Design of forest management planning DSS for wildfire risk reduction

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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m

w w w. e l s e v i e r. c o m / l o c a t e / e c o l i n f

Design of forest management planning DSS for wildfire risk reduction Spiridon Kaloudisa,⁎, Constantina I. Costopouloub , Nikos A. Lorentzosb , Alexander B. Sideridisb , Michael Karterisc a

Technological Education Institute of Lamia, Department of Forestry and Management of Natural Environment, Laboratory of Forest Management and Geographic Information Systems, 36100 Karpenisi, Greece b Agricultural University of Athens, Informatics Laboratory, Iera Odos 75, 11855 Athens, Greece c Aristotle University of Thessaloniki, Department of Forestry and Natural Environment, Laboratory of Forest Management and Remote Sensing, 54006 Thessaloniki, Greece

AR TIC LE I N FO

ABS TR ACT

Article history:

Forest management planning is generally a complicated task. The amount of data,

Received 18 July 2006

information and knowledge involved in the management process is often overwhelming.

Received in revised form 16 July 2007

Decision support systems can help forest managers make well documented decisions

Accepted 18 July 2007

concerning forest management planning. These systems include a wide variety of components, depending on the management goals of the forested land. Although an

Keywords:

increased growth of decision support systems in specific domains of forest management

Forest management planning

planning exists, there is no special design model for the deployment of forest management

Decision support systems

planning. To this direction, this paper has the following objectives: Firstly, to propose a

Geographic information systems

conceptual design model for developing goal-driven forest management planning decision

Wildfire Destruction Danger Index

support systems. Secondly, to apply the design model for a particular case of these systems, the wildfire risk reduction decision support systems. Thirdly, to present the deployment of a wildfire risk reduction decision support system as well as its results for a specific forest area. © 2007 Elsevier B.V. All rights reserved.

1.

Introduction

Forest management planning (FMP) is generally a complicated task due to (i) the complexity of environmental dynamics over time and space, (ii) the overwhelming amounts of data, information and knowledge in different forms and qualities, and (iii) the multiple, often conflicting, product needs (Janssen, 1992). Thus, when making decisions, forest managers are obliged to take into account all the above considerations in order to define the goals of management and treatments and build perspective management plans. Many researchers have recognized the considerable potential of decision support systems (DSS) in sustainable management of natural resources (e.g. Mowrer, 2000; Rauscher et al., 2000; Reynolds et al., 2000; Twery et al., 2000; Walker, 2002; Nute et al., 2004;

Poch et al., 2004; Iliadis and Spartalis, 2005; Turban et al., 2005; Mendoza and Martins, 2006; Richardson et al., 2006). This is due to the fact that DSS enable the modelling of complex processes and integration of knowledge across disciplines. DSS represent a class of interactive computer-based systems and subsystems intended to help decision makers in using communication technologies, data, documents, knowledge and/or models to complete decision process tasks, giving emphasis to semi-structured and unstructured decisions. According to Dunikoski and Mandell (1988), DSS can facilitate decision-making and improve their effectiveness and quality by speeding up the processing of big volumes of data, and by providing a number of alternative solutions to a problem that otherwise might not have been considered due to time constraints.

⁎ Corresponding author. Tel.: +30 22370 25063; fax: +30 22370 24035. E-mail address: [email protected] (S. Kaloudis). 1574-9541/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoinf.2007.07.008

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Fig. 1 – A conceptual design model of FMP-DSS. An increased growth of DSS in specific domains of FMP exists (Potter et al., 2000; Twery et al., 2000; Nute et al., 2004; Poch et al., 2004; Anonymous, 2005; Liao 2005; Mendoza and Martins, 2006), including spreadsheets, databases to record and manipulate inventories, growth and yield simulators to project future conditions of forested land, optimization programs to maximize timber production, financial analysis systems to track income from forest products, geographic information systems (GIS) and visualization tools. Such DSS are mainly developed on a case-by-case basis, but they do not address successfully every important aspect of FMP (Mowrer, 1997). They are less capable in addressing social and economic issues than biophysical issues. They do not address comprehensively ecological and management interactions across multiple scales. Also, the deployment of such DSS is usually complex and costly (Potter et al., 2000). As an overall observation, most of the existing DSS do not cope with uncertainty, which is present in many cases in FMP. Yet, there is no design model for the deployment of goaldriven forest management planning decision support systems (FMP-DSS). Given however that FMP is too complex for policy makers, forest managers and developers, such a model can prove useful to them. In this context, the objective of the present paper is threefold: Firstly, to propose a conceptual design model for developing goal-driven FMP-DSS, using a component-based approach. Secondly, to apply the design for a particular case of these systems, the wildfire risk reduction DSS (WRR-DSS). Thirdly, to present the deployment of the WRR-DSS as well as its results for a specific forest area. To be reliable for the WRR a method is also needed, capable of determining the long-term forest fire risk for each defined location. In addition such a method should be able to provide the so called wildfire destruction danger index (WFDDI) (Kaloudis et al., 2005).

