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An Expert system to predict the ecological effects of prescribed fire Johnston, Michael Macfarlane 1989

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AN EXPERT SYSTEM TO PREDICT T H E ECOLOGICAL EFFECTS OF PRESCRIBED FIRE by MICHAEL MACFARLANE JOHNSTON B.Sc, Carleton University,  1979  A THESIS SUBMITTED IN PARTIAL F U L F I L M E N T O F T H E REQUIREMENTS FOR T H E DEGREE OF MASTER OF SCIENCE  in T H E F A C U L T Y O F G R A D U A T E STUDIES Commerce and Business Administration  We accept this thesis as conforming to the required standard  T H E UNIVERSITY O F BRITISH CO L UMB I A June 1989 © Michael Macfarlane Johnston,  1989  In  presenting  degree freely  at  the  available  copying  of  department publication  this  of  in  partial  fulfilment  University  of  British  Columbia,  for  this or  thesis  reference  thesis by  this  for  his thesis  and  scholarly  or for  her  of  The University of British Columbia Vancouver, Canada  DE-6  (2/88)  I  I further  purposes  gain  the  shall  requirements  agree  that  agree  may  representatives.  financial  permission.  Department  study.  of  be  It not  that  the  be  Library  an  advanced  shall  permission for  granted  is  for  by  understood allowed  the  make  extensive  head  that  without  it  of  copying my  my or  written  ABSTRACT  This thesis reports the results of a project to develop an expert system to predict the ecological effects of prescribed fire. The central goal of this project was  to examine  the  suitability of the  application of expert systems technology  prescribed burning domain for the and to recommend a strategy for  further research in this area.  With the sponsorship of the Protection Branch of the B C Forest Service, several experts were contacted in order to ask for their participation in the Fire Effects Expert System project. Five different experts were consulted and several different conceptual models of the domain were developed. The limiting factor concept was used to model the effects of prescribed fire on the direct and indirect growth factors. A prototype of the Fire Effects system was eventually constructed using the V P - E X P E R T software package.  Many difficulties  in using expert  systems technology  in the prescribed  burning domain were identified. These difficulties include: conflicting viewpoints amongst the different experts, uncertainty of knowledge concerning fire effects, and lack of upper management commitment to the project. However, many favourable factors were identified and these include: the scarcity of domain experts, the qualitative nature of the decisions, the large solution space of the problems, and the  existence of a large amount of narrow domain-specific  ii  knowledge.  Several uses for the Fire Effects system were proposed. These uses include: aiding foresters in developing fire prescriptions, educating forest managers, and serving as a basis for group discussions concerning fire effects.  A strategy for further research in the prescribed burning domain was also proposed. The principal features of this strategy are to plan and coordinate the development of expert systems at a high management level, to draw extensively on academic work in the area, and to develop specific standards pertaining to all site information data that is collected by the Forest service.  iii  TABLE OF CONTENTS ABSTRACT  ii  TABLE OF CONTENTS  iv  LIST O F FIGURES  vi  ACKNOWLEDGEMENT  . .  C H A P T E R 1. INTRODUCTION . . . 1.1 Background 1.2 Prescribed Burning 1.3 Expert Systems 1.4 Prescribed Fire Research in B C 1.5 Fire Effects Expert System Project 1.6 Structure of the Paper  viii  . . .  1 1 2 4 5 7 8  C H A P T E R 2. E X P E R T SYSTEMS 2.1 Overview 2.2 Expert System Tasks 2.3 Expert System Lifecycle . . . 2.4 Selecting and Motivating Experts 2.5 Knowledge Engineering 2.6 Knowledge 2.7 Knowledge Representation 2.8 Knowledge Acquisition 2.9 Knowledge Validation . 2.10 Knowledge Engineering with Multiple Experts 2.11 Hypertext 2.12 Integration of Expert Systems into the Environment . . . . . . 2.13 Expert Systems in the Forestry Domain  10 10 13 16 17 18 21 23 26 32 33 36 37 39  C H A P T E R 3. PRESCRIBED FIRE A N D FIRE E F F E C T S 3.1 Introduction . . . 3.2 Prescribed Fire and Fire Effects Research in B.C 3.3 Effects of Fire on Soil . . 3.3.1 Physical Changes . . . . . . . . 3.3.2 Chemical Changes 3.3.3 Biological Changes 3.4 Fire and Ecosystem Processes •-. . . . . . . 3.5 Fire Severity and Fire Behaviour 3.6 Vegetation and Prescribed Fire 3.7 Prescribed Burning Procedures in B C 3.8 Prescribed Burning Decision Models 3.9 The Ecosystem Classification System in B.C 3.10 Soil Development and the Forest Floor  41 41 41 48 48 50 51 52 54 57 60 61 62 64  iv  3.11 Limiting Factor Concept 3.12 Prescribed Fire Decision Aids  66 68  C H A P T E R 4. BUILDING T H E E X P E R T S Y S T E M 4.1 Knowledge Engineering 4.1.1 Knowledge Engineering Sessions with E . Hamilton . 4.1.2 Knowledge Engineering Sessions with M . Curran . . 4.1.3 Knowledge Engineering Sessions with B. Hawkes . 4.1.4 Knowledge Engineering Sessions with other Experts 4.2 Prescribed Burning Procedures 4.3 Conceptual Model of Fire Effects . . 4.4 Motivations of Groups Involved in Prescribed Burning 4.5 Structure of the Expert System 4.6 Typical Interaction with the System 4.7 Comparison of Different Expert System Software  . . . . . . . .  71 71 75 77 83 84 85 91 93 95 97 99  C H A P T E R 5. DISCUSSION A N D CONCLUSIONS 5.1 Discussion of Knowledge Engineering 5.2 Discussion of Development and Production Environments . . . 5.3 Discussion of the Prototype System 5.4 Discussion of Expert Systems in Prescribed Burning 5.5 Strategy for Further Expert Systems Research .  103 103 108 Ill 114 119  BIBLIOGRAPHY  123  APPENDLX 1 - P R O J E C T PROPOSAL  129  APPENDLX 2 - P R E - H A R V E S T SILVICULTURAL PRESCRIPTION  . .  137  APPENDLX 3 - SITE PREPARATION GUIDE  140  APPENDLX 4 - BURNING  147  PLAN  APPENDLX 5 - PRESCRIBED B U R N ANALYSIS  151  APPENDLX 6 - V E G E T A T I O N C O N C E P T U A L M O D E L  154  APPENDLX 7 - G R O W T H F A C T O R S  164  APPENDLX 8 - FIRE BEHAVIOUR C O N C E P T U A L M O D E L  167  APPENDLX 9 - TYPICAL INTERACTION WITH S Y S T E M  v  . . . . . . . .  172  LIST OF FIGURES  FIGURE 1 Expert System Task Types  15  FIGURE 2  The Expert's Transition Curve  19  FIGURE 3  Stages of Knowledge Acquisition  30  FIGURE 4  Criteria for Selecting a Knowledge Engineering Technique  FIGURE 5  A Validation-Based Life Cycle Model of the Knowledge-Based  .  System Development Process FIGURE  6  Flow  of Research  34 Needs  from the  M O F to  Forestry  Researchers FIGURE 7a  31  42  Proposed Prescribed Fire Management System  . . . . . . . .  45  F I G U R E 7b Proposed Prescribed Fire Management System  46  F I G U R E 7c  47  Proposed Prescribed Fire Management System  FIGURE 8  Factors Determining the Impact of Prescribed Fire on Soils  53  FIGURE 9  Example Demonstrating Limiting Factor Concept  67  FIGURE 10 FIGURE 11a  Key to the Identification of Site Sensitivity to Fire Classes Products of Knowledge Engineering Sessions  70 73  F I G U R E l i b Products of Knowledge Engineering Sessions  75  F I G U R E 12  Proposed Implementation of Limiting Factor Concept . . . .  81  F I G U R E 13  Prescribed Burning Decision Process Model  87  F I G U R E 14  Conceptual Model of Prescribed Burning  92  F I G U R E 15  Structure of the Fire Effects Expert System  F I G U R E 16  Comparison of Four Expert System Development Tools . .  vi  . . . . . . . . . . . .  96 101  F I G U R E 17  Spectrum of Techniques for Analyzing Interaction Between  Fire and Site Factors  vii  ACKNOWLEDGEMENT The author would like to thank several different agencies and individuals for their support of this project. The Protection Branch of the B C Forest Service provided financial support for travelling and other expenses incurred during the course of this project. The Forest Economic and Policy Analysis Project provided financial support and office space for the project. Mike Curran and Evelyn Hamilton of the B C Forest Service gave much of their time and energy to this project. My friend, Chris, encouraged me and proofread draft versions of this thesis. To all of these parties, the author extends his sincere gratitude.  viii  C H A P T E R 1.  INTRODUCTION  1.1 B a c k g r o u n d  This thesis is one of the products of a research project involving the B C Forest Service, the Canadian Forest Service (CFS), the Forest Economics and Policy Analysis (FEPA) project, and the Faculty of Commerce and Business Administration at the University of British Columbia. This project involved investigating the feasibility of applying expert systems technology to prescribed burning decisions by developing an expert system to predict the  ecological  effects of slashburning.  Initial contact between the B C Forest Service and The Faculty of Commerce and  Business  Administration was  established  through F E P A .  project was first suggested following a June meeting between  The  specific  representatives  from the Faculty of Commerce and Business Administration, the B C Forest Service, and the Canadian Forest Service (CFS). Since the C F S had already started development work on an expert system for prescribed burn planning, it was suggested any other project should complement the C F S project. Therefore, the  Fire  Effects  Expert System  Project  was  proposed  with  the  goal  of  developing the Fire Effects module of the CFS-sponsored PB (Prescribed Burn) Planner expert system. However, the Fire Effects Expert System Project was sponsored by the Protection Branch of the B C Forest Service. Appendix 1 contains the proposal for this project.  INTRODUCTION / 2  1.2 Prescribed B u r n i n g  Prescribed burning may be defined as "the knowledgeable application of fire to a specific land area to accomplish designated land objectives. Implicit in this definition is the studied consideration of the probability and benefit of achieving the objectives, the potential for damage to other resources and the opportunity costs and benefits of alternate land treatments. Fire is not the only land management tool. It is however, an economical and natural tool which offers more potential for manipulation in response to specific site requirements than most other post-logging treatments."  [Muraro, 1977, page 26]  In B C , one of the major uses for prescribed burning is as a site preparation tool prior to replanting of the site. Prescribed burning can improve the planting accessibility, increase the number of planting spots, sanitize the site from insects and diseases, and increase the availability of the nutrients on the site. In 1985, over 45,000 ha of land were burned after timber harvesting [Hawkes and Lawson, 1986]. Cost savings of up to $200/ha can be achieved by using slashburning instead of other site preparation methods [D. Gilbert , pers. 1  comm.].  1  P r o t e c t i o n Branch,  BC F o r e s t  Service.  INTRODUCTION / 3 Prescribed burning has other uses in addition to its use as a post-logging site treatment. Its most common use throughout the world is to clear land prior to planting crops  [Chandler et  management  of grasslands  management  of  forest  and  fuels  al.,  ranges,  (hazard  1983]. Other uses include:  the  management  control),  and  the  of wildlife, maintenance  the the of  ecosystems. In 1985, 92,000 ha of land in B C were burned for range and wildlife habitat improvement [Hawkes and Lawson, 1986]. A n additional 1,500 ha were burned for hazard abatement.  Although fire can be a very useful tool for site preparation, it must be used carefully. Many factors must be considered before fire is used on a site. The impact of a fire on a site varies with the slash loading, the moisture content of the soil and fuels, the weather conditions at the time of burning, the site topography, and the method and pattern in which the fire is ignited. The forest manager can manipulate the impact of fire on a site by defining a fire prescription that states a certain time of year for burning, as well as weather conditions and ignition patterns to burn under. Of course, economic and safety concerns play a crucial role in this decision because the forest manager should only burn when there is a minimal chance of the fire escaping.  The effects of a particular fire on the ecology of a site cannot be predicted with much certainty. Fire can affect the site chemistry; the soil structure, the soil temperature as well as the different biochemical processes of the ecosystem. Because of the uncertain knowledge concerning the nature of how fire impacts  INTRODUCTION / 4 a site, many efforts have been made to document and evaluate prescribed burns. The goal of these efforts is to gradually improve the prescribed burning decision models that are in use.  1.3 Expert Systems  Professor Edward Feigenbaum of Stanford, one of the leading researchers in expert systems,  has defined an expert system as "an intelligent computer  program that uses knowledge and inference procedures to solve problems that are difficult enough to require human expertise for their solution. Knowledge necessary to perform at such a level, plus the inference procedures used can be thought of as a model of the expertise of the best practitioners of the field." [Harmon and King, 1985, page 5]  Expert systems derive much of their power from having the ability to model knowledge about a specific part of a problem domain. However, it is very difficult  to  incorporate what  is  generally  referred  to  as  "commonsense"  knowledge into an expert system. Therefore, expert systems technology is only suitable for problems that require extensive knowledge within a narrow domain.  Of course, it is also possible to represent knowledge about a narrow domain in a book or manual. However, it is difficult to represent knowledge about strategies that can be used to solve a problem. This is because of the problem of combinatorial explosion of the solution space of a problem as it increases in  INTRODUCTION / 5 complexity.  Expert  systems  technology  is  better  able  to  handle complex  problems because it can easily represent all the different types of knowledge that are commonly used to solve a task. The current technology also enables the system to explain the reasoning it used to solve a task in much the same way as a human expert does it.  Expert systems have become more popular with the advent of powerful microcomputers and computer languages and programs that allow for symbolic programming. The advent of expert systems have also led to the creation of a new= type of profession. The people who develop expert systems are now commonly referred to as knowledge engineers [Beerel, 1987], Many different expert systems are currently in use in industry [McDermott, 1982; Herrod and Smith, 1986].  1.4 Prescribed F i r e Research i n B C  There is extensive research presently occurring in B C relating to prescribed burning. The main categories of research are: 1.  Operational  monitoring  of  prescribed  burns  and  the  development of models to understand fire behaviour and the interaction of fire and site factors.  2.  Analyzing old sites that  have been burned many years  previously and the development of models to predict the long  INTRODUCTION / 6 term effects of fire on site productivity.  3.  Developing techniques to document prescribed burns for use in a prescribed burn computerized database.  4.  Developing improved decision aids to be used by the field staff.  The final category of research is becoming more and more important as the need to transfer knowledge from the researchers to the people in the field becomes more recognized. One of the original decision aids is the Prescribed Fire Predictor/Planner (PFP) [Muraro, desired impact (slash reduction or duff  1977]. The P F P takes as input the 2  reduction) and produces as output the  range of moisture codes that will result in that impact. This set of moisture codes forms a prescription. The user can run this prescription through a historical weather database program to obtain probabilities of the likelihood of a specific prescription occurring at a certain time of year.  Decision aids have also been developed to enable the site to be classified in terms of its sensitivity to fire. These decision aids usually take the form of onepage keys that use readily observable site characteristics to determine a site's 3  Also  r e f e r r e d t o as t h e f o r e s t  floor.  Small f l o w c h a r t s t h a t take as i n p u t a r r i v e a t a d e c i s i o n , (see F i g u r e 10) 3  several  site  factors  i n order t o  INTRODUCTION / 7 sensitivity to fire. The Vancouver Forest Region's Site Sensitivity to Fire key is an example of this type of approach [Klinka et al., 1984].  Recently, the Canadian Forest Service (CFS) has commenced development work on the PB Planner expert system. This expert system is being designed primarily by Bernie Todd of the CFS's Petawawa research centre. The goal of this project is to build a system that will help with all the different prescribed burning decisions. This includes the translation of silvicultural objectives into burn objectives, the scheduling of cutblocks for burning, and the modelling of fire behaviour as it relates to ignition patterns and topography.  1.5 F i r e Effects Expert System Project  The Fire Effects Expert System project was formally proposed in July of 1988 after a June meeting involving representatives of the C F S , the Protection Branch of the B C Forest Service as well as Michael Johnston and Professor Yair Wand of the University of British Columbia. The general goal of this project was to determine the applicability of expert systems technology  to  decisions within the prescribed burning field. A more specific goal was to work on a part of the overall PB Planner Expert System and to develop a prototype expert system to predict the ecological effects of slashburning. The objectives were: 1.  To study different development environments with the goal of recommending a particular environment for further systems  INTRODUCTION / 8 development.  2. To examine the integration of expert systems applications into the operating environment.  3.  To recommend a strategy for future research into  expert  systems.  The activities of the project included meeting with different experts as well as  meeting  with potential users.  Considerable effort  was  also devoted  to  developing a prototype system using an expert system shell.  The  outcomes of this project include a prototype system to predict the  ecological effects of slashburning. This paper also defines a strategy for further research in expert systems in the prescribed burning domain.  1.6 Structure of the P a p e r  In Chapter Two of this paper, the many issues involved in developing expert systems are described. Included in this chapter are sections describing the development engineering,  lifecycle types  of  an  expert  of knowledge  system,  representation,  the  process  of  knowledge  techniques  for  knowledge  acquisition and issues involving knowledge engineering with multiple experts.  INTRODUCTION / 9 Chapter Three of this paper describes prescribed fire and how it interacts with the ecology of a site. Specific sections include discussions of the effect of fire on soil as well as its effect on vegetation. The procedures involved in conducting a prescribed burn are also given.  Chapter Four describes the process that was involved in building the expert system. Included in this chapter is a section describing the conceptual structure of the domain as well as a section comparing different expert systems software.  The  final chapter of this paper discusses various aspects of this project  including the viability of expert systems technology in this domain. A strategy for further expert systems research is also proposed.  C H A P T E R 2. E X P E R T S Y S T E M S  2.1 Overview  Expert systems(ES) is an application of what is generally referred to as the field of artificial intelligence. Branches of this field include reasoning, natural language understanding, knowledge representation, and vision. Expert systems are also  commonly referred to as  knowledge-based  systems [Walters and  Nielsen, 1988]. This is because expert systems maintain their knowledge in the form of a separate knowledge base.  There are three basic components to an expert system: the knowledge base, the inference engine and the user interface [McGraw and Harbison-Briggs, 1989]. The knowledge base contains knowledge in the form of rules or some other type of representation formalism. The inference engine consists of rules that control how the knowledge in the knowledge base is used. The user interface controls the communication between the system and the user.  E S are different from conventional systems in that they can be used for problems that have no easy algorithmic solution [Beerel, 1987]. They simply try to model the reasoning of a human expert on a particular problem. Because of this approach to problem solving, they are not expected to give a correct solution for all situations/problems.  10  E X P E R T SYSTEMS / 11 The concept of expertise is crucial to the expert systems field. Turban (1988) defines expertise as facts and theories about the problem area, hard and fast rules and procedures regarding the problem area, rules (heuristics) of what to do in a given problem situation (i.e. rules regarding problem solving), global strategies for solving these types of problems, and meta-knowledge  (knowledge  about knowledge).  The many functions that experts provide is an important consideration for researchers in the expert systems field and expert system designers have to be aware of these different functions. Experts do more than simply just provide answers to problems. Experts perform a range of tasks including recognizing and formulating the problem, explaining the solution, learning from experience, and determining inconsistencies in logic. Expert systems have only been able to model some of these functions.  Despite the inability of expert systems to perform all the functions of an expert there are many advantages to using this type of technology [Hart, 1986]. These advantages include availability, consistency and comprehensiveness.  Availability refers to the fact that experts are busy people and there is considerable  expense  involved  in  training  new  experts.  Expert  systems  technology has the potential to increase the availability of expertise. This is because a computerized system can be made available at all times to many different users.  E X P E R T SYSTEMS / 12  Consistency is another advantage of expert systems technology. Experts do not always reason the same way when presented with a similar problem. The current emotional state of the expert can also have adverse affects on the reasoning process of an expert.  Comprehensiveness refers to the ability for expert systems technology to encapsulate knowledge from many different sources. Therefore, the user of an expert system can have immediate access to all the knowledge that is necessary to solve a problem. This can be done by linking the system to external databases and systems or by consulting different knowledge bases.  The main disadvantages of expert systems are summarized by Hart (1986) as follows: choice of domain, acceptability, uncertainty, updating and testing.  Choice of domain refers to the unsuitability of many domains for expert systems technology. For many domains, the problems are too complex and the experts disagree quite vehemently.  Acceptability refers to whether the users in the domain will want to use a computer to give them advice on problems.  The problem of uncertainty refers to the fact that most of the data that is handled by experts is vague and uncertain.  E X P E R T SYSTEMS / 13  Updating refers to the fact that knowledge is changing quite frequently in many different domains. Therefore, the knowledge base of an expert system has to be updated very frequently which can present many difficulties.  Testing an expert system can be difficult to do as well. There are many different solution paths and it is usually not possible to test each of these paths individually.  Expert systems are most useful for problems with a large number of possible combinations, and problems that involve interpreting a large amount of signal data, and problems where there are time constraints on the decision and there is a large amount of information to interpret.  In  order to construct an expert system, it is first necessary to j)btain  knowledge from an expert. This process is called knowledge acquisition (KA). There are many methods to do this as is discussed later in this chapter.  2.2 Expert System Tasks  Not all problems are suitable for expert systems technology. The knowledge domain and the task type will affect the suitability of the problem for expert systems technology, the potential success of the system, and the knowledge acquisition techniques that could be used effectively. Kidd (1987) classifies  E X P E R T SYSTEMS / 14 knowledge into four different domains based on how formalized is the language for reasoning and whether there is any clear, coherent theory underlying the domain.  She  proposes  that  expert  systems  development  is  much  more  problematic in the domains where there is no such theory.  The first domain class comprises domains where humans have developed a strong formal underlying  reasoning language  theory.  This  class  and where  include  there is  mathematics,  a clear, coherent, geometry  and  the  programming languages. The second class of domains includes chemistry and medicine. There is no formal reasoning language for these domains but there are some underlying theories. The third class includes applications software, management  and  marketing. These  domains  lack  formal  reasoning and  representation languages as well as a clear, coherent, underlying theory. The fourth domain is spatial reasoning tasks where experts have developed a formal and powerful language for reasoning but are not able to support this language on a machine.  Much work is presently being done on matching expert system techniques to tasks [Chandrasekaran, 1984]. Therefore, it is important to understand the nature  of the  different  types  of tasks.  Boose  (1988)  provides  a  fairly  comprehensive list of different types of tasks (see Figure 1). These task types are also classified into two broad categories: analysis and synthesis. Analysis tasks are ones where there are a limited number of solutions to the problem and the task is to map one of the pre-defined solutions to the problem at hand.  E X P E R T SYSTEMS / 15  ANALYSIS TASXS Classification Debugging Diagnosis Interpretation  SYNTHESIS TASKS Configuration Design Planning Scheduling  -categorizing based on observables -prescribing remedies f a r malfunctions - i n f e r r i n g s y s t e i n a l f u n c t i a n s from observables - i n f e r r i n g s i t u a t i o n d e s c r i p t i o n s from sensor data  -configuring c o l l e c t i o n s of objects under c o n s t r a i n t s i n r e l a t i v e l y small search spaces -configuring c o l l e c t i o n s of objects under c o n s t r a i n t s i n r e l a t i v e l y large search spaces -designing a c t i o n s -planning v i t h strong time and/or space c o n s t r a i n t s  TASXS COHBINIKG ANALYSIS AND SYNTHESIS Comnand and c o n t r o l -ordering and governing o v e r a l l system c o n t r o l Instruction -diagnosing, debugging, and r e p a i r i n g student behaviour Monitoring -couparing observations to expected outcomes Prediction - i n f e r r i n g l i k e l y conseguences of given s i t u a t i o n s Repair -executing plans to a d i i n i s t e r p r e s c r i b e d remedies __zz-  FIGURE 1  Expert System Task Types  (adapted from Boose, 1988)  Synthesis tasks involve constructing the solution from the subproblem solutions.  E X P E R T S Y S T E M S / 16 2.3 Expert System Lifecycle  There is much confusion presently occurring over what should be the standard expert system lifecycle. Much of this controversy concerns whether rapid prototyping is the best approach to use for building expert systems or whether a more formal methodology is preferable.  The term "prototyping" refers to a program development methodology where the system is built in an iterative fashion. The features of this methodology include  user  requirements  involvement, as  experience  learning is  gained  between  iterations  [Mathieson,  1988].  and This  evolving technique  contrasts with other, more structured system development methodologies such as the traditional Systems Development Life Cycle where there is no iterative component to the methodology.  Parsaye and Chignell (1988) describe a typical lifecycle for E S as: 1. feasibility analysis 2. conceptual design 3. knowledge acquisition 4. knowledge representation 5. knowledge validation 6. technology transfer and maintenance  Important features to note about this lifecycle are the distinction between a  E X P E R T SYSTEMS / 17 conceptual design phase and a knowledge acquisition phase and the inclusion of a knowledge validation phase.  2.4 Selecting a n d Motivating Experts  When building an expert system, it is important to select the right experts to use as sources of knowledge for the system. There are many characteristics that make some experts more desirable than others. The designer should also be aware of the phenomenon usually termed "paradox of expertise" whereby an expert may not be able to explain in detail the reasoning he/she used to solve a problem [Beerel, 1987].  The first desirable characteristic that an expert should possess is the ability to articulate his/her own knowledge. The expert must have time to devote to the knowledge acquisition process and the expert must be committed to the goals of the project. The credibility of the expert is also an issue because potential users may want to know that he/she is getting advice from a system based on the knowledge of a respected expert.  