Decision support system for the sustainable forest management

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1 Forest Ecology and Management 128 (2000) 49±55 Decision support system for the sustainable forest management Vivek K. Varma *, Ian Ferguson, Ian Wild School of Forestry, University of Melbourne, Parkville, Vic. 3052, Australia Accepted 24 September 1999 Abstract This paper discusses the methodological component of a decision support system being developed for the sustainable forest management at the forest management unit level. A geographic information system-based multi-criteria evaluation technique for measuring sustainability of forest management is under development. It integrates and utilises spatial and temporal data on diverse ecological, economic and social variables, while handling data and decision-rule uncertainty. A decision support approach using the concept of aspiration-based utility functions is proposed for formulating forest land use strategies to improve sustainability. It combines the use of linear programming and geographic information systems. As such, the paper attempts to bridge the gap between considerable research work done on developing the framework for measuring sustainability of forest management and a lack of similar efforts in monitoring and using these indicators as a formal part of the planning system. # 2000 Elsevier Science B.V. All rights reserved. Keywords: Sustainable development; Forest management planning; Geographic information system; Monitoring; Land-use; Resource allocation 1. Introduction Due to multi-faceted role of the resource, sustainable forest management necessitates decision-making which recognises and incorporates diverse ecological, economic and social processes; a multitude of variables; and con icting objectives and constraints. Sustainable forest management also encompasses spatial as well as temporal dimensions (Montreal Process, 1995). Although achieving sustainability has been recognised as a critical link in any sustainable development strategy (Brundtland's, 1987), few studies * Corresponding author. Tel.: ; fax: address: v.varma@landfood.unimelb.edu.au (V.K. Varma). integrate the various roles and values of the forest resource. Most of these studies have focussed on either the ecological, economic or social aspects of the forest management. This paper integrates these into a decision-making process aimed at achieving sustainability of forest management. Such decision-making involves three sub-goals: a definition of sustainability of forest management that recognises its ecological, economic and social underpinning; finding ways to measure sustainability with due regard to its spatial and temporal dimensions; and operationalising sustainability in terms of identifying strategies to improve forest management, wherever needed /00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved. PII: S (99)

2 50 V.K. Varma et al. / Forest Ecology and Management 128 (2000) 49±55 2. Literature review 2.1. Definition of sustainable forest management There is no universally accepted de nition of sustainable forest management. It is often regarded as a logical extension of Brundtland's concept of sustainable development (1987) (Ferguson, 1996). While recognising a responsibility for the current and future generations (Toman and Ashton, 1996), it does not help in guiding or achieving sustainability at operational level (Ferguson, 1996) or the treatment of tradeoffs with other objectives (Pelt, 1993). Ecosystembased de nitions have been found to be more useful (Dixon and Fellon, 1989; Lust, 1995; Toman and Ashton, 1996) ± in line with the current approaches to manage forests on the basis of principles of ecosystem management (Montreal Process, 1995; Toman and Ashton, 1996). Generally, the health and wellbeing of the people and the forest ecosystem together determine the progress of management practices towards the sustainability (Dovers and Norton, 1994; Maser, 1994; Colfer et al., 1995). This approach provides a conceptual framework for sustainability Criteria and indicators of sustainable forest management To be able to apply the concept of sustainable forest management as clearly and simply as possible, it has been necessary to describe it in terms of guiding principles, criteria and corresponding indicators. In this regard, the results of international initiatives (ITTO, 1992; Helsinki Process, 1995; Montreal Process, 1995) are signi cant. The criteria and indicators are derived from the guiding principles of sustainable forest management, embodying maintenance and enhancement of diverse forest values. An overview made by Food and Agriculture Organisation of such initiatives (Lanly, 1995), shows a consensus on the characterisation of sustainable forest management through six criteria; (1) extent of forest resources, (2) conservation of biological diversity, (3) forest health and vitality, (4) productive functions of the forest, (5) protective functions of the forest, and (6) forest-related