The motivations to study this case are: (i) Wildfires usually cause damages to forests, FMP for wildfire damage reduction is considered valuable, especially for forests with a medium to high fire risk and with a high commercial value; (ii) Wildfire behaviour depends on three major factors, namely the fuel characteristics, the meteorological conditions and the topography. Fuel is the only of these factors, which can be altered by applying treatments, such as crown thinning, branch pruning and slash removal. Such treatments can be used both for protection and production reasons but they are usually expensive and may have side effects; and (iii) There is a number of DSS dealing with FMP but none of them incorporates wildfire damage reduction in a multi-objective FMP environment.

2. Development of conceptual model for forest management planning DSS for wildfire risk reduction 2.1. DSS

The conceptual model of forest management planning

FMP-DSS consider the inventory, classification, collection, storage and retrieval of a huge volume of environmental and forest (EF) data. These data refer to values of factors which act on the forest alone and/or in combination. In order to have a common understanding, a classification of EF factors, namely into primitive and derived is proposed. Primitive factors act directly to the forest. Representative examples are air temperature, slope, soil depth, and soil texture. Derived factors are those that stem from the combined action of a set of primitive

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Table 1 – The analysis and design phases of WRR-DSS Phases

System entities

Identification of actors

Forest content provider Decision-maker Identification of use cases Enter forest data Get advice Identification of the Primitive Data Components: components that support use cases Database/DBMS Geodatabase/GIS Derived Data Components: Timber and non-timber production Wildfire simulator Soil erosion index Financial analysis Intelligent FMP Components: FMP KB WFDDI KB Design of the WRR-DSS First step sequence diagram model Second step sequence diagram

factors. Representative examples are wildfire risk, soil erosion risk, and site quality index. EF factors take exact numeric values or fuzzy values. Moreover, primitive data can be stored in database or geodatabase, relevant to their nature, and can be retrieved by a database management system (DBMS) or a GIS, respectively. Derived data are computed from primitive data (e.g. by a fire behaviour simulator). In FMP, it is essential to determine which derived data are necessary and which models are needed for computations. In the following a conceptual design model for deploying goal-driven FMP-DSS is proposed, using a component-based approach. The main purpose of this model is to record design

decisions as early as possible. More specifically, this model describes FMP-DSS in abstract terms, by expressing the components and their cohesion. The abstract representation of the model shows the general capabilities of the system and makes it possible to add various technical details at a later detailed design stage. It consists of three abstraction levels, as shown in Fig. 1. Each higher level is based on the specification of the lower levels. That is, the level of Primitive Data Components stores EF primitive data, which is used by the level of Derived Data Components and, in turn, it supports the level of Intelligent FMP Components. In addition, the level of Primitive Data Components supports the level of Intelligent FMP Components. The level of Primitive Data Components incorporates components for the storage and retrieval of EF factor values. To keep and retrieve the values of factors valid over the whole forest, a database and a DBMS are needed. The database is used to record descriptive data, such as climatic data, the names and characteristics of the fuel models, population and financial data. EF factors, with spatially varying values are retrieved by a GIS. The GIS handles spatial data, such as forest biomass, soil characteristics, site quality index, and performs spatial computations. Using a GIS representation, the spatial value distribution of each EF factor (e.g. slope, surface fuel model, stand age, crown closure) is described by an information level with its own resolution. This information level is divided into small uniform areas, termed cells. The use of forest cell as the basic forest management unit, has been adopted because it facilitates a more precise FMP. Specifically, it enables (i) providing better care for locations with high risk, (ii) increasing forest productivity, (iii) decreasing the disturbances to forest functions from excessive intervention,

Fig. 2 – A WRR-DSS model.