An  additional concern is that the experts may feel threatened by the  "deskilling" process of the new technology [Beerel, 1987]. For some experts, relinquishing control of knowledge may mean loss of power. Therefore, the concept of deskilling is very important to the motivation of the expert. This is a  term that describes the mechanization or automation of a task that is  E X P E R T SYSTEMS / 18 normally done by a human. A person who holds a special skill is likely to unreasonably defend against this deskilling process.  Beerel (1988) has studied the motivation levels of experts during the course of development of an expert system. She describes the motivation level of an expert in terms of a transition curve (see Figure 2). It is claimed that the motivation and enthusiasm for the  project will  change  quite dramatically  through the course of the project as is illustrated by the curve in Figure 2.  The shape of the curve is affected by the way in which the expert was introduced to the technology, the support of top management, the confidence and  abilities  of  the  expert,  the  skills  of the  knowledge  engineer,  the  understanding of the objectives set, and the manner in which these are to be achieved. By carefully considering these various factors, it should be possible to maintain a moderately high motivation level in the expert throughout the course of the project.  2.5 Knowledge  Knowledge  Engineering  engineering can be  defined as  the  "process of synthesizing  knowledge into a computer system so that the problems are electronically solved through symbolic manipulation and reasoning of the knowledge base" [Beerel, 1987, page 127]. The person who obtains the knowledge is referred to as the knowledge engineer. Some people do not like using this term because it seems  E X P E R T SYSTEMS / 19  E X P E R T SYSTEMS / 20 to imply that knowledge engineering is a science when it is in reality closer to an art. In fact, some authors give this occupation a different name such as "knowledge crafter" [Walters and Nielsen, 1988].  There is also some controversy as to whether the expert should be building the system himself/herself and whether it is really necessary and beneficial to employ  the  services  of an  intermediary (i.e.  knowledge  engineer).  If a  knowledge engineer is employed then there also is a debate as to whether that person should be from the expert's domain or not.  In addition to knowledge acquisition, the knowledge engineer is usually responsible for many different tasks. In particular, the K E is responsible for: 1.  The overall management of the project.  2.  The identification reading  existing  of  the  project.  documentation  on  This the  usually  involves  problem,  doing  extensive background reading, and locating the experts.  Many researchers have attempted to document what are the most important characteristics for a knowledge engineer to have [Davies and Hakiel,  1988;  Beerel, 1987; Hart, 1986]. These characteristics include good communication skills, proficiency in using expert system software, persistence, intelligence and domain knowledge. Good communication skills and domain knowledge are particularly important because the K E must have the ability to enter into the  E X P E R T SYSTEMS / 21 "expert's way of thinking" [Beerel, 1987]. This means that the knowledge engineer must be able to grasp new concepts very easily.  There are many phases to the knowledge engineering process. The central phase is the knowledge acquisition phase. However, the validation phase is becoming more and more important as more systems are being implemented and the need for system maintenance is recognized. These phases are described in more detail in subsequent sections of this chapter.  2.6 Knowledge  "Knowledge is not synonymous with information. Rather knowledge is information that has been interpreted, categorized, applied and revised." [McGraw and Harbison-Briggs, 1989, page 13].  Knowledge is what gives expert systems their power. This section discusses the concept of knowledge is and leads into the next section which describes different methods to represent knowledge. It is related to expertise in that expertise is a demonstration of the application of knowledge [Mcgraw and Harbison-Briggs, 1989].  Many authors have proposed many different categories of knowledge. HayesRoth (1984) classifies knowledge across three dimensions: scope, purpose, and validity.  E X P E R T SYSTEMS / 22  The scope dimension ranges from general, common statements to specific, focused statements. The purpose dimension ranges from descriptive (factual) to prescriptive (procedural) statements. The validity dimension ranges from 100% certain to 100% uncertain.  Presently, specific,  the knowledge  descriptive  bases of most expert  knowledge.  It  is  difficult  to  systems contain mainly incorporate  general  and  prescriptive knowledge in an expert system [Wolfgram et al., 1987]. Examples of knowledge that are difficult to incorporate into an expert system include general  problem-solving  knowledge  and  metaknowledge  (knowledge  about  knowledge). This limits the types of problems that expert systems technology can be applied to.  It is important to understand that there are many different components of._ knowledge in order to be able represent knowledge effectively.  Parsaye and  Chignell (1988) propose five components of knowledge. These components are: naming, describing, organizing, relating and constraining.  Naming involves selecting  a unique name for an object so there is no  confusion concerning which object is being referred to. Describing involves noting the important properties about an object. Organizing involves putting objects into conceptual categories such as classes or hierarchies. Once we have described and organized objects, we need to describe the relationship between  E X P E R T SYSTEMS / 23 them. Part of the skill in describing a relationship is in choosing the right level of  analysis  and  deciding whether  to  include  particular  entities  in  a  relationship. Constraints that govern the properties of objects should also be defined.  2.7 Knowledge Representation  Many approaches to representing knowledge have been suggested [Newell and Simon, 1972; Minsky, 1975; Quillian, 1968; Stefik and Bobrow, 1986]. Some of these knowledge representation techniques have evolved from studies of the way humans appear to store information. Since an expert system is attempting to model the reasoning of the human expert, a knowledge representation method must allow for knowledge structures that are similar to those of the human mind. A comprehensive list of knowledge representation techniques would include:  1.  Semantic  nets: Some  of the  earliest  proposed types  of  knowledge structures are referred to as semantic nets. These nets specify how objects are related and also allow for the inheritance  of  properties.  Nodes  and  links  are  the  fundamental units used to represent knowledge [Parsaye and Chignell, 1988].  2.  Production  rules: These are examples  of simple types of  E X P E R T SYSTEMS / 24 knowledge representation. This knowledge structure allows knowledge to be stored in simple IF - T H E N rule formats. The  structure makes it easy to encode heuristics (rules of  thumb) into an expert system.  3.  Logic: Knowledge can also be expressed in the form of logic. However, this representation method is cumbersome and is not commonly used. However, there are many languages that are suited for this type of representation such as P R O L O G and LISP.  4.  Frames, scripts: More recently proposed forms of knowledge representation  structures  are  frames  and  scripts.  These  structures are basically mechanisms to package knowledge. They allow for properties to be inherited. The concept of inheritance makes it possible to specify properties that apply to a whole class of entities. Research has also shown that people  store information in their brain in some form of  package  structure.  Scripts  permit  reasoning  based  on  expectations about what should happen next in stereotyped situations [Parsaye and Chignell, 1988].  5.  Objects:  The  object-oriented  methods  of  knowledge  representation share a number of features with frames and  E X P E R T SYSTEMS / 25 semantic networks. Knowledge is viewed as a set of objects, each of which is capable of exhibiting certain behaviours. Actions can be taken by invoking a method. A method defines how an object is allowed to behave in response to a message from another object.  6.  Blackboard:  Blackboard-based  representation  is  a  less  common form of knowledge representation. There are three major components to the blackboard representation method [Walters  and  (expertise),  Nielsen,  the  representation),  1988]  blackboard  :  the  knowledge  (knowledge  sources  storage  and  and the control (problem-solving strategy).  The advantage of this method is that knowledge  can be  stored in a modular way. Different knowledge representation methods can also be employed.  7.  Model: The model-based representation is best used when it is desirable to represent a system by more than a simple list of  facts  and  specification  rules.  of the  The  system  model  provides  a  complete  being examined [Walters and  Nielsen, 1988]. Reasoning is done within a complete model context by matching the current situation to the model. In essence, the reasoning is done from first principles [Turban, 1988].  The potential  exists  for  "transportability" of  the  E X P E R T SYSTEMS / 26 knowledge  base.  For example,  a  system  for  diagnosing  electronics problems could potentially be used for a wider class of electronic devices rather than just one specific device.  Parsaye and Chignell (1988) give a checklist of what to look for in a knowledge  representation  structures,  storage  method.  mechanisms,  The  quality  of  the  retrieval mechanisms,  basic and  knowledge  representation  environment are all important factors to be considered.  2.8 Knowledge Acquisition  Knowledge acquisition (KA) can be defined as the "process of obtaining the public and private knowledge used by an expert skillful in solving problems in a constrained and restricted domain." [Walters and Nielsen, 1988, page 5]. It is this process that is usually termed the "bottleneck" in the expert  system  development process. It is also the critical part of the development process because the success or failure of the system will be determined by the results of  the K A stage. Because of the importance of the knowledge acquisition  process, it has been discussed extensively in the literature in the past several years [Kidd, 1987; Hart, 1986; McGraw and Harbison-Briggs, 1989].  The  knowledge acquisition phase is complicated by several factors. First of  all, it may be difficult to find a suitable expert in the domain who has enough time to devote to the development of an expert system. In most cases, the main  E X P E R T SYSTEMS / 27 reason for building the system is to increase the availability of expertise in the domain. However, the  slow and lengthy K A phase  will actually serve  to  decrease the availability of the expert in the short term.  A second complicating factor concerning the knowledge acquisition phase is what was referred to earlier in this chapter as the "paradox of expertise". This phenomenon occurs when an expert is so proficient in reasoning through a problem that he is not able to explain the intermediate steps of reasoning in such a level of detail that is required to implement it on a machine.  There are three general categories of approaches to knowledge acquisition. These approaches are interviewing experts, learning by being told, and learning by observation [Parsaye, 1988].  The first category, termed "interviewing experts" is the classic knowledge acquisition technique. The knowledge  engineer interviews  the  experts  and  proceeds to elicit concepts and knowledge about the domain. This interview can be very structured or unstructured. A n unstructured interview might be used to elicit terms and concepts in an early stage of knowledge acquisition whereas a structured interview could be used in the later stages to elicit facts and problem-solving  strategies.  Hoffman  (1987)  describes  different  types  of  structured interviews (i.e. method of "familiar" tasks, constrained processing tasks, limited information tasks, etc.) and discusses how they can increase the effectiveness of the K A process.  E X P E R T SYSTEMS / 28  The second approach, learning by being told [McGraw and Harbison-Briggs, 1989], essentially involves the expert being able to communicate his knowledge through some sort of K A tool. This could involve using an automated K A tool or participating in tasks such as card-sorting or scale development in order to define the knowledge and expertise in a domain. In this approach, the expert is responsible for expressing and refining most of his/her own knowledge. The repertory grid is a much researched technique which has proved to be very useful  for classification-type  problems. Essentially, this  technique  involves  rating examples according to a series of different constructs. Algorithms are then available to translate these ratings into rules that can be incorporated into an expert system.  The third approach, learning by observation [Parsaye, 1988] can also involve automated or non-automated K A methods.  Induction, otherwise  known as  machine learning, has been studied quite extensively. This process involves the expert presenting detailed examples of problems as well as their solutions. Algorithms are then available which can determine a set of rules that could be used to obtain the same solutions. The more examples that are provided, the more comprehensive the set of rules that is produced. This technique has proved to be useful in situations where the paradox of expertise does not allow the expert to communicate his/her reasoning effectively. Protocol analysis is another technique that fits into this category. This technique involves observing the expert while he/she is doing the task. Usually, the expert is asked to  E X P E R T SYSTEMS / 29 provide a verbal transcript of his/her thought processes as he/she solves the problem.  Various authors have identified different stages of knowledge acquisition. McGraw and Harbison-Briggs describe five stages (see Figure 3): identification, conceptualization, formalization, implementation, and testing. Commonly, there is an iterative aspect to these stages such that many of them will probably be repeated several times during the development of the system. Sometimes, this development process is called rapid prototyping.  There  have  also  been  some  attempts  to  define  some  more  formal  methodologies. KADS [Breuker and Wielinga, 1987] is an attempt to define a more formal  methodology. They define  three  stages in their methodology:  orientation, problem identification, and problem analysis. For each of these stages they define the purpose, the type of knowledge, and the K A techniques that should be used.  Various models for the K A process have been proposed [Dhaliwal and Benbasat,  1988]. A n extensive  set  of criteria  for selecting  a  knowledge  engineering technique is illustrated in Figure 4.  There are many different factors which will affect the results of the K A process. These factors include the knowledge acquisition tool, the characteristics of the expert, the characteristics of the  knowledge engineer, the stage of  E X P E R T SYSTEMS / 30  * Identify Problei Characteristics  Identification  Retina Requicenents  i * Identify Concepts  Conceptualization  Refine Concepts  i * Organise Knowledge  Forualizatlon  Refine Design  * roruuiate Rules  Inpleientation  Refine Representations  i * Validate Rales  FIGURE 3 Briggs,  Stages 1989)  Testing  o f Knowledge A c q u i s i t i o n  (adapted  from McGraw and H a r b i s o n -  E X P E R T SYSTEMS / 31  P u r p o s e of T e c h n i q u e  Domain  -ellcltation -analysis -representation -validation  Characteristics -deep knowledge vs. Bballov knowledge  TasX  type - c l a s s i f i c a t i o n , diagnosis, debugging, etc.  Kno¥ledge Type -concepts -structure of knowledge -problen-solvmg s t r a t e g i e s  S t a g e of D e v e l o p m e n t - I n i t i a l stage vs. l a t e stage  Characteristics  of E x p e r t - a r t i c u l a t e vs. i n a r t i c u l a t e -personality type -novice vs. seasoned expect  Characteristics  of XE -doialn knowledge -Interpersonal s k i l l s - a b i l i t y to evaluate knowledge  C o s t of T e c h n i q u e - t i a e and aoney costs  FIGURE 4  Criteria  f o r S e l e c t i n g a Knowledge E n g i n e e r i n g  Technique  E X P E R T SYSTEMS / 32 development of the system, the type of knowledge, the domain characteristics, the task type, and the cost of the technique.  2.9 Knowledge V a l i d a t i o n  There are some special concerns regarding knowledge-based systems when compared to traditional information systems. Specifically, it is the problem of validation. Validation is usually handled informally - not in a  systematic  fashion. However, almost as many validation methods as acquisition methods have been proposed.  Validity is defined as "the degree of homomorphism between a representation system, i.e., expert system and the system that it is supposed to represent, i.e., expertise source" [Vandierendonck, 1975]. A n associated concept is the one of verification. Verification can be defined as "the demonstration of consistency, completeness, and correctness of the software at each stage and between each stage of  the  software development life cycle" [Adrion  et  al.,  1982]. The  distinction between validation and verification is important. The concept of validation includes elements of how well the conceptual model represents the real model and how well the implemented model represents the source and conceptual model. Validity can also be defined at many different levels such as how closely each minute chunk of knowledge in the system correlates with chunks of knowledge in the expert's memory.  E X P E R T SYSTEMS / 33 Validation is becoming a more important issue as system developers in the expert system domain build larger and larger systems that are having an increasingly important impact. The issue of maintaining these large systems is also becoming a critical issue.  Therefore, many authors are proposing a range of validation techniques corresponding to the range of elicitation techniques. These techniques include knowledge-base walkthroughs with source and non-source experts and the use of different K A tools such as protocol analysis to assess validity. Benbasat and Dhaliwal (1988) propose a validation-based life cycle model (see Figure 5). From this model they propose a range of techniques to be used at different stages of the development of the system. For example, a different validation technique would be used in the modelling phase and the system construction phase. It appears that increasingly formal approaches to validation will become more popular just as knowledge acquisition techniques have become more formalized.  2.10 Knowledge E n g i n e e r i n g with Multiple Experts  Expert systems are difficult enough to properly develop using just one expert. When multiple experts are consulted, a host of additional issues become important. More demands are placed on the knowledge engineer because he/she must be able to reconcile and integrate the knowledge from different sources. Several group K A techniques have also been proposed for use with multiple experts [McGraw and Searle, 1988].  E X P E R T SYSTEMS / 34  IDEALIZED' KNOWLEDGEBASE <noDexutent  iV;/:>.. b reality); :. :  OTHER 'ALTERNATIVE KNOWLEDGE-BASES . IN REALITY ; <c^, other cxpcrt»> i$  •SOURCE* - * KNOWLEDGE-BASE IN REALITY . <e.g., source expert >  IMPLEMENTED KNOWLEDGE-BASED SYSTEM  CONCEPTUAL KNOWLEDGE-BASE  Notes: Conceptual and alienation validation oonttltute Knuwtodtft Afq<*iWnn Valdalon. 'implementation validation can b* termed VWIficabon. *Bolh functional and representational validation constitute Knowtodga-Syitam Valdaflon. 1  FIGURE 5 A V a l i d a t i o n - B a s e d L i f e C y c l e Model o f t h e Knowledge-Based Development P r o c e s s ( r e p r i n t e d w i t h p e r m i s s i o n o f t h e authors)  System  E X P E R T SYSTEMS / 35  The first issue to consider is whether  the  knowledge  domains  of the  different experts are overlapping. If so, then the knowledge engineer will have to consider different problem solving strategies and viewpoints. The knowledge engineer can try to integrate these viewpoints by himself or he can do it in a group session.  Group K E sessions can take many forms. McGraw and Searle (1988) discuss three different techniques : brainstorming, consensus decision-making and the nominal group technique.  Brainstorming involves a group session where participants are encouraged to express their ideas in rapid succession. Each idea is evaluated afterwards by the group. The focus in this technique is on the quantity of ideas.  In consensus decision-making, the emphasis is placed on finding the best solution to the problem. Alternative solutions to a problem are proposed and then each one is voted on.  The nominal group technique involves soliciting ideas anonymously from each group member. Each idea is then ranked anonymously by each group member. This technique is useful  when there is the  members not being able to express their thoughts freely.  possibility of group  E X P E R T SYSTEMS / 36 Other issues associated with multiple experts are: how to validate the knowledge base, and how skilled the knowledge engineer should be i n group dynamics and in integrating knowledge.  2.11 Hypertext  The field.  concept of hypertext is becoming more relevant to the expert systems Many  expert  system  software  packages  now include hypertext-like  features [Stoddard, 1988]. Hypertext may be defined as "a combination  of  natural language text with the computer's capacity for interactive branching, or dynamic display ... of a nonlinear text ... which cannot be conveniently printed on a conventional page" [Nelson, 1967]. It is essentially a more complex method of handling information and has many advantages over the "linear" fashion of managing information.  Hypertext may be envisioned as a series of nodes and links. Each node corresponds to "chunks" of information. Each node may be connected to many other nodes via links [Conklin, 1987]. The user is able to "jump" to other nodes in a hypertext system by activating pointers. A popular hypertext system is the H Y P E R C A R D system that is available on the Macintosh computer [Williams, 1987]. This system also includes the use of graphic images so it could be termed a hypermedia system.  Many possibilities for managing information exist with a hypertext system.  E X P E R T SYSTEMS / 37 The user has the option of choosing his own path through the information base. There is much potential in the forestry field for this technology. This is because there is a large base of information that the forest manager has to use in order to help make his decisions. A n expert system coupled with a hypertext system would make it possible for a forest manager to use one computer system for all his/her information needs thus reducing the need to maintain large sets of reference manuals.  2.12 Integration of Expert Systems into the E n v i r o n m e n t  Although many expert system prototypes have been developed in the past ten years, very few have actually been successfully implemented  [Pedersen,  1988]. Many of the "technical success - operational failures" could have been avoided if the developers had properly considered how the expert system should have been integrated into the environment. This entails the examination of many different issues.  The  user interface of the expert system is very important. The system  should provide advice at a suitable level for the user. The focus should be on -having the  user .understand the  causal  structure underlying the problem  [Gordon et al., 1987]. Therefore, the system dialogue should be at a level the user can understand. Some expert systems vary the technical level of their dialogue in response to the perceived technical ability of the user.  E X P E R T SYSTEMS / 38 The  users  should also be educated as to the limits of expert  system  technology. This is necessary because research has shown that many expert systems are only used for the most difficult problems [Gordon et al., 1988]. Unfortunately, it is at this end of the problem spectrum that expert systems are  most  "brittle" in that  the  quality of their advice  rapidly  degrades.  Therefore, a user who uses the system mainly for these sort of problems will have a distorted view of the reliability of the system.  Another issue to consider is how the expert system will integrate with existing systems. Cholawsky (1988) states that data problems are a major cause of  implementation  failures.  These  types  of  data  problems  range  from  nonautomated data to incomplete data to inappropriate data. The exact data needs of the expert system must be analyzed carefully so that the user has to manually type in as little data as possible. Data checks should be placed to avoid erroneous data.  There are also business issues that need to be considered for a prototype system to be implemented successfully. The system must be cost-justifiable, for if it is not, then the commitment of senior management will be jeopardized. Also, maintenance of the system is very important. One of the most successful expert systems in use is DEC's X C O N system which is experiencing problems with high maintenance costs [Newquist, 1988]. It does not make good sense to replace  dependence  programmers.  on  an  expert  with  dependence  on  expert  system  E X P E R T SYSTEMS / 39  2.13 E x p e r t Systems i n the Forestry D o m a i n  Expert system prototypes  have  been built for several problems in the  forestry area. These problems include the planning of forestry roads [Thieme et al.,  1987], the  treatment  dispatch of fire control resources  of tree  fungi  [Rust,  [Kourtz,  1988]. A demonstration  1988], and the  of expert  system  technology for fire effects problems has also been built [Starfield and Bleloch, 1983]. A comprehensive system for developing fire prescriptions is now being built at the University of Missoula [Reinhardt, 1987]. Another system for prescribed burn planning is also being developed by the  Canadian Forest  Service [Hawkes and Lawson, 1986].  Expert systems technology has considerable potential in the forestry domain. Many of the problems and decisions can't be treated quantitatively and are best solved using the experience and skill of an expert. Many of these problems also require the integration of multiple sources of knowledge and expert systems technology can help in this regard.  The  fire effects domain is  quite suitable  for the  application of expert  systems technology. There are only a few experts in this field and there are many people who can make use of their expertise. Much of the  expertise  needed for problem solving consists of narrow domain knowledge. There are many different sources of knowledge that are relevant to the decisions and the  E X P E R T SYSTEMS / 40 decisions can't easily be treated algorithmically.  The ideal structure and form of a fire effects system is unclear. It is not immediately apparent as to how global the reasoning framework should be for a fire effects expert system. There are many diverse ecosystems in B C and prescribed  fire  interacts  quite  differently  with  each  of these  ecosystems.  However, it is not operationally or economically feasible to develop an expert system specifically for each type of ecosystem. Dyer (1989) discusses some of the problems of adapting an expert system to diverse geographical areas. She proposes a layered structure for the expertise in an expert system whereby the local  heuristics  can  be  incorporated  into  a  geographically-  standardized  knowledge base. A n expert system for predicting fire effects could make use of this approach.  C H A P T E R 3. P R E S C R I B E D F I R E A N D F I R E E F F E C T S  3.1 Introduction  Muraro (1971) defined fire effects as the combined result of the immediately evident effect of fire on the ecosystem in terms of biophysical alterations or population reduction as well as postfire influences. This definition indicates that there are both short term and long term components to fire effects. In order to study these two components, fire effects researchers have had to study old burns as well as monitor recent burns. The first section  of this  chapter  discusses fire effects research in B C . Subsequent sections discuss the various ways in which fire interacts  with site properties, the  terminology of fire  behaviour, the procedures involved in conducting a prescribed burn, the B C site classification system, the limiting factor concept and the status of prescribed fire decision aids in B C .  3.2 Prescribed F i r e a n d F i r e Effects Research i n B.C.  