economic and social needs. Based on various international efforts for developing indicators, Mengin-Lecreulx (1996) has compiled a list of eighty indicators applicable at the national level. In practice, only a limited number of indicators can be used (Opschoor and Reijnders, 1991), which are sensitive to spatial and temporal changes, and also meet the requirements of ease of data collection and application (Liverman et al., 1988). The criteria of sustainability are applicable both locally (at the management unit level) and at higher planning levels (regional or national levels). This is not true of the indicators. Some may be common to different levels, but others are more relevant at the local level (Lanly, 1995). The criteria and indicators provide a framework for measuring the sustainability of forest management. Despite considerable research in developing the criteria and indicators, comparatively little work (Prabhu et al., 1996) has been done to implement them to achieve sustainability Decision support approach The term `decision support system' refers to an approach that integrates decision maker's own insights with computer's information processing capabilities for improving the quality of decision making (Keen and Scott-Morton, 1978; Turban, 1993). This approach also involves an integration of data from a variety of sources (Turban, 1993). However, these systems do not automate management decisions simply by nding optimal solutions to a problem. The nal selection of management alternative is left to the manager (Cooney, 1986; Turban, 1993). The decision support role of a geographic information system is particularly notable. Geographic information systems assist the evaluation of a greater number of alternatives both spatially and otherwise (Cowen, 1990). The geographic information systembased multi-criteria evaluation of forest resources is increasingly being used (Carver, 1991; Eastman et al., 1993; Eastman, 1995; Jankowski, 1995). Furthermore, an integration of geographic information system and linear programming for land-use modelling (Chuvieco, 1993) offers a powerful tool for improving the management of forests. However, geographic information system's potential for use in sustainability-related studies (Orr, 1996) is yet to be fully realised. A realisation that every decision is actually a compromise (Simon, 1979; Mykkanen, 1994), has led to approaches that use reasonable or satisfactory levels of

3 V.K. Varma et al. / Forest Ecology and Management 128 (2000) 49±55 51 objectives ± known as aspiration levels ± to nd a compromise solution (Davis and Olson, 1985; Lewandowski and Wierzbicki, 1989). As aspiration levels re ect preferences of the decision maker (Mykkanen, 1994), an aspiration-based decision support enables us to incorporate these preferences in the decision-making process (Lewandowski and Wierzbicki, 1989). Mykkanen (1994) has proposed a technique for determining utility functions based on aspiration levels. Since maximisation of utility derived from the forest resources has long been regarded as a goal of the forest management (Mykkanen, 1994), use of aspirationbased utility functions may provide a useful basis for handling such problems Uncertainty in decision making Uncertainty plays an important role in any decisionmaking process. The uncertainty related to data and decision rules have a bearing on this study. An estimate of uncertainty attached to eld data can be obtained from the root mean square error of the measurement and sampling errors (Philip, 1994; Eastman, 1997b). Depending upon the nature of decisionrule uncertainty, Bayesian probability theory (Daellenbach et al., 1983), fuzzy sets of Zadeh (1965) or Dempster±Shafer theory of evidence (Shafer, 1976) can be used to deal with this source of uncertainty. Fuzzy sets are useful for dealing with imprecision by way of the fuzziness of a decision rule with respect to the categories of decision (Zadeh, 1965). The Dempster±Shafer theory of evidence is particularly useful in the cases where support provided by an uncertain piece of evidence is to be taken into account (Shafer, 1976; Bhatnagar and Kanal, 1986). Bayesian probability theory is a special case of the latter (Shafer, 1976), and thus merits no further discussion. Another advantage of Dempster±Shafer theory of evidence is that it explicitly considers ignorance associated with the decision-rule (Shafer, 1976; Bhatnagar and Kanal, 1986). 3. Decision support methodology 3.1. Measuring sustainability Selection of criteria and indicators In order to build on the results of earlier research (ITTO, 1992; Helsinki Process, 1995; Montreal Process, 1995), the following criteria and indicators were chosen for this study: 1. State of forest resources: the distribution and area of forest by forest type, by native or exotic species, and by age-classes. 2. Conservation of biological diversity: the distribution and area of the forest managed specially for the conservation and use of forest genetic resources, distribution and number of endemic species, and distribution and number of species according to protection-status (endangered, threatened, rare, extinct). 