Jefferson Way, Corvallis, OR 97331.
Abstract Reynolds, Keith M. 2001. Fuzzy logic knowledge bases in integrated landscape 

assessment: examples and possibilities. Gen. Tech. Rep. PNW- G T R - 5 2 1 .

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest

Research Station. 24 p.

The literature on ecosystem management has articulated the need for integration

across disciplines and spatial scales, but convincing demonstrations of integrated

analysis to support ecosystem management are lacking. This paper focuses on integrated ecological assessment because ecosystem management fundamentally is

concerned with integrated management, which presupposes integrated analysis.

Knowledge-based solutions are particularly relevant to ecosystem management

because the topic is conceptually broad and complex and involves many abstract

concepts whose assessment depends on many interdependent states and processes.

Logic constructs are useful in this context because the problem can be evaluated

as long as the entities and their logical relations are understood in a general way

and can be expressed by subject matter authorities. As an example, ecosystem

management decision-support system provides a formal logic framework for integrated analysis across multiple problem domains, has the ability to reason with

incomplete information, and assists with optimizing the conduct of assessments by

setting priorities on missing data. Most significant, however, is the possibility that

knowledge-based reasoning could readily be extended to networks of knowledge

bases that provide logical specifications for integrated analysis across spatial

scales.

Keywords: Knowledge base, fuzzy logic, hierarchy, network, integration, ecosystem

management, ecological assessment, landscape analysis.
This page was intentionally left blank.
Introduction Ecological assessment is fundamental to ecosystem management, which has

emerged as a basic principle of natural resource management in the United States

since the 1990s (Committee of Scientists 1999). Ecosystem management has been

defined as “...the use of skill and care in handling integrated units of organisms and

their environments... to produce desired resource values, uses, or services in ways

that also sustain the diversity and productivity of ecosystems” (Overbay 1993, p. 5).

The primary goal of ecosystem management is ecosystem sustainability (Daly and

Cobb 1989, Dixon and Fallon 1989, Gale and Cordray 1991, Greber and Johnson

1991, Maser 1994), which in its broadest sense means achieving an operational

balance among concerns for ecological states and processes, economic feasibility

of management actions, and social acceptability of expected management consequences (Bormann et al. 1994, Salwasser 1993). 

Conceptual models for an adaptive ecosystem management process have been

proposed (fig. 1) as ways to implement ecosystem management (FEMAT 1993,

Maser et al. 1994). The process is conceived as a continuous cycle that includes

monitoring, assessment (evaluation in fig. 1), planning, and implementation. A

process that actively supports adaptation is necessary because ecosystems are

complex entities and our knowledge about them is limited. In addition, both the

social and biophysical components of ecosystems are highly dynamic and unpredictable. Ecological assessment is fundamental to ecosystem management because

it simultaneously is the concluding step of an iteration on the cycle and generates

revisions to current knowledge that become the basis for adaptation in the next

iteration (Committee of Scientists 1999). 

This paper focuses on integrated ecological assessment because ecosystem management fundamentally is concerned with integrated management, which presupposes integrated analysis. The literature on ecosystem management indicates the

need for integration across disciplines and spatial scales. However, convincing

1

Figure 1—The adaptive ecosystem management process (adapted from Maser et al. 1994).
demonstrations of integrated analysis to support ecosystem management have

been lacking. This paper discusses practical approaches to integrated assessment

across disciplines and spatial scales, landscape-level application of the latter, and

near-term prospects for extending the approaches to embrace much of the full

adaptive management process. 

Current Approaches Several broad-scale ecoregional assessments have been conducted in the U n i t e d

to Ecosystem States in recent years (Anon. 1996, 1997; Everett et al. 1994; FEMAT 1993), and 

A s s e s s m e n t several more are in progress. Each assessment has used a now well-standardized

approach to define the analytical problem. A scoping process was used to identify

and evaluate critical issues deserving consideration. A needs assessment was performed to identify data requirements and analytical methods needed to respond to

the issues. Various statistical, simulation, and optimization procedures have been

used to address various components of the overall assessment problem. To assert

that analyses used in these assessments were conducted ad hoc would be a disservice. However, although assessment teams may have carefully coordinated the

conduct of analyses with integration in mind, there is little evidence in the reports

that effectively integrated analysis was achieved. 

Improving Expert systems (Jackson 1990, Waterman 1986), known more generically as

Integration in knowledge-base systems (Waterman 1986), began to appear in significant numbers

Assessments with in natural resource management in 1983 (Davis and Clark 1989). More recently,

Knowledge-Based Durkin (1993) catalogued over 100 knowledge-base applications in the environmental

Reasoning sciences. O’Keefe (1985) envisioned an important role for knowledge-base systems

as components of larger decision-support systems in the future.

A knowledge base is a formal logical specification for interpreting information and is

therefore a form of metadatabase in the strict sense. A knowledge base is a logical

representation of a problem in terms of relevant entities in the problem domain and

logical relations among them. Interpretation of data by a knowledge-base engine

provides an assessment of system states and processes represented in the knowledge base as topical entities. Use of logical representation for assessing the state of

systems frequently is desirable or necessary. Often, the current state of knowledge

about a problem domain is too imprecise for statistical or simulation models or optimization, each of which presume precise knowledge about relevant mathematical

relations. In contrast, knowledge-based reasoning provides solutions for evaluating

more imprecise information. 

Adaptation and Knowledge-based solutions are particularly relevant to ecosystem management

Limitations because the topic is conceptually broad and complex and involves many, often 

abstract, concepts (e.g., health, sustainability, ecosystem resilience, ecosystem 

stability, etc.) whose assessment depends on many interdependent states and

processes. Logical constructs are useful in this context because the problem can 

be evaluated as long as the entities and their logical relations are understood in 

a general way and can be expressed by subject matter authorities.