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and (iv) reducing the cost of the treatment application by applying them wherever they are really needed. The level of Derived Data Components includes components, which are dedicated to the goal(s) of FMP-DSS. Examples of such components may be a timber and non-timber production component and a financial analysis component. The level of Intelligent FMP Components involves the knowledge bases and an inference engine that enables providing consultation to the system user (forest manager).

2.2.

Application of DSS for wildfire risk reduction

The proposed conceptual design model is applied for the development of a WRR-DSS model. For the analysis and design of the WRR-DSS system, the Unified Modeling Language (UML) notation is used. UML is widely accepted in the industrial and academic communities (Rumbaugh et al., 1999; Rational, 2001) and has an increased acceptance in the world of artificial intelligence and knowledge engineering (Schilstra, 2001). UML can be useful for specifying WRR-DSS requirements and capturing design decisions, as well as for promoting communication among forest managers and developers during the early phase of system development. UML allows developers to represent multiple independent perspectives or views of a system using a variety of graphical diagrams, such as use case diagrams and sequence diagrams. Use case modelling helps in gaining a clear understanding of the functional requirements of the system, without any worry related to how these requirements will be implemented. A use case model consists of actors and use cases; an actor is an external entity that interacts with the system. A use case represents a sequence of actions initiated by an actor (Rumbaugh et al., 1999). Its task is to yield a measurable value to the system actor. The set of the use case descriptions specifies the complete functionality of the system. Moreover, a UML-based analysis and design of a system, involves in general the following phases: Phase 1: Identification of actors. Phase 2: Identification of use cases. Phase 3: Identification of the components that support use cases. Phase 4: Design of a system model, including identification of sequence diagrams. Taking into account the abstract representation of the FMPDDS model (Section 2.1) and the UML-based analysis and design phases, the process for the development of a WRR-DSS design model has been specified (Table 1). The UML terminology is used in the following, to the degree possible.

2.2.1.

Phase 1: Identification of actors

Two types of actors can be identified, which are described bellow. • Forest content provider, who is either a forester, a group of foresters or a data provider (public services/companies). One's duty is to supply the level of Primitive Data Components, with data about the forested land to be managed. • Decision-maker, who is either a forester, a group of foresters, a forest owner, a forest manager, a public official, a scientist or

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a forest trainer. One seeks for advice that concerns the FMP of each cell of the forest area under study.

2.2.2.

Phase 2: Identification of use cases

Two use cases have been identified, according to the WRR-DSS functionality: • ‘Enter forest data’ use case: This is performed by the forest content provider actor. It provides data to the level of Primitive Data Components. • ‘Get advice’ use case: This is performed by the decision-maker actor, who receives advice for primary and secondary forest management objectives, treatments combination and their application intensity.

2.2.3. Phase 3: Identification of WRR-DSS components that support the use cases Taking into account the abstract representation of the FMP-DDS model (Fig. 1) and the acquired knowledge for wildfire damage reduction, the WRR-DSS components are identified (Fig. 2). Thus, the level of Primitive Data Components includes a Database/DBMS and a Geodatabase/GIS. The level of Derived Data Components contains a timber and a non-timber production component, a soil erosion index component, a wildfire simulator component, and a financial analysis component. These components are described later in this section. The level of Intelligent Component includes the FMP knowledge base (KB) component and the WFDDI KB component which is analysed below. The composition of a WRR plan requires the knowledge of the long-term wildfire threat. In practice, it is impossible to know the precise time and location of a future wildfire, the behaviour of the fire, and the objects under threat. For a given period, however, it is possible to estimate the expected wildfire risk of an area. Existing wildfire danger rating indices are usually used for short-term prediction. These indices comprise an integration of weather elements and other factors affecting wildfire risk over an area. For long-term forest management applications, a more comprehensive index is needed (Bachmann and Allgöwer, 1998; Iliadis, 2005; Kaloudis et al., 2005; Spartalis et al., 2007). Its calculation must take into consideration the probability of wildfire occurrence, the expected wildfire behaviour and the threatened values. Therefore indeed, the WFDDI can provide reliable information about the expected wildfire risk of an area. It takes into account the following parameters:

2.2.3.1. Fire incidence probability. This parameter denotes the probability of a wildfire incidence within an area, for a certain period. It can be computed by the past wildfire incidence and the future trend for changes in the number of wildfires. Since fire incidence due to natural reasons can be considered constant, the future changes in the number of wildfires can be attributed to human intervention. Fires caused by humans can be attributed to the need for land (agricultural fields, houses, etc.) and common practices relevant to fire use. Human demands for land depend directly on the number of people and the level of consumption. Practices depend on the level of wealth and technology. Therefore, future changes in the number of wildfires can be estimated by the change rate of

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Fig. 3 – A sequence diagram for the first step. Note: FMP stands for Forest Management Planning Expert System, WFDDI stands for Wildfire Destruction Danger Index Expert System, WS stands for Wildfire Simulator, SEI stands for Soil Erosion Index, PR stands for Timber and non Timber Production, FA stands for Financial Analysis, GB stands for Geodatabase, DB stands for Database.

the human population and the change rate of income per capita. Due to the small size of cells, the probability for wildfire ignition in a cell is low but wildfire transfer from neighbouring cells may be high. As a consequence, spatial relationships between cells play an important roll in the calculation of the WFDDI at a cell level.

2.2.3.2. Fire severity.

This represents the magnitude of a wildfire event and, consequently, the size of threat to the objects. For the calculation of wildfire severity, wildfire characteristics are used. Four main wildfire behaviour parameters are considered: wildfire type, flame length, wildfire line intensity and rate of spread. These parameters can be calculated from existing wildfire simulation models.

2.2.3.3. Fire severity probability. This represents the probability for a certain fire severity to occur within a certain period, in a given area. Fire severity probability values can be calculated either for a certain period of the year (e.g. months with high wildfire activity) or for the whole year. 2.2.3.4. Values in threat. These are values of objects and environmental entities, which are threatened by wildfire. They can be classified into two categories: forest objects such as timber, fruits etc., and non-forest objects such as human lives, public infrastructure etc. Due to the fact that some entities, though useful, have no commercial value, as it is the forest soil, a distinction must be made between commercial and utility values.

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2.2.3.5. Values of fire sensitivity. This parameter concerns the estimation of the loss of the value of an object from a wildfire. The loss of value depends on the sensitivity of the object to wildfire and may be estimated on the basis of its (i) flammability and (ii) destruction degree. The WFDDI is applicable to either large or small areas, and can be used for short and long-term predictions (Kaloudis et al., 2005). 2.2.4.

Phase 4: Design of the WRR-DSS model

According to the FMP-DSS conceptual design, the WRR-DSS model is proposed. It consists of three abstraction levels, and it is described as follows (Fig. 2):

Table 2 – Messages used in the first step sequence diagram Message

Sender Receiver

Request FMP_GB

Forest FMP manager FMP GB

FMP_DB

FMP

DB

FMP_WFDDI FMP WFDDI_GB WFDDI

WFDDI GB

WFDDI_DB

WFDDI

DB

WFDDI _WS WFDDI

WS

WS_GB

WS

GB

WS_DB

WS

DB

2.2.4.1. The level of Primitive Data Components.

It stores the values of EF factors in a database and a geodatabase, described below. The database records the 2.2.4.1.1. Database/DBMS. values of non-spatial factors, either discrete or fuzzy. Representative factors are the name of the fuel model and the relevant parameters that each model uses, human and animal populations, and development characteristics. The DBMS retrieves the EF factor values and provides the results to other system components. It is also used to maintain nonspatial data. The geodatabase records the 2.2.4.1.2. Geodatabase/GIS. values of spatial factors. Such factors are topographical characteristics, vegetation characteristics, soil characteristics, and the site quality index. The GIS provides data to the other system components and performs the necessary spatial computations. It also provides information that enables the user to perform, at some future time, comparative studies and to investigate, how much satisfactory the system consultation is. It displays the results in the form of thematic maps, tables and diagrams.