3. Forest health, vitality and integrity: the distribution and area affected by disease, insects, fire, competition from exotic species and unregenerated part of the ecosystem. 4. Production of wood and other forest products: the distribution and area of forest managed according to a management plan, distribution and area available for the production of wood and nonwood forest products, distribution and area dedicated to sustained production of wood and non-wood forest products. 5. Soil and water protection: the distribution and area of forests managed specifically for the purpose of soil and water conservation, distribution and area subject to poor organic content, and compacted soil. 6. Socio-economic functions: the distribution and area of forest managed primarily for leisure and tourism, for cultural values, for landscape and for meeting the needs of the forest-dependent population Multi-criteria evaluation of sustainability The purpose of spatially based multi-criteria evaluation of sustainability is to determine the sustainability hotspots, i.e., the units of forest land that are not progressing toward the sustainability of forest management. The basic unit of analysis is a cell, the distinct unit of forest land represented on the geographic information system map. Thus, this decisionprocess can be viewed as whether a cell supports the following hypothesis or not: S: Progress is being made toward the sustainability of forest management.

4 52 V.K. Varma et al. / Forest Ecology and Management 128 (2000) 49±55 Therefore, the set of alternative hypotheses, known as frame of discernment, would be {S, S 0 }, where S 0 denotes negation of S. To deal with the spatial and temporal dimensions, evaluation is carried out in two stages. First, a time-series analysis is carried out for an indicator, which consists in `differencing' a map layer corresponding to time t with that of time (t 1). This is done to nd out the changes in distribution and extent of area related to an indicator. The numerical trends exhibited by the indicator are thus detected. A threshold value, based on the aspirations of the decision maker, is now applied to the numerical trends to determine the cells supporting the hypothesis S. In the next stage, the map layer corresponding to the most recent time is scored by reference to the sustainability-thresholds, which represent the values accepted as satisfactory by the decision maker in respect of the indicators. This is done for all the indicators. Combining the results of the two stages provides us a map that shows the cells supporting hypothesis S. All the other cells fall into the category of sustainability hotspots, and require management intervention. In the future it is planned to use a raster-based geographic information system called IDRISI for Windows (Eastman, 1997a) to carry out this analysis. P j;k m C ˆ m 1 A j m 2 B k 1 P j;k m 1 A j m 2 B k when A j \ B k ˆ C (1) when A j \ B k ˆ f where m 1, m 2 and m(c) are, respectively, basic probability assignments for focal elements A j, B k and combined evidential support for the subset C over the same frame of discernment. In this case, the theory of evidence (Shafer, 1976) also provides us the following relationships: BelieffSg ˆm S (2) BelieffS 0 gˆm S 0 (3) Ignorance ˆ m S; S 0 ˆ1 m S m S 0 (4) Thus, theory of evidence provides us with a crude measure of sustainability in the form of belief {S} given by Eq. (2). This approach does not obscure the distinction between the evidence for and against hypothesis S. A high value of ignorance indicates an inability of the decision maker to assign greater support to S or S 0. It also highlights the need for more information or data collection in order to reduce ignorance Uncertainty management As noted earlier, data and decision rule uncertainty are important for this study. Data uncertainty is measured by estimated root mean square error (Philip, 1994; Eastman, 1997b). To handle the decision-rule uncertainty, the Dempster±Shafer theory of evidence (Shafer, 1976) is used. The assignment of the numeric values to the measures of uncertainty, known as belief value, is made from basic probability assignments (Shafer, 1976). In the absence of suf cient data, the basic probability assignments (m) are made on the basis of subjective judgment (Bhatnagar and Kanal, 1986; Eastman, 1997b). Drawing on his or her experience, the decision maker makes these assignments for all the indicators, which represent the support that a threshold value provides for one of the hypotheses S or S 0 (Shafer, 1976). These values are stored in a tabular form for their subsequent use. For the aggregation of evidence on all the indicators, the Dempster±Shafer theory of evidence (Shafer, 1976) provides following relationship: 3.2. Aspiration-based forest land-use strategy for sustainability After identifying the units of forest land not meeting