Logic-based analysis is not in direct competition with other, more traditional forms 

of analysis. Instead, knowledge-based representations can be used as logical framew o r k s within which results from many specific mathematical models are integrated.

2
Traditional knowledge-based systems, dating from the early 1970s, have used

rule-based reasoning. Such systems conventionally have only been suitable for narrow, well-defined problems (Jackson 1990, Waterman 1986). As discussed in the

next section, however, newer forms of knowledge-based representation, based

on object models and fuzzy logic, substantially improve the ability to model large,

general problem domains such as ecosystem assessment. 

A New Approach The USDA Forest Service, Pacific Northwest Research Station released the first 

to Ecosystem production version of the ecosystem management decision-support (EMDS) system 

Assessment in February 1997. The EMDS system integrates a knowledge-base engine into the

ArcView GIS (Environmental Systems Research Institute)1 to provide k n o w l e d g e -

based reasoning for landscape-level ecological analyses. As of November 1 9 9 9 ,

about 325 sites worldwide have requested EMDS, including about 60 USDA

Forest Service sites, 125 national research institutes, and another 110 universities.

The implications of this new hybrid technology are examined from several points 

of view.

Object-Based Logic NetWeaver was initially developed in 1988, based on concepts originally proposed

Networks for Problem by Stone et al. (1986). It has steadily evolved since and now provides a substantially

Specification different form of knowledge representation that offers several advantages over production-rule systems that make it highly suitable for landscape-level ecosystem

analyses. Key features of the system include an intuitive graphical user interface,

object-based logic networks of propositions, and fuzzy logic. Implementation of the

user interface together with NetWeaver’s object-based representation supports

design of highly modular knowledge bases. Modularity in turn enables effective,

incremental evolution of knowledge-base structures from simple to complex forms.

Modern systems theory asserts that incremental evolution is a virtual requirement

for design of complex systems (Gall 1986).

Fuzzy Logic Fuzzy logic representations are more intuitively satisfying than classical Boolean 

and Compact (bivalent) logic, as well as more precise and compact compared to classical ruleRepresentation based representations. Zadeh (1965, 1968) first presented basic concepts of approximate reasoning with fuzzy logic. Subsequent concept papers (Zadeh 1975a, 1975b,

1976) elaborated on the syntax and semantics of linguistic variables, laying the

foundation for what has now become a significant branch of applied mathematics.

Fuzzy logic is concerned with quantification of set membership and associated set

operations. Formally (Kaufmann 1975),

let E be a set, denumerable or not, and let be an element of E. Then

a fuzzy subset A of E is a set of ordered pairs { , μA( )}, E, in

which μA( ) is a membership function that takes its values from the set

M = [0, 1], and specifies the degree of membership of in A.

Because fuzzy set theory is a generalization of Boolean set theory, most Boolean

set operations have equivalent operations in fuzzy subsets (Kaufmann 1975, p. 11). 

1 The use of trade or firm names in this publication is for 

reader information and does not imply endorsement by the 

U.S. Department of Agriculture of any product or service.

3
Application of fuzzy logic to natural resource science and management is still relatively new. General areas of application include classification in remote sensing

(Blonda et al. 1996), environmental risk assessment (Holland 1994), phytosociology

(Moraczewski 1993a, 1993b), geography (Openshaw 1996), ecosystem research

(Salski and Sperlbaum 1991), and environmental assessment (Smith 1995, 1997).

More specific applications include catchment modeling (Anon. 1994), cloud classification (Baum et al. 1997), evaluation of plant nutrient supply (Hahn et al. 1995), soil

interpretation (Mays et al. 1997, McBratney and Odeh 1997), and land suitability for

crop production (Ranst et al. 1996).

To appreciate the compactness of fuzzy set representation versus bivalent logic 

in production-rule systems, consider the following example. Suppose that risk of a

landslide depends on three factors, A, B, and C. Rather than simply concluding that

risk is true, we want to refine our conclusion to selection from among the outcomes

risk.low, risk.mod, risk.high, and risk.extreme. If each factor also has a rating of low,

moderate, high, or very high, then there are potentially 256 rules that at least need

to be considered. The number of rules tends to increase combinatorially. With a large

number of discrete rules, the potential for logical incompleteness also is very high.

This is a small example. Reflection on the relative complexity of the example versus

that of the ecosystem management domain should make it clear how impractical

bivalent reasoning becomes in the context of large problems. 

Now consider the fuzzy logic implementation of the same landslide risk problem. In

N e t We a v e r, each factor is represented by a data object, each having an associated

fuzzy membership function. Risk is evaluated by a single network object that contains a graphically constructed logic expression, typically involving a limited number

of combinations of the three data objects. In the simplest case, for example, the

N e t Weaver representation of the risk problem would require one network object

with a single logical expression to evaluate the three data objects. A more complex

formulation might require a single network object with a compound logical expression involving multiple references to the three data objects, and with each elementary expression using varying combinations of fuzzy arguments on the data objects.

Even in this more complex case, there would still only be one network object, three

data objects (because they are reusable by multiple reference), and now perhaps

six to nine fuzzy arguments. 

Similarly, fuzzy logic has significant practical advantages over Bayesian belief networks (Ellison 1996, Howard and Matheson 1981) in some contexts. Bayesian belief

networks may be preferable to fuzzy logic networks when conditional probabilities of

outcomes are known. However, Bayesian belief networks, like production rule systems, are difficult to apply to large, general problems because the number of conditional probabilities that must be specified can quickly become extremely large as the

conceptual scope of a problem increases. In such situations, model design not only

becomes difficult to manage but many probabilities will not be well characterized

and will therefore need to be supplied by expert judgment, thus negating much of

the value to be gained by a more statistically based approach to knowledge representation. 