2.2.4.2. The level of Derived Data Components.

It includes four components, namely a timber and non-timber production component, a wildfire simulator component, a soil erosion index component, and a financial analysis component. It provides data to the level of Intelligent FMP Components. For 2.2.4.2.1. Timber and non-timber production component. each cell, it evaluates its production potential, and determines which products, in what quantity and quality this cell can produce. In particular, it calculates both the current quantity and quality of the products as well as those, after the application of the proposed treatments. It provides the results to the level of the Intelligent FMP Component. It requires data relevant to the EF factors from both the database (forest management type, climatic conditions, social needs, etc.) and the geodatabase (slope, altitude, aspect, soil characteristics, site quality index, vegetation characteristics, timber increment, timber capital, forest age etc.). 2.2.4.2.2. Wildfire simulator component. It is based on wellknown wildfire behaviour models (e.g. Rothermel, 1972; Byram, 1973; VanWagner, 1977; Andrews and Rothermel, 1982). It has both a forward and a backward functionality. During forward functionality, it calculates the wildfire characteristics, such as wildfire type, rate of spread, flame length, and wildfire intensity.

WS_ WFDDI WS

WFDDI

WFDDI_FMP WFDDI FMP_SEI FMP SEI_GB SEI

FMP SEI GB

SEI_DB

SEI

DB

SEI_FMP

SEI

FMP

FMP_PR

FMP

PR

PR_GB

PR

GB

PR_DB

PR

DB

PR_FMP

PR

FMP

FMP_FA FA_GB

FMP FA

FA GB

FA_DB

FA

DB

FA_FMP Response

FA FMP

FMP_GB

FMP

FMP Forest manager GB

Description Request consultation (main and secondary objectives). Request data on visibility and distances from areas with human activity etc. Request data on population, etc. Request WFDDI value. Request data on values in threat, fire history, etc. Population change rate, income per capita change rate, etc. Request wildfire characteristics. Request data on surface fuel model code, fuel humidity, crown bulk density, slope, aspect etc. Request data on surface fuel models, meteorological data etc. Send expected wildfire characteristics (fire type, flame length, fireline intensity and rate of spread). Send WFDDI value. Request soil erosion value. Request data on vegetation density, soil texture, soil organic matter content, slope etc. Request data on precipitation quantity etc. Send the calculated soil erosion value. Request values on the estimated forest products (kind, quantity and quality) etc. Request data on site quality index, tree species composition, slope, aspect, grazing etc. Request data on forest management type etc. Send the calculated values on forest products. Request financial analysis. Request data on slope, distances, roads network, visibility etc. Request data on wages, machines cost, products prices etc. Send financial analysis results. Objectives of forest management planning. Updates with main and secondary objectives of the forest management planning, WFDDI and SEI values.

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Fig. 4 – A sequence diagram for the second step.

During backward functionality, it calculates the necessary degree of fuel treatments to decrease wildfire characteristics such as the amount of slash removal. The wildfire simulator follows a default order of treatment applications. Alternatively, the user can ask the simulator to follow a customised order of treatments examination by setting the order, the application limits (e.g. maximum height of branch pruning) and the step (e.g. increase the height of tree branch pruning by one meter at a time) for each treatment. It provides the results (i.e. wildfire

characteristics and fuel treatments) to the level of Intelligent FMP Components. It gets data relevant to the EF factors from the database (e.g. air temperature, wind velocity and surface fuel models) and the geodatabase (e.g. slope, altitude, aspect, surface fuel model name, crown bulk density and fuel humidity). It calculates the soil 2.2.4.2.3. Soil erosion index component. erosion index of each cell before and after the application of the combination of treatments that have been proposed by the

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expert system of the upper layer. It provides its results to the level of Intelligent FMP Components. It gets data relevant to the EF factors from the database (e.g. raindrop velocity and quantity of precipitation) and the geodatabase (e.g. soil texture, vegetation density and slope). 2.2.4.2.4. Financial analysis component. It assists in the determination of primary and secondary objectives. In addition, it evaluates various combinations of treatments, thus making a cost/benefit analysis. It uses linear programming techniques for the evaluation of the various solutions of primary and secondary objectives and treatment applications. It provides these results to the level of the Intelligent FMP Components. It gets data relevant to the EF factors from the database (e.g. wages, product prices, machine costs) and the geodatabase (e.g. distances between cells, distances from roads and markets).

2.2.4.3. The level of Intelligent FMP Components. It is an expert system that incorporates two discrete components, namely the FMP KB component and the WFDDI KB component.