the criteria of sustainable forest management, the next step is to attempt to make them sustainable. One of the ways to achieve this objective is to determine a land use strategy that maximises the utility derived from the forest resources. It stems from the recognition of the fact that the extent of the area devoted to a particular forest value is important for its continued enjoyment by current and future generations. The inclusion of the indicators related to the extent of forest for all the criteria of sustainable forest management (ITTO, 1992; Helsinki Process, 1995; Montreal Process, 1995) further strengthens this approach. A utilitybased approach to land use planning also facilitates consideration of a trade-off between or among the competing objectives. The two stages of this process are discussed below.

5 V.K. Varma et al. / Forest Ecology and Management 128 (2000) 49± Aspiration-based land allocation Forest values related to biodiversity, production, protection and social functions are of importance from the viewpoint of the long-term commitment of forest land to a particular use. They will be referred to as candidate forest values. The efforts to deal with unsustainability arising from the criteria related to state of forests and forest health, vitality and integrity do not con ict with these forest values in terms of land use. To identify a land-use pattern that maximises the utility derived from the forest resources, the concept of aspiration-levels is used. The method of Mykkanen (1994) uses these aspiration levels and lowest acceptable values to determine the partial utility functions of the candidate forest values. The desirable values (aspiration levels) and lowest acceptable values of the number of cells for each forest value are decided on the basis of decision maker's preferences. The utility maximisation problem is modelled as a linear programming problem: Maximise X i a i u i x i such that x i G i ; x i M i ; X i where: x i ˆ L; 8x i 0 (5) x i ˆ decision variable, i.e., the number of cells allocated to the forest value i, i ˆ biodiversity, production, protection and social values, a i ˆ relative importance of the forest value i, u i ˆ partial utility function of the forest value i, G i ˆ desired value of the number of units of forest land assigned to the forest value i, M i ˆ lowest acceptable value of the number of units of forest land assigned to the forest value i, L ˆ total number of the units of forest land available. This model presupposes a knowledge of the relative importance of objectives (a i ). These values can be determined by methods recommended by Mykkanen (1994). The land-use problem stated above lends itself to solution by any standard linear programming software. It gives us optimal land allocation that yields maximum total utility from the forest resources. Rounding off of any fractional values of cells has very small effect on optimality. Use of aspirationlevels in this model improves adaptability to revised goals in view of the decision maker's learning from previous goals and feedback. This is particularly advantageous as the dynamic character of forest resources and forest-dependent people necessitates periodic revision of goals Spatial distribution of forest values The next issue is to determine the `best' spatial distribution of the various forest values in a forest management unit. This is an ill-structured problem given the issues concerning aggregation, fragmentation and adjacency. There is no accepted method to solve this problem. Nor is it possible to identify feasible solutions. Heuristics are therefore used to identify the best solution under the given circumstances (Daellenbach et al., 1983). The preferences of the decision maker play an important role in the heuristic method as described below: 1. Name each cell uniquely for the ease of identi cation. 2. Find the most important forest value for all the cells. This is done by the decision maker by matching the condition of the cell with the requirements of the candidate forest values. This may be difficult in the case of sustainability hotspots, particularly if a cell is to be assigned to a forest value in which it was judged unsustainable. A decision has to be made by the decision maker on the basis of his preferences, while considering the long-term interests of sustainability served by such an assignment. 3. Similarly, other less important forest values for all the cells would be determined. 4. For each cell, arrange various forest values in the decreasing order of importance. 5. For each forest value, rank all the units of forest land in the decreasing order of importance. Its purpose is to facilitate preferential selection of the highly ranked cells within a particular forest value. For example, to make a group of cells contiguous, potential cells may be ranked higher. This is done on the basis of the decision maker's preferences. As a result of this, four columns of the ranked cells are obtained, each column corresponding to a forest value.