4
This argument is not meant to imply that fuzzy logic networks are inherently superior

to Bayesian belief networks or other forms of knowledge representation. On the

c o n t r a r y, the alternative forms of representation just discussed may be highly complementary to one another in practice. In particular, fuzzy logic networks are well

suited as logic frameworks for integrating model results from various analytical

systems such as simulators, linear programs, Bayesian belief networks, and 

p r o d u c t i o n-rule systems.

Integration Across Problem specification for ecological assessment may well deserve to be classified

Disciplines as a “wicked problem” (Allen and Gould 1986). As discussed in the introduction,

ecosystem assessment must consider many states and processes of biophysical,

social, and economic components of an ecosystem. Many entities may have both

deep and broad networks of logical dependencies as well as complex interconnections. Although constructing such complex representations in NetWeaver is not trivial,

it is at least rendered feasible by the precision and compactness of fuzzy logic relations, and by the graphic, object-based representation of logic networks in the system interface. No subject-matter authority, nor for that matter any group of authorities,

is capable of holding a comprehensive cognitive map of such a complex problem

domain in their consciousness. On the other hand, the graphic, object-based form 

of knowledge representation in NetWeaver is highly conducive to the incremental

evolution of knowledge-base design from simple to complex forms.

Landscape Major components of the EMDS system include the NetWeaver knowledge-base

Implementation system, the EMDS Arcview application extension, and the Assessment system 

(fig. 2). This section briefly summarizes system structure and function in terms of

s y s t e m-level objects and their methods and relations. More detailed descriptions 

of the system are provided in Reynolds et al. (1996, 1997a, 1997b) and Reynolds

(1999a). 

The NetWeaver knowledge-base system (Reynolds 1999b) is composed of an

engine and a graphic user interface for knowledge-base developers that provide

controls for designing, editing, and interactively evaluating knowledge bases (fig. 2).

Primary components of the EMDS Arcview application extension are the DataEngine

and MapDisplay objects that customize the Arcview environment with methods and

data structures required to integrate NetWe a v e r’s knowledge-based reasoning

schema into A r c v i e w. The Assessment system is a graphic user interface to the

N e t Weaver engine for end-users of the EMDS application that controls setup a n d

running of analyses, runtime editing of knowledge bases, and display of maps,

tables, graphs, and evaluated knowledge-base state related to analyses. 

5
Figure 2—Object diagram of the ecosystem management decision-support system.

6

Figure 3—Knowledge-based integration across scales.
Integration Across It is relatively easy in principle to extend integrated analysis via knowledge-based

Spatial Scales reasoning over multiple spatial scales (fig. 3). Data from fine-scale landscape features such as watersheds are first processed by a knowledge base designed for that

scale. Knowledge-base output, shown as evaluated states in the middle of the figure, then go through an intermediate filter (typically implemented in a spreadsheet

or database application) to synthesize information for input to the next coarser

scale. A second knowledge base processes the synthesized information to provide 

an assessment of landscape attributes at the top of the figure. Finally, knowledge

base outputs at the broader landscape scale may feed back to the fine scale as

context information that influences evaluations at the fine scale. This simple conceptual model (fig. 3) provides the basis for a formal logical specification of analyses

that is consistent across scales. Hierarchies, or even networks, of knowledge-based

analyses as suggested would be highly consonant with ecosystem theories concerning the hierarchical organization of ecosystems (Allen and Starr 1982).

Practical Discussion thus far already has alluded to some practical advantages to integrating

Advantages for knowledge-based reasoning into a GIS system, including use of logical frameworks

Landscape for integrating knowledge over numerous and diverse problem domains. This section

Assessment considers three additional advantages that are more specifically associated with use

of the NetWeaver engine in EMDS.

Evaluation with In the future, assessment teams may be able to assemble a list of all topics they

Incomplete want to include in an assessment, as well as a list of data requirements needed to

Information address those topics, and find they have all the requisite data. At present, however,

assessments routinely start with incomplete data. There may be some missing

observations or no data for several to many data types. One solution to the problem of missing data is to limit the scope of analyses to those topics for which data

already exist or can be easily acquired. Tailoring analyses to suit existing data is

undesirable because the assessment becomes driven by the data at hand rather

than the questions that are really of interest. Moreover, acquiring missing data usually is both time-consuming and expensive. Even if there is no conscious decision

to limit the conduct of analyses to existing data, it may be difficult to avoid subconscious rationalization. 

7
NetWeaver was chosen as the inference engine for EMDS primarily because it supports robust analyses under conditions of missing data. Recall from the earlier discussion that NetWeaver implements a fuzzy propositional logic. NetWeaver also

could be described as an evidence-based reasoning system; propositions of network

objects are neutral to missing data, and whatever data are available incrementally

contributes to, or detracts from, the strength of evidence supporting a proposition.

In NetWeaver, influence of data is computed as a function of the number of times 

a data object is referenced with the knowledge-base structure, and the level(s) at

which the data enter. The EMDS system computes synoptic measures of influence

that also account for the number of records in which a data field has missing values.

Influence of Missing The influence of missing data is closely related to the ability to reason with incomData on Completeness plete information. Influence, in this context, refers to the degree to which missing

of an Assessment data would contribute to completeness of an assessment. The NetWeaver inference

engine uses simple rules to compute influence, based on how many states and

processes use the information and at which levels the missing information would

enter a knowledge-base structure (Reynolds 1999b). The Data Acquisition Manager

of EMDS (Reynolds 1999a, DAM in fig. 2) uses synoptic information about data

influence to assist EMDS users with prioritizing new data acquisition needs (fig. 4).

The ability to compute influence and set priorities for collecting data is particularly

useful owing to the highly dynamic nature of data influence. Due to interdependencies between data, influence not only depends on which fields in a database are

populated but also on the values in those fields (Reynolds et al. 1997b).

8

Figure 4—Evaluation of the influence of missing data in the ecosystem management 

decision-support system.
9

Figure 5—Graphic display of evaluated knowledge-base state for a selected landscape

feature in an ecosystem management decision-support system analysis.