Many different groups in B.C. are involved in research concerning prescribed fire and fire effects. The B C Ministry of Forests (MOF) and the Canadian Forest Service (CFS) are two of the more influential groups involved. Figure 6 shows the links between the researchers, the various coordinating groups and the Ministry of Forests. The Ministry of Forests has the critical responsibility of identifying research needs and transferring technology from the researchers  41  PRESCRIBED FIRE / 42  Research Reeds C r o i tbe n i n i a t r y of f o r e s t s  A3 3B33ieilt  OJLA  Coordinating Oroups (A •» C)«  He3eo.1cb.er3  ~ 1  B.C. Forest flesaarch cooacll (A • c. BasearcB Beads]  Land Hanageant Orgaaisatioi, (•a Biuatrj ol Foiamlt lestercB Braicn)  (tor Beeional a. Provincial Besaarcn Ad>laor, C a u l t t t n (A • C) organisation (eg. CFS. CIS]  crs/Bor BecaarcB wresignt n u g a n i earning (BesaarcB item, A » o mit  (tg Oittrict)  t t t  nb-Vx  OpiHllonil  Provincial-Industrial coordinating Counties {BoseercB Hods and A « c. e.g. silviculture coanlttoss] MlnivercltLes aetignaL Besearci council (Raitaxcb loading] Private [Rdmtrr  rtBIC (RosearcD isnain BID u-BouseJ FOBIinX and tha Palp aad Paper Besearcn Instiling ol Canada (Baaearch lending aad la-houie)  consultants CPS Science SibTenlioa Prograas (A I C] • . j . paor contracts to nniversity  Land Banageatnt Acumias •Assessaeat aid Coordinating troops aar Be Brpagsad ana faaaarcb neede n y flev directly to tat raaaarchars  FIGURE 6 Flow o f Research Needs (adapted from Hawkes e t a l . , 1984)  from  42  t h e MOF  to Forestry  Researchers  PRESCRIBED FIRE / 43 to the operational staff.  In  order to make the best use of limited research funds and research  personnel, a Prescribed Fire Research Advisory Committee (PFRAC) has been established. This group includes representatives from the Ministry of Forests, the Canadian Forest Service, industry and academia. The primary functions of this group are as follows [Hawkes et al, 1984]: 1. to  further  develop  and  revise  the  strategic  plan  for  prescribed fire and fire effects research.  2. to prioritize, advise and help facilitate research projects to meet the requirements of the strategic plan.  3. to be given the mandate to develop cooperative, multi-agency research projects and studies.  4. to develop implementation plans for research results, decision  —  aids and training needs.  5. to recommend policy, planning, and operational procedure changes required in various agencies in B C to improve the use of prescribed fire.  The basic goal of this group is to promote closer links between fire research  PRESCRIBED FIRE / 44 and the forest resource management process. In order to accomplish this goal, the strategic plan defines a prescribed fire management system comprised of seven phases. These seven phases are: 1. Operational plan. 2. Pre-harvest assessment. 3. Treatment alternatives evaluation and selection. 4. Prescribed fire prescription and plan development. 5. Prescribed fire application. 6. Prescribed fire monitoring. 7. Prescribed fire evaluation.  For each of these phases the strategic plan attempts to define operational problems and technology transfer needs. Standardized methodologies are also proposed to document and evaluate prescribed burns. Figures 7a, 7b, and 7c illustrates these seven phases.  The Ministry of Forests conducts its prescribed fire research through several different  internal groups. In Victoria, the  Protection Branch, the  Research  Branch, and the Silviculture Branch do research into prescribed fire and fire effects. The Ministry of Forests also conducts much of its prescribed fire and fire effects research through its six regional offices located in: Smithers, Prince George, Nelson, Kamloops, Williams Lake, and Vancouver. Most of these offices have a research silviculturalist, a research pedologist, a research ecologist, a wildlife ecologist, a pathologist and a hydrologist.  PRESCRIBED FIRE / 45 OPERATIONAL PLAH Pbyscial and b i o l o g i c a l s i t e paraaeters* t o t a l resource Inventory  s i l v i c u l t u r e end p r o t e c t i o n l i r e ueasgeaeat expertise p o l i c y end p r a c t i c e  I  what coabinstions of barvest systeas. tree regeneration systeas. and s i t e treataente v l l l occur l a t i e harvesting ores?  0  I 0  I  f i l l prescribed l i r e De part ot tbe s i t s treataent alternatives''  pre«jCTln«1  t\ra  PHE-HARVEST ASSESSMENT ( C u t DlOCk species s i l v l c s seed and seedling a v a i l a b i l i t y  I  Hov should harvesting schedules, segue&ces. systeas. and block layout be developed to use prescribed ( I r e costoffactively? _____  -block layout standards s e n s i t i v e t o prescribed burning reguiresents -logging sequence to ainlouae  general s u i t a b i l i t y o( prescribed ( I r e on s p e c i f i c s i t e s -road developaent plan  advanced regeneration status  engineering end (Ire aaaageaent eipertlse  rrmtrnl  T l i k  level)  engineering and cost constraints  predicted post-harvest residue and organic s e t t e r conditions  vatersbed  plantable spots  acceptable regeneration delay  insect and disease concerns  stocking _sta"i=»rii« ^  ecological constraints  Vast p o s s i b l e regeneration aetbods?  Vkst p o s s i b l e s i t e preparation technlgues to p e r a l t regeneration a l the area? .  Vbet p o s s i b l e harvest systeas?  Treatment A l t e r n a t i v e s  TREATMENT ALTERNATIVE EYALUATIO^AND^SELECTION cost/benefit a n a l y s i s vkicb coul consider: -treataent costs  - p o s i t i v e or negative long tare p r o d u c t i v i t y changes - p o s i t i v e or negative p r o t e c t i o n e i l e c t s ( l i z e . i n s e c t , disease)  X  -regeneration e s t a b l l s - i e n t . s u r v i v a l , and growth standards - p o s i t i v e or negative vatarshed changes - p o s i t i v e or negative v l l d l i f a . range and l i s b h a b i t a t e t f e c t s  Vbat i s tbe optinua a n at post-harvest s i t e conditions on tbe cut block to achieve the s p e c i t l c land aanagenent ohjectives?  O p L i i l i i i o treatment a c t i v i t i e s (e.g. nigD leaa yara, DioadcasL Dura and plant)  aa Undoing y  PRESCRIBED FIRE PRESCRIPTION AND PLAN DEVELOPMENT  FIGURE 7a Proposed e t a l . , 1984)  Prescribed Fire  Management  System  (adapted  from  Hawkes  PRESCRIBED FIRE / 46 PRESCRIBED FIRE PRESCRIPTION AJTD PLAN BEVEL OP KENT post-bervest s i t e treetaent o b j e c t i v e s translated i n t o ( l r c lapact c r i t e r i a  ( I r e lapact predictor  post-barvest s l o s h and s o i l organic natter c o n d i t i o n  predicted  fire  v a t e i . acce and topograpny  saoke aenageaent  bebavlour i n cot Block and adjacent stand  ecological constraints (translate* Into ( I r e lapact c r i t e r i a  1 Harvesting  Fire  Regeneration  Vbat a r e tae rrurn o b j e c t i v e s ' e g alasb f u e l and d u l t reduction, and i l n e r a l s o i l exposure layout  tlaing  logging aetnod  I I  species  are tbe f i r e c o n t r o l requirements''  l t l B t a r  planting aetbod  ""^""aa**" Ibat i b a t bu burn techniques. I g n i t i o n systeae. and patterns w i l l be used? Vbat are tbe saoke aanageaent xequlxeaeata' Vaat a r e tbe aop-up requirements'  Prescribed F i r e P r e s c r i p t i o n . Plan and Burning P e r a l t  PRESCRIBED FIRE APPLICATION  a v a i l a b i l i t y o( ignition systeis  Burn day acceptable'' -vitnin presciption' -burn conditions -control -ignition feasibility -BOp-Up  -saoie d i s p e r s i o n  o Tbe B u r n BtsnlardiMd Mttedolnqr  PRESCRIBED FIRE MOKITORIXG FIGURE 7b  Iteas per hectare  Vbat a arre tae rrurn conditions needed' e g I Ire veatber and l u s l n o i s t a r e  Proposed P r e s c r i b e d F i r e Management  System  PRESCRIBED FIRE / 47 PRESCRIBED FIRE tiOUITORIHG aiesb trio soil  rtra control •eiion  conditions tor the burn eey  organic M t t « l  Burn tecknieues. Ignition  reduction  Harvesting  8«ok« dispersal  Hop-vp n e t L O S S .  nrstena. and patterns used  Pire  pattern  I  Regeneration  I  V i n t vere tbe M r s m p a c t s ' Vbat vere tbe barn conditions'' Vbat c o n t r o l a c t i o n s vere  Advanced regeneration''  taken''  Plantation  vnat Banting tecnnigues. l a m c i o n systems, and patterns vere used''  Harvest to n l n l a i z e residue  establishment,  s u r v i v a l , and grovth''  Vbat vere tbe nop-op r e q o l r e n e n t s ' Vbat was tbe saoke d i s p e r s a l ' Vbat vara tbe presribed burning  costs'  I  I  vaste assessment  P o s t - b a r v B s t assessment Burn Docunentation ( i n c l u d i n g escaped l i r e a n a l y s i s )  Survival plots  PRESCRIBED FIRE EVALUATION  ( i r e lapact t r a n s l a t e d i n t o s i t e treatment o b j e c t i v e s  I  Vere tbe s i t e treatment objectives net'  burn c o n d i t i o n s , prescription  Vere p r e s c r i b e d burn c o n d i t i o n s net'  t i r e actions f i r e c o n t r o l plan  Any t i r e c o n t r o l problems'  vere tzeatnent c o s t s acceptable  burn tecnnigues and bum plan techniques  I  01b burn techniques i n c l u d i n g equipeaent. I g n i t i o n systeas. and p a t t e r n meet tbe f i r e c o n t r o l p l a n and s i t e t r e a t n e n t objectives''  Prescribed f i r e a n a l y s i s  standardised netnodolagy Feedback  FIGURE 7 c  P r o p o s e d P r e s c r i b e d F i r e Management  System  saoke d i s p e r s a l smoke nanageaent  pien  I  Vere there any saoke management problens'  PRESCRIBED FIRE / 48  The goal of much of this region-based research is to develop ecosystemspecific guidelines for using prescribed fire [Mackinnon, 1988]. Through the use of biogeoclimatic maps, these guidelines can be extrapolated to similar sites throughout the province. This is seen as the best method to translate research results to operational procedures.  3.3 Effects of F i r e o n Soil  Fire can have a very significant impact on soil properties. The effect of fire on soil properties will vary with the fire intensity and the amount of organic matter that is consumed. Fire can also be beneficial or detrimental to the short and long term productivity of the site. These changes can be temporary or permanent. Although it is convenient to classify these changes into different categories, it is important to note that most of these factors tend to interact with each other resulting in a complex series of changes to the site [Feller, 1982].  Kimmins (1987) classifies the effects of fire on soil into three types of changes: physical, chemical and biological.  3.3.1 P h y s i c a l Changes  Physical property changes can be further classified into four  categories:  PRESCRIBED FIRE / 49 organic matter, structure and porosity, moisture, and temperature.  Organic matter: Fire consumes organic matter by transforming dead foliage and  fine roots into its constituent minerals. Although the mineralization of  dead organic matter occurs naturally through the action of microbes, fire can accomplish this transformation much faster. As well, although much of the nutrients may be lost from the site in the form of fly-ash, the amount of readily available nutrients on the site almost always increases immediately after a fire.  Structure a n d Porosity: Many instances have been documented concerning fire-induced changes in structure and porosity. Fire has been known to break down the structure of the soil and result in a hydrophobic (water-repellent) layer. Since fire consumes organic matter, the porosity of the soil will almost always be affected by fire. Increased surface water flow can occur after a fire resulting in accelerated erosion of the topsoil. The amount of erosion that occurs will be influenced by the slope, the depth of the organic and/or mineral soil layers, the texture, the intrinsic erodibility of the soil, the fire depth of burn, the climate, and the revegetation rate [Feller, 1982].  Moisture: Moisture is the third type of physical change. Most of the changes in soil moisture content are due to the loss of foliage. The soil becomes wetter as less moisture is lost through interception and transpiration. The soil can also become drier if the soil is quite coarse textured [Kimmins, 1987]. This  PRESCRIBED FIRE / 50 results from the decreased moisture retaining capacity of sandy textured soils as compared to humus rich soils or clay type soils. The moisture status of the soil is affected by the organic matter content of the soil, the soil moisture content during the burn, the fire intensity and depth of burn, and the climate.  Soil Temperature: Soil temperature is affected by the aspect, the forest floor thickness, the fire intensity and depth of burn, and the climate. The effect of fire is an increase in soil temperature because fire blackens the soil surface resulting in greater heat absorption. In B.C., the soil temperatures during the growing season are cold compared to the temperatures required for optimal seedling  growth. Therefore, fire is  almost  always  beneficial  for the  soil  temperature regime of the site.  3.3.2 C h e m i c a l Changes  Chemical changes to the soil can be classified into two types [Kimmins, 1987]: changes  to p H , and changes  to site nutrient capital and nutrient  availability.  pH: Changes in soil p H primarily result from the basic (alkaline) nature of ash. Therefore, fire will almost always raise the p H of the soil. The pH value of the soil relates to the solubilities of key nutrients and correlates positively with the rate of decomposition of organic matter.  PRESCRIBED FIRE / 51 Site  Nutrient  Capital  a n d Nutrient  Availability: The second type  of  chemical changes are changes to site nutrient capital and nutrient availability. Fire almost always results in a reduction in total site nutrient capital due to volatization (rapid evaporation during fire). This occurs because much of the minerals are lost through smoke and fly-ash. However, there is usually an increase  in  the  amount  of  available  nutrients  because  fire  converts  undecomposed organic matter into a soluble form that is more readily available to plants.  3.3.3 Biological Changes  The  final type of soil property changes are biological changes. Fire induces  changes in the biological properties of the soil primarily due to soilheating although fire-induced p H changes can also affect the biological properties of the soil.  Fauna: Most of the meso fauna and micro fauna are killed during a fire. These fauna  can be  important to  energy  flows  between different  parts  of  the  ecosystem because of the contribution of microorganisms to the process of decomposition. A site can usually be recolonized with meso and microfauna in just a few years. However, this new set of fauna can be quite different from the old set of fauna.  Vegetative Cover: Fire will also cause changes to the vegetative cover on the  PRESCRIBED FIRE / 52 site. Although fire will kill most of the existing vegetation cover on the site, the vegetation that regrows may be lusher than the vegetation that existed before the fire. The increased nutrient availability is usually regarded to be the cause of the lusher vegetation regrowth. However, the vegetation complex may be quite different than that which existed before the burn.  Many different factors will influence the degree to which fire will affect each of these soil properties [Kimmins, 1987; Feller, 1982]. Figure 8 summarizes the relationship between these factors and the soil properties.  3.4 F i r e a n d Ecosystem Processes  The previous section discussed how fire induced changes in soil properties. The real effect of these changes is a change in energy flows between the different parts of the ecosystem and the biogeochemistry of the site. This in turn affects the productivity of the site.  Fire can have a major impact on ecosystem processes. It should also be noted that fire-induced changes to energy flows are not usually static. For example, changes in p H can be temporary or permanent.  The major processes that affect energy flow between the different parts of the ecosystem are the processes of leaching, decomposition and erosion. Fire can affect each of these ecosystem processes.  PRESCRIBED FIRE / 53  SOIL CHARACTERISTIC  Physical  SOIL FACTORS  OTHER FACTORS  Properties  • S t r u c t u r e & P o r o s i t y and Organic H a t t e r Content  1. s l o p e 2. depth of o r g a n i c and/or a i n e i a l s o i l layers 3. t e x t u r e 4. c r e d i b i l i t y -aggregate s t a b i l i t y -parent n a t e x i a l  1. f i r e depth of burn 2. c11nate 3 revegetation rate  - tlolsture Status  1. o r g a n i c B a t t e r content 2. a o i s t a r e c o n t e n t of s o i l d u r i n g tbe burn  1. l i r e i n t e n s i t y and depth of burn  - Teaperature  2. c l l B o t a  1. a s p e c t 2. ( o r e s t f l o o r t h i c k n e s s  1. f i r e i n t e n s i t y and depth of burn 2. c l l D B t e  - S i t e l u t n e n t C a p i t a l and Mutrient A v a i l a b i l i t y  1. 2. 3. *.  1. f i r e i n t e n s i t y and depth of burn 2. c l l n a t e 3. s l a s h consumption  - pa-  1. o r g a n i c n a t t e r c o n t e n t 2. c l a y a l n e r a l c o n t e n t  1. f i r e i n t e n s i t y and depth of b u m 2. s l a s h consumption  - fauna  1 o r g a n i c n a t t e r content 2. n o i s t u r e c o n t e n t of s o i l d u r i n g the burn 3. presence o r absence  1. fire I n t e n s i t y and depth of burn 2. type of p l a n t s p e c i e s regenerated a f t e r t h e burn  - V e g e t a t i v e Cover  1. presence o r absence 2 t o l e r a n c e t o burning  1. l i r e i n t e n s i t y and depth of burn  cnealcal  Properties  Biological  FIGURE  8  c a t i o n exchange c a p a c i t y f o r e s t f l o o r depth f o r e s t ( l o o r n u t r i e n t content mineral s o i l n u t r i e n t content  Properties  Factors  (adapted from F e l l e r ,  Determining 1982)  the  Impact  of  Prescribed  Fire  on  Soils  PRESCRIBED FIRE / 54  For example, fire can initially induce a change in the p H of the soil. This will affect the concentration of microorganisms in the soil which will affect the rate of decomposition.  The  solubility  of the  various mineral ions  will also be  affected by the p H . This relates to the amount of leaching that will occur.  1  The p H will also affect the vegetation that is on the site which will in turn affect the amount of erosion.  3.5 F i r e Severity a n d F i r e Behaviour  In order to define the effect of fire on the ecology on a site, it is necessary to clarify some of the fire behaviour terminology. There presently exists much confusion in the literature because researchers have been using terms such as "cool" fire and "hot" fire. These fire classifications are very subjective and do not  easily  allow  for  direct  comparisons  between  fires  on  different  sites  [Alexander, 1982].  The terms "fire intensity" and "fire impact" are used quite commonly in the literature. A suitable definition of fire intensity can be found in Alexander (1982). The fire intensity,  or energy output, is defined as the product of  PRESCRIBED FIRE / 55 available fuel energy and the fire's rate of advance. However, fire intensity can be a misleading number because a fire can be intense but last only a short time. Conversely, a fire could be not very intense but could last a long time. Both these fires could have similar effects on a site. Therefore, fire effects researchers usually use other terms besides fire intensity.  "Fire impact level" is a term first proposed by Muraro (1977). His prescribed fire predictor/planner uses a single impact level to characterize a fire in terms of its effects on the duff thickness, amount of mineral soil exposure, and slash loading. The duff thickness is the thickness of the forest floor (see Section 3.10 for a more complete definition), the mineral soil exposure is the amount of soil that is exposed on the surface (i.e. where there is no forest floor), and the slash loading refers to the weight of slash (logging detritus) still present on the site. Although the term impact level is widely used, it is not always the most appropriate term to use because it portrays the impact of fire on a site in terms of one single number. It would be better to think of fire impact levels in terms of a number of different site properties.  Therefore, the term fire severity could be a more appropriate term (M. Feller , pers. comm.). The severity of a fire would be defined in terms of the 4  amount of duff consumed or the amount of mineral soil exposed. For example, a fire could be classified as being severe in terms of duff consumption and  4  Professor  o f F o r e s t r y , U n i v e r s i t y o f B r i t i s h Columbia.  PRESCRIBED FIRE / 56 moderate in terms of mineral soil exposure.  Most of the research concerning fire severity has been directed toward predicting slash consumption, duff consumption and mineral soil exposure by relating these parameters to the preburn moisture codes (i.e. Drought codes and Duff Moisture codes) and the slash loading on the site. The moisture codes can be determined quite easily for most sites and are already used quite extensively by the Forest Protection Branch to determine fire danger ratings for sites throughout the province.  These moisture codes are supposed to reflect the moisture at various depths within the duff. The codes define a moisture gradient which has an important effect on the impact of a fire on a site. For example, a burn conducted on a site where the surface is dry and the underlying soil layers are wet will probably result in a "low" severity fire.  For any given site, it is also possible to manipulate the impact of a fire on a site by burning under particular weather conditions and at certain times of the year. A spring burn will generally be less severe than a fall burn because the  underlying duff layers  are  still  quite  moist  due  to  the  snow  melt  [Anonymous, 1985] . 5  T h i s document i s a manual the BC M i n i s t r y o f F o r e s t s . 5  jointly  published  by M a c m i l l a n - B l o e d e l  and  PRESCRIBED FIRE / 57 3.6 Vegetation a n d Prescribed F i r e  Fire can have a dramatic effect on the vegetation complex of a site. In particular, successional  fire  usually converts  state  such  as  a  the  vegetation  grassland. Fire  on a is  site  used  to  an earlier  quite  extensively  throughout the world to convert areas for use as grazing pastures. Without the intervention of fire, a site will reach its climax successional state, that is, the most mature state that the vegetation complex will reach.  Fire usually changes the vegetation  complex on a site because  of the  different tolerances of the species to fire. A very severe burn can actually kill all the vegetation on a site. Any subsequent regrowth on the site will only result from inseeding or through some sort of human intervention such as seeding or planting.  Vegetation control can be the primary objective for a prescribed burn. Burning is done on the site to establish a time window for the tree seedlings to grow  and  take  hold.  However  under  certain  situations,  dramatically increase the vegetation cover on the site.  burning  can  For example, a site  with a moderate concentration of aspen on it before the burn will almost always become a dense aspen thicket after a burn (E. Hamilton , pers. comm.). 6  This is because fire induces suckers in the stems of the aspen plant.  6  Research Branch, BC F o r e s t  Service.  PRESCRIBED FIRE / 58  Kimmins (1987) has defined the following five categories of mechanisms by which vegetation adopts to fire: 1. adaptations to fire in the vegetative stage. 2. adaptations to fire in the reproductive stage. 3. effects of fire on the germination phase. 4. evolution of increased inflammability. 5. other adaptations.  Adaptations to fire in the  vegetative  stage include  species that  have  developed fire-resistant bark. Other adaptations in this category include species that have nonflammable tissues, as well as species with rhizomes (horizontal, underground stems).  Adaptations  to  fire  in  the  reproductive  phase  include  stimulations  of  flowering in some species. For some species, seed dispersal can also be affected by fire.  Seed germination of some species can be influenced by fire. Some species are termed seedbankers in that they commonly have dormant seeds below the surface of the soil. The heat from a fire induces germination of these seeds. The  mineral  soil  exposure  induced  by  burning can  germination success of some  species. This  attributable to  moisture  the  increased  phenomenon  content  also  influence  the  may be partially  of some mineral soils (E.  PRESCRIBED FIRE / 59 Hamilton, pers. comm.).  Evolution toward inflammability is believed to be another adaption to fire. That is,  the  species may evolve towards  having more flammable tissues.  Although this species will be consumed by fire, most of its competitors will be consumed as well. The adapted species can usually recblonize the site quite easily.  Other adaptations to fire include fire-resistant bark, fire resistant needles and rapid elevation of the terminal bud and foliage.  Fire heating.  primarily influences  vegetation  For any given burn, it is  through duff consumption and soil  possible  to define  a depth of lethal  temperatures: any species which sprouts from a point above this depth of lethal temperature will usually be killed by a fire. However, since fire does not usually affect a site uniformly, a certain percentage of the species will normally survive.  The tolerances of different species to fire is only generally known. Much of the information regarding fire tolerance is summarized in Haeussler and Coates (1986).  PRESCRIBED FIRE / 60 3.7 Prescribed B u r n i n g Procedures i n B C  If prescribed burning is to be done on a site then this information should be considered at an early stage in the forest resource planning process. The cutblock must be laid out so that a treatment can be applied uniformly and economically over the whole site. The method for handling slash on the site will also be affected by the site preparation method. On sites where whole tree harvesting is done, an effort must be made to leave some slash on the site if burning is to be done afterwards.  The site preparation treatment to be used is first noted on the Pre-Harvest Silvicultural Prescription form (FS 711A). Appendix 2 contains a copy of this form. A more thorough analysis of site preparation alternatives is usually done after harvesting of the  site. The Site Preparation Guide (Appendix 3) is  designed to help lead the land manager through the evaluations of the various alternative treatments.  If a prescribed burn is to be done, then a burning plan (Appendix 4) is usually required by most forest regions. The resource manager records his/her burning prescription on this form.  When the burn is done, a Prescribed Burn Analysis form (Appendix 5) is required. This form is used to document the site and weather conditions at the time of burn, the costs associated with this burn and a summary of how the  PRESCRIBED FIRE / 61 fire affected some readily observable site characteristics.  Chapter 4 discusses prescribed burning procedures in more detail. The prescribed  burning  decisions  are  also  modeled  using  a  system  analysis  technique known as a Data Flow Diagram (DFD).  3.8 Prescribed B u r n i n g Decision Models  Many prescribed burning decision models have been developed to aid forest resource managers in decisions decision  involves  considering  involving prescribed burning.  whether  fire  is  the  best  The primary  site  preparation  alternative for any given site both economically and ecologically. Secondary decisions involve determining the combination of moisture indices and weather conditions to burn at.  Fullerton and Martell (1984) have used a decision analysis framework to define  management  alternatives  for a jack pine  sand  flat  cutover.  This  framework is primarily concerned with detennining whether fire is the most suitable site preparation treatment. They defined a total of seven decision variables which the land manager can manipulate to achieve his/her objectives. The site preparation method, the regeneration stock type, the seeding intensity, the thinning policy and the thinning year are included in these decision variables. A total of forty-eight state variables defined the environment. Both these set of variables were incorporated into a F O R T R A N program which could  PRESCRIBED FIRE / 62 show how each management strategy would affect the economic value of a site. The output of this model is the net present worth of the site as a result of a particular management strategy and inclusive of the costs of following that strategy.  Radloff and Yancik (1983) have also attempted to model the prescribed burning decision framework. Their model considers the important variables such as fire behaviour, operational costs, risks to resources and property as well as the  uncertainties  of these variables. Probabilities are used to  assess  the  potential for fire escapes given different weather scenarios. The output of their model is an expected value of the cost of a burn which reflects the various probabilities of events occurring.  Raybould and Roberts (1983) have defined a prescription matrix. This matrix aims to show how different elements of a fire prescription can interact with each other. These factors can be offsetting and therefore it may still be possible to burn to obtain the desired effects even when the weather and site conditions are out of range for the prescription.  3.9 T h e Ecosystem Classification System i n B.C.  The biogeoclimatic ecosystem classification system (BEC) forms the basis for much of the forestry interpretation work in the province. Land is mapped on the  basis  of ecosystem type  in  order to  prescribe  guidelines  for  forest  PRESCRIBED FIRE / 63 management on an ecosystem-specific basis. The B E C uses vegetation, climate and soil data to map an area, thus, it is an integrative classification system [Pojar et al., 1987].  The first step involved in classifying an area is to identify zonal (climatic climax) ecosystems. These are ecosystems that are intermediate in properties (i.e. soil moisture, soil nutrients, light and heat) to the other ecosystems in the area. These sites are generally found on gently sloping areas. The vegetation and soil on these sites are primarily a product of the climate in the area.  