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They both make extensive use of fuzzy logic techniques for the management of uncertainty, and can provide non-fuzzy consultation via a defuzzification process. It makes decisions about 2.2.4.3.1. FMP KB component. primary and secondary FMP objectives and provides treatment consultation. It requires data relevant to the EF factors from the other system components, and records data to the database and the geodatabase. It provides WFDDI values 2.2.4.3.2. WFDDI KB component. to the FMP component before any treatment and for each combination of examined treatments. The WFDDI component uses data relevant to the EF factors. These data can be obtained from the database and the geodatabase. In order to model the execution flow between WRR-DSS components and actors, UML sequence diagrams have been developed. A sequence diagram represents the interaction between various components of the system that support use cases and actors. The important aspect is that a sequence diagram is time-ordered, i.e. the sequence of interactions

Table 3 – Messages used in the second step sequence diagram Message Request

Sender

FMP_GB FMP_DB FMP_WFDDI1 WFDDI_GB1 WFDDI_DB1 WFDDI _WS1 WS_GB1 WS_DB1 WS_ WFDDI1 WFDDI_FMP1 FMP_FMP1 N FMP_WFDDIn WFDDI_GBn WFDDI_DBn WFDDI _WSn WS_GBn WS_DBn WS_ WFDDIn WFDDI_FMPn FMP_FMPn FMP_SEI SEI_GB SEI_DB SEI_FMP FMP_PR PR_GB PR_DB PR_FMP FMP_FA FA_GB FA_DB FA_FMP FMP_FMPn + 1 Responses

Forest manager FMP FMP FMP WFDDI WFDDI WFDDI WS WS WS WFDDI FMP N FMP WFDDI WFDDI WFDDI WS WS WS WFDDI FMP FMP SEI SEI SEI FMP PR PR PR FMP FA FA FA FMP FMP

FMP_GB

FMP

Receiver

Description

FMP

Request consultation.

GB DB WFDDI GB DB WS GB DB WFDDI FMP FMP N WFDDI GB DB WS GB DB WFDDI FMP FMP SEI GB DB FMP PR GB DB FMP FA GB DB FMP FMP Forest manager GB

Request data on visibility, distances from human activity areas, WFDDI, etc. Request data on population, etc. Request reduction of WFDDI value. Request data on values in threat, fire history, etc. Request data on Population change rate, income per capita change rate, etc. Request reduction of fuels and new wildfire characteristics. Request data on surface fuel model code, fuel humidity, crown bulk density, slope, aspect etc. Request data on surface fuel, meteorological data etc. Send wildfire characteristics (fire type, flame length, fireline intensity and rate of spread). Send WFDDI value. Evaluation of WFDDI value. N Request reduction of WFDDI value. Request data on values in threat, fire history, etc. Request data on ppopulation change rate, income per capita change rate, etc. Request reduction of fuels and new wildfire characteristics. Request data on surface fuel model code, fuel humidity, crown bulk density, slope, aspect etc. Request data on surface fuel, meteorological data etc. Send wildfire characteristics (fire type, flame length, fireline intensity and rate of spread). Send WFDDI value. Evaluation of WFDDI value. Request soil erosion value. Request data on vegetation density, slope etc. Request data on rain intensity, precipitation quantity etc. Send the calculated soil erosion value. Request values on the forest products (quantity and quality) etc. Request data on site quality index, tree species composition, slope, aspect, grazing etc. Request data on forest management type etc. Send the calculated values of forest products. Request financial analysis. Request data on slope, distances, roads network, visibility etc. Request data on wages, machines cost, products prices etc. Send the calculated financial analysis. Evaluation of the whole data. Send forest management treatments. Updates with new values of fuel load, WFDDI and SEI.

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Fig. 5 – An output screen with system results.

between components is represented on a step-by-step basis. Different components in a sequence diagram interact via messages.

3.