Interpretation Logic structures in the domain of ecological analysis and assessment can be highly

complex owing to potentially large numbers of logical entities and their interrelations.

The NetWeaver hotlink browser in EMDS (fig. 2) has a NetWeaverlike interface that

displays an expandable outline view of the evaluated knowledge base as well as the

evaluated state of networks selected in the outline (Reynolds 1999a, fig. 5). Whereas

the graphic interface of NetWeaver is useful for knowledge-base testing and validation in the NetWeaver development environment, the graphic interface of the hotlink

browser in EMDS facilitates tracing the underlying logic that leads to observed states

in a relatively intuitive manner.
10

Table 1—Primary networks in the knowledge base for assessing watershed

condition 

Network name Proposition evaluated by network 

Watershed processes: Watershed processes are within acceptable ranges.

Hydrologic processes Hydrologic processes in the watershed are within

acceptable ranges compared to reference conditions

(table 2).

Erosion processes Erosion processes in the watershed are within an

acceptable range compared to reference conditions

(table 2).

Fire processes Fire processes in the watershed are in good condition

compared to reference conditions (table 2).

Watershed patterns: Watershed patterns are within acceptable ranges

Upland patterns Upland patterns in the watershed are within acceptable

ranges compared to reference condition. Evaluation

includes vegetation composition and structure.

Valley bottom patterns Valley bottom processes in the watershed are within

acceptable ranges compared to reference conditions.

Evaluation includes vegetation composition and structure, stream type composition, sinuosity, woody debris,

pool frequency, bank stability, and sediment transport

capacity.

Channel patterns Channel characteristics in the watershed within

acceptable ranges compared to reference conditions.

Evaluation includes bankfull width to depth ratio, pool

depth, flood-plain width, and fines in riffles.

Human influence Aggregate effects of human influence are within

acceptable ranges compared to management standards. Evaluation includes effects of roads, dams,

diversions, channelization, groundwater extraction,

mines, grazing, and recreation.

Aquatic species: Likelihood of long-term viability of aquatic species is

good.

Fish habitat Fish habitat potential in watershed is good. Evaluation

includes effects of baseflow, substrate, water temperature, and cover.
Examples of Several knowledge-base development projects are underway at the Pacific 

Current EMDS Northwest Research Station and Pennsylvania State University. Initial efforts are 

Applications focusing on integrated analysis for specific spatial scales. Progress to date provides

strong evidence that it is feasible to construct NetWeaver knowledge bases for

ecosystem management despite the conceptual scope and complexity of the problem domain. A few example prototype knowledge bases have been completed in 

the past 12 months. 

Assessment of Reynolds et al. (1999) designed a knowledge base for assessment of watershed-l e v e l

Hydrologic Integrity hydrologic integrity for the U.S. Environmental Protection A g e n c y. Primary logic

networks for assessing integrity are watershed processes, watershed patterns,

human influence, and aquatic species. Each network evaluates a specific propos i t i o n about the state of watershed condition (table 1). A simplified hierarchy of the

logic structure under the network for watershed processes illustrates the scope of

the watershed processes topic (table 2). Topic structure (table 2) has been simplified

for brevity by omission of intermediate calculated data links and terminal data links.

We trace the logic structure from watershed processes (fig. 6) down to total yield

(figs. 7-11) as a typical example of knowledge-base structure. The truth value for the

proposition that watershed processes are within suitable ranges of conditions depends

on the degree to which the premises, or logical antecedents, of watershed processes

are true (fig. 6). The logic structure of the network (fig. 6) makes the meaning of the

proposition (table 2) explicit. The network for hydrologic processes (fig. 7) similarly

has logically antecedent conditions, represented by networks, that determine the truth

of its proposition (table 2). The AND nodes (figs. 6 and 7) are fuzzy logic operators.

In conventional fuzzy logic, an AND operation is implemented mathematically as a

min function over the set of logical antecedents, xi(Kaufman 1975). However, the

NetWeaver implementation of AND is a minimum-biased weighted average of its

logical antecedents expressed as

AND = min +( – min)( min +1)/2 ,

in which min = min( i, i=1,.., n), and is the weighted average of the i. Other logic

operators in NetWeaver include OR, SOR, XOR, and NOT (table 3).

Determination of suitable hydrologic processes depends, among other things (table

2), on suitable stream flow (figs. 8 and 9). Evaluation of the stream flow network

depends on the evaluation of four other networks (table 2), but whereas the network

for hydrologic processes is evaluated by a fuzzy AND expression (fig. 7), stream

flow is evaluated by calculating a sum of products in the calculated data link stream

flow sum calc (fig. 9). The difference in formulations is semantically significant. The

computation of stream flow sum calc and its use in the fuzzy node in the stream

flow network (fig. 8) effectively asserts that networks contributing to evaluation of

stream flow can compensate for one another to some extent. If, for example, the

network for total yield evaluates to completely false, but some other network, say

peak flow, evaluates to completely true, then peak flow at least partially compensates for total yield. The stream flow network also is typical of many networks in

the knowledge base in its use of weights that are read as data for weighting the

importance of networks contributing to evaluation of stream flow (fig. 9).