The plant association is the basic unit of vegetation classification and is defined as a floristically uniform vegetation unit. Each plant association can be transformed into a site association because  the vegetation  function of the biotic potential of the site. Species  on a site is a  with relatively narrow  ecological amplitudes form the basis for much of the mapping. However, if the vegetation on a site has been disturbed by events such as fire or logging, then other factors must be given more weight in order to classify the site. These factors include soil properties such as texture and humus form.  A plot of soil moisture regime versus soil nutrient regime for a subzone defines an edatrophic grid. Site associations can be represented as unique areas on this grid.  Forestry interpretations and management recommendations are done on a  PRESCRIBED FIRE / 64 site association basis. Each site association has a recommended tree species and a recommended treatment.  However, even though two sites may have  an  equivalent vegetation complex, they can have quite different site properties. Some site factors may compensate  for each other. Fire can therefore  affect  these two sites differently and this is why the recommended site treatments for an ecosystem should not always be adhered to.  3.10 Soil Development a n d the Forest F l o o r  It is important to discuss how the various soil layers are formed in order to understand how fire affects soils. The development of soil horizons is related to energy flows within the ecosystem and of course has a direct impact on the potential productivity of the site.  Organic matter is deposited continually on the site in the form of litterfall. Depending on the chemical and biological conditions, this organic matter will gradually be decomposed into its mineral constituents. Leaching (percolation of waters through the soil) may remove much of the minerals from the site and this process will be the major determinant of soil fertility. In fact, most soils are classified by the extent to which they have been leached.  Because  additions  decomposition,  a forest  of litterfall  to  the  site  usually  occur  floor will normally develop. This forest  faster  than  floor is  a  permanent buildup of organic matter. In mature forest floors, three distinct  PRESCRIBED FIRE / 65 layers can generally be identified. These are the L , F , and H layers. The L layer is the litter layer. The F layer is the fermentation layer and is an intermediate layer composed of fine roots and partially decomposed Utter. The H layer is the humus layer and is generally darker in colour and moister than the overlying F layer.  Forest floors can be classified on the basis of their humus type. Three major categories are usually used to classify humus forms. These categories are mors, moders  and  mulls.  Mors  are  formed in  cool  moist  climates  where  the  decomposition process is slow [Kimmins, 1987]. There is usually very little intermixing of the forest floor with the underlying mineral soil. The forest floor is very important for these types of sites. The opposite extreme is a mull humus form. This humus is quite rich and there is usually a lot of intermixing of  the  forest  floor with  the  underlying mineral soil.  Frequently, an A h  (organically rich mineral soil) layer is formed in the underlying mineral "soil as a result of this intermixing. The forest floor is not as important to this site's productivity. Moders are humus forms that are intermediate  in properties  between mors and mulls.  The term duff is also sometimes used when forest floors are discussed. The term duff refers to the intermediate layer of more or less decomposed organic material underlying the litter layer [Klinka et al., 1981]. However, the term duff does not refer to any part of the underlying mineral soil even in the case of mulls where there may be a lot of organic material in the mineral soil. A  PRESCRIBED FIRE / 66 common measure of the severity of a fire is the amount of duff that is consumed.  3.11 L i m i t i n g Factor Concept  The  limiting factor  concept  is  used  quite commonly in the  ecological  modelling field. This concept states that the growth rate of a plant species will be determined by the level of the factor that is the most limiting (least optimum). Figure 9 illustrates the limiting factor concept as applied to an agricultural example [Brady, 1974]. In this example, potassium is the most limiting factor in the figure on the left hand side. As long as the amount of potassium was not increased, it would not be possible to increase the crop production by increasing the amounts of the other elements. On the right hand side of the figure, the potassium level has been increased and it is now nitrogen that is the most limiting factor.  The hmiting factor concept can also be applied to tree production in forestry. The Site Preparation Guide uses the limiting factor concept when it asks the user to indicate whether the moisture or nutrients is limiting on a site. Although the limiting factor concept is an oversimplification of reality (some factors  can in fact compensate  for others),  it is  a very easy concept  understand and implement in any sort of manual or computerized system.  to  PRESCRIBED FIRE / 67  FIGURE 9 1974)  Example Demonstrating L i m i t i n g F a c t o r Concept  (adapted from Brady,  PRESCRIBED FIRE / 68 3.12 Prescribed F i r e Decision Aids  A major goal of the prescribed fire research advisory committee (PFRAC) is to transfer technology from the research groups to the field staff. Educational workshops, field manuals and decision aids are some of the different ways to enable this technology transfer.  One  of the  earliest  decision  aids  developed  was  the  Prescribed Fire  Predictor/Planner (PFP). This decision aid is composed of a series of tables that relate weather, fuels and topography to fire behaviour parameters (ease of ignition, rate of spread, and difficulty of control) and measures of fire severity (duff consumption, slash consumption, and mineral soil exposure)  [Muraro,  1977]. The P F P was an attempt to ease the task of developing prescriptions to meet burn objectives. However, this decision aid was never intended to produce definitive predictions of the impact of a fire given a set of site conditions. The user of the decision aid is supposed to incorporate much of his own knowledge into the development of the prescription [Anonymous, 1985] . 7  A need was also recognized in the early 1980's to determine the sensitivity of a site to fire. It was known that fire could not be used on all sites and an attempt was made to classify a site's sensitivity to fire on the basis of several readily observable site characteristics. Klinka et al. (1984) developed a one page  T h i s document i s a manual the BC M i n i s t r y o f F o r e s t s . 7  jointly  published  by M a c m i l l a n - B l o e d e l  and  PRESCRIBED FIRE / 69  key to classify sites in the Vancouver Forest Region (see Figure 10).  The  emergence  of expert systems technology has created some renewed  interest concerning new possibilities for decision aids. These decision aids can now be made more complex and more comprehensive than a set of tables or a one page key could allow.  The  PB Planner expert system was conceptualized in 1987 in order to  address the need for decision aids incorporating this form of technology. This system is being developed by Bernie Todd of the C F S at the  Petawawa  National Forest Research Centre. The system is envisioned to cover all aspects of the prescribed burning decision process from the development of prescriptions to the scheduling of multiple burns to the modelling of ignition patterns and fire behaviour. The Fire Effects Expert System Project is attempting to build one module from this overall system knowledge engineering in this area.  as well as examining the issue of  PRESCRIBED FIRE / 70  START  I Soil <25cm arid/or coarse fragment content >80%  YES  Jtio Sbpe >80%  YES  4 NO Sbpe 50-80%  I YES  NO  Humus form <20cm Buck 6 well developed Ae OR sail particle sin coarse OR soil <S0cm  YES  •0 0 • O  4 NO  0 M  Slope 33-50%  YES NO  Seepage water, water table, gkrying. or periodic flooding  YES  NO  Humus form Mull or Moder with Ah horizon  YES  Humus orm Mult or Moder wiith Ah horizon  YES  o 0  Soil panicle sin coarse  YES  YES  YES  Wall developed Aa horizon  4 NO  o  NO  Well developed Ac OR toil parbcle sire coarse OR soil <50cm  • O  • NO Soil tenure silly  kNO Soil dark coloured fligh C content)  YES  • NO  NO  Humusterm>20cm trick  Humus form >20cm thick  YES  NO  YES  0 0 •o  0 0 0  " 1  FIGURE 10  Key t o the I d e n t i f i c a t i o n  of S i t e S e n s i t i v i t y to F i r e  Classes  C H A P T E R 4. B U I L D I N G T H E E X P E R T S Y S T E M  4.1 Knowledge E n g i n e e r i n g  The  Knowledge Engineering phase was started in late August of 1988. A  general memo was sent out in July to most of the fire effects researchers in the province to make them aware that a project was being started in this area. Experts were identified in the various domains and agencies . The first priority 8  in selecting experts was to cover expertise of the various subdomains of the fire effects domain. These subdomains were identified to be: vegetation, nutrients, and fire behaviour. Secondary priorities for selecting experts included having representatives  from the B C Forest Service, the  Canadian Forest Service,  industry, and academia. It was also desirable to choose experts with experience in different regions in B C so as to maintain a "global" view of the fire effects problem.  With these priorities in mind, various experts were contacted. Most were quite busy during the summer season and did not have any free time until the fall. Several did not have time at all to participate in the project. As the project continued, most of the knowledge  engineering work was done with  experts that seemed most committed to the success of the project.  8  these  John Parminter experts  of the Protection  Branch  71  helped  to identify  most o f  BUILDING T H E E X P E R T S Y S T E M / 72 Figures 11a and l i b illustrate the flow and products of the  knowledge  engineering sessions.. Of the 21 sessions, 13 were devoted almost entirely to conceptual modelling of the domain. Three different types of models were developed: 1) an overall conceptual model was produced which illustrated the major  factors  which  determined  the  impact  of  prescribed  fire  on  the  productivity of the site. This model was later modified to incorporate the direct and  indirect growth factor as  well  as  the  limiting factor concept.  2) A  vegetation response to burning model was developed to predict the growth of vegetation following a burn. Much effort was devoted toward developing this particular model because one of the primary uses of prescribed burning is to control vegetation. 3) A fire behaviour model was developed to illustrate the relationship between site factors (e.g. soil moisture and slash loading) and fire severity (as measured by duff consumption, slash consumption, and mineral soil exposure).  A prototype of the expert system was also developed during the knowledge engineering phase of the project. The expert system shell V P - E X P E R T  was  selected for the prototype development because of the author's familiarity with the system and because of its availability at the University of BC.  A n initial prototype of the system was demonstrated in sessions 14-16. The conceptual  models  that  were  developed  in  the  implemented in this prototype. This prototype was sessions 17^21.  preceding  sessions  subsequently  were  refined in  MONTH  JULY  AUGUST  SEPTEMBER  SESSION HUMBER AND LOCATION"  EXPERT I I E. Banliton  EXPERT «2 H. Carran  EXPERT »3 B. Havkes  Hike T e l l e r *1 - Vancouver.  Evelyn Baailton n - v i c t o r i a  OVERALL CONCEPTUAL MODEL FI1E BEHAViaUX COHCEPTOAL MODEL VEGETATION COICEFTtfAL H0DEL OVERALL COICEPTDAL HODEL  FIDE BEHAVlOtn CONCEPTUAL HODEL  Brad Havkes 12 - V i c t o r i a  B i l l BBeje i l - Hanalao  Hike Curian 15 - l e l s o n Hike Curian *6 - l e l s o a  B. Beese  OVERALL COICEFTUAL HODEL  Brad Havkes «l - V i c t o r i a  OCTOBER  EXPERT *5  VEOETATIOR COICEFTuAL MODEL  Hike F e l l e r 12 - Vancouver  Hike Curian »3 - l e l s o n Hike Darren M - l e l s o n  n. T e l l e r  OVERALL COJCEPTtfAL HODEL  HikB C i r r i n M - l a l s o n Hike Cur ran 12 - l e l s o n  Evelyn Kaalltan *2 - V i c t o r i a  EXPERT 14  DIRECT AID IIMRZCT GIOVTH FACTOR HODEL  REVIEV OF CONCEPTUAL KODSL  MONTH  SESSIOJ  XUHBER  AND LOCATION  NOVEMBER  Ivelyn Basilton "3 - Vancouver  EXPERT 11 Z. Bon It on  VEGETATIOI SYSTEM PHOTOTYPE LIMITIIG rACTOl COICEPT PROTOTYPE  Hike Curran a7 - Vancouver Mile Corron of) - Vancouver  DECEMBER  JANUARY  FEBRUARY  Mile Cuiran «9 - l a l s o a Hire Curran »10 - Nelson  Evelyn lamiltctn *i - V i c t o r i a  l i k e Curron i l l - l e l s o a Hike Curran «13 - l e l s o a  EXPERT 12 It. Cur ran  LimTTHC FACTOR CONCEPT PROTOTYPE  VEGETATION cinmira FACTOR COICEPT PROTOTYPE  LIHITIIO FACTOR COICEPT PROTOTYPE  EXPERT »3 B. Kavkes  EXPERT 14 H. Feller  EXPERT 15 B. Beese  BUILDING T H E E X P E R T S Y S T E M / 75 The  subsequent sections of this chapter discuss in detail the format  and results of the knowledge engineering sessions.  4.1.1 Knowledge Engineering Sessions w i t h E. Hamilton  Evelyn Hamilton is a research scientist with the Research Branch of the B C Ministry of Forests. She is an expert in vegetation burning.  Her recent  publications  include  a  and its responses to  Forest Resource  Development  Agreement (FRDA) study titled "Vegetation Development after Clearcutting and Site Preparation in the SBS Zone" [Hamilton and Yearsley, 1988].  Four knowledge engineering sessions were conducted with this expert. Three of these were done at the Research Branch offices in Victoria and the other was done in the MIS lab at U B C . The first two Victoria sessions were done in the conference room without distractions since there were no phone calls routed to this room. A tape recorder was used to record these sessions.  The format of the first session was fairly unstructured. The expert was not accustomed to being asked to predict what would be the vegetation response to burning on a particular site given some initial site and burn parameters. For much of the interview session, the expert defined concepts in the domain. Several times during the interview, the knowledge engineer reviewed what was said to ensure that no important details were omitted in the notes and to clarify several points that had been mentioned. At the end of the session a  BUILDING T H E E X P E R T S Y S T E M / 76 review  of the  conceptual model of the  domain was  also done. This  first  interview lasted almost four hours.  After the first interview, several conceptual models of the domain- were constructed by the knowledge engineer using a microcomputer graphics software package. These models took the form of block diagrams that illustrated how vegetation responded to fire. This series of block diagrams consisted of about ten charts which were sent to Ms. Hamilton after the first interview  (see  Appendix 6 for the finalized versions of these block diagrams).  This series of block diagrams were reviewed extensively in the next session. Notes about changes to the model were made directly onto the diagram. At several  times  during the  meeting,  the  expert  brought in several  of her  publications to illustrate some of the vegetation studies she had been doing in different parts of the province.  The third session was conducted at U B C in the MIS lab. At this session, a small  prototype  of the  expert  system  was  demonstrated.  This  prototype  attempted to implement most of the conceptual models that were discussed in the previous interviews. The demonstration turned out to be very valuable because it uncovered some misunderstandings between the knowledge engineer and the expert. At this time, the expert also suggested that the model that was being built was too complex and should be simplified somewhat.  BUILDING T H E E X P E R T S Y S T E M / 77 The  fourth session was conducted in the general offices of the Research  Branch in mid January of 1989.  In the preceding month, a  substantially  revised prototype of the expert system had been built. Many of the revisions occurred as a result of discussions  with another expert, Mike Curran. The  limiting factor concept had also been incorporated into this prototype.  This session turned out to be very productive. A copy of the screens and the dialogue were sent to Ms. Hamilton to allow her to do further evaluation of the system. A case example from her research was also set up.  4.1.2 Knowledge E n g i n e e r i n g Sessions w i t h M. C u r r a n  Mike Curran is a research penologist with the B C Ministry of Forests in the Nelson Forest Region. He is currently on leave from the Phd program in Soil Science nutrients  at  the  University of British  Columbia. His expertise  pertains  to  and the long term effects of fire on soil properties. His recent  publications  include  a  paper  titled  "Short  and  Long  Term  Effects  of  Slashburning on Soil Properties, Tree Growth and Nutrition on Some Coastal B.C. Sites" [Curran, 1986].  Twelve knowledge engineering sessions were done with this expert. These sessions were all about four hours long and were done in sets of two. A typical set of two sessions would consist of a four hour afternoon session followed by another four hour session the next morning. The cost of travelling between  BUILDING T H E E X P E R T S Y S T E M / 78 Nelson and Vancouver was the primary reason for doing these sessions in sets of two.  The first six sessions were held in Nelson during the months of September and October. The first four of these sessions were done in the conference room of the Ministry of Forests building in Nelson. This room was not totally free from distractions as Mike Curran's phone was routed to the conference room for part of the time.  Much of the time in the first four sessions was spent on developing a conceptual model of the domain. The expert seemed to prefer sketching models on paper for most of the session. Since the expert was effectively taking the notes  for the  session,  the  knowledge  engineer  was  able  to  concentrate  intensively on understanding the conceptual model.  The expert frequently referred to a list of direct and indirect growth factors that was contained in an appendix of one of his publications. This list also noted how fire affected each one of these growth factors. The expert commonly referred to this list as "Mikey's albatross" (see Appendix 7).  Different approaches to modelling the domain were tried in the first few sessions.  A n attempt  was  made to list  all the  growth factors  and the  interrelationships between them. The level of detail involved in this analysis proved to be quite formidable.  BUILDING T H E E X P E R T S Y S T E M / 79  During these sessions, the expert would frequently break away from the session to retrieve relevant field manuals and papers to serve as illustrations for his points or to provide the researcher with background material. As well, several times the expert phoned some other specialists from the region to obtain their views on certain phenomena.  Although conceptual models of the domain were extensively discussed in these sessions, an approach to implement these conceptual models was still lacking. The mathematical modelling approach used by Dr. J . P. Kimmins of U B C was discussed. This modelling approach attempts to model the nutrient levels in the different parts of the ecosystem and the flows between them. At times during the first few knowledge engineering sessions, it appeared that the Fire Effects system would follow as detailed and complex an approach as that used by the F O R C Y T E system of Dr. Kimmins.  However,  during  the  fourth  session,  the  limiting factor  concept  was  discussed. It was still not completely obvious how to implement this concept. By the fifth session, the knowledge engineer devised a method to implement the hmiting factor concept. This method is described as follows:  A  numeric index would be  assigned  to  each growth factor.  For each  ecosystem association, values would be defined for when that numeric index value would be extremely limiting, moderately limiting, etc. The factors believed  BUILDING T H E E X P E R T S Y S T E M / 80 to be the most limiting for any particular ecosystem association would also be defined. This would serve to restrict the search for the most limiting factor for any given ecosystem association.  Figure 12 illustrates the process that would be involved in determining the limiting level of a particular growth factor. For this example, soil temperature is the growth factor being modelled. On this site, soil temperature was defined to be at "-.5" on the numeric index scale. This corresponded to a "moderate" limiting level. However, three different site factors could influence the position of this factor on the numeric index scale. These factors were aspect, slope and humus form. A n east or west aspect, a 0 - 5 degree slope, and a 5 - 10 cm mor would involve no modification of the position on the numeric index scale. However, if these site factors had other values such as a south aspect or > 30 degree slope, then this would change the position of soil temperature on the numeric index scale. The effect of fire on the site would be modelled by examining how fire affected each site factor. In this example fire would only affect humus form.  However, the expert did not believe that this would be a completely viable approach. It would not be necessary to define a numeric index corresponding to each growth factor. Instead, he believed that it would be possible to directly evaluate the degree of limitation of all growth factors, given a set of site conditions.  The most limiting factor could then be determined by comparing  the limiting level of all these factors. The degree of limitation of each of the  "1  H O  G  ECOBTSTEB H O TJ O C/> CD  0-  ASSOCIATIOI  CDTj/Ql  DIRECT OR iroiBECT GBOVTH FACTOR  V0RNAL LIMITING LEVEL  HUMESIC INDEX  NUMERIC IITIO  SITE FACTOR 1  SITE FACTOR 2  SITE FACTOR 3  RELATED TO LIMITATION  SOIL TEMPERATURE  ASPECT  lUHtrs FORM  NORTH  HOR > 15 CM.  • 1.0 »0.9  .0 8 »0. 7 •0.6  3 CtUI  rt HO 3  t" H3 1  Hrt H-  •0. S •0 « •0.3 •a. 2 •0. 1 •0.0 -a  I  -0.2  SLIGHT  -0.3  3  -a. 4  iq  —o s  00 DERATE  EAST OR VEST  noR < i a ca.  -0.6  o  -0.7  o  -0. 8  rt H O  a  HOR < S CM. > 30 DEGREES  -0.9, -1.0  o 3  O CD •O rt  p  COMMEJTS: F i r e a f f e c t s only raie of tae s i t e factors (I.e. aliens fore). Tberefore. t i r e v u i nave a negative efect on tnis ecosystea.  A illicit auans layer acts t o Insulate tbe s o i l .  s GO 173  oo  BUILDING T H E E X P E R T S Y S T E M / 82 growth factors would also be determined after burning.  The seventh and eight sessions were conducted at U B C . They were done in the MIS lab on the second floor of the Henry Angus building. Part of the sessions were devoted to demonstrating system prototypes that the researcher had  developed at various stages of the project. None of the systems had  implemented the limiting factor concept though.  For most of the time in these two sessions at U B C , the limiting factor concept was discussed for each growth factor. The expert took most of the notes for  these sessions. Because several growth factors weren't covered in these  sessions, it was decided that the expert would work on these back in Nelson and send the results to the knowledge engineer.  Between the eighth and ninth sessions, the researcher implemented the limiting factor concept for most of the growth factors. Much of the system dialogue had to be written at this point and the researcher had difficulty composing some of this  dialogue. This occurred because the concepts were  complex but had to be explained in simple terms.  The ninth and tenth sessions were done back in Nelson in December. These sessions were primarily conducted around the research branch computer in the basement of the Ministry of Forest building. The dialogue was reviewed in these sessions and much time was spent showing the expert how the reasoning  BUILDING T H E E X P E R T S Y S T E M / 83 strategy of the system worked. The rule format of V P - E X P E R T made this process quite easy. Work was also started on how the limiting factor concept would incorporate vegetation. It appeared that this would be a complex part of the program to implement.  The  eleventh and twelfth sessions were also conducted in Nelson. These  sessions were devoted to refining the system. Particular attention was paid to the system dialogue. At one point in the eleventh session, another expert was brought in to clarify the reasoning in one of the modules. The knowledge engineer spent several hours discussing the root rot module with the regional pathologist, Don Norris. These last two sessions were quite productive.  4.1.3 Knowledge E n g i n e e r i n g Sessions w i t h B. Hawkes  Brad Hawkes is a fire researcher with the Canadian Forest Service ~at the Pacific Forestry Research Centre in Victoria. Most of his research relates to models predicting burn impacts as well as models to describe fire behaviour. He is currently working with Bernie Todd of the C F S in the development of the PB Planner expert system.  Two sessions were conducted with this expert. Both were held in Victoria at the C F S bunding i n the conference room. The first' session was devoted to developing a conceptual model of how fire impacted site factors. Models for predicting slash consumption, duff consumption, etc. were discussed as well. A n  BUILDING T H E E X P E R T S Y S T E M / 84 attempt was also made to clarify the terminology of the domain. The expert constructed the models using the blackboard in the conference room.  The  second session with the expert was mainly devoted to reviewing the  current status of the project. Computer produced block diagrams of the previous sessions were also reviewed (see Appendix 8).  It became apparent that there was a lack of research results in terms of models for predicting fire impact. More would be known when the previous summers research would be analyzed. It was decided that this part of the expert system would be constructed primarily using the P F P to predict impacts. The  expert  would provide advice  as to where  these predictions could be  improved. Slash loading was noted as being missing from the PFP model.  4.1.4 Knowledge E n g i n e e r i n g Sessions w i t h other Experts  Professor Mike Feller of U B C and Bill Beese of Macmillan-Bloedel were two other experts that were consulted. Mike Feller is known as an expert in the ecological  effects of slashburning. He authored a  1982  publication on the  ecological effects of Slashburning in B C [Feller, 1982].  He  appeared to be a very articulate expert and appeared quite able to  explain concepts in the domain. Both sessions were conducted in his office at U B C . There were many distractions as students from the university came to  BUILDING T H E E X P E R T S Y S T E M / 85 his office for advice.  He appeared to be aware of all the slashburning research that was going on in B C . However, he was not of the viewpoint that it would be easy to predict the  ecological  effects  of  slashburning on  any  particular  site.  From  his  experience, he has observed a substantial amount of conflicting research results.  Professor Feller stated in the second session that he would probably not have enough time to devote to the project.  One session was done with Bill Beese of Macmillan Bloedel in Nanaimo. He is regarded as an expert in silviculture. He also has a lot of general expertise in prescribed burning. Most of this session was devoted to going over the conceptual  model that  had been  developed  with the  other  experts. The  assumptions that had been made were reviewed. Much time was also spent discussing  the  typical decisions  that  have  to  be  made  when  a  site  is  slashburned.  4.2 Prescribed B u r n i n g Procedures  In order to develop a system to model the ecological effects of slashburning, it is first necessary to study the procedures and decisions involved. This depends on the objectives of the decision maker. Hence in this section, the motivations  of the  different  players, the  information needs and types  of  BUILDING T H E E X P E R T S Y S T E M / 86 decisions are analyzed.  Figure 13 shows the decision process for the prescribed burning problem. This figure shows the information needed and the sequence in which the decisions are made.  The decision making process starts when the land is initially evaluated as to its value for commercial timber harvesting. Other possible uses include wildlife, recreation and tourism.  If a parcel is deemed suitable for commercial harvesting then depending on visual concerns or environmental concerns, it is either selected for clearcutting or selective cutting. The site is mapped, classified ecologically and a cutblock is then laid out for the company to harvest. This cutblock should be laid out so that a site treatment can be applied uniformly over the site once it has been harvested. The size of the cutblock should also fit the needs of the company that wants to harvest it. In the B C interior, many of the cutblocks are quite small in order to accommodate the needs of the small-sized  A  pre-harvest  silvicultural prescription form must be  logging companies.  