Deploying WRR-DSS

For a better understanding of the WRR-DSS functionality, a specific scenario is presented. Initially, it is assumed that an authorized forest content provider has already inserted data into the database and the geodatabase of WRR-DSS. It is also assumed that a forest manager wants to get consultation about a forest cell. Next the forest manager invokes the WRRDSS for consultancy. For each cell, the WRR-DSS operates in two steps. In the first step, it determines main and secondary objectives. In the second step, it determines treatment combinations and their application intensity. The interactions between the WRR-DSS components and the forest manager are described for the first step via a sequence diagram that shows the explicit sequencing of messages (Fig. 3). Table 2 describes the list of messages used in this diagram. The message name consists of two parts. The first part declares the sender and the second part declares the receiver. For example, the message name WS_GB means that the wildfire simulator (WS) component requests data from the geodatabase (GB). A sequence diagram for the second step has been also developed (Fig. 4). Table 3 describes the list of messages used in this diagram. Based on the WRR-DSS model and the UML diagrams, the software components has been developed and integrated in a prototype version of the WRR-DSS system. In particular, the FMP KB component, the WFDDI KB component, the wildfire simulator component, the database and the geodatabase have been implemented, and are currently under evaluation. These components have been deployed using the following software tools: • For the FMP KB and the WFDDI KB components, the expert system shell Exsys Professional (version 5.1.0) has been used. The Exsys Professional shell supports the manage-

ment of uncertainty thought fuzzy logic, and can run in forward or backward chaining. It is also support a multiple functionality for the integration with external software components, such as a DBMS. • For the wildfire simulator component, the programming language Visual Basic Professional for windows in combination with ActiveX Data Object (ADO) and the Structured Query Language (SQL) for the access and query of the database have been used. • For the construction of the database, the MS Access software has been used. It is worth noticing that the database has been stored in the Dbase IV format for compatibility reasons with Exsys Professional shell. • For the geodatabase and spatial queries, the Arc-Info GIS software (version 9.0) has been used. The WRR-DSS can run in two modes namely, interactive and batch command execution. The developed pilot system has been evaluated with actual input data of a Pinus halepensis Mil. forest, located at north of Evia island, in Greece. The input variables of a cell of the specific forest under study and the corresponding values are given in Annex A. The system's results are displayed on the screen and they are also recorded in a magnetic file, in text format. One screen page is shown in Fig. 5. Notice that these results incorporate a degree of uncertainty. This uncertainty stems from two sources, (i) the vagueness in the data, due to the difficulty to get precise measurements for each forest cell, and (ii) the ambiguity in forest treatments selection, due to the difficulty in the precise modelling of the impact of vegetation treatments on the forested ecosystem. Due to the uncertainty of the results, the system also accompanies the consultation with a degree of confidence, also displayed on the screen. For instance, it can be seen that the main objective Fire protection has confidence value equal to 0.697 (see Fig. 5 and Table 4). For system evaluation purposes, the system's results have also been compared with the proposal of the official forest management plan (Gofas and Mihtatidis, 1999). Table 4 presents a representative analytical consultation of the system (third

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Table 4 – Comparison of the WRR-DSS consultation with the official forest management plan proposal Step First

Objectives/ treatments Main objective Secondary objective

Second Treatments combination and application intensity, where applicable

WRR-DSS consultation It is suggested: Fire protection as main objective of forest management planning, Confidence = .697 It is suggested: Protective use as secondary objective of forest management planning, Confidence = .697 It is suggested: Timber production as secondary objective of forest management planning, Confidence = .697 It is suggested: Resin tapping as secondary objective of forest management planning, Confidence = .8 It is suggested: Aesthetic use as secondary objective of forest management planning, Confidence = .558 It is suggested: Management shape of the Pine forest “even aged”, Confidence = 1 It is suggested: Seeding thinning until crown closure (%): = 50

Not available Not available Not available Not available Not available

Indented management shape of the Pine forest “even aged”. Seeding and light-giving thinning until crown closure 60 %. It is suggested: Completion of resin tapping and cutting of the trees subject to Cutting of the trees that have finished “Lethal tapping”, Confidence = 1 resin tapping at the previous year before the year of logging. It is suggested: Bushes thinning in order to Thinning of the dense understory with a) Facilitate natural regeneration and b) Reduce WFDDI value, confidence = 1 positive selection of the best sapling of each bush nog, to facilitate regeneration of Pine trees. It is suggested: Protection from grazing, Confidence = 1 Protection from grazing, after the logging. It is suggested: Scratching of the soil to facilitate natural regeneration. Scratching of the grassy areas in order to improve natural regeneration of Pine trees. It is suggested: Encouragement of broadleaf trees, Confidence = .9 Not available It is suggested: Pruning of the dead branches of the seeding trees, those Not available remain after the logging, Confidence = 1 It is suggested: Duration of the cutting cycle (years): = 4 Not available It is suggested: Management starting priority: = 6.2/10 Not available