11

–

–

Text continues on page 18.
Table 2—Propositions associated with networks antecedent to the watershed processes network 

Source of 

Network name Proposition comparisona

Hydrologic processes: Hydrologic processes are within suitable ranges. --

Stream flow— Stream flow characteristics are within suitable ranges. --

Total yield Total water yield is within a suitable range. Reference 

Peak flow Peak flow is within a suitable range. Reference 

Base flow Base flow is within a suitable range. Reference 

Bankfull discharge Bankfull discharge is within a suitable range. Reference 

Water quality— Attributes of water quality are within suitable ranges. --

Sediment-- Sediment attributes are within a suitable range. --

Bedload Bedload is within a suitable range. Reference 

Dissolved solids Concentration of dissolved solids is within a suitable range. Reference 

Suspended solids Concentration of suspended solids is within a suitable range. Reference 

Coliform Coliform count is within a suitable range. Regulation 

Dissolved O2 Concentration of dissolved oxygen is within a suitable range. Regulation 

Water temp— Water temperature characteristics are within suitable ranges. --

Temp max 7-day running average for summer maximum water temperature 

is within a suitable range. Regulation 

Temp thresh Number of days that daily maximum water temperature exceeds 

threshold is within a suitable range. Regulation 

Nutrients— Nutrient concentrations are within suitable ranges. --

Nitrogen concn Nitrogen concentration is within a suitable range. Regulation 

Phosphorous concn Phosphorous concentration is within a suitable range. Regulation 

Metals— Metal concentrations are within suitable ranges of regulatory 

requirements. --

Aluminum concn Aluminum concentration is within a suitable range. Regulation 

Arsenic concn Arsenic concentration is within a suitable range. Regulation 

Copper concn Copper concentration is within a suitable range. Regulation 

12
Table 2—Propositions associated with networks antecedent to the watershed processes network 

(continued)

Source of 

Network name Proposition comparisona

Mercury concn Mercury concentration is within a suitable range. Regulation 

Zinc concn Zinc concentration is within a suitable range. Regulation 

Erosion processes: Erosion processes are within suitable ranges. --

Surface erosion Amount of surface erosion is within a suitable range. Reference 

Mass wasting Amount of mass wasting is within a suitable range. Reference 

Debris avalanche Amount of debris avalanche is within a suitable range. Reference 

Sediment delivery Amount of sediment delivery is within a suitable range. Reference 

Fire processes: Fire processes are within suitable ranges. --

Fire frequency Fire frequency is within a suitable range. Reference 

Fire hazard Amount of expected fire damage is within a suitable range. Reference 

a Observed data values are compared to fuzzy membership functions, representing either reference conditions or regulatory requirements, to

determine if an observed value falls within a suitable range of values. Data defining fuzzy membership functions for reference conditions and

regulatory requirements are read by the knowledge base to parameterize the fuzzy membership function.

13

Figure 6—Network for watershed processes. The truth of the proposition that watershed processes are within a suitable range of conditions depends on the degree to

which its three premises, represented by the networks hydrologic processes, erosion

processes, and fire processes, are true.
14

Figure 7—Network for hydrologic processes. The truth of

the proposition that hydrologic processes are within a suitable range of conditions depends on the degree to which its

two premises, represented by the networks stream flow,

and water quality, are true.

Figure 8—Network for stream flow. The rounded box represents a fuzzy curve that is

dynamically defined. The fuzzy curve object compares the value of stream flow sum

calc (fig. 9) to the x coordinates in its xy nodes to interpolate the truth value for stream

flow. The calculated data link, stream flow calc, computes the sum of the weights for

terms in fig. 9 to dynamically define the x coordinates of the xy nodes.
15

Figure 9—Calculated data link for stream flow sum calculation. The calculated data link computes a sum of products. The weights for

each term in the sum are read as data.

Figure 10—Network for total water yield from the watershed. The rounded box represents a fuzzy curve that is dynamically defined. The

fuzzy curve object compares the value of totYldCurrent to the x coordinates in its xy nodes to interpolate the truth value for total yield.

The x terms in each xy node are calculated data links (fig. 11).
Table 3—NetWeaver logic operators

Operator Type Truth value returned 

AND Set Minimum-biased weighted average of the truth values of its

logical antecedents. 

NOT Unary Negation of the truth value of its antecedent. 

OR Set Maximum truth value in the set of its logical antecedents. 

SOR Set Truth value of the first logical path (ordered left to right) 

with sufficient data. 

XOR Set Logical distance between its two most true logical 

antecedents. 

16

Figure 11—X terms for xy nodes (fig. 10) are either computed from the

mean and standard deviation (read as data) or computed as quantiles

(read as data).
17

Figure 12—Network for evaluation of ecological site suitability of Douglas-fir in Great Britain. AT, MD, con, wind, SNR and SMR suit

indicate logic networks that evaluate site suitabilities for accumulative air temperature, moisture deficit, continentality, wind, soil nutrient

regime, and soil moisture regime, respectively, with respect to Douglas-fir.

Figure 13—Network for evaluating yield suitability of Douglas-fir in Great Britain. AT, MD,

con, wind, SNR and SMR indicate computational networks that compute site indices for

accumulative air temperature, moisture deficit, continentality, wind, soil nutrient regime,

and soil moisture regime, respectively, for Douglas-fir.
Total yield (fig. 10) is typical of all terminal (that is, lowest level) networks in the

knowledge-base hierarchy (table 2) in that it evaluates a simple data link to get the

current condition (totYldCurrent in this case) and compares this value to a dynamically defined fuzzy membership function that specifies a suitable reference condition

for total water yield. X Y nodes under the fuzzy curve object (fig. 10) define the

shape of the fuzzy membership function. The form of the curve for total yield also

is typical of most dynamically defined fuzzy membership functions for all terminal

networks in the knowledge base; y terms are constants and x terms are computed

from data. With a few exceptions, x terms are evaluated by calculated data links in

which a switch object is used to select one of two methods for computing x (fig. 11).

X is computed from the mean and standard deviation (totYldMean and totYldSD,

respectively, in fig. 11) of a reference condition if the data value of refMethod = SD,

or x is assigned a quantile value (e.g., totYldQ1 in fig. 11).

Ecological Site Ray et al. (1998) designed a knowledge base for ecological site classification (ESC)

Classification that evaluates site suitability of commercial tree species for the reforestation program

of the British Forestry Commission. Each species has a network that evaluates

ecological suitability (fig. 13) and a network that evaluates yield suitability (fig. 13).