submitted to  the  government at this point. The site needs to be classified according to the Biogeoclimatic Ecosystem Classification (BEC) system in order to use this form. Stocking standards for replanting the site must also be noted. These standards must be met in order to conform to Section 88 of the Silviculture Act. The  BUILDING T H E E X P E R T S Y S T E M / 87  COMPANY FORESTER  \ t l lE o b a r  I  RESOURCE ASSISTANT  "  \  Doteruine bianagenent  bjectlves  regional  regulreaents  for area priorities  f  —\ Assess side  pbaracterla lea and classic/ potential site site hi r v e s t l n g s l t e l s bbaracteristlcb  Lay o a t cutDlocx  SILVICULTDHAt FICEH  catblock spacitications Determine barvesting letboo  Evaluate Site lPreparatlon| Alternative p r e s c r i p t i o n aXa s i t e data  prescription i t e and i n i t i a l f e a s i b i l i t y data  Pre-Barvest Silvlcultural Prescription  Site Preparation Guide  PISE PI OTECTION  regional knavledge  1  Determne Zeeslbllit r ot  tire  historical veatber data BUSKER  F i r e Veatber Database  t burn p l a Eiecnte burn plan  FIGURE 13  P r e s c r i b e d B u r n i n g D e c i s i o n Process Model  d a i l y veatber lnforaation  BUILDING T H E E X P E R T S Y S T E M / 88 standards are set for each ecosystem association by the forest region.  A  site  treatment  method  is  tentatively  identified  in  order  to  start  anticipating how the site should be prepared for planting. The way the slash is treated is critical to how the site is to be prepared. If the site is to be burned then slash has to be left on the site, preferably scattered uniformly rather than in  piles so as to achieve a uniform burn coverage on the site. A set of  silvicultural objectives should also be drafted at this stage. These could include the objective of sanitizing the site from insects, improving the soil temperature, achieving a minimum number of planting spots or enabling easy planting access.  An initial feasibility assessment is done to make sure that it is possible to meet the silvicultural objectives with the preferred treatment. In the case of burning, an initial estimation is done to determine what the burn objectives would have to be in order to meet the  silvicultural  objectives. Then an  assessment must be made of how easy it would be to meet these burn objectives.  Once the site is harvested, the site treatment options must be reevaluated. This is done using the Site Preparation Guide (form F S 117). The user of this form is led through the decisions necessary to evaluate the different site preparation alternatives. Risks of burning are evaluated too. There is also an economic assessment of the cost of the different treatments and an assessment  BUILDING T H E E X P E R T S Y S T E M / 89 of  the  site sensitivity  to fire. If the  site is  to be burned then a burn  prescription must be defined as well.  These burn prescriptions are usually developed in the winter before the site is to be burned. From the burn objectives, a set of moisture indices (Duff Moisture Codes, Drought Codes and Fine Fuel Moisture Codes) for burning needs to be determined because the moisture content and moisture gradient of the soil correlate with the impact that fire will have on a site. These moisture indices  are usually derived by using the  Prescribed Fire predictor (PFP).  Alternatively, the user could look at previous burns in the area.  The site sensitivity to fire must always be kept in mind when a prescription is developed. For several areas of the province, site sensitivity to fire keys have been developed. The user of these keys is able to obtain as output a qualitative rating of how sensitive the site is to fire. One burning manual has related these sensitivity ratings to P F P impact levels [Anonymous, 1985].  The forest manager can use other sources of information to complement the site sensitivity to fire keys. The publication, "Autecological Characteristics of Selected Species that Compete with Conifers in British Columbia: A Literature Review" [Hauessler and Coates, 1986] provides information on the response to burning of selected vegetation species. The regional ecologist and silviculturalist might be consulted as well.  BUILDING T H E E X P E R T S Y S T E M / 90 Most prescriptions are also run through a weather database program in order to predict the chances of obtaining a particular prescription i n a given week. The manager might need to reassess the situation if the probability of achieving a prescription is quite small.  Once the decision is made to burn, a burning permit must usually be obtained. The district office would make sure that the chance of escape is acceptably small. The district staff might also provide some input into the burning prescription. Of course, there may be many given sites scheduled for burning in a particular area. The stock that is coming in and the potential productivity of the site will affect the decision of which sites to burn first and whether to burn at all.  If the site is to be burned, then a decision must be whether to extensively document this burn as part of the Prescribed Fire Assessment Research. When a  site  is  documented  for  measurements must be made. and  this  database,  then  a  large  number of  site  These measurements involve sampling the soil  measuring the slash within the different size classes at various points  within the site [Trowbridge et al., 1987]. The results of all these measurements are filed with the Protection Branch. At present, this data is partially analyzed but the results of this analysis is not given to the people who conducted the burn or the people who made the slash measurements.  Even if the site is not documented as a research burn, a Prescribed Fire  BUILDING T H E E X P E R T S Y S T E M / 91 Analysis form must be completed. Site measurements pre and post burn are noted on this form but the level of detail is much lower than the Prescribed Burn Assessment form.  Currently, there does not appear to be any procedures in place to evaluate how well a site treatment worked. If the planting was a total failure then the forest  company could be asked to replant. However, besides the  stocking  standards guidelines, there are no firm policies in place to ensure that the quality of the restocking will be good.  4.3 Conceptual Model of F i r e Effects  To understand the fire effects problem, it is necessary to have a conceptual model of how fire affects site properties which in turn affect  the overall  productivity of the site. Figure 14 illustrates the conceptual model developed in this project. In the expert systems field, it is actually the knowledge engineer's conceptual model that is implemented in the system, not the expert's [Addis, 1987]. However, it is  the  goal of the  knowledge  engineer  to produce a  conceptual model that is as close as possible to the expert's conceptual model.  In this model, fire is a process which acts upon the different properties of the  site. Fire  affects  the  pre-burn vegetation  complex  and pre-burn soil  characteristics such as duff thickness and mineral soil exposure. The impact of fire  on these two  sets of factors  is  determined by the  burning weather  FRE-BITB1" VEGETATION COMPLEX  ST-BURH VEGETATIOH COMPLEX IEE-BURJ son. CHARACTERISTICS  -fall  tnlclness -aineral s o i l BIJOSttlB  7 IRE  SITE FACTORS AFFECTITO FIRE SEVERITY -slash loading -slope -dull loistare code -drDuglt code  POST-RURI SOIL dARACTEXISTICS -doit tbiciness -nlneral s o i l eiposure  1THER IIDIRECT GROlTH FACTORS -root rot  IRE-BURN DIRECT iROVTB FACTORS BURIIIG COIDITIOHS -Bind  -leather flaring burn  -light -aaisttue -nutrients -teaperature  PHT3ICAL. CHEMICAL AID BIOLOGIC SOIL PROCESSIS  POST-BDRI DIRECT GIOTTE FACTORS -ligat -aoistaie -nutrients -teiperatare  BUILDING T H E E X P E R T S Y S T E M / 93 conditions and other site factors which affect fire severity.  The  post-burn vegetation  complex and the post-burn soil characteristics  affect the direct and indirect growth factors for the site because of their effect on  the soil processes of leaching, decomposition and erosion. These growth  factors in turn affect the growth of the tree seedling.  4.4 Motivations of Groups Involved i n Prescribed B u r n i n g  There  are  several  different  groups involved in the  prescribed burning  decision framework. There are the research personnel, the regional and district management within the Forest Service, the logging companies, and the crews that do the burning. The motivations of all these groups must be considered because they all could affect the success of an expert system in the prescribed burning domain.  The objectives of the Prescribed Fire Research Advisory Committee (PFRAC) have already been mentioned in a previous section of this thesis. This group's main goal is to see that prescribed fire is used properly in B C . Some of the public is looking for an opportunity to condemn prescribed fire even though the alternatives  such as chemical or mechanical treatments  are probably more  detrimental to the environment. A new focus in prescribed burning research has been on smoke management. It is now a priority in many areas to burn when the smoke ventilation will be best which is not always the best conditions  BUILDING T H E E X P E R T S Y S T E M / 94 for the optimal preparation of the site. It should be mentioned that prescribed fire has almost been legislated out of existence in Oregon because of smoke management problems.  Many of the goals of the PFRAC are also similar to those of the Forest Service's regional and district management. However, in the Forest Service the emphasis is on implementing policies uniformly and staying within budget. In some forest regions, the Forest Service does most of the burning. This occurs when smaller companies are involved. In these cases, the government also assumes all the  costs of reforestation. However, the regional and district  management is also committed to staying within budget. For example, they might not be able to pay overtime to burning crews when all of the cutblocks' optimal burning prescription windows coincide.  The companies involved in logging have differing views concerning the whole reforestation process. The smaller companies appear to take a shorter-term view and are mostly interested in just meeting the standards set by the Silviculture  Act. The larger companies  appear to  have  a much stronger  commitment to the quality of the reforestation process. These companies usually harvest under tree farm licenses. Their logging licenses are usually for longer time periods and are usually renewable. This means that the same company will be harvesting an area rotation after rotation. Therefore, the logging companies  involved  are  motivated  to  take  a  longer-term  view  of  the  reforestation process. In fact, wood volume forecasting models are sometimes  BUILDING T H E E X P E R T S Y S T E M / 95 generated so that the needs of the sawmills and the wood production from the site (which include wood from spacing and thinning) are in balance.  Another issue involved in the reforestation process is that the length of time necessary to generate optimal seedling stock. Since the tree nurseries in the province have been privatized, it has been possible to obtain seedling stock that is more mature and is of higher quality. Companies now order their stock several years in advance to ensure the stock has reached optimal maturity. Therefore, the harvesting and replanting schedules of the various sites must be coordinated to ensure the availability of suitable seedling stock.  4.5 Structure of the Expert System  The prototype of the Fire Effects expert system was implemented in VPE X P E R T . This tool is generally not considered to be a good shell to use on large projects partly because of its inherent limitations in terms of rule base size and partly because of its slow speed [Press, 1988]. However, it does allow some types of graphics and it allows hypertext.  The Fire Effects expert system knowledge base is segmented into modules. For each separate growth factor, at least one module is used. Figure 15 shows the structure of the Fire Effects expert system. Besides the growth factor modules, there are some additional modules that serve to obtain parameters that are needed for several different modules. It was advisable to obtain most  BUILDING T H E E X P E R T S Y S T E M / 96 D e t s r m n e g e n e r a l s i t e p a r a n e t e r s and access s i t e i n t o n a t i o n Iron database  FIREFFEC  i  D e t e m i n e f a c t o r s a f f e c t i n g f i r e s e v e r i t y and p r e d i c t d u f f c o n s u m p t i o n , m n e r a l s o i l e x p o s u r e and s l a s h consumption  FUELCHAR  • VEG  D e t e m i n e i n i t i a l v e g e t a t i o n complex  1-7  including  a e l g t i t and d e n s i t y  ROOTROT  Assess degree  o l i m i t a t i o n of f u n g n  • SOILNUTR  SOILTEHP  Assess degree of I m i t a t i o n  ol soil  nutrients  Assess degree of i m i t a t i o n  of s o i l t e n p e r a t u r e  Assess degree of i m i t a t i o n  of a i r t e n p e r a t u r e  Assess degree of i m i t a t i o n  of s o i l  i AIRTEMP  V HYGROTOP  "  EVALLItl  "  '  "  ~  ~  •  """"  D e t e m i n e vhicn i s nost l i n i t l n g presence or absence of f i r e  •  FIGURE 15  S t r u c t u r e of the F i r e E f f e c t s Expert  System  noisture  '  factor i n  "  BUILDING T H E E X P E R T S Y S T E M / 97 of these parameters all at the same time rather than asking for them only when the system needs them. This was done in order to improve the flow of the system dialogue and to allow the user to concentrate on the outputs of the system.  Typical parameters that are prompted for by the system include information on the ecological classification of the site, the slash loading, the duff depth, and the presence or absence of an A h layer (organic layer in mineral soil). The output of the  system is  a determination of whether  fire is beneficial or  detrimental for the site. A n explanation as to how fire will influence each growth factor is also given.  The overall structure of the system is to have it function as a management gaming and learning tool. The users are encouraged to experiment with several different burning prescriptions to see how each are predicted to affect the site. The  output of the system is not viewed as the most important part of the  system, but rather the learning process involved in seeing how fire will affect a site is the most important part.  4.6 T y p i c a l Interaction with the System  The typical user of the system is a forest manager who has received an education in forestry but is only vaguely aware of how fire influences  site  factors. This user would be the person who is developing the fire prescription  BUILDING T H E E X P E R T S Y S T E M / 98 to be used to burn the site. The system could also be used as an auditing tool to check parameters on certain prescriptions that have been executed.  The  first  series  of screens lets  the  user  specify  all  the background  information needed for the site (see Appendix 9). This information includes data concerning the ecosystem classification for the site. The F U E L C H A R module then prompts the user for moisture codes and slash loading parameters in  order to obtain a prediction of the duff consumption and mineral soil  exposure following the burn.  The user is then lead through the vegetation module, the root rot module, etc. The dialogue structure for each module is very similar in format. For each growth factor, the system describes the principal site characteristics that affect it.  The limiting values  for that  factor,  before  and  after  fire,  are  then  determined. Questions that are not applicable for an area are not asked. For example, only certain types of root rot are problems in some parts of the province. The system will only ask for information if it is pertinent to the particular site. As much dialogue as possible is incorporated into the system to make sure that the user understands the output and the limitations of the system's predictions.  A graph of the limiting values of all the growth factors is presented in the E V A L L I M module. The user is then prompted to see if he/she wants to vary the moisture codes to see how different severity fires will affect the ecology of  BUILDING T H E E X P E R T S Y S T E M / 99 the site. If so, then the system redisplays all the modules, but the user is not permitted to change data other than the moisture codes.  4.7 Comparison of Different Expert System Software  There are many expert system development tools on the market today. These products range from user-friendly expert system shells  such as V P -  E X P E R T to logic-based programming languages such as PROLOG. Recently, there has been a trend toward very sophisticated expert system shells. In fact, the  term "knowledge programming environment" better  characterizes these  types of systems.  Thus, it might be that no software will be the right tool for every project. In fact, the features desired of a tool will change as the project progresses in the expert system lifecycle [Citrenbaum et al., 1988]. For example, features that are important in the feasibility stage will differ from the features that will be important in the knowledge acquisition phase and the implementation phase.  For the feasibility phase of the project, the tool should have an easy to understand rule format. It should also be easy to develop the user interface to simulate how the final system might look.  The  knowledge acquisition and prototype development phase requires an  extensive range of programming support features. These features could include  BUILDING T H E E X P E R T S Y S T E M / 100 system  checks for consistency  and completeness  of the knowledge  debugging facility, a. bridge to external databases and languages,  base,  a  knowledge  dictionary support and a knowledge base browsing facility.  The implementation phase of the project requires a different set of features. Portability is important as any system would probably have to implemented in several different hardware environments. The flexibility of the user interface is important too. Finally, the performance of the implemented system is very important because this will affect user acceptance of the system.  For this project, V P - E X P E R T was used as a development tool, and three other development tools were evaluated. V P - E X P E R T was used for development work primarily because of its ease of use and flexibility. It is important to be able to quickly alter a prototype of the expert system between knowledge engineering sessions. The rule format employed in V P - E X P E R T seems quite easy for the expert to understand. However, V P - E X P E R T is a comparatively slow tool when used in a production environment. It also lacks the ability to incorporate forms of knowledge representation other than rules (i.e. frames or objects). Therefore, other tools such as TURBO-PROLOG might be better suited for a production environment. For this project TURBO-PROLOG and the expert system  shells  GURU  and N E X P E R T  were  assessed  through the  use  of  demonstration packages and industry software reviews.  Figure 16 compares the different products on a range of criteria. There are  o o  I  i  0) H H-  01  O  Type of Tool ;  VP-EXPERT Elaple rale-based s h e l l  3  O t-h  "3  O C H  Xnovlaflge B i p n t n t i t i o n s supported  Bales  TURBO PROLOG r e s t aacrocaaputer version a l PROLOG Rales a rrases  GDRU sophisticated rule-based shell Rales  W  cost l o r Basic systen  - J i s o con.  Co •< CO ft  Bun-Tine aodule cost  -1150  3 o  Usage i n the narlstplace  TJ (D h r+  cdn.  •11SO  cdn  free  -16000 cdn.  -1300  cdn.  NEXPERT Sophisticated object-oriented shell Roles a Objects (fraaes)  -17000 cdn  -I1SQ0 cdn  (0  (D < (D (-•  Peitoraance  o 3  r+  O O  very popular  very popular  Slav  very cast  soaevhat popular  quite popular  aodcrate  aoaerace  good  very goad  o  i  i Graphics Capability Zase o l Learning  .  i  goad  vary easy  very good  hard  aoderataly easy  noderately easy  s  BUILDING T H E E X P E R T S Y S T E M / 102 large price differences between the four systems. V P - E X P E R T is one of the least expensive packages but it is also a very slow package [Press,  1988;  Stoddard, 1988]. N E X P E R T is quite sophisticated but is much more expensive than the other tools. Although the development environment of N E X P E R T is easy-to-use, the production environment must be programmed almost completely in  the C language. This makes it time-consuming to prototype a  system  interface. TURBO-PROLOG is very fast and inexpensive but it is cumbersome to program rules directly into the system.  C H A P T E R 5. D I S C U S S I O N A N D C O N C L U S I O N S  5.1 Discussion of Knowledge Engineering  The knowledge engineering phase is the critical phase of any expert systems development  project.  As  was  mentioned  in  the  previous  chapter,  the  development of the Fire Effects expert system presented many difficulties of which the main ones are listed below:  1. Decision-making experience: The experts in the domain aren't accustomed to making the types of decisions that this system is attempting to model. The experts that were consulted for this  project are primarily researchers  who  are normally  accustomed to designing experiments to test their conceptual ideas  and  theories.  The  experts  were  very  seldomly  approached with a problem that involved predicting the ecological effects of a given fire.  2. Area-specific  knowledge: Many  of the  experts  have  had  experience in only a few different geographical areas. This appeared to bias the approach they used to reason about fire effects.  3.  Commitment: It was noted in the previous chapter that the  103  S U M M A R Y A N D CONCLUSIONS / 104 experts were all quite busy people. This is usually the case with experts in every field. However, in this project all of the experts did not report to the sponsor of the project. Therefore, it was difficult to develop the commitment that was needed for the project. It appeared that some sort of upper-level approval of the project would be beneficial so that the experts would feel that they were partly responsible for the success of the project.  4.  Conflicting viewpoints: Each expert had diverging views on how  fire  affected  site  properties.  Because  of  knowledge engineer had to ensure that he was  this,  the  able to  reconcile these conflicting views. Each expert also had an issue  that  he/she  was  particularly  concerned with. For  example, one of the experts was concerned that whether the site was winter-logged or summer-logged had more ecological impact a site than whether it was slashburned. However, the other experts did not judge this issue to be as significant.  5. Knowledge engineering time interval: The time gap between KE  sessions caused some difficulties. Because there were  several experts involved in the project, it was not possible to see each expert as frequently as would be desirable. Much time was also spent at the start of each session reviewing  S U M M A R Y A N D CONCLUSIONS / 105 the results of the previous session.  6. Lack of Familiarity with Technology: Most of the experts in the  field  technology.  were This  not very had  familiar  some  with  negative  expert effects  systems on  the  productivity of the sessions. This lack of familiarity was a hindrance when it was necessary to demonstrate the system and explain the flow of reasoning to the experts.  7.  Uncertainty of Knowledge: The uncertainty of the knowledge in the domain caused problems in the knowledge engineering phase. Many experts did not know why certain phenomena occurred and were not sure about their own ideas regarding how different factors interacted.  8.  Familiarity of KE with Fire Effects Domain: The knowledge engineer was not very familiar with the problem area. This was viewed as a serious problem because of the complex theories involved in fire effects models.  Most of these factors acted to hinder the development of the expert system, however, there were many factors about this project which have a positive effect on the viability of the system. These are listed below:  S U M M A R Y A N D CONCLUSIONS / 106 1. Experts: They were researchers in the domain and they were also articulate people. All of them had advanced degrees and were  used to presenting, defending and explaining their  ideas. They did not react adversely to having their ideas challenged. However, it was their  intuitive  ideas  sometimes  because  they  difficult to obtain  were  not  used  to  presenting ideas that they could not factually defend.  2.  Practice in model development: The experts all had much practice  developing  conceptual  models.  Some  of  them  preferred to take the lead in the construction of the model.  3. Expert's motivation: The experts  were  all  transferring technology to the field staff.  committed  to  Therefore, they  were interested in any new approach to accomplish this transfer. Also, they were used to acting in a consultative role.  •  . ...  Many different knowledge engineering techniques proved to be successful in this environment. However, the main point to note is that the technique had to be adjusted to each expert. Some experts required a more structured interview format than others.  1. Block diagrams; prototyping: It was important to have some  S U M M A R Y A N D CONCLUSIONS / 107 sort of product after each session. If the elicited knowledge could not be implemented in a prototype, then it was useful to produce some sort of a block diagram of the conceptual model  of the  expertise.  This  block diagram  served  two  functions. First of all, it forced the knowledge engineer to review the elicited knowledge in his mind. It also served to refresh the expert's memory at the start of the next session.  2. Discussion of background issues: At several points during the series  of  sessions,  the  knowledge  engineer  shifted  the  discussion away from the conceptual modelling and discussed various issues concerning expert systems technology. These diversions served to allow the expert a break from intense thinking.  3. Alternative problem approaches: One thing that was apparent in the knowledge engineering sessions was the vast amount of work that was accomplished when the knowledge engineer presented a different way of approaching the problem. This forced the expert to do some deeper thinking. The experts seemed  to enjoy participating in extensive brainstorming  discussions about how to solve the problem.  summary, there were positive and negative factors that affected the  S U M M A R Y A N D CONCLUSIONS / 108 success of the knowledge engineering phase of this project. Much of the work was accomplished in the sessions when the knowledge engineer was able to motivate the expert to do some intense thinking. For this to be possible, the knowledge engineer has to be very familiar with concepts in the domain. It was also necessary for the knowledge engineer to adapt the format of the session to suit each expert.  5.2 Discussion of Development a n d Production Environments  The previous chapter summarized the characteristic features of four different software environments. Different features are required of an expert system environment at different phases of the project. At the feasibility and knowledge acquisition phases of the project, it is desirable that the environment is flexible and the coding format is quite readable so that the experts can understand it easily. It also has to be easily modifiable in order to demonstrate to the expert what effects changes in his/her reasoning will produce.  V P - E X P E R T was found quite suitable for most of the development work that occurred. Its hypertext and screen mapping features were an improvement over the traditional text scrolling display that most expert systems use. A missing feature was the ability to import graphics such as scanned images directly into the system. Many concepts in forest ecology are most easily explained using diagrams and it would be beneficial to have the ability to display these diagrams in an expert system.  S U M M A R Y AND CONCLUSIONS / 109  Also,  VP-EXPERT  was  lacking  in  sophisticated  forms  of  knowledge  representation. Because most of the entities in the forestry domain can be put into some sort of hierarchical structure, much time could have been saved if the development environment had incorporated frame, or object-like properties. Attribute values could have been specified at the class level. If necessary, these values could have been overridden at the object level. For example, much of the knowledge in the vegetation module could have been implemented in some sort of hierarchical structure. Separate classes could be specified for trees, shrubs, and herbs. Default tolerances to burn severity could then be specified at the class level instead of the entity level.  A production or implementation environment needs its own set of features. It needs to be able to access or integrate easily with current and planned databases and systems. Some expert system tools have special links to different types  of databases.  For example,  NEXPERT  can interface  directly  with  O R A C L E databases. Other expert systems such as TURBO-PROLOG can be called up as executable modules from within other programs. This is important for the system to be successful as it has to be incorporated into other systems within the Ministry of Forests such as the system to document prescribed burns.  Cholawsky (1988) indicates that many prototype implementation problems result from data problems. These are: voluminous data, nonautomated data,  S U M M A R Y A N D CONCLUSIONS / 110 incomplete  data, erroneous  data, and inappropriate data. Therefore, much  thought should be given as to the data requirements of the expert system. Much of the information that the Fire Effects Expert system uses is currently recorded on various forms such as the Pre-Harvest Silvicultural Prescription form and the Site Preparation Guide. However, much of the information that is recorded on these forms is not recorded in a standardized format. For example, information concerning vegetation is recorded differently on both forms.  