column of Table 4) and the respective proposal of the official management plan (forth column of Table 4). Differences between the two are discussed as follows: Official management plan does not mention the forest objectives for every part of the forest. Instead, it makes the assumption that timber production is the main objective of the whole of the forest. Also, it gives less weight to the aesthetic use of the forest. Finally, it proposes undertaking a new study for fire protection (not shown in Table 4). Based on this comparison, it can be concluded that the WRR-DSS performs very satisfactory. Additional system evaluations are under way.

4.

Official management study proposals

Discussion and conclusions

Initially, this paper presents a conceptual design model for deploying goal-driven FMP-DSS, using a component-based approach. The model aims at providing guidelines for a design process to forest managers and developers, reducing the complexity of this kind of deployment. It describes goal-driven FMP-DSS in abstract terms, by expressing the components and their cohesion. It is a layered, component-based approach. As such, it is flexible in that improvements and addition of components or replacement of components can easily be incorporated. The abstract representation of the model shows the general capabilities of the system and makes it possible to add various technical details at a later detailed design stage.

Next, the conceptual model of the FMP-DSS is elaborated in the design of the WRR-DSS model. A UML-based analysis and design of WRR-DSS is used, since it provides a rich set of graphical artefacts to help in the elicitation and top-down refinement of software systems from the capturing of requirements to the deployment of software components. WRR-DSS can facilitate and optimize FMP for wildfire damage reduction, despite uncertainty. This is mainly because fuzzy logic can handle the uncertainty. This is also complemented by the facts that software programs have the ability to execute intensive spatial and non-spatial calculations and the adoption of forest cells that enable a more precise FMP for each location of the forest. WRR-DSS uses an open architecture that enables integration of the proposed system with other systems such as systems for wildfire suppression tactics and systems for protection from insects and pathogens. Finally, based on the presented model a prototype version of the WRR-DSS has been development that are currently under evaluation. The system runs smoothly and the first results are encouraging. Although, the system has been developed for research purposes, in fact, it may be operated by a non-profit organization or a third party such as a forest public agency. Future research is concerned with the modelling of forest functions and treatment effectiveness in WRR, the investigation of forest fuel growth after the treatment application, the detailed modelling of fuel, especially that of crown and the modelling of vegetation structure for long time intervals with/without fuel treatment applications.

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Acknowledgement We would like to express our warm thanks to the reviewers, who greatly assisted in the improvement of the contents of the paper.

Appendix A Annex A: Input data of WRR-DSS for a forest part under study Environmental and forest factor

Value

Adjacency to agricultural fields Adjacency to main road Adjacency to urban area Aesthetic value of the forest cell Average burned area per fire (Ha) Average Income per Capita Change Rate (%) Average Population Change Rate (%) Crown closure (%) Destruction degree of the values in threat Ecological value of the forest cell Existence of protective works from soil erosion Fire Incidence History (number of fires per year per sq.km) Flammability of the values in threat Forest health Forest stand stage Main forest species Management period of the forest cell Maximum tree age Minimum distance of the stand to human activities (m) Natural beauty of the forest cell Human population of the area under study Price of the values in threat Protection from fire with natural or artificial firebrakes Protection value of the forest cell Regeneration success Resin tapping Rotation management time Site Quality Index Soil suitability for natural regeneration Stage of the regeneration

No No No Medium 128.16 4 0.46 100 High Medium No 0.0749 High Good Regeneration Pinus halepensis First 90 800 Medium 2000 High No

High Poor Yes 80 Moderate Moderate Seedlings and saplings Stand management shape Uneven aged Surface fuel model Local 1 Total forest area (Ha) 5736.17 Tourist value of the visible area Medium Utility of the values in threat High Visibility from urban areas OR main roads OR Yes tourist areas

Note: In addition to the above data, the WRR-DSS has been fed up with data of local surface fuel models, topographic data and meteorological data from a period of 9 years.

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