Ecological suitability is determined by the most limiting environmental condition (fig.

13), whereas yield suitability is determined as the product of realized growth potential

based on accumulative air temperature (AT) and the most limiting of other factors

(fig. 13).

The knowledge base was developed as a literal translation of the ESC expert 

system (Ray et al. 1996). The current version is somewhat unusual for NetWeaver

knowledge bases insofar as computations are primarily numeric rather than logicbased. Nevertheless, the NetWeaver implementation has been considered highly

successful owing to both conversion of categorical outcomes to continuous indices

based on fuzzy logic, and to the ability of EMDS to process hundreds of forest management units in a single analysis.2 Ray and colleagues plan continued development of the current prototype, including assessment of ecological suitability for 20

native woodland types and addition of topics related to evaluation of biodiversity 

and sustainability. Both lines of enhancement are expected to rely more heavily 

on logic-based processing.

Forest Ecosystem One of the major accomplishments of the 1992 Earth Summit (Rio de Janeiro,

Sustainability Brazil) was the enunciation of a set of principles for sustainable development of the

world’s forest resources (United Nations 1992). Subsequently, signatory nations to

the 1995 Santiago Declaration, representing about 90 percent of the world’s boreal

and temperate forest cover, affirmed the recommendations of the Montreal Process

that prescribed a set of seven criteria and 67 indicators for evaluating forest ecosystem sustainability. The specifications of the Montreal Process are notable in two

respects. First, the specifications provide relatively clear definitions of ecosystem 

2 Ray, D. 1998. Personal communication. Soil scientist, British 

Forestry Commission, Forest Research, Roslin, Midlothian, 

Scotland, UK.

18
attributes requiring evaluation. Second, however, the Montreal Process does not

prescribe the manner in which criteria and indicators are to be interpreted to draw

conclusions about the state of forest ecosystem sustainability. Additional specifications that enable consistent interpretations of monitoring data on sustainability

clearly would be useful.

In 1999, I designed a prototype knowledge base for comprehensive evaluation of all

Montreal criteria and indicators (Reynolds 2000). The knowledge base contains two

complementary, primary logic networks; the first evaluates current conditions in relation to long-term desired future conditions, and the second evaluates trend by comparing conditions between the current and a past assessment. As with the knowledge

base for hydrologic integrity, all fuzzy membership functions in the knowledge base

for the Montreal prototype are dynamically defined. Thus, the logic specification for

evaluation of forest ecosystem sustainability is extremely general and could be

applied to any country or major bioregion. Readers are referred to Reynolds (2000)

for additional details of the specification because the knowledge base is extremely

large. 

Conclusions Awareness of the value of fuzzy logic for environmental analysis has been increasing in the natural resource science community over the past 7 to 8 years. Increased

attention to the possible benefits of fuzzy logic-based approaches to analysis and

assessment has paralleled shifts in systems analysis from relatively narrow, welldefined problems such as harvest schedule optimization to much larger, more poorly

defined problems such as maintaining ecosystem health. Note Zadeh’s (1975a,1975b)

early thoughts on this:

...the ineffectiveness of computers in dealing with [biological]

systems is a manifestation of what might be called the principle

of incompatibility—a principle which asserts that high precision

is incompatible with high complexity. Thus, it may well be the

case that the conventional techniques of system analysis and

computer simulation...are intrinsically incapable of coming to

grips with the great complexity of human thought processes 

and decision-making. ...Indeed, it is entirely possible that only

through the use of [approximate reasoning] could computer 

simulation become truly effective as a tool for the analysis of

systems which are too complex or too ill-defined for the application of conventional quantitative techniques.

Zadeh’s (1975a, 1975b) comments on complexity seem no less compelling today

than when first published nearly 25 years ago. Indeed, if anything, organi-zations

and individuals who have had to confront the complexities of ecosystem assessment over the past 8 to 9 years should probably have a keener appreciation for

Zadeh’s (1975a,1975b) remarks than his contemporaries at the time. 

19
Integration of the NetWeaver knowledge-base engine into a GIS environment, as

described for the current version of EMDS, is not the ultimate solution to ecosystem

management and ecological assessment; it does suggest, however, some promising

possibilities for continued evolution of knowledge-based systems in landscape

analysis. As a starting point, EMDS provides a formal logic framework for integrated

analysis across multiple problem domains, has the ability to reason with incomplete

information, and assists with optimizing the conduct of assessments by setting priorities on missing data. Perhaps most significantly, however, was the relatively recent

insight that the advantages of knowledge-based reasoning could readily be extended

to logic networks of knowledge bases that provide logic specifications for integrated

analysis across spatial scales.

Author’s Note EMDS version 2.0 for ArcView 3.2 is currently available from the download page of

the EMDS website (www.fsl.orst.edu/emds). EMDS version 3.0 for ArcGIS 8.1 will

be available at the same site beginning about December 1, 2001. Requests for CDs

of either version also can be submitted from the download page.

Acknowledgments I thank Frank Davis (University of California at Santa Barbara), Mike Rauscher

(USDA Forest Service, Southern Research Station), and Mark Twery (USDA Forest

Service, Northeastern Research Station) for their many helpful review comments. I

thank Mike Saunders and Bruce Miller (Pennsylvania State University) for their generosity and invaluable contributions to the creation of the freeware version of

NetWeaver distributed with EMDS version 2.0.

20
Literature Cited Anon. 1994. Fuzzy logic applied to catchment modelling. Water and Wastewater 

International. 9: 40-46.

Anon. 1996. The southern Appalachian assessment summary report. Tech. Pap. 

R8-TP-25. Atlanta, GA: U.S. Department of Agriculture, Forest Service, Southern

Region. 118 p.

Anon. 1997. Sierra Nevada ecosystem project. Davis, CA: University of California, 

Centers for Water and Wildland Resources; final report to Congress; 328 p.