The programming code should be locked out from the users; that is, they should not be able to access the source code once the system is implemented. Usually, the best way to do this is to have a compiler for the system. This has the dual effect of making the system run faster as well as hiding the actual program code from the users.  Much effort has to be devoted to the user interface of the system. Many companies  have  found that  70% of the  development  effort  goes into  the  interface of the system and only 30% goes into the reasoning part of the system [Berry and Broadbent, 1987]. Since most of the potential users of Fire Effects system have limited familiarity with computers, it is important that this system has a good user interface.  The system also has to be portable across many different environments. A desirable  feature  would  be  the  ability  to  distribute  runtime  versions  inexpensively. This would make it easier for the forestry companies to adopt  S U M M A R Y A N D CONCLUSIONS / 111 the system. Another consideration is version control. It has to be easy to distribute new versions of the program. Finally, the microcomputer environment is preferable because with microcomputer-based systems there is no need for special communication facilities and expenses.  5.3 Discussion of the Prototype System  The  prototype  EXPERT.  of the  fire effects expert  system was  This expert system shell is quite flexible  developed in VP-  and it incorporates a  number of advanced features such as hypertext and graphic images. However, the graphics were cumbersome to the programmer and it was difficult to produce graphic objects. A simple bar graph requires a substantial amount of code to produce and is not generated very quickly on the computer when the code is executed. The system also runs slowly on anything less powerful than an I B M PC-AT. Typical response times for some operations such as generating a bar graph were in the order of thirty seconds to one minute on an A T computer.  The limiting factor approach was easy to implement on the system. At least one module was devoted to the reasoning for each growth factor. A pre-burn limiting level is determined initially for each growth factor. The limiting level after the fire is then determined. Since these limiting levels are all based on a common numeric scale from 1 to 8, it is a very easy process to determine which is the most limiting growth factor.  S U M M A R Y A N D CONCLUSIONS / 112  Currently, the system only interacts on one level with the user, that is, each time the system is consulted, the user obtains the full dialogue. A desirable feature of the system would be the ability to provide different levels of user interaction. For example, a user with a strong academic background in forestry would probably desire a strong technical emphasis to the dialogue, whereas a forester with limited formal training would probably want the dialogue to explain each concept in simpler terms. In addition, the user should be able to adjust the level of detail of the system dialogue.  The include:  system is envisioned to perform many different functions. Its uses site  management,  education,  a  basis  for  group  discussions,  prediction/validation purposes, and standardization purposes.  1. Site preparation: A primary function of the system would be as a site preparation tool to assist foresters in developing fire prescriptions.  The user would input all the information  for a particular fire. The system then makes predictions as to the ecological effect of a particular fire. Much of the usefulness of the system derives from the ability of the user to easily vary several parameters within a fire prescription to  test  the  effects  of  these  changes  on  the  system's  predictions. This can be viewed as a sensitivity analysis on a particular fire.  S U M M A R Y A N D CONCLUSIONS / 113  2. Education: The education of forest  managers is another  important use for the system. Users can learn from the explanation of the reasoning that the system provides for its predictions.  3.  Group discussion: This system could also serve as the basis for group discussions. The beliefs of the different experts could be discussed quite easily because contradictory views about how fire interacts with site factors would become quite apparent. This is because the knowledge of the different experts would be documented in the system and in the conceptual models that are developed.  4.  Prediction I validation: The system can also be used to give a prediction based on the actual parameters of a fire that occurred. This is so the system prediction can be kept on file and compared to what actually occurs. This is seen as a good way to test the validity of the experts' predictions and improve the data/knowledge base.  5.  Standardized  reasoning: The system is also set up so that  the same reasoning framework is standardized across the province. However, the system accesses a number of D B A S E  S U M M A R Y A N D CONCLUSIONS / 114 files that contain specific information about each ecosystem association.  These  DBASE  files  could  be  modified  by  Research Branch personnel in the forest regions.  Finally, it should be mentioned that most of the system's predictions are purposefully formulated using imprecise wording. This is to ensure that the user understands that the system is only producing crude estimates of fire effects and that the system's predictions should be treated with caution.  5.4 Discussion of Expert Systems i n Prescribed B u r n i n g  There  is  considerable  potential  for  prescribed fire domain. The previous  expert  section  systems mentions  technology the  many  in  the  different  functions an expert system could perform. Essentially, the main benefit derived from the system would be an increased transfer of technology between the researchers and the field staff.  Expert system technology is suitable for the prescribed burning domain for many different reasons: 1. Qualitative nature of decisions: Most of the decisions in the prescribed burning field are qualitative in nature. Many of the experts in the field use heuristics to aid in making their decisions.  S U M M A R Y A N D CONCLUSIONS / 115 Large  Solution  Space: Most  decisions  in  the prescribed  burning field involve a large number of possible solutions. Expert systems technology is particularly well suited for handling these types of problems.  Diversity of Knowledge Sources: Much  of the  knowledge  needed to make a decision is located in many different sources. These sources include: manuals and guides produced by the Forest Service, experts within the Forest Service and within academia, and databases on various computers. A n expert system would essentially allow centralized access to most of the knowledge that is relevant to a decision.  Scarcity of experts: There prescribed  burning  are very few  domain. Most  experts  of these  in  experts  the have  extensive research commitments and have very little time to act in a consultative role for the field staff.  Narrow breadth of domain: Most of the knowledge in the prescribed burning domain consist of very narrow, domainspecific knowledge. These factors make this domain ideal for expert systems technology. However, an expert system must be  adaptable  to  a  wide  geographical area so  system's benefits will outweigh its costs.  that  the  S U M M A R Y A N D CONCLUSIONS / 116  There are potentially many difficulties in applying expert systems technology to this field. These difficulties include:  1.  Uncertainty of knowledge and complexity of domain: Most of the experts in the prescribed burning field are very unsure about how prescribed fire interacts with site factors due to the complex nature of the domain. Additionally, many of these experts have conflicting views.  2.  Uncertainty of payoff: The benefits derived from the use of expert systems technology are very intangible. Substantive economic benefits won't be realized in the short term.  3.  Lack of commitment of senior management: Experts systems technology requires the commitment of senior management. The development of an expert system requires many years and requires much time from the experts. In the prescribed burning domain, many of these experts are in  different  agencies. This means  of these  that the  agencies must cooperate  senior managers  in order to properly develop  an  expert system.  4.  User acceptance of technology: Potential problems could occur  S U M M A R Y A N D CONCLUSIONS / 117 regarding user acceptance of an expert system within this domain. Many of the potential users of the system are not very familiar with computer technology.  Many different decision aids have been developed for prescribed burning domain.  Each  disadvantages.  type  of decision  aid has  The author of this  its  own  thesis proposes  set  of advantages  that  decision  aids  and be  categorized into six types: the "cookbook", the "workbook", the simple expert system, the detailed expert system, the mathematical modelling, and the expert system simulation approaches (see Figure 17). For illustration purposes, these six types have been plotted on an axis labelled "Complexity of Technique". Any approach can be made as  complex or as  simple as  the  designer  wants.  However, it is proposed that each approach is best suited for dealing with a problem at a particular complexity level. It should be noted that it is difficult to establish clear definitions of the six different types of approaches.  The "cookbook" approach is currently used extensively by the Forest Service. This approach may be defined as the use of a limited number of factors in order to produce a small decision hierarchy. Usually, these decision hierarchies take the form of one page keys. Site Sensitivity to Fire keys are examples of this type of approach. Although these keys are easy to use, they do not appear to result in an appreciable increase in user understanding of the relationship between prescribed fire and ecology.  S U M M A R Y A N D CONCLUSIONS / 118 The "workbook" approach has not been used in the prescribed fire domain but it represents a viable approach to the ecological effects of fire problem. A n example  of the  "workbook" approach is  the  "Decision Making Profile for  Vegetation Management Options" [MacDonald, 1987]. This approach may be defined as a technique in which the user is asked a number of questions that are relevant to the decision. Usually, the reasons for asking each question are given. The user then combines the results of the questions using a procedure specified  in the  workbook. The result is  a recommendation concerning a  particular decision. This technique enables various factors to be weighted to arrive  at a conclusion with the  constraining factor being the  amount of  computation that it requires the user to perform.  The author of this thesis suggests that the expert systems approach for this area can be categorized into two levels: simple and complex expert systems. A simple expert system is differentiated from a complex expert system on the basis of how it models  physical processes. A complex  expert  system can  explicitly model the external physical processes. It might also be able to model the time varying nature of the effects of fire on ecology. A simple expert system would just contain knowledge about how some site factors affect some other site factors.  The mathematical modelling approach is best exemplified by the F O R C Y T E program. This program was developed by Dr. J . P. Kimmins of U B C [Kimmins, 1987] in order to model the flows of nutrients within an ecosystem. Although  S U M M A R Y A N D CONCLUSIONS / 119 not specifically designed for slashburning, it nevertheless  could be used to  model the effects of slashburning on ecology. This approach may be defined as the use of quantitative models to directly model physical processes.  The final type of approach can be termed an expert system simulation. This technique would involve elements of mathematical modelling and expert system technology. It could provide interpretations of the modelling results and answer queries about the actual model itself.  As Figure 17 illustrates, expert systems technology has the potential to address problems at a more complex level. However, we feel that it is best to progress gradually along the "complexity of technique" axis. Therefore, at this time, no attempt should be made to develop complex expert systems that model directly physical processes. Instead, simple expert systems should be developed to enable researchers and users to become more familiar with the technology.  5.5 Strategy for F u r t h e r Expert Systems Research  Expert  system  technology  is  expensive  to  implement  —  because  of  the  knowledge engineering effort. Expert systems usually take a long time to develop. Not only does the knowledge engineer have to be spend time but so do the experts. The knowledge engineering effort could be a substantial drain on the expert's time while the benefits can be quite intangible.  Expert systems  might also  once they  require a considerable amount of maintenance  are  Complexity of tecnnlque Low  High  COOKBOOK  &EBFU EXPERT BT8TE8 -generellly mart eaaplti lata sorkbool lyyroaeh  - a i e a p l l f l M By l i t e • e i i t l l i * l t y to r i r i ley -approach I t m r - H - t i i •no alapla bat does net iced to a n t u a i n t a n a i M Mderlylag physical factort t proeesata  -assert ayataa dees a l l the calealatiaaa  otnuirarsicuIOKLUIO -eieapllfled by FORCYTft syete -coaaUtri factor! and processes -eaa't give erplanatlan  -only cenidsrs factors tad loaa aat try ta • i l l i c i t l y aadal tsysical processes  DETAILED EXPERT 8T&TEM  vomaor -ao carreat eieaple l a prescribed ( i r e doaala  -eeaelders processes as v e i l as factors  -spproacn allots leigatlag factors to be asad -caa erplala purpose of each euestloa  i  -eaa s i mi dyaaalc ehaage la factor aita tlae 1 e states of astrleats soils processes aacb as leaching aad decdeposition are atfectlag I t  m i r r s r s m iiniAfiov -caa give aiplanatlen -caa aaeeer general eaestlon* about aadal  S U M M A R Y A N D CONCLUSIONS / 121 implemented. This occurs because knowledge is seldom stable within a domain.  Therefore, the following strategy is proposed for further expert systems research within the domain:  1.  Future  planning  and  coordination  of  expert  systems  development should be done at a high level. This allows for experts  from different  project.  It will  also  departments to be allow for the  expert  assigned system  to  a  to be  integrated into current and future databases and systems.  2. It is possible to draw on academic work in the field of expert systems. Labour might be less expensive in this environment. Current research in the academic environment concerning automated  K A tools  has  the  potential  to  decrease  the  development time and expense of an expert system.  3.  The emphasis should be on developing a number of small systems rather than one large system. In this way, it is possible to review the development strategy frequently in order  to  direct efforts  towards  the  systems  which will  produce the most benefits. Also, the introduction of each new system will serve to gradually familiarize the users with expert systems technology.  S U M M A R Y A N D CONCLUSIONS / 122  4.  Specific standards should be developed for all the information regarding site factors. These standard should be developed in conjunction with the expert system developers. The standards would help alleviate "data problems" that might occur (i.e. inappropriate data, erroneous data, missing data).  5.  Establishing some contact with American researchers doing work in the forestry expert systems field [Reinhardt, 1987; Martin,  1987]  would be useful to examine how they are  approaching the same types of problems.  6.  Research into prescribed burning should be coordinated with the development of expert systems. This is necessary because there are many "gaps in knowledge" in the domain. For example, the response of vegetation to fire is only known for a few ecosystems in the province.  / 123 BIBLIOGRAPHY Addis,  T. R., "The Role of 'Explanation' in Knowledge Elicitation", International Journal of Systems Research and Information Science, 1987, Vol. 2, pp. 101-110.  Adrion, W.R., Branstad, M.A., and Cherniavsky, J . C . , 'Validation, Verification, and Testing of Computer Software", A C M Computing Surveys. Vol. 14, No. 2, June 1982, pp. 159-192. Alexander, Martin E . , "Calculating and Interpreting Forest Fire Intensities", Canadian Journal of Botany. Vol. 60, No. 4, 1982, pp. 349-357. Anonymous, A Guide to Prescribed Broadcast Burning in the Forest Region. B C Min. For., 1985.  Vancouver  Beerel, Annabel C , Expert Systems: Strategic Implications and Applications. Ellis Horwood, 1987. Benbasat, Izak and Jasbir S. 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Haeussler, S. and D. Coates, Autecological Characteristics of Selected Species that Compete with Conifers in British Columbia: A Literature Review. B C Min. For., 1986. Hamilton, Evelyn H . and H . Karen Yearsley, Vegetation Development after Clearcutting and Site Preparation in the SBS Zone. B C Min. For., 1988.  / 125 Harmon, Paul and David King, Expert Systems: Artificial Intelligence in Business. John Wiley & Sons, 1985. Hart, Anna, Knowledge Acquisition for Expert Systems. Kogan Page, 1986. Hawkes, Brad and Bruce Lawson, "Documentation of Prescribed Fire Behaviour and Effects on Forest Fuels", in Prescribed Fire-Forest Soils Symposium Proceedings, edited by R.L. Trowbridge and A. Macadam, BC Min. For., 1982, pp. 93-114. Hawkes, B.C. and B.D. Lawson, "Prescribed Fire Decision-Aids in B.C. : Current Status and Future Developments", Northwest Forest Fire Council Annual Meeting Proceedings. 1986. Hawkes, Brad, Lawson, Bruce and John Parminter, "Prescribed Fire and Fire Effects Research in Forest Resource Management in B.C.", Unpublished B C Forest Service document, 1984. Hayes-Roth, F . , "Knowledge-Based October 1984, pp. 263-273.  Expert  Systems",  IEEE  Computer.  Herrod, R. and M . Smith, "The Campbell Soup Story: A n Application of A l Technology in the Food Industry", Texas Instruments Engineering Journal. Vol. 3, No. 1, 1986, pp. 16-19. Hoffman, Robert R., "The Problem of Extracting the Knowledge of Experts from the Perspective of Experimental Psychology", A l Applications in Natural Resource Management. Vol. 1, No. 2, 1987, pp. 35-48. Kidd, Alison L . , "Knowledge Acquisition-An Introductory Framework", in Knowledge Acquisition for Expert Systems: A Practical Handbook, edited by Alison L . Kidd, Plenum Press, 1987, pp. 1-15. .Kimmins, J.P., Forest Ecology. Macmillan Publishing, 1987. Klinka, K. 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Muraro, S.J., "Prescribed-Fire Impact in Cedar-Hemlock Logging Slash", Can. For. Serv. Publ. No. 1297. 1971. Muraro, S. J . , "The Prescribed Fire Predictor", Fire Control Notes. January, 1977, pp. 26-29. Nelson, T. H . , "Getting It Out of Our System", Information Retrieval, edited by G. Schechter, Thompson Books, 1967. Newell, A. and H . Simon, Human Problem Solving. Prentice-Hall, 1972. Newquist, Harvey P. Ill, "Struggling to Maintain", A l Expert. August 1988,  / 127 pp. 69-71. Parsaye, Kamran, "Acquiring and Verifying Knowledge Expert. May 1988, pp. 48-63.  Automatically", A l  Parsaye, Kamran and Mark Chignell, Expert Systems for Experts. John Wiley & Sons, 1988. Pedersen, Ken, "Connecting Expert Systems Environments", A l Expert. May 1988, pp. 26-35. Pojar,  and  Conventional  J . , Klinka, K., and D.V. Meidinger, "Biogeoclimatic Ecosystem Classification in British Columbia", Forest Ecology and Management. No. 22, 1987, pp. 119-154.  Press, Larry, "Eight-Product Wrap-Up: PC Shells", A l Expert. September 1988, pp. 61-65. Quillian, M.R., "Semantic Memory", Semantic Information Processing, edited by M . Minsky, MIT Press, 1968, pp. 227-270. Radloff, David L . and Richard F. Yancik, "Decision Analysis of Prescribed Burning", Fire Management Notes. Vol. 44, No. 3, pp. 22-29. Raybould, Steven and Tom Roberts, "A Matrix Approach to Fire Prescription Writing", Fire Management Notes. Vol. 44, No. 4, 1983, pp. 7-10. Reinhardt, Elizabeth, "An Expert System for Designing Fire Prescriptions", U.S. Department of Agriculture Gen. Tech. Rep. PSW-101. 1987, pp. 223-225. Rust, Marc, "White Pine Blister Rust Hazard Rating: A n Expert Systems Approach", A l Applications in Natural Resource Management. Vol. 2, No. 2-3, 1988, pp. 47-50. Smith, Donald L . , "Implementing Real World Expert Systems", A l Expert. December 1988, pp. 51-57. Starfield, A . M . and A . L . Bleloch, "Expert Systems: A n Approach to Problems in Ecological Management that are Difficult to Quantify", Journal of Environmental Management. No. 16, 1983, pp. 261-268. Stefik, M . and D. G. Bobrow, "Object-Oriented Programming: Themes and Variations", A l Magazine. Vol. 6., No. 4, 1986, pp. 40-64. Stoddard, Joan, "VP-Expert v.2.0" A l Expert. October 1988, pp. 73-77.  / 128 Thieme, Ronald H . , Jones, Don D., Gibson, Harry G., Flicker, Jon D. and Reisinger, Thomas W., "Knowledge-Based Forest Road Planning", A l Applications in Natural Resource Management. Vol. 1, No. 1, 1987. Trowbridge, R., Hawkes, B., Macadam, A., and J . Parminter, Field Handbook for Prescribed Fire Assessments in British Columbia: Logging Slash Fuels. B C Min. For., 1987. Turban, Efraim, Decision Support Publishing Company, 1988.  and  Expert  Systems.  Macmillan  Vandierendonck, A. "Inferential Simulation: Hypothesis-Testing by Computer Simulation", Nederlands Tijdschrift voor de Psycholgie. Vol. 30, 1975, pp. 677-700. Walters, John and Norman R. Nielsen, Crafting Knowledge-Based Systems. John Wiley & Sons, 1988. Williams, Gregg, "HyperCard", Bvte Magazine. December 1987, pp. 109-117. Wolfgram, Deborah, Dear, Teresa and Craig S. Galbraith, Expert Systems for the Technical Professional. John Wiley & Sons, 1987.  / 129 APPENDIX 1 - PROJECT PROPOSAL  /130  FACULTY OF COMMERCE AND BUSINESS ADMINISTRATION UNIVERSITY OF BRITISH COLUMBIA PRESCRIBED FIRE EXPERT SYSTEMS PROJECT PROPOSAL Introduction The purpose of this document is to provide a brief outline of a research project that will investigate the feasibility of applying expert systems technology to the prescribed burning (PB) field. The goals of this project, the manner in which the study will be done, and the required resources are also described in this document. Nature of the Problem Area Prescribed burning refers to the use of fire as a site preparation tool to achieve specific land management objectives. In British Columbia, it is used primarily to prepare logging sites for replanting. Although mechanical and chemical methods can be used to prepare a site, prescribed burning is usually the least expensive site preparation tool. However, prescribed burning is not suitable for all sites because it can have very deleterious effects on some ecosystems and because it can be unsafe to conduct a prescribed burn under certain environmental conditions. Much research has been conducted concerning the complex decision-making process of how severe a burn should be and whether the current environmental conditions are acceptable for a burn. At this time, various expert system projects are currently underway in the U.S. and Canada to investigate the suitability of expert systems to this problem domain. Project Goals The primary goal of this project is to investigate the suitability of expert systems technology for tbe prescribed burning decision-making process. The potential benefits of expert systems in this area will be examined as well as problems associated with knowledge engineering. From the results of this project, it should be possible to recommend the direction for future work in this area.  /131  Background In early 1988, Bernie Todd of the Canadian Forestry Service commenced work on an expert system called "The PB Planner". The general outline of this system is described in Appendix 1. This expert system was envisioned to include just about all the decisions that relate to prescribed burning on clear-cut areas. It is expected that the system will be implemented in three stages. Stage 1 involves the decisions that relate to a specific site whereas Stage 2 and Stage 3 relate to the regional scheduling of burns on different sites and simulating fire behaviour, respectively. Presently, Bernie Todd is working on the Silvicultural Objectives and Site Sensitivity to Fire Modules of the PB Planner system. Within the next several months, he expects to complete a prototype system using experts from the Prince George region. From this prototype, he then expects to extend the system to the rest of B.C. and Canada. The Management Information Systems division in the Faculty of Commerce and Business Administration at UBC became interested in the use of expert systems in this area through discussions with people in the FEPA (Forestry Economics and Policy Analysis) project and the Protection Branch of the BC Ministry of Forests. This project interested us because expert systems are an important development in information systems and, therefore, some of our current research is in this field. Systems Approach to this Problem An essential component of an expert systems project is an analysis of the "systems" issues that pertain to a particular system. It is important that the objectives of the system and how they relate to the users are examined. A system can be a technical success but an operational failure. With expert systems there are several additional issues to be considered. These issues include what will be the sources of expertise and how will the knowledge base be maintained once the system is operational. Another concern is how this system will fit in with existing procedures and systems. Issues Concerning Expert Systems Technology Expert systems technology is only suitable for certain types of problems. These problem domains primarily involve situations where there are only a few experts in the  /132  area and there are many people who could benefit from their expertise. It is important to note that an expert system relates to a well defined and narrow domain that does not require general common knowledge. These problem domains are also characterized by the existence of many different factors that must be considered in making a decision. In these problem domains the experts use heuristics or "rules of thumb" to combine these factors in their reasoning process. Usually, the experts will apply different weights to these factors depending on the situation they are analyzing. Although manuals embody lots of useful knowledge and, in particular, list the factors that should be considered in a particular problem, their use can be problematic. First, people may have difficulties in integrating knowledge from varied sources and second, manuals do not accurately describe how to combine the various factors. However, it is generally possible to simulate this reasoning process in an expert system. An essential component of the development of an expert system is the use of knowledge engineering techniques to elicit expertise from the experts. Although many automated tools have been developed to facilitate this process, they are only suitable for relatively simple types of problems. For most types of problems, a knowledge engineer must interview the expert to determine the reasoning process the expert uses. In certain situations, it is useful to provide the expert with test problems and then examine the experts reasoning process in each of these test problems. The knowledge engineering process is generally an iterative process whereby the knowledge engineer models the reasoning process of the expert by constructing a prototype of the system. The knowledge engineer shows this prototype to the expert after each knowledge engineering session and the expert is queried as to how closely the system models his reasoning process. The knowledge engineer refines the prototype according to the results of each session. This prototyping process is necessary because most experts have problems describing their own reasoning processes. Only after the knowledge engineer and expert are satisfied with the prototype, should the detailed programming of the system be done. It is at this stage that a decision is made as to exactly how the system will be programmed. Integration with existing systems, maintainability of the knowledge base and efficiency of the system will be concerns at this time. It is important that the programming environment decision is made after most of the knowledge engineering has occurred. Otherwise, the selection of the programming environment will influence the way the reasoning process is modeled. The final stage of an expert systems development project is the operational testing  /133  of the system. This testing usually involves the comparison of the performance of experts (including ones not interviewed) and the expert system on test cases. From the results of this comparison, it should be possible to determine how closely the system replicates the knowledge of the expert and how similar the expert reasons relative to other experts. It should also be remembered that the expert system performance as assessed in this way might not reflect the performance in reality. This is because, in practice, the user does not need an' expert system to handle simple problems and hence, tbe expert system will probably only be used for problems that are considerably more complex than thee test scenarios. The user should also be queried at this stage to see that he is familiar with the terms that the system uses and whether the human interface of the system is adequate. Proposed Problem We propose to develop an expert system that will encompass a well defined part of the overall PB Planner system. This expert system relates to the Fire Effects module. This system would focus on the short and long term effects of fire on the productivity of a particular site. As we view it, the inputs to this system will comprise of the following: -all the fuel parameters that are necessary to assess the burn severity (fuel loading and continuity, fuel moisture, fuel species type and size class distribution) -the species type, coverage, and characteristics of the existing site vegetation -the species type and characteristics of the species to be planted -various soil and site characteristics The system would then calculate the response of various ecological parameters to the severity of the burn. The erodibility of the soil, the long and short term nutrient availability, and the long and short term effects on competing vegetation would be some of the parameters that would be calculated. Using these intermediate parameters, the system would then be able to derive an estimate of the effect of a particular fire on long and short term site productivity. Appendix 2 describes a preliminary conceptual model for this expert system. An important concern in this project will be tbe integration of this work with the work presently being done by Bernie Todd. It may eventually be necessary to reconsider the structure of the PB Planner system. It will also be beneficial to  /134  It would be advisable to bring Bernie Todd out to Victoria to discuss the overall system and how this project will integrate with the overall project. At this time, it might also be beneficial to reevaluate the structure of the PB Planner system. Finally, some funds may be required to purchase software (expert system 'shells') and possibly to upgrade a microcomputer's memory to be able to run these shells. It is assumed that in meetings with experts there will be access to microcomputers on which the knowledge base prototype will be demonstrated and examined for feedback. Immediate Work Plan The first step in the work plan would be to meet in Victoria with a few experts as well as the project sponsors. At this meeting, the objectives of the system would be clarified. A conceptual model of the system should be discussed at this time and users and experts be identified. A reassessment of the viability of expert system technology for this problem domain could also be done at this time. The knowledge engineering process would occur over the next several months. This will include knowledge acquisition and prototype implementation. It is planned to complete this process during the summer. At the end of the summer, a meeting with Bernie Todd could be scheduled. The prototype of the expert system will also be demonstrated to the project sponsors at this time.  