6 54 V.K. Varma et al. / Forest Ecology and Management 128 (2000) 49±55 6. In each column of the ranked cells, assign cells from the top to each forest value until the number of cells equal to the optimal distribution is reached. This gives us the utility-maximising forest land use pattern. 7. The spatial distribution of the forest values is displayed with the objective of revising the same, when necessary, to ensure contiguity of cells assigned to a forest value. For example, if a small number of cells are surrounded by a large number of cells assigned to a different forest value, it is advisable to assign the small number of cells to the dominant forest value, provided such loss can be compensated for elsewhere. This heuristic method is expected to produce a better spatial distribution of the various forest values. However, the optimum level of utility predicted by the linear programming solution may not be achievable in practice as each cell assigned to a forest value cannot be expected to be identically same and thus to yield equal utility. In future this heuristic method will be implemented by using IDRISI for Windows (Eastman, 1997a). 4. Discussion and conclusions This paper describes the methodological component of a decision support system being developed for the sustainable forest management at the forest management unit level. Speci cally, it addresses two main goals: nding ways to measure sustainability of forest management with due regard to its spatial and temporal dimensions; and operationalising it in terms of identi cation of utility-maximising land use strategies. A review of the literature revealed few efforts devoted to achieving these goals, perhaps due to the dif culties associated with integration and processing of various types of data coming from a variety of sources. The decision support approach offers a means of dealing with the complexity of this resource problem. The key to this approach lies in the blending of decision maker's subjective knowledge and the capabilities of geographic information systems to integrate and analyse spatial as well as non-spatial information. These systems also make possible a multi-criteria evaluation for the identi cation of sustainability hotspots. Such an evaluation is not easily performed by using traditional maps and overlay techniques, specially when there are several criteria and large amount of data involved. The Dempster±Shafer theory of evidence, besides dealing with the uncertainty associated with decisionrules, provides an indirect measure of sustainability of forest management. This measure discerns between the evidence for and against the hypothesis of sustainability. It also recognises ignorance, which is not uncommon in the cause-and-effect relationships in the ecological and social domains. This is an improvement over the normally adopted approaches which completely neglect ignorance involved with the natural resource decision-making. The use of the aspiration-based approach to decision support facilitates the incorporation of the decision makers' preferences in decision making. It can also help in accommodating the viewpoints of forestdependent people, which is the core of the participatory approach to forest management. The aspirationbased partial utility functions offer a computationally ef cient method for the consideration of trade-offs. The utility-maximising forest land-use strategy is potentially a way of ensuring the more ef cient use of forest resources to meet the needs of current and future generations. However, the dynamics of changing needs and aspirations necessitates periodic revision for continuing improvement. The proposed methodology has been implemented on some imaginary data sets. These data sets were created so as to be as close as possible to real world forestry situation in developing countries like India. Its implementation on real world information is in progress and will be reported later. References Bhatnagar, R.K., Kanal, L.N., Handling uncertain information: a review of numeric and non-numeric methods. In: Kanal, L.N., Lemmer, J.F. (Eds.), Uncertainty in Artificial Intelligence, Elsevier, Amsterdam, pp. 3±26. Brundtland, H., Our Common Future. Oxford University Press for World Commission on Environment and Development, Oxford, 400pp. Carver, S.J., Integrating multi-criteria evaluation with geographical information system. Int. J. Geographical Information Sys. 5(3), 321±339.

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