(Addendum)

Allen, G.M.; Gould, E.M., Jr. 1986. Complexity, wickedness, and public forests. 

Journal of Forestry. 84: 20-23.

Allen, T.F.H.; Starr, T.B. 1982. Hierarchy: perspectives for ecological complexity.

Chicago: University of Chicago Press. [Pages unknown].

Baum, B.A.; Tovinkere, V.; Titlow, J.; Welch, R.M. 1997. Automated cloud classification of global AVHRR data using a fuzzy logic approach. Journal of Applied

Meteorology. 6: 1519-1526.

Blonda, P.; Bennardo, A.; Satalino, G.; Pasquariello, G. 1996. Fuzzy logic and 

neural techniques integration: an application to remotely sensed data. Pattern

Recognition Letters. 17: 1343-1347.

Bormann, B.T.; Brookes, M.H.; Ford, E.D. [and others]. 1994. Eastside forest 

health assessment. Volume V: A framework for sustainable-ecosystem management. Gen. Tech. Rep. PNW-GTR-331. Portland, OR: U.S. Department of

Agriculture, Forest Service, Pacific Northwest Research Station. 61 p. 

Committee of Scientists. 1999. Sustaining the people’s lands: recommendations 

for stewardship of the national forests and grasslands into the next century.

Washington, DC: U.S. Department of Agriculture. 181 p.

Daly, H.E.; Cobb, J.B., Jr. 1989. For the common good: redirecting the economy 

toward community, the environment, and a sustainable future. Boston, MA:

Beacon Press. 482 p.

Davis, J.R.; Clark, J.L. 1989. A selective bibliography of expert systems in natural 

resource management. AI Applications. 3: 1-18.

Dixon, J.A.; Fallon, L.A. 1989. The concept of sustainability: origins, extensions, 

and usefulness for policy. Society and Natural Resources. 2: 73-84.

Durkin, J. 1993. Expert systems: catalog of applications. Akron, OH: Intelligent 

Computer Systems, Inc. 88 p.

Ellison, A.M. 1996. An introduction to Bayesian inference for ecological research 

and environmental decision-making. Ecological Applications. 6: 1036-1046.

21
Everett, R.; Hessburg, P.; Jensen, M.; Bormann, B. 1994. Eastside forest ecosystem health assessment. Volume I: Executive summary. Gen. Tech. Rep. PNWGTR-317. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific

Northwest Research Station. 61 p.

Forest Ecosystem Management Assessment Team [FEMAT]. 1993. Forest 

ecosystem management: an ecological, economic, and social assessment.

Portland, OR: U.S. Department of Agriculture; U.S. Department of the Interior

[and others]. [Irregular pagination].

Gale, R.P.; Cordray, S.M. 1991. What should forests sustain? eight answers. 

Journal of Forestry. 89: 31-36..

Gall, J. 1986. Systematics: how systems really work and how they fail. Ann Arbor,

MI: The General Systematics Press. 342 p.

Greber, B.J.; Johnson, K.N. 1991. What’s all the debate about overcutting? 

Journal of Forestry. 89: 25-30.

Hahn, A.; Pfeiffenberger, P.; Wirsam, B.; Leitzmann, C. 1995. Evaluation and 

optimization of nutrient supply by fuzzy logic. Ernahrungs-Umschau. 42: 367-375.

Holland, J.M. 1994. Using fuzzy logic to evaluate environmental threats. Sensors. 

11: 57-62.

Howard, R.; Matheson, J. 1981. Influence diagrams. In: Howard, R.; Matheson, J., 

eds. Readings on the principles and applications of decision analysis. Menlo

Park, CA: Strategic Decisions Group. 2: 721-762.

Jackson, P. 1990. Introduction to expert systems. Reading, MA: Addison-Wesley 

Publishers. 526 p.

Kaufmann, A. 1975. Introduction to the theory of fuzzy subsets: fundamental 

theoretical elements. New York: Academic Press. 416 p. Vol. 1.

Maser, C. 1994. Sustainable forestry: philosophy, science, and economics. Delray 

Beach, FL: St. Lucie Press. 373 p.

Maser, C.; Bormann, B.T.; Brookes, M.H. [and others]. 1994. Sustainable forestry

through adaptive ecosystem management is an open-ended experiment. In:

Maser, C., ed. Sustainable forestry: philosophy, science, and economics. Delray

Beach, FL: St. Lucie Press: 303-340. Chapter 16.

Mays, M.D.; Bogardi, I.; Bardossy, A. 1997. Fuzzy logic and risk-based soil interpretations. Geoderma. 77: 299-309.

McBratney, A.B.; Odeh, I.O.A. 1997. Application of fuzzy sets in soil science: fuzzy

logic, fuzzy measurements and fuzzy decisions. Geoderma. 77: 85-91.

22
Moraczewski, I.R. 1993a. Fuzzy logic for phytosociology 1: syntaxa as vague 

concepts. Vegetatio. 106: 1-12.

Moraczewski, I.R. 1993b. Fuzzy logic for phytosociology 2: generalizations and 

prediction. Vegetatio. 106: 13-27. 

O’Keefe, R.M. 1985. Expert systems and operational research mutual benefits. 

Operations Research. 36: 125-129.

Openshaw, S. 1996. Fuzzy logic as a new scientific paradigm for doing geography.

Environment and Planning A. 28: 761-767.

Overbay, J.C. 1993. Ecosystem management. In: Taking an ecological approach to 

management. Watershed and Air Management WO-WSA-3. Washington, DC:

U.S. Department of Agriculture, Forest Service: 3-9.

Ranst, E. Van; Tang, H.; Groenemans, R.; Sinthurahat, S. 1996. Application of 

fuzzy logic to land suitability for rubber production in peninsular Thailand.