Mike Johnston Yair Wand June 16,1988  /135  General  FLQhCHAST  of t n e P i P l a n n e r  APPENDIX 1 I i  SILVILCULTURAL OBJECTIVES  r I  | I  V.. S I T E .* I SENSITIVITY-., I  I ' P F P IMPACT I L E V E L S  J I  I  I v  BURN O B J E C T I V E IMPACT L E V E L . I ON-SITE FIRE I FACTORS / RISKS  OFF SITE RISKS/VALUES  I I  I I  I I—>  FIRE EFFECTS VALUE  I  I  AVAILA6LE WEATMERCMXaX)  I—>  <—I  | |—>  I <--I  -FUEL HAZARD FACTOR  I G N I T I O N TECHNIQUES ANO P A T T E R N S  I <—I  C05T I C O N S I D E R A T I O N S |-  EFFJCT/RISK O r NO B U R N  1 1 K — 1  AVAILABLE • RESOURCES  I —>  ->l I  1 1 1 1 1 V  1  I PB  | |  —  1  PRE-BURN WEATHER  I  S P O T WX FORECAST  I  SMOKE MANAGEMENT  K ~ l  I MOP-U? FACTO*  /  j  1 1 1 1 1 1 1 1 1 1 1 V  I |-  P F P RANKS LEVELS  PLAN  I  <—|  <—|  | I  APPENDIX 2  PRELIMINARY C O N C E P T U A L MODEL OF FIRE E F F E C T S • TREE SPECIES CHARACTER 6TICS IN THIS ECOSYSTEM Shod* Totoranc* Sta<]« R«qulr»m«nts Nutria nt R«qulnsm«nts Molstura Raqulrennnts SOIL AND SITE CHARACTERISTICS Nutriant Supply Awllabk Nutrients Molstura Supply Erodlbinty  FIRE SEVER IIY CHARACTERISES Fu«l Loading Fu«l Continuity Sb« Class DM. Fusl Spaclaa Ago of Fat I  FIRE SEVERITY! LEVEL  HK4C DC  VEGETATION CHARACTERISTICS \<e-jel'jtkn Typa \<>'j«t<ltk41 CtM.  Bum Threshold Growth Abcva Thr. Growth Babw Thr. Short Tarm Comp. Abll. Long Tarm Comp. Abll. Erosion Cont. Abll. Nutria nt Danvonds Nutria nt Cycling Atyl. Shod* Prod. Abll.  SYSTEM  SHORT AND LONG TERM BALANCE IN ABSENCE OF FIRE  SOIL AND SITE CHARACTERISTICS AFTER FIRE  EFFECTS OF FIRE ON SHORT TERM GROWTH  SHORT AND LONC TERM BALANCE Erosion Bo Id not Moist ura Sola not Vag Comp. Bala no* Nutria nt Bala no* Shod* Bolonoa  7  SHORT AND LONG TERM VEGETATION GROWTH AFTER FIRE  EFFECTS OF FIRE ON LONG TERM GROWTH  / 137 APPENDIX 2 - PRE HARVEST SILVICULTURAL PRESCRIPTION FORM  /138  ^,1  Ministry of  Province ol British Columbia  1  PRE-HARVEST SILVICULTURE PRESCRIPTION  Forests  FINAL PRESCRIPTION  Forest Service T  REGION  F D  U M to  LOCATION  OwtOQ  E N n E  D  E S c  LICENCE NO  CP.  LICENSEE  BLOCK  ASSESSED BY (PMntl  PHOTO NO  • 0  DATE H  ha»)I cwttKoch prMcnpfia* fYtun inrft#i tmeTtmvftl unit pnjtC'ipt'ortf on Form 71 tA TSA Vse AREA (hi)  B C G S / OPENING NO. / SETTING NO.  PLOT NO  ECOLOGICAL CLASSIFICATION  V  1 1 t  CUTTING  PRIORITY  S E A S O N / VR  LOGGING  METHOD  CONSTRAINTS  R E C O M M E N D E D CUTTING SPECIFICATIONS  OPENING SIZE AN0 CONFIGURATION RATIONALE  A C C E S S ROADS TO BLOCK  R O A D L A Y O U T IN  BLOCK  SKID TRAILS IN BLOCK  LANDINGS IN BLOCK  CULVERTS AND  BRIDGES  R A N G E RECREATION WATERSHED AND  FISHERIES  CONSTRAINTS  METHOD TYPE  SEASON i YEAR  PES* T R E « T V £ N I  R'^'EC'iON  TO  START  SEASON / VR TO FINISH  CONSTRAINTS  J-'TE P f l f P C O N S T R A I N T S  /139  PROPOSED USE  E S c 5  MEASURES REQUIRED  PROPOSED USE  SITE TREATMENTS / TIMING  OTHER  IMPROVEMENTS  STOCKING STANDARDS  TARGETS Stocking StjnOanj (w*n t p t c M ' hi)  fmm Growing Cltly LSI*  STANDARDS Animurn StrxtxiQ  SITE PREPARATION  Target v ,  So*  u  Plant  REFORESTATION S*^CIM Sp«-t  S*I«CI<M S(oc» Tyt*  SEN E R A ! C O M M E N T S  METHODS'TIMING  BRUSHING AND WEEDING  CONSTRAINTS  GENERAL C O M M E N T S  GENERAL OBSERVATIONS  PROCESSING SUMMARY  Date Compififa  Actio" 1 £rtl«r Mi»IO*y RfCOrrJ  •  A D' M  MAP.PMOTO ATTACHED  MAP SCALE  J  l _  I N I s T R  A  AFFIX SEAL HERE  T I O N  A P P E N D I X 3 - SITE P R E P A R A T I O N GUIDE  /141 Province of  M i n i s t r y of  British Columbia  Forests  SITE PREPARATION GUIDE  BIOGEOCLIMATIC SUB ZONE  FOREST REGION  FOREST ECOSYSTEM TYPE  District  #  P. S. Y. IJ. T h i s Guide i s designed t o a s s i s t t h e f i e l d o f f i c e r i n making a d e c i s i o n t o p r e s c r i b e a s u i t a b l e s i t e treatment on a s i t e s p e c i f i c b a s i s . T h i s treatment should be c o n s i s t e n t w i t h the manaoement o b j e c t i v e s f o r the area c o n s i d e r i n n t h e f i r e hazard and r i s k , the r e f o r e s t a t i o n o b j e c t i v e s , and the f e a s i b i l i t y and impact o f treatment. Concerns about t h e impact o f treatment on f i s h , w i l d l i f e o r o t h e r resources s h o u l d be r e f e r r e d t o the a p p r o p r i a t e resource apency.  CP. t  1.  Tenure  2.  Treatment R e s p o n s i b i l i t y  3.  Management O b j e c t i v e s (See e x p l a n a t o r y notes"]  4.  5.  Hectares  L i m i t a t i o n s on S i t e Treatment (See e x p l a n a t o r y notes) Present stocking Yes •  6.  Block*  No  Sat. •  HSR •  Patchy f ~ )  Map G r i d  Attainable  Yes •  *o  •  Attainable  Yes •  No  •  Do you expect i t t o r e s t o c k n a t u r a l l y  •  Number o f years s i n c e d i s t u r b a n c e  FIRE DANGER RATING A.  1  HAZARD  (Circle  appropriate  rating  Needles and f i n e s sparse o r absent o r mixed with s o i l  FUEL SIZE  1 2  Less than 30 cm  -UEL DEPTH  1 3  FUEL CONTINUITY  Fuel f r e e areas l a r g e r than f u e l areas 1  4  5  VEGETATION* None cured**  0  oreen  0  SPECIAL FACTOR  and enter  in right-hand  Needles and fines present, but on ground 2  f i n e s abundant and p a r t i a l l y elevated  .60 cm t o 100 cm More than 100 cm 4  2 Fuel f r e e areas s m a l l e r than f u e l areas 2  5  Continuous f u e l area broken by roads  Fuel i s nenerally continuous 5  3  Partially hidden 1  Fines abundant and mostly elevated 7  5  30 cm t o 60 cm  -1 •  colvrrr.)  Mostly hidden  S l a s h obscured  2  4  -2  -3  T h i s f a c t o r r a t e s from -2 to +4 t o cover t h e f o l l o w i n o s i t u a t i o n  TOTAL HAZARD RATING * Prefect etctc cf venetaticr rf tire **Either "cured" or "preen" vepctaiion F.S  117 P R O 79 I  of treat-erf ahouli' ~e cc^zlete.i  rut ict bcth Page 1  /142  HAZARD RATING VALUES L i t t l e hazard =-2 to 6 .federate t o High = 7 to 14 Extreme hazard = 15* B.  RISK  ( c i * c / e affrc^riate  1  SIZE OF AREA  2  ASPECT OF AREA  ratine  Less than 20 ha 1 N, NE,  and enter  in rinht-hand  20 to 60 ha  0  More than 120 ha  60 t o 120 ha  2 HV  coium)  4  3  E, S 1  SE, l e v e l o r variable 2  S. Sw 3  3  SLOPE OF AREA  Less than 20% 1  21 - 35? 2  36 - 453  «1ore than 45*. 4  4  RISK ON AREA*  Low  Moderate  High  Extreme  5  6  Public  ]  industrial Lightninq  2  3 .  1  1  2  3  1  2  2  4  4  EXPECTED" BUILOUP INDEX g r e a t e r than 40  Less than 10 days per year  SPECIAL FACTORS  This f a c t o r r a t e s from -3 to +4 to cover the f o l l o w i n g s i t u a t i o n  1  10 to 20 days per year  21 t o 30 days per year  More than 30 days per year  4  2  TOTAL RISK RATING RISK RATING VALUES Low r i s k = 3 to 10 Moderate to High = 11 t o 20 Extreme r i s k = 21+ CAN PROTECTION OBJECTIVES BE MET HITH NO TREATMENT  Yes •  No f ^ j  FROM A FIRE DANGER POINT OF VIEV HAZARD REDUCTION IS: HIGHLY NECESSARV  NECESSARYQ MARGINAL •  UNNECESSARY £j  HIGHLY UNNECESSARY  * A ratirc e-hculd aypear for- all three categories under RisV or. Area ** Fefcrs tc the average nurtler of dai*s per ~ear that the B.V.J, t s exj-ected  RANGE ASSESSMENT Mere Present on Area o r AdjacentvArea  1  L i v e s t o c k Use  2  P o t e n t i a l f o r L i v e s t o c k Use (see explanatory notes)  Yes  3  Grass Seeding Recommended  Yes  4  I f Yes, 1s Seedbed Adequate  Yes  CAN RANGE OBJECTIVES BE MET WITH NO TREATMENT-  -Yes  • • • • •  Q  to exceed 40  arrUcahle) No Present Use No No No No  • • • • •  FROM A RANGE MANAGEMENT POINT OF VIEW SITE PREPARATION IS: HIGHLY NECESSARY •  NECESSARY •  MARGINAL •  UNNECESSARY •  HIGHLY UNNECESSARY  Q  Page 2  /143  SILVICULTURAL ASSESSMENT S i l v i c u l t u r a l Objective (see e x p l a n a t o r y n o t e s ) Attainable NATURAL REGENERATION PI  Yes f j  ( S e l e c t a p p r o p r i a t e f o r e s t type - r e f e r t o S i l v i c u l t u r a l  Objective)  • S u f f i c i e n t V i a b l e Cones per Hectare  Yes Q  No | ~  S u f f i c i e n t Disturbance o r M i n e r a l S o i l Exposure  Yes Q  No | |  A c c e p t a b l e Advance Regeneration  Yes Q  No  S u f f i c i e n t Disturbance o r M i n e r a l S o i l Exposure  Yes r j  No | |  W i l l Seed Source Restock Area  Yes f j  No  •  Yes Q .  No  Q  Yes r j  No f~1  Other  No  Q  , S p e c i f y F o r e s t Type  Seed Source Adjacent  Yes f~]  S a t i s f a c t o r y Cone Crop Expected PLANTING  .  No •  On Area  This Year  (Refer t o S i l v i c u l t u r a l  [~1  Objective)  S l a s h O b s t r u c t i o n to P l a n t i n g  Little •  Moderate •  High f j  P o t e n t i a l Vegetation Competition  L i t t l e rj  Moderate Q  High  PJ  S u f f i c i e n t P l a n t a b l e Spots  Yes •  No  •  I n s e c t s o r Disease Present i n R e s i d u a l s o r S l a s h  Yes Q  No £3  Yes •  No  Spec i f y I f Yes, I s S p e c i a l Treatment Necessary Spec i f y CAN REFORESTATION OBJECTIVES BE MET WITH NO TREATMENT  •  FROM A SILVICULTURAL POINT OF VIEW SITE PREPARATION I S : HIGHLY NECESSARY •  NECESSARY •  MARGINAL •  UNNECESSARY •  DETRIMENTAL  •  OTHER COMMENTS:  Page 3  •  TREATMENT IMPACT AND FEASIBILITY Chech one box for each item under the treatment that appears to be indicated. A concentration cf checks in the left hand colurm indicates the treatment is not suitable; a conce.nti£tta* of checks on the right indicates treatment is suitable. A.  BROADCAST BURN  s.  Soil Productivity  1  O r g a n i c Layer ( L , F a H)  2  Mineral Soil  3  N u t r i e n t Regime  4  Soil  5  E x i s t i n g Erosion  Impact  • 0-30 cm • Poor • Coarse • High • 50? • • 0-10 cm  (A, B, H o r i z . )  Texture  e  Slope  7  Aspect  10-20 cm 30-50 cm  20 cm* •  •  60 cm* f H  Q  Medium Medium  •  •  .  Medium  Q  20-50*  •  Rich  •  Fine  •  Low  •  0-20?  •  «n  THE IMPACT OF BURNING ON SOIL PRODUCTIVITY I S : HIGHLY ACCEPTABLE •  ACCEPTABLE •  DOUBTFUL  b.  Burn  1  Layout (consider potential f o r escape)  Major i — | Problems l —  A d j a c e n t Values  High  3  Smoke S e n s i t i v i t y  High  4  Special Factors (See e x p l a n a t o r y n o t e s )  2  Q  UNACCEPTABLE •  DETRIMENTAL  •  Feasibility  FROM A FEASIBILITY  Minor |—| Problems  1  •  Medium  Q  F a v o r a b l e |~~|  • •  Low Low  Medium  POINT OF VIEW BURNING I S :  HIGHLY ACCEPTABLE •  ACCEPTABLE •  B.  MECAHNICAL TREATMENT  a.  Soil Productivity  DOUBTFUL  •  UNACCEPTABLE •  HIGHLY UNACCEPTABLE  Impact  1  S o i l Texture*  Fine  2  Drainage  Poor  3  Mineral Soil  Q  0-30 cm •  Medium  f~]  Coarse  Q  Hell  •  Rapid  •  30-60 cm [ ]  60+ cm Q  THE IMPACT OF MECHANICAL TREATMENT ON SOIL PRODUCTIVITY I S : HIGHLY ACCEPTABLE •  ACCEPTABLE •  DOUBTFUL •  UNACCEPTABLE •  DETRIMENTAL  * The soil productivity impact of heavy disturbance on coarse textured soil Conpactior. on vet, fine textured soils can also create problems.  •  can be serious.  Page 4  •  b.  Mechanical Treatment F e a s i b i l i t y  1  Slope ( t )  /14'5 30T.+  21-30*  0-20?  ( I n c l u d e 2 i n each c l a s s ) 2  Stump Height  21 cim •  rj-20 cm  [1  3  Stump D i e a e t e r  40 cm-» f_J  0-40 cm  £~J  4 • Stump D i s t r i b u t i o n 5  400/ha»Q  Terrain  Variable  FROM A FEASIBILITY  0-400/ha  Q  Q  Uniform f ~ I  POINT OF VIEW MECHANICAL TREATMENT IS:  HIGHLY ACCEPTABLE f j  ACCEPTABLE •  DOUBTFUL •  UNACCEPTABLE •  HIGHLY UNACCEPTABLE  RECOMMENDED TREATMENT No Treatment  Q  Spot Burn  Blade S c a r i f y  f~]  Drag S c a r i f y  Bunch and Burn r j  f j  Broadcast Burn  Q  Landing Burns f ~ l Combination  rj  (Specify)  Area t o be Treated (Hectares) f.xments Regarding Treatment  Special Considerations  Is I t necessary t o have s p e c i a l i s t s view the area b e f o r e f i n a l d e c i s i o n I f y e s , what s p e c i a l i s t s w i l l view t h e area  Yes Q  No 1 I  •_  ATTACH MAP Show areas t o be t r e a t e d and areas t o be r e s e r v e d from treatment and show any s p e c i a l areas which may need t o be t r e a t e d d i f f e r e n t l y o r under s p e c i a l c o n d i t i o n s .  Include  p e r t i n e n t i n f o r m a t i o n such as i n s t r u c t i o n s f o r o p e r a t i o n , t i m i n g s , equipment s p e c i f i c a t i o n s and. standards f o r o p e r a t i o n .  Include a S c a l e and a North Arrow.  COMMENTS:  Examined by: Approved:  Position:  Date: Date:  Page 5  •  EXPLANATORY NOTES  Manaqenent O b j e c t i v e s (page 1) Management o b j e c t i v e s f o r t b e general area and , f o r the s p e c i f i c block being assessed should be s t a t e d . These o b j e c t i v e s r e l a t e t o r e f o r e s t a t i o n (method and s p e c i e s c h o i c e ) , g r a z i n g , watershed management, w i l d l i f e management, hazard r e d u c t i o n . O b j e c t i v e s should be taken from the p r e - l o q g i n g S i l v i c u l t u r a l Report ( i f there i s one). Lacking such a r e p o r t , o b j e c t i v e s should be determined now and s t a t e d on l i n e 3.  L i m i t a t i o n s on S i t e Treatment (paoe 1) S t a t e l i m i t a t i o n s t o the p o s s i b l e methods of s i t e treatment. L i m i t a t i o n s may r e l a t e t o p h y s i c a l s i t e f e a t u r e s , p u b l i c concerns, watershed concerns, o r management o b j e c t i v e s . Refer t o t h e S i l v i c u l t u r a l Report. Examples - a v o i d broadcast burning or g r a s s i n g o f w e s t - f a c i n g slopes -. Type I I , block I I should regenerate of s i t e treatment.  n a t u r a l l y without any form  - ensure n a t u r a l drainage channels on s l o p e s over 15t a r e l e f t open. P o t e n t i a l f o r L i v e s t o c k Use (page 2) C r i t e r i a f o r determinino s i t e s w i t h p o t e n t i a l f o r l i v e s t o c k use w i l l he a v a i l a b l e s h o r t l y . I f p o t e n t i a l f o r l i v e s t o c k use i s h i g h , s i t e treatment f o r grass seeding may be d e s i r a b l e . However s i t e treatment p r e s c r i p t i o n s f o r grass seedinq should be compatible w i t h r e f o r e s t a t and range o b j e c t i v e s where dual use o f s i t e s i s contemplated. Intensive s i t e p r e p a r a t i o n that reduces t r e e o r o d u c t i v i t y f o r t h e b e n e f i t o f i n c r e a s e d grass p r o d u c t i o n , f o r example, should be c l o s e l y examined. S i l v i c u l t u r a l O b j e c t i v e s (page 3) S t a t e t h e s i l v i c u l t u r a l o b j e c t i v e s as determined p r i o r t o l o g g i n g i n the S i l v i c u l t u r a l Report, o r s e t them now. Be s p e c i f i c . O b j e c t i v e s r e f e r t o method o f r e f o r e s t a t i o n ( n a t u r a l , p l a n t i n g , seeding) and a l s o the d e s i r e d s p e c i e s combinations. S p e c i a l F a c t o r s (page 4) I n d i c a t e any s p e c i a l f a c t o r s such as adjacent p l a n t a t i o n ' or immature stands, seed p r o d u c t i o n a r e a s , spacing p r o j e c t s , b u i l d i n g s o r p u b l i c c o n s i d e r a t i o n s which may l i m i t the use o f broadcast b u r n i n g , o r r e q u i r e s p e c i a l p r e c a u t i o n s . I n d i c a t e any adjustments t o l e a v e block shape o r s p e c i a l salvage o r boundary clean-up which would l e s s e n t h e chance o f damage.  M28-237  Page 6  / 147 APPENDIX 4 - BURNING  PLAN  /148  BURNING P L A N - V A N . B E O I O N XHPAKY:  MOKE:  •WRtSS•"P. I (tenure. Slock. Settinq) fjrpe o f «.  turn:  SAOAKAST MCSCP.1PT10W  ____________ ha;  SPOT  fca;  IHrtWW/LATOIIiGS  (1) Objective: Desired lapact • _ « » : (2)  Required C . F . M . I . Waives:  a . r.r.w.c. ».  t. mod:  O.K.C.  e. D.c. (3)  Prescribed M r e Predictor Ranks: a.  M)  (S)  J.  Ignition:  __________  a.  Spread:  _ _ _ _ _ _ _  C.  Control:  ________  Heather S t a t i o n : a.  location:  ft.  Similar Aspect:  C.  Pittance f r o * s i t e :  Additional Data:  ( l o c a l trtikd conditions, fuel tjrpe. t f e » o f jrear t o B u m , e t c . )  ftttPAMTlDN (1)  • h / s t o l o g l c s l Factors (to bt shewn on sketch vdiere possible) a.  Topograph/:  ___________  It.  Itater Source:  C.  Values Protected:  d.  Preblen S i t e s :  (slope, rock; _ (distance fron twin sourer]  (plantation, structures, tletbtr, aesthetic •tc  (duff, large landings, potential escape n  /149  a.  l i t r e Precautions  (spawning b c o i . erosion p o t e n t i a l .  streaaoann.etc)  Pre-Supprestion (shown «n sketch)  3.  («)  fireguards (hand)  (0)  Fireguards (eta chine)  (c)  Fireguards (retard)  (d)  Sprinkler l i n e s  (e)  lUtwral treats  (k)  Cther  (f}  Brevity Lines  (g)  Fiaw> Setting  (h)  Tinker S i t e  (1)  Equip. Locations _  fj)  iUdio/Phonc Sites  •  _  ____________  ___  equipment on s i t e p r i o r t o Ignition (sketch) Tankers  _ _ _ _ _ _ _ _ _ _ *•*«  (Cats  . Hand tank pumps  ___  Sprinklers  Skldders  , Retardent  Helicopter  , Crip  Ptrm  Torches  Hand Tools  IC'ilTlo*. - cowrao'. - «JQP VP A d d i t i o n a l a*n and eci1pn*nt the • e r a l t t e e •111 be l i a b l e f o r In the event o f escape s h a l l be discussed with the Forest O f f i c e r . This w i l l be spelleo out In tt* burning p e n s ' l . 1.  (a)  Light-up e r e -  Jb)  Oeslejment on reap?  (c)  Ignition Sequence?,  (d)  Escape routes a e t e t ? ,  ( f ) ftee-rfci  (e) <»r_ps f o r cre«?  t. 3.  n  |a)  Control crew  (c)  Rack tip forces a v a i l within  (e)  Comments _ _ _ _ _ _ _ _ _ _  (b)  liability  Is  alnutes  (d)  Where?  A n t i c i p a t e d Kop-up crew: _ _ _ _ _ _ _ _ _ _ _  en f o r ,  .days  A n t i c i p a t e d Patrol crew:  en f o r .  .days  •  (To be retained u n t i l released by Forest Officer) Estimated date o f burn Impact rank desired  . ________————  /150  - I  I.  RISC.  -  - ( e t h t r tefe er n w r t i net previously covered - adjtcent landowners* aotl f l e d ! B*d>»?)  r. tw ircrwr) Boundary a f a r t * to t » burned ( K O ) F i r e Boards  a  c  Access:  4 WD a n l /  • / • / • /  * w p Setting  fT|  t V a v l t j Setting  |TI  Sprinklers S u b - H i e d tor  Cretardant)  Tankers Nest l e y  a u t * X " l " * * X 1*71 C - C - C 1>  • c  is  mi  lllcenseer  IMte}  i i s t r i c t Manager  (Bote)  Apa raved  FCST mm  F.S.  1171  tYALIMTION:  B -  Mashed  mad  C - C - C -  t  (McMne)  icnar  Complete Prescribed Bum Analysts fan* F . S . 117A.  APPENDIX 5  - PRESCRIBED B U R N ANALYSIS  /152 MHrtyef PRESCRIBED BUM WLYSfS I T U . MfoU tkeuU t t  KM"m  Ml*: , tumnf  C10B  •U> RET.  _ C.».  n a n n BO.  MAJBCT t a n . M n r . s . at nuaimm miwiim, BATS i v n u c T i a i LCTTD IOTD *>•  it it  •ATT nCATHBrr C O C L R B l r.i.  PBOJtcT n  t m  or r x o a i i D  it  •ton.  emi won-.  tJftvtM— T  WKWXJ&I  tax.. **ncr.  m  can wow  ran  •cm t  •  ton  •  a m wan' O  leomm. n u ttcnai n s u n rotxi)  rtDicno*  • •  on> tr t o o A K A R t o u I S t s s u u i D  n a a » i > ar n u a i i D n u i — i n o t U A D I K S - n o t ruunriK sue nsa  •nnzs I knocnot  a s j t e n n or s n u  IA)  ssnoa » — u n a  (S)  BDOSt n s c _  ton  •  [O  t u K i M n o s or m u  •  SWALOAT10"  • B U D  or  WOACI  D  BBUTIK ODTOITIOBS  tJDJCriSD ID S B R f t l W T •BJBLIMIS  won  BUIIXS carnoL Buua  DC*ACT •Mac  ix>  •BO—T  CBOl  BBimsu COM  •MUD  tonne*  SPUAD SHI  tm  POXX  SBurau CO*  coxsrnoxs SAT.  /  m s u . i  «• U  t u n a• mm r u s a u i o n u  tlOADCAST SLASH tos*. O D W U T I m s  rtDicrat B X A C I K S  <SIM I)  S K A T S K I anrmTICKS m nss rjn. •01S1VXI  BATTS or ic>: T I C *  CODE  SCTT tBirnnu coot  t s i u T t x n i — • B U S D B S ai M T WOO s a n t u •B0OCR cote  SUA svTjrtr ( i run  nres:  CSASSZS s n c r u n o x tSVSM L0SCXK  SLASH  OTJIt P V t U KTCALS F S MT A  taoASCAST  wmma  tonneo  •>BJLt>  *AH  SUIR  AUI  OuVltdJL  avAcr  am  wum) aroT  wtwos  •DIAL  j 1.  /153  • a t c * _ T ( tonno«  rurirs:  _—n<  «s_Tf  tauno* tut: r m I A T T  TODAY i  SPUAE _  :  TOD-TS C D A D l HAS:  TOT  Br  •  BAST  tuw Q  BUM D  tBtT BAST •  EAST •  eoALin or B O W MAS : m i root •  toss •  tut SPOT I V U C A S T Bsouzsns?  TBS  •OUCAST:  atrnsc  -  —U  aiman.T  •  Brnac •  TBS D.  *>  •  TBBT  M Mn. nmc-T  SAT.O  O  BC  O  O s  anaii or caST tarcciuu O i m  nm—neu  aocc O  PAST  m i aceo  wiscnoa s  RBICASTI  » _ ) B I B E S _Cf_UB»Cll): BO.  D  • O  nuxm  _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  _____________  OT •SCAM _ _ _ _ _ _ _ _ _ _ _ _ _ _  i ? _u_rt occouzs BBOV n u _wt _  n u s i i D  , ir  TSXT BimCUl.T D •StCapTTT—TLIA11I  BTK»  D »  •  FAIT D  BAP I D •  MfTICDLT •  BD-flBC M A I CABJMD OUT SATTSFATTOULTT TBS •  B I D At BSCAM OCOrt  . twee  b.  —<  _ _ _ _ _ _ _ _ _  snui o t u t r r r v r s : I S AAI>I BEAD! BOB rUUTTIBC?  WfXI CBJICTIVBS _ _ i r n _ ?  •AT.  BOG  COST or BOMlir. ( S K V B O U A I * A L ~ )  COST SDtVJtY:  B__KA3T  ACDtCT [ B C U T K ALL R Z f A U n O I , t l C B T - W . XMTtDl. M D ABMlraSTMTIOII COSTS:  anorirrp  RID  aror  TOTAL  in. r.s.  X> SOT IBCLODt COST OT CCBTMIU.IK  f I V  fAVsrc _ V—tvcr #M** ABB* a_r_an t u U v A T U *» nsX&s. OL7_T> MIX MWI< I P n u r u - L n u BETOKT.  mus  IVT.  TOTAU  Km:  0D5T m  t* s  I n tkt tvciur a c X _ - C U b 4i|*tf« A C ~ N _ t | fw— _uU_f.U4 CM<*«Jb _ i _ i  pw r t ^ a f . ' i  p £ _ U f C l — L l f t«_M>:  ygUSKS A—) BICWQgTIPATTOItS: fits-it ummuiX on l « f . a i M A l (M — tiptcttrf p t - U t a U t - w f t m r f «* C«M»—«J SB* CW pustiUptitn <tflnrf<i^ —taCiM, JJ«_—f a>«-UX_?~. c*-t*»C, A t e . 9*4 tkt t p n t t P * MapCy mUA pUtu Mrf w h t c t J w u T I f net. •*# tV gov ore*— «A tti arottifirat impact te S« 4M — H « * • <tXuj*~t-C! r u O He • At* f i t , a c _ u t « « f _ u a .  MATT.  BIOUTSU  — — BAVT ftfTlAt  MAPS ABB B1STOKT CAIDS  _=__  BIBIX OtTlCBI BEEN  _»B*T_5?  U  aimicT  aura i m w D_u>- ATTACKED m o t t t s i A T iO oti: noTSCTlOX: SOME roots n t : BSCIOXAL MJUCE1  / 154 APPENDIX 6 - VEGETATION CONCEPTUAL  MODEL  /155  1_LLJ r  I  O r C L  LO-  CO -iJ LJJ  O  CD —-  !  1 I  I  8  E  L  F A C T O R S A F F E C T I N G F I R E S E V E R I T Y  FIRE SEVERITY MEASURES FIFE ICMTIOJ F*TTEFN3  MNFFvtt. SOIL  \  \  _..  A  3.OPE  XI IX'FF M3BTURE CON1ENT  /  /  OI.MATIC y'  DJFF CCr«UfcPTION  an: rrPE  ^  30IL HEATWC  /157  CO  O LU Q_ i CO'  -<  o o  UJ  __  LU  CD  O  CO  o  co  F A C T O R S A F F E C T I N G M O R T A L I T Y - OF E X I S T I N G V E G E T A T I • ( O b s e r v a b l e and S e e d b a n k " ) ; \ L.  HEPTrl OF VABIE 3rXD«M<i  rXPTrk OF PfJSPfOUTNr) POINT:  Li: !•-•'(  SPCOES MORTAL)TY WE  TO FIFE  DEPTH OF LETHAL TO.<POWJRE3 CEPTH OF BURN  'IT Hi}  li'ilil i. i  in  iI  : I I. !  F A C T O R S A F F E C T I N G R A T E OF R E S P R O U T I M G OF S P E C I rFPTH OF LETHI roxrawirars J  i1  SfJtl. M . - T O f N T F B 3 M F .  •«1L TtMPEWTl, E RECIr€ -  R*TF: OF PESPRC'jnNr; OF  oesEPV«»£  SPECIES  F A C T O R S A F F E C T I N G !R A T E OF E S T A B L I S H M E N T OF j S E E D B A N K I N G S P E C I E S j nCPTH OF l.tW TD/IPERATLWES  9Ctl. CJjfMISIFTf ]  f£P).«N*.TION SlJ>XF.3S OF SEEDS  POST-SITE COMMONS  i  r  3311. MJTPJENT FfOME  «Ol TEMPERATURE REGIME  93L MOISTURE RECIME OJMATIC COMMONS  rPESEMCE OF COMPETINC SPECIES  SUCCESS AM> RATE OF ESTAEUSHMENT OF 3EEDB*N< SPECIES  F A C T O R S A F F E C T I N G S U C C E S S OF I N V A D I N G S P E C I E PROMMITY OF 3FFI>  30VFGF.  WWUNT OF SITF. I W y X L P I E D  * aJL rtxnj~.  #*)I.,NT O T M I j l i R M . S O L b!POTiO> '!  W1E OF ESr«U5H.CNr OF NVffilNtJ SPECCS  ~ O E S SEECflED TOIERAN3E  90lli IEMFER*PvfE P f O M E  01  GO I  iI  ' Biepliiiad sugary of short-ten effects of slashburmm Type at factor -'  factor  :  FACTUM  . ill  Available Light i •; Available eater ;  j  on factors affection tree arovtn led nutrition  Possible effects of burning  Possible Effects on Tree  increased (decreased abiding)  Positive, aaytre net. i f 8. facing slope or sheds-rewlrine; spp.  ftacxeasad l n f i l t r t b i l i . t r . and forest dear (end asaally upper s o i l ) voter holding capacity  l e g t t i r i I t serious saner droagat as tree sicro-slte  Increeead l a recsinag areas  positive i f not excessive  A m i a b l e Carbon Dloiide tax Photosynthesis i  logllolblei  legligibJe'  Teapereture regleee ( s i r end s o i l ] ;. ,  Iscressed range above ground, at sarfsce. and belov ground  Positive i t respiration decreased at algbt and/or eoll teap. lacxsasa. •egative If too estrone tar species (slope, aspect lapertant)  Decreased range at surface possible i f alneral s o i l eipotod  Positive If decreased heat stress 01 ' stea sad/or increased heating of root eaTlronaeat legativa i f frost boating occurs  Rooting Substrate j 3  •1 .. V •j 3  if . . ..] i: ' j . I  BoiljpH •  -j  '  negative  Decreased i f species relies beavily on forest floor  legative i t significant forest floor losses occur  Seedling frost-heaved dee to teap. j effects  legativa  increased i f sediaeat gain  Positive i f not eicesslve  increases up to 3 pH units doe to asU  i ,:;[ |.  Decreasea i t erosions! losses  :  S o i l Iutri«Bt» - t o t a l Cantont .ii ! [,! • • •'!! Relative Availabll lty of > isnalalng Sutrionts  h  J.'  : !  •••  '  Positive i i not eicessive (oz spades reqelreaeats or nutrition, •egative i f eiceealva tor apecies regulroneate or nutrition  ;  lege t i n I t any natriaat pool f a l l s belov raguiraaants  Decraaead dee to ataospaeue losses, laacalag and erosLOB  Increased tor S.S.K.Ca.Hg, doe to rapid aiasrallsation et organic aatter ay t i r e ; A M sosetloes eabanced l t n a t i o n 1  Positive unless eicesslve supply of noreiat aatafonisee uptake of another (e.g. ca induced re deficiescy)  I  1  ii'  :  ;:  1  V  • .  !• -  i  K !  1  .f  i-  . 1 i'  i  1  \  ! •"• 1 v  j:  |  i  • •• • '  ;  • ' 1:  t  Increased pi-dependent CEC ,  i  CQDJ Bting  Vegetation  Increased i f propagation encouraged (e g s a l a l roots not burned)  legatlve because of decreased nutrient retention and s o i l buffering to change ! legatlve because of lacreased i competition Positive  ; Increased i f propagation or gnrath '! encouraged (e.g. Rkislna unduiata spore gertinatlon. graves encouraged by f i r e  Root Pathogens ! i:  j :||  negative  Decreased i f apposite effect ( i . e . . only If persistent deep burning a l roots occur)  ii  Decreased i f dependent on retaining slasb (e.g.. n s l e t o e )  Pathogens ; '.  i' ; :  :  !;••  ; 1  If' , i, j, :  Increased i f reduces antagonistic agents or encourages alternate t o s t (e.g. Ribas for vnite pine b l i s t e r rust)  Positive  '.  Positive • •  '  negative ;  !  i:  '  i 1 "  • • -1 t:  ; P o s i t i v e If Increased ! Negative i f decreased (e.g. P  Decreased i f propagules consumed dr discouraged  !  i  P o s i t i v e If othervlse Growth-1 l u t i n g ( l i t t l e kaoea)  P o s i t i v e because of increased nutrient | retention and s o i l buffering to change  Decreased i f ercesslve organic t a t t e r consumption  I  legatlve i f any one f a l l s t o grovtb, H a l t i n g levels  j  •''  ' I !:•:'•!•  1  Possible E f f e c t s a t Tree  j j  I l l d l i f a Grating  (cont'd)  absorption by charcoal)  i i•' • •'  !• '  oa (actors a f f e c t i n g tree groitlt and n u t r i t i o n  Increase or Decrease for P. B depending on lalance of pB. org. t a t t e r e f f e c t s  S o i l Cation Iichaage Capacity (CEC) ;  1  '  ,  Increased tor Ho Because oc e l t e e t s a l increased pH  • • ;.l  Otie  •  •  ;  i i"  ' " - ;'  !  .  Possible effects of burning  j, •' SOHE i n m c T GDon g TACTORS :  :  Decreased for Fa. Hn. 2n. Cu Because of eftects of increased pE  I '  i  i'  !