Creating land allocation zones for forest management: a simulated annealing approach

This paper describes the Zone Allocation Model (ZAM) that uses the simulated annealing algorithm to create forest management zones. ZAM partitions the landscape into the Timber, Habitat, and Old Growth zones by allocating small land tiles into contiguous areas. The zone allocation process is guided by landscape-level targets and size and shape objectives. An ecological representation objective proportionally distributes all ecosystem types into each of the three zones. Priority objectives control allocation of identified lands that are targeted for specific zones. All objectives are combined within an objective function, with a penalty-weighting system specifying relative importance of each objective. The ZAM model found 1.7%–4.4% of theoretical optimum scores from small to large problems, respectively. A demonstration on a 1.2 × 106–ha landscape from coastal British Columbia illustrates the iterative exploration of compromises between objectives that leads to informed zone allocation decisions.

[1]  John Sessions,et al.  Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems , 2002 .

[2]  B. Driver,et al.  Wilderness management zoning , 1987 .

[3]  Richard L. Church,et al.  Reserve selection as a maximal covering location problem , 1996 .

[4]  C. Lockwood,et al.  Harvest scheduling with spatial constraints: a simulated annealing approach , 1993 .

[5]  Justin C. Williams,et al.  Reserve assemblage of critical areas: A zero-one programming approach , 1998 .

[6]  E. Gunn,et al.  A model and Tabu search method to optimize stand harvest and road construction schedules , 2000 .

[7]  A Crossroad in the Forest: The Path to a Sustainable Forest Sector in BC , 1997 .

[8]  John Sessions,et al.  A new heuristic to solve spatially constrained long-term harvest scheduling problems , 1994 .

[9]  Kevin Boston,et al.  An analysis of Monte Carlo integer programming, simulated annealing, and tabu search heuristics for solving spatial harvest scheduling problems. , 1999 .

[10]  B. Csuti,et al.  A Gap Analysis of the Management Status of the Vegetation of Idaho (U.S.A.) , 1995 .

[11]  Jan Bos,et al.  Zoning in Forest Management: a Quadratic Assignment Problem Solved by Simulated Annealing , 1993 .

[12]  C. Margules,et al.  Introduction to some Australian developments in conservation evaluation , 1989 .

[13]  John Sessions,et al.  Using Tabu search to schedule timber harvests subject to spatial wildlife goals for big game , 1997 .

[14]  Robert G. Haight,et al.  An Integer Optimization Approach to a Probabilistic Reserve Site Selection Problem , 2000, Oper. Res..

[15]  Daniel P. Faith,et al.  Integrating conservation and forestry production: exploring trade-offs between biodiversity and production in regional land-use assessment , 1996 .

[16]  J. Vincent,et al.  Efficient Multiple-Use Forestry May Require Land-Use Specialization , 1993 .

[17]  C. Binkley Preserving nature through intensive plantation forestry: The case for forestland allocation with illustrations from British Columbia , 1997 .

[18]  Kevin A. Crowe,et al.  Solving the area-restricted harvest-scheduling model using the branch and bound algorithm , 2003 .

[19]  J. D. Brodie,et al.  Comparison of a random search algorithm and mixed integer programming for solving area-based forest plans. , 1990 .

[20]  Daniel Granot,et al.  A tabu search algorithm for finding good forest harvest schedules satisfying green-up constraints , 1998, Eur. J. Oper. Res..

[21]  G. A. Jordan,et al.  Forest landscape management modeling using simulated annealing , 2002 .

[22]  Steffen Stræde,et al.  Strategic multiple-use forest planning in Lithuania -- applying multi-criteria decision-making and scenario analysis for decision support in an economy in transition , 2001 .

[23]  Roger A. Sedjo,et al.  Using Foret Plantations TO SPARE Natural Forests , 1997 .

[24]  Guoliang Liu,et al.  A target-oriented approach to forest ecosystem design - changing the rules of forest planning. , 2000 .

[25]  K. Boulding The Economics of the Coming Spaceship Earth , 2013 .

[26]  John Sessions,et al.  Intensifying a heuristic forest harvest scheduling search procedure with 2-opt decision choices , 1999 .

[27]  Bart Muys,et al.  A methodology to select the best locations for new urban forests using multicriteria analysis , 2002 .

[28]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[29]  Richard L. Church,et al.  Heuristic solution approaches to operational forest planning problems , 1995 .

[30]  C. Binkley Ecosystem management and plantation forestry: new directions in British Columbia , 1999, New Forests.

[31]  John E. Estes,et al.  Species RichnessA geographic approach to protecting future biological diversity , 1987 .

[32]  Manuela M. P. Huso,et al.  A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon , 1997 .

[33]  John Sessions,et al.  Designing Compact and Contiguous Reserve Networks with a Hybrid Heuristic Algorithm , 2002, Forest Science.

[34]  K. Öhman,et al.  The core area concept in forming contiguous areas for long-term forest planning , 1998 .

[35]  Eric J. Gustafson,et al.  Clustering Timber Harvests and the Effect of Dynamic Forest Management Policy on Forest Fragmentation , 1998, Ecosystems.

[36]  B. Bruce Bare,et al.  Spatially constrained timber harvest scheduling , 1989 .

[37]  R. Hosie,et al.  Native Trees of Canada , 1979 .

[38]  Mike Fenger,et al.  Implementing biodiversity conservation through the British Columbia Forest Practices Code , 1996 .