Geoderma. 70: 1-12.

Ray, D.; Reynolds, K.; Slade, J.; Hodge, S. 1998. A spatial solution to ecological 

site classification for British forestry using ecosystem management decision support. In: Abrahart, R.J., ed. Proceedings of the 3rd international conference on

GeoComputation. [CD-ROM]. [Place of publication unknown]: [Publisher

unknown]. http://divcom.otago.ac.nz/SIRC/GeoComp/GeoComp98/37/gc_37.htm.

(May 22, 2001).

Ray, D.; Suarez, J.; Pyatt, D.G. 1996. Development of an ecological site classification system decision support system for British Forestry. In: Proceedings of the

1st international conference on GeoComputation, Leeds University. [Location of

publisher unknown]: [Publisher unknown].

http://www.ashville.demon.co.uk/gc1996/abs079.htm. (May 22, 2001).

Reynolds, K.M. 1999a. EMDS users guide (version 2.0): knowledge-based decision

support for ecological assessment. Gen. Tech. Rep. PNW-GTR-470. Portland,

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research

Station. 60 p. 

Reynolds, K.M. 1999b. NetWeaver for EMDS version 2.0 user guide: a knowledge 

base development system. Gen. Tech. Rep. PNW-GTR-471. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific Northwest Research Station.

75 p. 

Reynolds, K.M. 2000. NetWeaver knowledge base specifications for evaluation of 

forest ecosystem sustainability as prescribed by the Montreal Process.

www.fsl.orst.edu/emds/sustain/sustain2s.htm. (May 22, 2001).

Reynolds, K.; Cunningham, P.; Bednar, L. [and others]. 1996. A design framework for a knowledge-based information management system for watershed

analysis in the Pacific Northwest U.S. AI Applications. 10: 9-22.

23
Reynolds, K.; Jensen, M.; Andreasen, J.; Goodman, I. 1999. Knowledge-based 

assessment of watershed condition. Computers in Agriculture. 27: 315-333.

Reynolds, K.; Saunders, M.; Miller, B. [and others]. 1997a. An application framework for decision support in environmental assessment. In: GIS 97 conference

proceedings of the 11th annual symposium on geographic information systems.

Washington, DC: GIS World: 333-337.

Reynolds, K.; Saunders, M.; Miller, B. [and others]. 1997b. Knowledge-based 

decision support in environmental assessment. In: Resource technology 97 conference proceedings of the ACSM 57th annual convention; and the ASPRS 63rd

annual convention. Bethesda, MD: American Society for Photogrammetry and

Remote Sensing and American Congress on Surveying and Mapping: 344-352.

Vol. 4.

Salski, A.; Sperlbaum, C. 1991. Fuzzy logic approach to modeling in ecosystem 

research. Lecture Notes in Computer Science. 20: 520-527

S a l w a s s e r, H. 1993. Perspectives in modeling sustainable forest ecosystems. In: 

Le Master, D.C.; Sedjo, R.A., eds. Modeling sustainable forest ecosystems.

Washington, DC: American Forests: 176-181.

Smith, P.N. 1995. A fuzzy logic evaluation method for environmental assessment. 

Journal of Environmental Systems. 24: 275-279.

Smith, P.N. 1997. Environmental project evaluation: a fuzzy logic based method. 

International Journal of Systems Science. 28: 467-471.

Stone, N.D.; Coulson, R.N.; Frisbie, R.E.; Loh, D.K. 1986. Expert systems in 

entomology: three approaches to problem solving. Bulletin of the Entomological

Society of America. 32: 161-166.

United Nations. 1992. Forest principles: report of the United Nations conference on

environment and development. New York: United Nations. 5 p.

Waterman, D.A. 1986. A guide to expert systems. Reading, MA: Addison-Wesley 

Publishers. 419 p.

Zadeh, L.A. 1965. Fuzzy sets. Information and Control. 8: 338-353.

Zadeh, L.A. 1968. Probability measures of fuzzy events. Journal of Mathematical 

Analysis and Applications. 23: 421-427.

Zadeh, L.A. 1975a. The concept of a linguistic variable and its application to 

approximate reasoning, part I. Information Science. 8: 199-249.

Zadeh, L.A. 1975b. The concept of a linguistic variable and its application to 

approximate reasoning, part II. Information Science. 8: 301-357.

Zadeh, L.A. 1976. The concept of a linguistic variable and its application to 

approximate reasoning, part III. Information Science. 9: 43-80.

24
This page has been left blank intentionally.

Document continues on next page.
This page has been left blank intentionally.
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle

of multiple use management of the Nation’s forest resources for sustained yields of

wood, water, forage, wildlife, and recreation. Through forestry research, cooperation

with the States and private forest owners, and management of the National Forests

and National Grasslands, it strives—as directed by Congress—to provide increasingly

greater service to a growing Nation.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs

and activities on the basis of race, color, national origin, gender, religion, age, disability,

political beliefs, sexual orientation, or marital or family status. (Not all prohibited bases

apply to all programs.) Persons with disabilities who require alternative means for

communication of program information (Braille, large print, audiotape, etc.) should

contact USDA's TA R G E T Center at (202) 720-2600 (voice and T D D ) .

To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, Room

326-W, Whitten Building, 14th and Independence Avenue, SW, Washington, DC

20250-9410 or call (202) 720-5964 (voice and TDD). USDA is an equal opportunity

provider and employer.

Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw

Telephone (503) 808-2592

Publication requests (503) 808-2138

FAX (503) 808-2130

E-mail desmith@fs.fed.us

Mailing address Publications Distribution

Pacific Northwest Research Station

P.O. Box 3890

Portland, OR 97208-3890
U.S. Department of Agriculture

Pacific Northwest Research Station

333 S.W. First Avenue

P.O. Box 3890

Portland, OR 97208-3890

Official Business

Penalty for Private Use, $300