•:.-.•  1 Tactor  ' .  :  '• :  u p l i f t e d suasary of s h o r t - t e n effects of slas-bornlng  Type ofirector.  j  i  ' !' 1 ^  1  1  Increased l o change i n grating habits j  ,1  i  '•  j !  !  ; l ii • ! • Mil; Nit' •  •  ' !  -'  fegative i f trees p r e f e r e n t i a l l y grated P o s i t i v e If busb p r e f e r e n t i a l l y grated to e f f e c t :  !i t '-1; ! ! :| : ;  '• j  :  : J j-  '!•  !•  '  '  !  •  :  i  :  / 167 APPENDIX 8 - FIRE BEHAVIOUR CONCEPTUAL MODEL  if!  F I R E I S E V E R I T Y - S O I L H E A T I N G  CEPTHOF BURN iii  SLASH CONSIWPTION  FIRE DURATION  i SOIL HEATNC P,'FF MOISTURE CRANENT  SOIL MOISTURE  CUFF DEPTH  CO I i  i: ' i ' 1  i  D E P T H O F B U R N  cn  F I R E S E V E R I T Y ! i! i i  rra;^ei..RN  FHE-BURN MNERAL SOIL EXPOSURE . <TJP, WINTER LOGGING— SUMMER LOCOING)  nEPTH OF  BURN*  II I  Ii MCROSITE VARIATION  <MCRoroPoaww)  F I R Ei S E V E R I T Y -: S L A S H C O N S U M P T I O N it I i •..iii?1• , ,S t • •: • SLASH FUEL MO STURE  -  -a •  33. L X )  j 3  l:  / 172 APPENDIX 9 - TYPICAL INTERACTION WITH SYSTEM  /173  [  FIRE E F F E C T S EXPERT SYSTEM  ]  UBC FACULTY OF COHNEHCE AND BUSINESS ADMINISTRATION  PROTECTION BRANCH, BC FOREST SERVICE, 1989  P r e s s ang key t o cemtimue  Hotlule==>riXHTEC T h i s i s the FIREFFEC Module o f t h e FIRE EFFECTS EXPERT SYSTEM. T h i s program e s t i m a t e s t h e response o f e c o l o g i c a l t r e e growth f a c t o r s t o p r e s c r i b e d f i r e . The LIMITING FACTOR concept i s used t o determine t h e n e t e f f e c t o f p r e s c r i b e d f i r e on t h e p r o d u c t i v i t y o f t h e s i t e . F o r each growth f a c t o r , t h e l i m i t i n g l e v e l t o growth i s determined, PRE- and POST- B U R N . -Program Author I M i o h a e l Johnston Principle .  Expertsi Mike C u r r a n , BC F o r e s t S e r v i c e E v e l y n H a m i l t o n , BC F o r e s t S e r v i o e ^ , Brad Hawkes, Canadian F o r e s t S e r v i o e  P r o j e c t C o o r d i n a t o r : John P a r m i n t e r , P r o t e c t i o n Branch JTJiere ar^^  model:  _ „•„ -  1. U n i f o r m c u t b l o c k ........ .. 7.* :  — 2 . Confounding F a c t o r s such a s w i l d l i f e and mass wasting/erosion are not considered in. t h i s  model.  P r e s s mnw k*S **• coatimue  •..  /174 AssunprioDS  (cont'd)  nodale-->FIR£FFXC  3. That growth f a c t o r s cannot compensate f o r each o t h e r . T h i s s i m p l i f i c a t i o n was Made i n o r d e r t o use the l i m i t i n g f a c t o r approach. 4. The l e n g t h o f time i n v o l v e d i s from p l a n t i n g t o the <<free - t o - grow>> s t a g e . ILLUSIR-TIBH  I F LIIIITIHS  FACTOR CBHCEPT  Ho i l l 1 e==>ri»EFTEC _ EXTREME _ VERY SEVERE _SEVERE —  MODERATE  M06T LIMITING  _ MILD _  VERY*  MILD  •."INSIGNIFICANT _ NONE AT ALL J h e U n i t i n g F a c t o r oonoept i s implemented i n t h i s system i n a numerio 1 - 8 s o a l e . Each growth f a c t o r i s ranked on t h i s s o a l e b o t h PRE- and POST- BURN. A s r a o h i n t h e EVALLIH noduLe o f t h i s program s u w e a r i z e s trie v a l u e s f o r the l i m i t i n g l e v e l s . •  P r e s s aim keg t o comti-ut?  /175  FIRE EFFECTS EXPERI SYSIEH  MUDULI==)riROTLC  SPECIFV SITE CLASSIFICATION Zone : KRS Subzcme : BBSj U d i i a i i l : (iJBS.jl Ecosysten Association : jSBSjl/07 J S i t e Nanc  > Devil's Club  ** Proceed t o next screen **  Next-Screen  /176  FUELCHAR MODULE  PttEs  any  keg  to  continue  Ho4ale==>Fll ESCHAR T h i s i s the FUELCHAR nodule. The F i r e s v e r i t y , the d u f f consumption, and the M i n e r a l s o i l exposure are e s t i m a t e d i n t h i s n o d u l e . The nethod used i s a M o d i f i e d v e r s i o n of the P r e s c r i b e d F i r e P r e d i c t o r (PFP). S e v e r a l a d d i t i o n a l s i t e f a o t o r s have been i n c o r p o r a t e d i n t o t h i s p r e d i c t i o n Model i n c l u d i n g s l a s h l o a d i n g . The s l a s h oonsuned by the p r e s c r i b e d f i r e w i l l - ^ d e t e r m i n e d by 4 d i f f e r e n t parameters: ••••• .^ • .  be  primarily — =—  1. f u e l f l a m m a b i l i t y '~ • 2. i n i t i a l s l a s h l o a d i n g 3. s l a s h f u e l M o i s t u r e 4. c o n t i n u i t y / d e p t h / p a c k i n g r a t i o  "~ "^The f u e l f l a m m a b l l i t y i s p r i m a r i l y d e t e r twined From acknowledge of the s p e c i e s t y p e s o f the s l a s h . Two d i f f e r e n t s p e c i e s t y p e s , a major and a minor can be s p e c i f i e d i n t h i s model. "  k I n i t i a l s l a s h l o a d i n g i s c a l c u l a t e d by e s t i m a t i n g the amount of s l a s h w i t h i n t h e ^ i f F e r e n t ' s i z e c l a s s e s . ~ ~ " the  S l a s h f u e l m o i s t u r e codes are c u r r e n t l y under development by CFS and are not used i n t h i s model. _ , ^  the  C o n t i n u i t y / d e p t h / p a c k i n g r a t i o s r e l a t e to the d i s t r i b u t i o n o f slash. •'~ • • . r~ :  P r e s s ang  key  to c o n t l n a e  /177 FIRE EFFECTS EXPERT SYSTEM  MUDU.LE==>FUELUIAR  ** SLASH PARAMETERS «• FUEL FLAMMABILIIx Major species type : R 1 - S u h a I p i n c f i r  V. of slash :  Minor species type : Dl Subalpine f i r  •/. of slash i DO  Iff)  INITIAL SLASH LOADINUSIZE CLASS 0-7cfi  SIZE CLASS >7cn  Slash loading : L i t t l n  Slash loading : [Fair  CANT IHIJ11 V/DEPTH/rACK ING RATIO" Hou even In distributed i s the slash?  [Evenly  Ncxt-Screcn  MUDOLE==>FULLlTIAK  FIRL" EFFECTS EXFERl SVSIEM ** ESTIMATION OF DUFF CONSUMPTION *• MOISTURE PARAHETFRS •Drought Code : QOU  Duff Moisture Code ! UU  SLASH PARAMETER; Flannahi 1 it.y sea In factor  :  Slash scale f a c t o r :  |l  Colt ./Depth/Packing scale factor : |l. 1 | SHE PARAMETERS I n i t i a l duff depth (en) : IS  > Tine since logging (years) :  Slope i n '/. '• 15 F i r e t;evei i t-vj : U.b  Duff uoiiyunplion (cn) '• Next-Screen  /178  FIRE EFFECTS EXPERT SYSTEM  MUDULE==}FUELCIIAR  *« EST1MAIIQN OF MINERAL SOIL EXPOSURE »* DUFF PARAMETERS Duff covjsunptiov) (cn) : |B.5 |  I n i t i a l duff depth (nn) :  |l5  /179  VEGETATION MODULE  P r e s s any ken t o c o n t i n u e  HodBle==>VKGl T h i s I s r h e VEG1 nodule. T h i s nodule end nodules UEG2 through UEG7 c a l c u l a t e t h e l l n l t a t i o n due t o v e g e t a t i o n b e f o r e and a f t e r the f i r e . T h i s i s done i n s e v e r a l s t e p s : 1. The d e f a u l t s p e c i e s c o m p o s i t i o n ( c o v e r and h e i g h t v a l u e s ) f o r t h e v e g e t a t i o n a s s o c i a t i o n t y p i c a l l y found on t h i s ... s i t e i s d i s p l a y e d and t h e u s e r i s asked t o modify these -~~-~'~^~- i xv r e f l e c t t h e v a l u e s observed on t h e s i t e . va  7 2 .  iUW  The program Then compares The c a l c u l a t e d a c t u a l b l o n a s s t o the d e f a u l t biomass. The biomass i s c a l c u l a t e d by m u l t i p l y i n g t h e h e i g h t times t h e c o v e r . ....  3. The a c t u a l / d e f a u l t biomass r a t i o i s used t o a d j u s t t h e l i m i t i n g l e v e l v a l u e f o r t r e e growth a t t r i b u t a b l e t o t h e vegetation i d e n t i f i e d for t h i s s i t e association.  P r e s s ang key t o c o n t i n u e  /180  4. F o r eaoh o f these i n i t i a l s p e o i e s w i t h a cover o f More than 5/., t h e program summarizes r e l e v a n t c h a r a c t e r i s t i c s p e r t a l n t l n g to t h a t s p e o i e s . 3. P o t e n t i a l soedbanking s p e c i e s are t h e n I d e n t i f i e d and t h e cover f o r each s p e c i e s i s m o d i f i e d . SEED!ANKERS  6.  S u r v i v a l r a t e s f o r a l l the species are c a l c u l a t e d on the p r e d i o t e d depth o f burn.  based  7. A p r e d i o t e d b i o n a s s ourve i s then o a l o u l a t e d f o r y e a r s 1 through 6. Bieieiass  YEAR The b i o n a s s l e v e l a t year t h r e e i s compared t o t h e a c t u a l bioMass. T h i s r a t i o i s used t o deterMine t h e v e g e t a t i o n U n i t i n g l e v e l 3 years a f t e r f i r e .  Ptiss  any kog t o c o n t i n u e  urn I MODULE This i s the default vegetation conplex on the s i t e Please chanye the default values tu r e f l e c t the current status uf your s i t e . Ox cuver neans l h a l i t i s not present on the s i t e . Press Next-Screen button t o go on t o next page Species Nane  V. Cower  Douglas Maple b i g l e a f naple nountain alder red airier green and S i t k a a l lady fern pa pel' b i r c h b l u e jui ill  pinegrass red-osier doguoodheaknd h a z e l n u t — f ireueed Salal black t u i n b e r r y — Help-Button  Hppr. height (n)  unci MODULE INDEX This i s the naster help index f o r the vegetation p r e d i c t i o n part uf the FIRE EFFECTS Expert Syslen. To s e l e c t the help screen you d e s i r e , c l i c k on the hyperuord that i s e i t h e r upper case or appears i n c o l o r . This i s the l i s t of hyperuords associated u i t h the f i l e . SCREEN-OPERATIONS UEG1-SCREEN jUEGZ-SCREEN iBIOHASS-CALCULATION LIMIIING-FACIOR To see SUBZONF.-INDEX c l i c k on t h i s uord.  ur.ni Mnnin.r  SIlSj-SUIBOHi:  The average annual p r e c i p i t a t i o n i n the SBSj i s about 800 nn u i t h a range of 542 t o 1102 nn. Mean annual tenperature i s 2.5 degrees C. There are approxiantely 073 growing degree days above 5 degrees C. ECOSYSTEMS: SBSJ 1/01-OAK-FEP.N SBSJ1/0G-Q0EENS-CUP SBSJl/n7-DF.UII. '.S-CI.IIR SBSJl/08-HORSETAII. To yo back^to the SBS-ZUNE , c l i c k the nouse on i t , To go back to the INDEX , c l i c k tlie nouse on i t .  Exit-Help  Ncxt-Scrccn  /182 UE02 MODULE Species Ncine f a l z e azalea sword f e r n balsan poplar black cottonuood t r e n b l i n g aspen bracken uhite flouered rhododendrons t i n k currant red raspberry tliinbleben y salnonberry ui llous elderberry siiou berry black huckleberry oval-leaved blueberry hiyliLusb cranberry  7. Cuver  fippr. h e i y l i l (n)  /183  FIRE EFFECTS EXPERT SYSTEM  MUDULE==)UEG4  ** FKE-BUKN LU1I11NU LEVEL ** Expected Eco. Assoc. U n i t i n g level : Competition type : L e t REMARKS : The vegetation and the crop tree species w i l l be competing for l i g h t rather than uater. These are the type of s i t e s that are n o r r n l l i i burned f o r vegetation c o n t r o l . Actual/Expected bionass r a t i o !  p  !  REMARKS : There i s the sane anniint of vegetat ion on t h i s s i t e as i s norM.nl ly found on a s i t e that has an ocnsystcn a s s o c i a t i o n of t h i s type. Adjusted 1 i n i t i n y level : 3  **  L1SIINU UF P0IENT1AL PRUBLEM SPECIES  «*  Devil's c l u b lias an i n t i a l cover before f i r e o f : 39. It i s Moderately t o l e r a n t to burning. I t s prinary node of establishment, following a burn i s by resprouting. PRESS ANY KEY TO CONTINUE  /184  FIRE  ErrECIS E X P F R I  SYSTEM  MODULE==>UEGb  This i s the UEUb nudule. l h i s nudule tie l e i nines the c o n t r i b u t i o n o f seedbanking to the vegetation conplex. Severity range f o r vegetation s u r v i v a l : JHigh This f i r e i s c l a s s i f i e d as being of a <<high>> s e v e r i t y i n terns of i t s e f f e c t s on vegetation. This i s because the depth of l e t h a l tenperatures i s eijual to b.b. The « h i y h » value f o r f i r e s e v e r i t y i s used t u deternine the s u r v i v a l of each species.  Next-Screen  ** LISTING OF SEEDBANK1NU CONTRIBUTION ** Red raspberry is a seedbanking species on t h i s s i t e . Therefore, there is the p o t e n t i a l f o r an increase in the surface coverage o f t h i s species as a r e s u l t of f i r e . The estimated cover due t o seedbanking is 2 t o give a t o t a l coverage of 2. PRESS ANY KEY TO CONTINUE Thinbleberry i s a seedbanlting species on t h i s s i t e . Therefore, there i s the p o t e n t i a l f o r an increase in the surface coverage of t h i s speciesas a r e s u l t of f i r e . The estinated cover due t o seedbanking i s 3 t o give a t o t a l coverage of 7. PRESS ANY KEY TO CONTINUE  FIRE EFFECTS EXPERT SYSTEM  MQDULE==>UEGG  This i s the UEG6 nodule. This nodule p r e d i c t s the pust~f ire biomass curves and the l i m i t i n g level due to vegetation a f t e r the burn. Average s u r v i v a l r a t i o : |fl. 1 j This number i s tlie r a t i o betueen the species biomass before the f i r e and the species biomass a f t e r the f i r e . Is the s i t e being managed f u r aspei product ion : Aspen Factor : Hegative The aspen f a c t o r r e l a t e s to uhether aspen i s on the s i t e and whether the presence of aspen i s desired. Since aspen suckering i s stimulated s i g n i f i c a n t l y by f i r e , i t i s very important t o be aware of the consequences of burning a s i t e u i t h aspen on i t .  11RE EFFECTS EXI'ERl SYSTEM  MODULE==)UEGG  ** POST-BURN LIMITING LEUEL ** Predicted Predicted Predicted Predicted Predicted Predicted  biomass biomass biomass biomass biomass biomass  - year year year - year - year - year  1 2 3 4 5 G  : : : : : :  Year three/present biomass the r a t ipredicted o : |0.20biomass j This number i s the r a t i o betueen at year three and Die present biomass. Ibe year three value i s used because there i s normally a regeneration delay betueen burning and p l a n t i n y . L i m i t inn  level  /187  ROOTROT MODULE  MOBULK--)  T h i s i s the ROOTROT nodule. T h i s nodule e s t i m a t e s the e x t e n t which r o o t r o t l i m i t s the growth of the t r e e s e e d l i n g s . Root f u n g i can be v e r y l i m i t i n g t o t r e e growth. There a r e c l a s s e s of f u n g i t h a t need to be c o n s i d e r e d . The f i r s t c l a s s c o n s i s t s of F u n g i such as:  lOOTSOf  to two  A r m i l l a r i a ostoyae Phyllenus w e i r i i Inonotus tomentosus These t y p e s o f f u n g i do not appear t o be a f f e c t e d by f i r e . Tree s p e c i e s type and p e r c e n t o f stand c u r r e n t l y i n f e c t e d appear t o a f f e c t the degree t o which they l i m i t growth. The f i g u r e on the next s o r e e n i n d i o a t e s the r e g i o n s o f BC where each one of t h e s e f u n g i i s most p r o b l e n a t i o . The second c l a s s of f u n g i are s t i m u l a t e d by f i r e s of moderate i n t e n s i t y . B h i z i n a undulate i s an example o f t h i s c l a s s o f f u n g i . T h i s i s because f i r e s t i m u l a t e s s p o r e g e r m i n a t i o n . The e x t e n t t e which s e e d l i n g growth i s l i m i t e d by t h i s c l a s s o f f u n g i i s r e l a t e d t o burn s e v e r i t y and i n c i d e n c e of f u n g i i n the a r e a ( i . e . p r e s e n t i n n e i g h b o u r i n g e u t b l o e k , present i n the same watershed, •tc.). For both c l a s s e s of f u n g i i t i s u s u a l l y p o s s i b l e t o c o n t r o l t h e l i m i t i n g l e v e l oF the Fungi by r o o t e x t r a c t i o n p r o c e d u r e s , s p a c i n g s e e d l i n g s from the stumps, ar by not p l a n t i n g For s e v e r a l years.  PRESS ANY B X I TO C O H I I H B E  /188 Class 1: Fungi unaffected by fire G E O G R A P H I C DISTRIBUTION - Phyllenus w e i r i l n a i n l y a f f a c t s Douglas F i r . S p e c i e s a l t e r n a t i v e s can e l i m i n a t e problem.  L  Ao  - A r m i l l a r i a ostoyae aFfects a l l species, s e l e c t i v e l o u s i n g can enhance it.  It  Inonotus  tomentosus  t h e r e a r e spruce and p i n e v a r i e t i e s of t h i s disease. I t has t o have a l i v i n g h o s t to s u r v i v e . PKESS  BflV  KEY  TO  CONTINUE  Class 2: Fungi affected by lire  File Severity LOW  Neighbou: cutblocl  Sane Pre-burn Vatershefj;: Proximity of Rhlzin* Within outbreak c o u p l e watershed  MEDIUM  HIGH  Interaction of Fire and Rhlzlna undulata F i r e s of Moderate severity M i l l stimulate spore g e m i n a t i o n o f the R h i z i n a fungus. However, F i r e s o f high s e v e r i t y w i l l k i l l the s p o r e s .  -In t h e District Very severe outbreak of Rhiane Severe outbreak ol Rhiana Moderate outbreak of Rhizina Sight outbieak of Rhiarw  f e e s s mem lies tm • o n t i o u e  /189  MODULL==>RUOTRUT  EIRE EFFECTS EXPERT SYSTEM PRE-BURN LIMITING LEUEL  Restocking spec ins : ffil Subalpine F i r  Subzone  I  Type o l Fungus present  Inonotus towentosus  Percent uf stand v i s i b l y infected ! |9 ^ I.initing Level : H  ] |  REMARKS: Tbe MODERATE incidence level f o r t h i s fungus i s considered to r e s u l t i n an SEUERE l i m i t a t i o n to growth ( l e v e l 3). If i t i s v i s i L l e i n B - 14'Aof tbe stand Llieu i t i s probable that 35/. of the stand i s infected and 50/ of tbe stand w i l l be infected by r o t a t i o n age. Inonotus is j u s t l i k e Phellinus except i t doesn't have t o be stunped. I t has t o have t o a l i v i n g host. Therefore, i t i s advisable t u u a i t tuu years f o r routs tu d i e , then plant anything »ere are spruce ana pine v a r i e t i e s  -  - •  1  "  ;  .....  .."  ....  "  v  .  TIRE EFFECTS EXPERT SYSTEM  M0DULE==>RO0TROT  . ** POST-BURN LIMIT1NG LEUEL ** F i r e Severity : 3.5 Incidence of Rhizina undulata : (SAME WATERSHED Note: NEIGHBOUR COIBLOCK neans present i n neighbouring cutblock. SAME WATERSHED neans - present in sane watershed. WITHIN CUUP WTRSHD neans - present a couple of uatersheds auay. IN THE DISTRICT neans - present i n d i s t r i c t or s i n i l a i — s i z e d area, L i n i t i n g Level  : 2  REMARKS: The f i r e has heen hot enough t o induce good spore g e m i n a t i o n . Moreover, the incidence l e v e l of Rhizina i s Iiiyh enuuyh tu r e s u l t i n an UERV SEVERE yruuth l i n i l a t i u n ( l e v e l 2). Main and n u i s t ueather f u l l u u i u y the burn u i l l further s t i n u l a t e the fungus, Next-Screen  TIRE EFFECTS EXPERT SVSTEM  M0D0LF==)R0UTR0T  ** ROOT ROT SUMMARY ** IVe burn l i n i t i n g level : B Post-burn U n i t i n g level : 2 E f f e c t of f i r e : jDetrinental  .rccn  /191  Moalule==>SOIUUTl T h i s i s t h e SOILNUTR nodule. T h i s nodule e s t i n a t e s t h e e x t e n t to which s o i l n u t r i e n t s i r e a U n i t i n g growth f a c t o r f o r t h i s site. The degree o f growth l i m i t a t i o n due t o n u t r i e n t s i n t h e absenoe o f a burn i s determined p r i m a r i l y by t h e s o i l n u t r i e n t regime o f t h e s i t e . F i r e oan be b e n e f i c i a l o r d e t r i m e n t a l t o t h e n u t r i e n t s o f t h e s i t e . The p r i n e f a o t o r s t h a t determine whether i t w i l l be b e n e f i o l a l o r detrimental t o the n u t r i e n t s of the s i t e a r e i 1. d u f f oonsunption 2. i n i t i a l d u f f depth 3. presence o f nh l a y e r The p r e s e n c e o f t h e Ah l a y e r i s i m p o r t a n t because t h i s i n d i c a t e s whether t h e humus form i s a m u l l o r a moder ( s e e f i g u r e on n e x t s c r e e n ) . T h i s r e l a t e s t o t h e r a t e o f n u t r i e n t c y c l i n g as w e l l a s how i m p o r t a n t the f o r e s t f l o o r i s t o the s i t e .  P e e s i e t i keg t o c e n t i m e  /192  NOR  <  HODER  >  NULL  In t h e NOR humus Form t h e o r g a n i c m a t e r i a l i s c o n t a i n e d s o l e l y i n t h e f o r e s t f l o o r ( n o t i n t h e m i n e r a l s o i l ) . The r a t e of decomposition oF o r g a n i o m a t e r i a l i s u s u a l l y v e r y s l o w . In t h e NULL humus f o r m t h e o r g a n i o m a t e r i a l i s i n t e r m i x e d with the mineral s o i l .  P r e s s aoy key to c o n t i n u e  HRE EITEC1S LXl'LKl SYS1EM  MUDULE==>SU1LNIJ1R  ** PRE-BURN LIMITING LEUEL ** S n i l Nutrient Regime : Hosotropliic L i m i t i n g Level I  REMARKS : The s n i l nutrient regime lias r e s i t Iter! i n an MII.D 1 in i tat ion to growth ( l e v e l 5) i n the absence of f i r e . Although nutrient uptake depends on water, t h i s i s considered i n the HYURU1UP mudule. Ht least one nutrient w o u l d be expected to be of very low av<*i l a b i 1 i t y r e s u l t i n g i n a s l i g h t n u t r i e n t deficiency.  /193  I"IRC EFFECTS EXPERI SYSTEM  MUDULE==>S01LNUIR  ** FUST-BURN LIMITING LtUEL ** Is the Ah layer present I n i t i a l duff depth : 15  : j¥es ^ Duff consumption : ^.5 |  L i m i t i n g Level : REMARKS: The f o r e s t f l u u r i s a Mull ur Moder. The s o i l n u t r i e n t a v a i l a b i l i t y i s considered t o lie marginally increased a f t e r a s l i a l l o u burn. The b e n e f i t s i n increased a v a i l a b i l i t y of most n u t r i e n t s i s considered t o outucigh the losses in the t o t a l c a p i t a l of other n u t r i e n t s such as nitrogen, and f o r e s t f l o o r organic matter.  Next-Screen  /194  H*anL>—'>SOILTEHP T h i s i s t h e SOILTEMP nodule. T h i s nodule e s t i m a t e s t h e e x t e n t t o which s o i l temperature U n i t s t h e growth o f t h e t r e e s e e d l i n g s . The degree o f l i m i t a t i o n o f t h e s o i l temperature i n — the absence o f f i r e i s a f f e c t e d p r i m a r i l y by t h e subzone, t h e s l o p e , t h e a s p e c t , and t h e s o i l m o i s t u r e regime o f t h e s i t e . The subzone component o f t h e l i m i t a t i o n i s based on mean - . sucuae-r s o i l temperature e s t i m a t e s from b i o g e o c l i m a t i c databases.. The s l o p e and a s p e c t a f f e c t t h e s o i l temperature regime because o f t h e i r e f f e c t on t h e a n g l e o f i n c i d e n c e o f the sun's rays. -The s o i l M o i s t u r e regime a f f e c t s t h e s o i l t e n o e r a t u r e because — i t i n f l u e n o e s f o r e s t f l o o r deoth and t h e a b i l i t y o f t h e s o i l t o oonduot and s t o r e heat depends on m o i s t u r e o o n t e n t . F i r e i s always b e n e f i o i a l f o r t h e s o i l t e n p e r a t u r e because t h e — — d u f f a o t s a s an I n s u l a t i n g l a y e r f o r t h e s o i l below and r o o t z o n e temperatures r e s u l t i n g f r o n deoreased f o r e s t f l o o r are never considered detrimental. ••••••«<•  P r e s s ang keg t o c e n t i n a e  MUDUn>=>SUlLTj:r11'  1IRE EFFECTS EXl'ERl SYSTEM ** PRE-BURN LIHITING LEVEL *» Subzone ; hBSj Slope : [15  Subzone I m i t a t i o n  Aspect : South  :  S o i l Moisture : jSubhygric  L i n i t i n g level : j4 REMARKS : The U n i t i n g effect of soil temperature due s o l e l y t o the subzone is SEUERE. This i s because the subzone i s c l a s s i f i e d as being CULD. This translates to a U n i t i n g l e v e l of 3 on the Uniting f a c l u r s c a l e (1 is nust U n i t i n g and 8 i s least U n i t i n g ) . The south aspect of t h i s s i t e w i l l b e n e f i c i a l l y a f f e c t the s o i l temperature regime.  /196  FIRE EFFECTS EXPERT SVSTEM  MUDULE==>SU1LTLMP  ** FUST-BURN LIHIIING LEUEL ** Is Ah layer present : |Yes I n i t i a l duff depth : 15  Duff consunption : |F>.5 |  L i n i t iny level : REMARKS: The f i r e has had a b e n e f i c i a l e f f e c t on s o i l tenpertature because of a reduction in the thickness of tbe i n s u l a t i n g duff layer.  riRE EFFECTS EXPERT SYittLM ** SOIL TEMPERATURE SUMMARY ** Pre-btirn U n i t i n g  level  Post'burn l i n i t n g  level :  E f f e c t of f i r e  : [Beneficial  MUDULE==>SU1LTLMP  AIR TEMPEFIATURE MODULE  P r e s s ana  key  to  continue  Itodul e-->  lirtenp  T h i s i s the fllRTEMP nodule. T h i s n o d u l e e s t i m a t e s the e x t e n t to which a i r temperature U n i t s - t h e growth of -the-tree s e e d l i n g s . The d e g r e e of l i m i t a t i o n of a i r t e n p e r t a t u r e i n the absence o f f i r e i s a f f e c t e d p r i m a r i l y by t h e : subzone .. . ^__nicrotopography ' _ — — nesotopography • •'— " ". " " v e g e t a t i o n / s e e d l i n g height r a t i o . ~~~ The subzone eoaponent o f the l i m i t a t i o n i s based on the f r e q u e n c y of c l e a r , low h u m i d i t y n i g h t s i n the subzone. The — n i c r o t o o o g r a p h y conoonent r e l a t e s to the amount of nounding p r e s e n t and thus the a b i l i t y f o r c o l d a i r pockets t o f o r n (see f i g u r e on next s c r e e n ) . The nesotopography component r e l a t e s to the presence o f b a s i n s o r l a r g e d e p r e s s i o n s . The r a t i o between the h e i g h t o f the s e e d l i n g and the h e i g h t of the v e g e t a t i o n i s — i m p o r t a n t because of the boundary l a y e r phenomena. : _^ F i r e can be b e n e f i c i a l o r d e t r i m e n t a l t o t h e a i r temperature depending on how the r a t i o between t h e s e e d l i n g h e i g h t and vegetation height i s a f f e c t e d .  P r e s s ang  key  to  continue  —  -Z.  ^  /198  Mesotopography Cold a i r pockets • f i l l form i n these depression:  * M;M  •i'i  I N  11 t I I  f, V i'i l l  * 1414  '[ I t^l H I 1^1 I -  •<§t., v>'t, w,.: ,  ,  i : . > ;  ,  , ; r ,  f  1 i *i f!i i i111 I'I i I'I I ' I 11 t-t  1  MWf'" "  Microtopography Cold a i r pockets w i l l form between t h e nounds.  P r e s s ang key t o c u t i n u  Vegetation / Seedling Height Ratio  The r a t i o between t h e v e g e t a t i o n h e i g h t and t h e s e e d l i n g h e i g h t w i l l a f F e c t the a i r temperature. T h i s i s because o f t h e boundary l a y e r phenomena. . S e e d l i n g " h e i g h t "<< ^ e g e t a t l 6 n * T i i e i s i h t Seedling height < vegetation height Seedling height > vegetation height OF c o u r s e , limitation.  vegetation cover  •==  w i l l affect  P r e s s any kern t o t e n t t i u  M i l d l y Unfauourable Uery U n f a v o u r a b l e Mildly Favourable t h e degree o f  FIRE  ,  EFFECTS  EXPERT  SVSTEM  MUDULE==>H I R T E M P *  ** PRE-BURH LIMITING LEUEL »» Subzone : SBSj Subzone l i m i t a t i o n  Mounding i n nir.rotopog. : ¥es  Pre-bum veg. cover  Eb"  Mar.rotopng  has i n present.  Ueg/Seedl ing h e i g h t r a t i o  :  Mo U.bHb  Limiting level ; 2 REMARKS: The l i n i t i n g effect, of a i r temperature due s o l e l y t o the subzone i s MILD. I h i s i s because of the lou frequency of c l e a r , lou huniditg nights i n t h i s suLzone. Ihis t r a n s l a t e s tu a U n i t i n g level uf b on the l i n i l i l i y fautur scale (1 i s nost U n i t i n g and 8 i s least U n i t i n g ) . The hounding present i n the nicrotopography w i l l act to lnuer the a i r tenperature. air  The height and cover of the vegetation creates unfavorable tenperature conditions. Nextc-Screei  /200 FIRE E1TECIS EXPERT SYSTEM  MODULE==>flIRTEMF  ** FUST-BURN L1MU1NU LtUEL ** Post-burn veg. cover : 18,94  Ueg/Seedling height ratio : p.506  U n i t i n g level  : F~  REMARKS: The burn has altered the vegetation complex i n such a uay as t o inprove the a i r temperature.  FIRE EFFECTS EXl'ERl SYSIEM ** AIR TEMPERATURE SUMMARY «* Pre-burn U n i t i n g level  : ?,  Post burn l i m i t i n g level  : J  Effect uf f i r e : (Beneficial  MUDULE==>H1KTEMP  /201  HYQROTOP MODULE  E c o l o g i c a l M o i s t u r e Regino and g e o l o g i c M a t e r i a l .  In r e l a t i o n to landscape  P r e s s ans key  position  to c o n t i n u e  Itodale-->BYC10TOP  to  1  T h i s - i s - t h e HYGROTOP module. T h i s nodule e s t i m a t e s - t h e e x t e n t which s o i l moisture l i m i t s the growth o f the t r e e s e e d l i n g s .  Most ecosystem a s s o c i a t i o n s a r e d e f i n e d p r i n a r i l y on the b a s i s of s o i l M o i s t u r e . Each ecosystem a s s o c i a t i o n u s u a l l y c o r r e s p o n d s .to a uniaue s o i l m o i s t u r e regime v a l u e . For each ecosystem 'I^s^T a s s o c i a t i o n , a p r o d u c t i v i t y index i s o f t e n d e f i n e d . T h i s index-;—::.....-„^.\ " r e l a t e s t o how l i m i t i n g the s o i l m o i s t u r e i s t o the s i t e . two  F i r e i s considered t o only a f f e o t s o i l moisture d i r e o t l y types of s i t e s i 1. the u n p r o d u c t i v e "wet 2. t h e v e r y d r y s i t e s  For site.  sites ™"™' -  on  -  both o f t h e s e t y p e s o f s i t e s , f i r e I s d e t r i m e n t a l t o the •. .  .Press ajig key  to o n t i n n e  ; _-. • • . •  /202  FIRE EFFECTS  EXPERT  SYSTEM  MUDULE==>HYGR01QP  ** PRE-BURN LIMITING LEUEL *» S o i l Moistum Rngine : Suhliygrio Ecosysten P r o d u c t i v i t g Index :fied1UM L i n i t i n g Level :  J5~  REMARKS: Tlie p r o d u c t i v i t y index of t i n s cnnsystcM association indicates that the l i m i t a t i o n due t o s o i l noisture i s MILD ( l e v e l 5).  Next-Screen  /203  FIRE EFFECTS EXPERT SYS1LM ** SOIL HOISTURE SUMMARY ** Pre-Burn L i n i t h i g Level : Post-Burn L i m i t i n g Level : E f f e c t of F i r e : NuChdiige  MUDULE==>HYUR01UF  EVALLIM MODULE  P r e s s ang keg to o o a t l n u e  Hodo.le==>KVALI.IN T h i s i s t h e E U B L L I PI M o d u l e . T h i s n o d u l e compares t h e l i m i t i n g l e v e l s f o r t h e v a r i o u s growth f a c t o r s . The l i m i t i n g v a l u e s f o r t h e v a r i o u s growth F a c t o r s are d i s p l a y e d i n g r a p h i c a l format. The most l i m i t i n g F a c t o r c o r r e s p o n d s to t h e t a l l e s t b a r . However, i t i s i m p o r t a n t t o c a r e f u l l y examine any growth f a c t o r t h a t a p a r t i c u l a r F i r e would have an adverse a f f e c t upon even though i t nay not be the most — 'limiting. • „..::..':. .~."""' '  , ™  A f t e r the bar graph i s d i s p l a y e d , t h e user i s prompted as t o whether he/she wants t o v a r y the f i r e p r e s c r i p t i o n . I f he/she answers yes, then t h e u s e r i s r e t u r n e d t o t h e FUELCHnR nodule ,and i s prompted f o r new v a l u e s f o r tbe n o l s t u r e oodes. The user,„..,_, ^ i s n o t allowed t o vary any o t h e r p a r a M e t e r s i n any o f the o t h e r nodules.  Peoss  a n y keg  to  continue  -  „  /205  /206  Mould you l i k e t o wary the f i r e p r e s c r i p t i o n ? Yes No  t I -» *•  Enter t o s e l e c t  END to complete  /Q to Quit  ? f o r Unknown  


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